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Cox E, Wade R, Hodgson R, Fulbright H, Phung TH, Meader N, Walker S, Rothery C, Simmonds M. Devices for remote continuous monitoring of people with Parkinson's disease: a systematic review and cost-effectiveness analysis. Health Technol Assess 2024; 28:1-187. [PMID: 39021200 DOI: 10.3310/ydsl3294] [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] [Indexed: 07/20/2024] Open
Abstract
Background Parkinson's disease is a brain condition causing a progressive loss of co ordination and movement problems. Around 145,500 people have Parkinson's disease in the United Kingdom. Levodopa is the most prescribed treatment for managing motor symptoms in the early stages. Patients should be monitored by a specialist every 6-12 months for disease progression and treatment of adverse effects. Wearable devices may provide a novel approach to management by directly monitoring patients for bradykinesia, dyskinesia, tremor and other symptoms. They are intended to be used alongside clinical judgement. Objectives To determine the clinical and cost-effectiveness of five devices for monitoring Parkinson's disease: Personal KinetiGraph, Kinesia 360, KinesiaU, PDMonitor and STAT-ON. Methods We performed systematic reviews of all evidence on the five devices, outcomes included: diagnostic accuracy, impact on decision-making, clinical outcomes, patient and clinician opinions and economic outcomes. We searched MEDLINE and 12 other databases/trial registries to February 2022. Risk of bias was assessed. Narrative synthesis was used to summarise all identified evidence, as the evidence was insufficient for meta-analysis. One included trial provided individual-level data, which was re-analysed. A de novo decision-analytic model was developed to estimate the cost-effectiveness of Personal KinetiGraph and Kinesia 360 compared to standard of care in the UK NHS over a 5-year time horizon. The base-case analysis considered two alternative monitoring strategies: one-time use and routine use of the device. Results Fifty-seven studies of Personal KinetiGraph, 15 of STAT-ON, 3 of Kinesia 360, 1 of KinesiaU and 1 of PDMonitor were included. There was some evidence to suggest that Personal KinetiGraph can accurately measure bradykinesia and dyskinesia, leading to treatment modification in some patients, and a possible improvement in clinical outcomes when measured using the Unified Parkinson's Disease Rating Scale. The evidence for STAT-ON suggested it may be of value for diagnosing symptoms, but there is currently no evidence on its clinical impact. The evidence for Kinesia 360, KinesiaU and PDMonitor is insufficient to draw any conclusions on their value in clinical practice. The base-case results for Personal KinetiGraph compared to standard of care for one-time and routine use resulted in incremental cost-effectiveness ratios of £67,856 and £57,877 per quality-adjusted life-year gained, respectively, with a beneficial impact of the Personal KinetiGraph on Unified Parkinson's Disease Rating Scale domains III and IV. The incremental cost-effectiveness ratio results for Kinesia 360 compared to standard of care for one-time and routine use were £38,828 and £67,203 per quality-adjusted life-year gained, respectively. Limitations The evidence was limited in extent and often low quality. For all devices, except Personal KinetiGraph, there was little to no evidence on the clinical impact of the technology. Conclusions Personal KinetiGraph could reasonably be used in practice to monitor patient symptoms and modify treatment where required. There is too little evidence on STAT-ON, Kinesia 360, KinesiaU or PDMonitor to be confident that they are clinically useful. The cost-effectiveness of remote monitoring appears to be largely unfavourable with incremental cost-effectiveness ratios in excess of £30,000 per quality-adjusted life-year across a range of alternative assumptions. The main driver of cost-effectiveness was the durability of improvements in patient symptoms. Study registration This study is registered as PROSPERO CRD42022308597. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135437) and is published in full in Health Technology Assessment; Vol. 28, No. 30. See the NIHR Funding and Awards website for further award information.
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Affiliation(s)
- Edward Cox
- CHE Technology Assessment Group, University of York, York, UK
| | - Ros Wade
- CRD Technology Assessment Group, University of York, York, UK
| | - Robert Hodgson
- CRD Technology Assessment Group, University of York, York, UK
| | - Helen Fulbright
- CRD Technology Assessment Group, University of York, York, UK
| | - Thai Han Phung
- CHE Technology Assessment Group, University of York, York, UK
| | - Nicholas Meader
- CRD Technology Assessment Group, University of York, York, UK
| | - Simon Walker
- CHE Technology Assessment Group, University of York, York, UK
| | - Claire Rothery
- CHE Technology Assessment Group, University of York, York, UK
| | - Mark Simmonds
- CRD Technology Assessment Group, University of York, York, UK
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Spooner RK, Bahners BH, Schnitzler A, Florin E. Time-resolved quantification of fine hand movements as a proxy for evaluating bradykinesia-induced motor dysfunction. Sci Rep 2024; 14:5340. [PMID: 38438484 PMCID: PMC10912452 DOI: 10.1038/s41598-024-55862-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 02/28/2024] [Indexed: 03/06/2024] Open
Abstract
Bradykinesia is a behavioral manifestation that contributes to functional dependencies in later life. However, the current state of bradykinesia indexing primarily relies on subjective, time-averaged categorizations of motor deficits, which often yield poor reliability. Herein, we used time-resolved analyses of accelerometer recordings during standardized movements, data-driven factor analyses, and linear mixed effects models (LMEs) to quantitatively characterize general, task- and therapy-specific indices of motor impairment in people with Parkinson's disease (PwP) currently undergoing treatment for bradykinesia. Our results demonstrate that single-trial, accelerometer-based features of finger-tapping and rotational hand movements were significantly modulated by divergent therapeutic regimens. Further, these features corresponded well to current gold standards for symptom monitoring, with more precise predictive capacities of bradykinesia-specific declines achieved when considering kinematic features from diverse movement types together, rather than in isolation. Herein, we report data-driven, sample-specific kinematic profiles of diverse movement types along a continuous spectrum of motor impairment, which importantly, preserves the temporal scale for which biomechanical fluctuations in motor deficits evolve in humans. Therefore, this approach may prove useful for tracking bradykinesia-induced motor decline in aging populations the future.
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Affiliation(s)
- Rachel K Spooner
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany.
| | - Bahne H Bahners
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Esther Florin
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany.
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Moreau C, Rouaud T, Grabli D, Benatru I, Remy P, Marques AR, Drapier S, Mariani LL, Roze E, Devos D, Dupont G, Bereau M, Fabbri M. Overview on wearable sensors for the management of Parkinson's disease. NPJ Parkinsons Dis 2023; 9:153. [PMID: 37919332 PMCID: PMC10622581 DOI: 10.1038/s41531-023-00585-y] [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: 05/25/2023] [Accepted: 10/02/2023] [Indexed: 11/04/2023] Open
Abstract
Parkinson's disease (PD) is affecting about 1.2 million patients in Europe with a prevalence that is expected to have an exponential increment, in the next decades. This epidemiological evolution will be challenged by the low number of neurologists able to deliver expert care for PD. As PD is better recognized, there is an increasing demand from patients for rigorous control of their symptoms and for therapeutic education. In addition, the highly variable nature of symtoms between patients and the fluctuations within the same patient requires innovative tools to help doctors and patients monitor the disease in their usual living environment and adapt treatment in a more relevant way. Nowadays, there are various body-worn sensors (BWS) proposed to monitor parkinsonian clinical features, such as motor fluctuations, dyskinesia, tremor, bradykinesia, freezing of gait (FoG) or gait disturbances. BWS have been used as add-on tool for patients' management or research purpose. Here, we propose a practical anthology, summarizing the characteristics of the most used BWS for PD patients in Europe, focusing on their role as tools to improve treatment management. Consideration regarding the use of technology to monitor non-motor features is also included. BWS obviously offer new opportunities for improving management strategy in PD but their precise scope of use in daily routine care should be clarified.
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Affiliation(s)
- Caroline Moreau
- Department of Neurology, Parkinson's disease expert Center, Lille University, INSERM UMRS_1172, University Hospital Center, Lille, France
- The French Ns-Park Network, Paris, France
| | - Tiphaine Rouaud
- The French Ns-Park Network, Paris, France
- CHU Nantes, Centre Expert Parkinson, Department of Neurology, Nantes, F-44093, France
| | - David Grabli
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - Isabelle Benatru
- The French Ns-Park Network, Paris, France
- Department of Neurology, University Hospital of Poitiers, Poitiers, France
- INSERM, CHU de Poitiers, University of Poitiers, Centre d'Investigation Clinique CIC1402, Poitiers, France
| | - Philippe Remy
- The French Ns-Park Network, Paris, France
- Centre Expert Parkinson, NS-Park/FCRIN Network, CHU Henri Mondor, AP-HP, Equipe NPI, IMRB, INSERM et Faculté de Santé UPE-C, Créteil, FranceService de neurologie, hôpital Henri-Mondor, AP-HP, Créteil, France
| | - Ana-Raquel Marques
- The French Ns-Park Network, Paris, France
- Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand University Hospital, Neurology department, Clermont-Ferrand, France
| | - Sophie Drapier
- The French Ns-Park Network, Paris, France
- Pontchaillou University Hospital, Department of Neurology, CIC INSERM 1414, Rennes, France
| | - Louise-Laure Mariani
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - Emmanuel Roze
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - David Devos
- The French Ns-Park Network, Paris, France
- Parkinson's Disease Centre of Excellence, Department of Medical Pharmacology, Univ. Lille, INSERM; CHU Lille, U1172 - Degenerative & Vascular Cognitive Disorders, LICEND, NS-Park Network, F-59000, Lille, France
| | - Gwendoline Dupont
- The French Ns-Park Network, Paris, France
- Centre hospitalier universitaire François Mitterrand, Département de Neurologie, Université de Bourgogne, Dijon, France
| | - Matthieu Bereau
- The French Ns-Park Network, Paris, France
- Service de neurologie, université de Franche-Comté, CHRU de Besançon, 25030, Besançon, France
| | - Margherita Fabbri
- The French Ns-Park Network, Paris, France.
- Department of Neurosciences, Clinical Investigation Center CIC 1436, Parkinson Toulouse Expert Centre, NS-Park/FCRIN Network and NeuroToul COEN Center, Toulouse University Hospital, INSERM, University of Toulouse 3, Toulouse, France.
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Smid A, Oterdoom DLM, Pauwels RWJ, Tamasi K, Elting JWJ, Absalom AR, van Laar T, van Dijk JMC, Drost G. The Relevance of Intraoperative Clinical and Accelerometric Measurements for Thalamotomy Outcome. J Clin Med 2023; 12:5887. [PMID: 37762828 PMCID: PMC10532071 DOI: 10.3390/jcm12185887] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 08/30/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
Thalamotomy alleviates medication-refractory tremors in patients with movement disorders such as Parkinson's Disease (PD), Essential tremor (ET), and Holmes tremor (HT). However, limited data are available on tremor intensity during different thalamotomy stages. Also, the predictive value of the intraoperative tremor status for treatment outcomes remains unclear. Therefore, we aimed to quantify tremor status during thalamotomy and postoperatively. Data were gathered between January 2020 and June 2023 during consecutive unilateral thalamotomy procedures in patients with PD (n = 13), ET (n = 8), and HT (n = 3). MDS-UPDRS scores and tri-axial accelerometry data were obtained during rest, postural, and intention tremor tests. Measurements were performed intraoperatively (1) before lesioning-probe insertion, (2) directly after lesioning-probe insertion, (3) during coagulation, (4) directly after coagulation, and (5) 4-6 months post-surgery. Accelerometric data were recorded continuously during the coagulation process. Outcome measures included MDS-UPDRS tremor scores and accelerometric parameters (peak frequency, tremor amplitude, and area under the curve of power (AUCP)). Tremor intensity was assessed for the insertion effect (1-2), during coagulation (3), post-coagulation effect (1-4), and postoperative effect (1-5). Following insertion and coagulation, tremor intensity improved significantly compared to baseline (p < 0.001). The insertion effect clearly correlated with the postoperative effect (ρ = 0.863, p < 0.001). Both tremor amplitude and AUCP declined gradually during coagulation. Peak frequency did not change significantly intraoperatively. In conclusion, the study data show that both the intraoperative insertion effect and the post-coagulation effect are good predictors for thalamotomy outcomes.
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Affiliation(s)
- Annemarie Smid
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (D.L.M.O.); (R.W.J.P.); (K.T.); (J.M.C.v.D.); (G.D.)
| | - D. L. Marinus Oterdoom
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (D.L.M.O.); (R.W.J.P.); (K.T.); (J.M.C.v.D.); (G.D.)
| | - Rik W. J. Pauwels
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (D.L.M.O.); (R.W.J.P.); (K.T.); (J.M.C.v.D.); (G.D.)
| | - Katalin Tamasi
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (D.L.M.O.); (R.W.J.P.); (K.T.); (J.M.C.v.D.); (G.D.)
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Jan Willem J. Elting
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.W.J.E.); (T.v.L.)
| | - Anthony R. Absalom
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
| | - Teus van Laar
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.W.J.E.); (T.v.L.)
| | - J. Marc C. van Dijk
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (D.L.M.O.); (R.W.J.P.); (K.T.); (J.M.C.v.D.); (G.D.)
| | - Gea Drost
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (D.L.M.O.); (R.W.J.P.); (K.T.); (J.M.C.v.D.); (G.D.)
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.W.J.E.); (T.v.L.)
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Xie J, Zhao H, Cao J, Qu Q, Cao H, Liao WH, Lei Y, Guo L. Wearable multisource quantitative gait analysis of Parkinson's diseases. Comput Biol Med 2023; 164:107270. [PMID: 37478714 DOI: 10.1016/j.compbiomed.2023.107270] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/24/2023] [Accepted: 07/16/2023] [Indexed: 07/23/2023]
Abstract
As the motor symptoms of Parkinson's disease (PD) are complex and influenced by many factors, it is challenging to quantify gait abnormalities adequately using a single type of signal. Therefore, a wearable multisource gait monitoring system is developed to perform a quantitative analysis of gait abnormalities for improving the effectiveness of the clinical diagnosis. To detect multisource gait data for an accurate evaluation of gait abnormalities, force sensitive sensors, piezoelectric sensors, and inertial measurement units are integrated into the devised device. The modulation circuits and wireless framework are designed to simultaneously collect plantar pressure, dynamic deformation, and postural angle of the foot and then wirelessly transmit these collected data. With the designed system, multisource gait data from PD patients and healthy controls are collected. Multisource features for quantifying gait abnormalities are extracted and evaluated by a significance test of difference and correlation analysis. The results show that the features extracted from every single type of data are able to quantify the health status of the subjects (p < 0.001, ρ > 0.50). More importantly, the validity of multisource gait data is verified. The results demonstrate that the gait feature fusing multisource data achieves a maximum correlation coefficient of 0.831, a maximum Area Under Curve of 0.9206, and a maximum feature-based classification accuracy of 88.3%. The system proposed in this study can be applied to the gait analysis and objective evaluation of PD.
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Affiliation(s)
- Junxiao Xie
- Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Huan Zhao
- Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Junyi Cao
- Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Qiumin Qu
- Department of Neurology, The First Affiliated Hospital of Medical College of Xi'an Jiaotong University, Xi'an, 710061, China
| | - Hongmei Cao
- Department of Neurology, The First Affiliated Hospital of Medical College of Xi'an Jiaotong University, Xi'an, 710061, China
| | - Wei-Hsin Liao
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, 999077, China
| | - Yaguo Lei
- Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Linchuan Guo
- Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
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Guerra A, D'Onofrio V, Ferreri F, Bologna M, Antonini A. Objective measurement versus clinician-based assessment for Parkinson's disease. Expert Rev Neurother 2023; 23:689-702. [PMID: 37366316 DOI: 10.1080/14737175.2023.2229954] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/18/2023] [Accepted: 06/22/2023] [Indexed: 06/28/2023]
Abstract
INTRODUCTION Although clinician-based assessment through standardized clinical rating scales is currently the gold standard for quantifying motor impairment in Parkinson's disease (PD), it is not without limitations, including intra- and inter-rater variability and a degree of approximation. There is increasing evidence supporting the use of objective motion analyses to complement clinician-based assessment. Objective measurement tools hold significant potential for improving the accuracy of clinical and research-based evaluations of patients. AREAS COVERED The authors provide several examples from the literature demonstrating how different motion measurement tools, including optoelectronics, contactless and wearable systems allow for both the objective quantification and monitoring of key motor symptoms (such as bradykinesia, rigidity, tremor, and gait disturbances), and the identification of motor fluctuations in PD patients. Furthermore, they discuss how, from a clinician's perspective, objective measurements can help in various stages of PD management. EXPERT OPINION In our opinion, sufficient evidence supports the assertion that objective monitoring systems enable accurate evaluation of motor symptoms and complications in PD. A range of devices can be utilized not only to support diagnosis but also to monitor motor symptom during the disease progression and can become relevant in the therapeutic decision-making process.
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Affiliation(s)
- Andrea Guerra
- Parkinson and Movement Disorder Unit, Study Center on Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | | | - Florinda Ferreri
- Unit of Neurology, Unit of Clinical Neurophysiology, Study Center of Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
- Department of Clinical Neurophysiology, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Matteo Bologna
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed, Pozzilli, Italy
| | - Angelo Antonini
- Parkinson and Movement Disorder Unit, Study Center on Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
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Kanellos FS, Tsamis KI, Rigas G, Simos YV, Katsenos AP, Kartsakalis G, Fotiadis DI, Vezyraki P, Peschos D, Konitsiotis S. Clinical Evaluation in Parkinson's Disease: Is the Golden Standard Shiny Enough? SENSORS (BASEL, SWITZERLAND) 2023; 23:3807. [PMID: 37112154 PMCID: PMC10145765 DOI: 10.3390/s23083807] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 04/04/2023] [Accepted: 04/04/2023] [Indexed: 06/19/2023]
Abstract
Parkinson's disease (PD) has become the second most common neurodegenerative condition following Alzheimer's disease (AD), exhibiting high prevalence and incident rates. Current care strategies for PD patients include brief appointments, which are sparsely allocated, at outpatient clinics, where, in the best case scenario, expert neurologists evaluate disease progression using established rating scales and patient-reported questionnaires, which have interpretability issues and are subject to recall bias. In this context, artificial-intelligence-driven telehealth solutions, such as wearable devices, have the potential to improve patient care and support physicians to manage PD more effectively by monitoring patients in their familiar environment in an objective manner. In this study, we evaluate the validity of in-office clinical assessment using the MDS-UPDRS rating scale compared to home monitoring. Elaborating the results for 20 patients with Parkinson's disease, we observed moderate to strong correlations for most symptoms (bradykinesia, rest tremor, gait impairment, and freezing of gait), as well as for fluctuating conditions (dyskinesia and OFF). In addition, we identified for the first time the existence of an index capable of remotely measuring patients' quality of life. In summary, an in-office examination is only partially representative of most PD symptoms and cannot accurately capture daytime fluctuations and patients' quality of life.
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Affiliation(s)
- Foivos S. Kanellos
- Department of Physiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
- PD Neurotechnology Ltd., 45500 Ioannina, Greece
| | - Konstantinos I. Tsamis
- Department of Physiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
- Department of Neurology, University Hospital of Ioannina, 45110 Ioannina, Greece
| | | | - Yannis V. Simos
- Department of Physiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | - Andreas P. Katsenos
- Department of Physiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | - Gerasimos Kartsakalis
- Department of Physiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, 45110 Ioannina, Greece
| | - Patra Vezyraki
- Department of Physiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | - Dimitrios Peschos
- Department of Physiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | - Spyridon Konitsiotis
- Department of Neurology, University Hospital of Ioannina, 45110 Ioannina, Greece
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Han Y, Liu X, Zhang N, Zhang X, Zhang B, Wang S, Liu T, Yi J. Automatic Assessments of Parkinsonian Gait with Wearable Sensors for Human Assistive Systems. SENSORS (BASEL, SWITZERLAND) 2023; 23:2104. [PMID: 36850705 PMCID: PMC9959760 DOI: 10.3390/s23042104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 01/28/2023] [Accepted: 02/09/2023] [Indexed: 06/18/2023]
Abstract
The rehabilitation evaluation of Parkinson's disease has always been the research focus of human assistive systems. It is a research hotspot to objectively and accurately evaluate the gait condition of Parkinson's disease patients, thereby adjusting the actuators of the human-machine system and making rehabilitation robots better adapt to the recovery process of patients. The rehabilitation evaluation of Parkinson's disease has always been the research focus of rehabilitation robots. It is a research hotspot to be able to objectively and accurately evaluate the recovery of Parkinson's disease patients, thereby adjusting the driving module of the human-machine collaboration system in real time, so that rehabilitation robots can better adapt to the recovery process of Parkinson's disease. The gait task in the Unified Parkinson's Disease Rating Scale (UPDRS) is a widely accepted standard for assessing the gait impairments of patients with Parkinson's disease (PD). However, the assessments conducted by neurologists are always subjective and inaccurate, and the results are determined by the neurologists' observation and clinical experience. Thus, in this study, we proposed a novel machine learning-based method of automatically assessing the gait task in UPDRS with wearable sensors as a more convenient and objective alternative means for PD gait assessment. In the design, twelve gait features, including three spatial-temporal features and nine kinematic features, were extracted and calculated from two shank-mounted IMUs. A novel nonlinear model is developed for calculating the score of gait task from the gait features. Twenty-five PD patients and twenty-eight healthy subjects were recruited for validating the proposed method. For comparison purpose, three traditional models, which have been used in previous studies, were also tested by the same dataset. In terms of percentages of participants, 84.9%, 73.6%, 73.6%, and 66.0% of the participants were accurately assigned into the true level with the proposed nonlinear model, the support vector machine model, the naive Bayes model, and the linear regression model, respectively, which indicates that the proposed method has a good performance on calculating the score of the UPDRS gait task and conformance with the rating done by neurologists.
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Affiliation(s)
- Yi Han
- The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
- Department of Intelligent Mechanical Systems Engineering, Kochi University of Technology, Kochi 782-8502, Japan
| | - Xiangzhi Liu
- The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
| | - Ning Zhang
- The National Research Center for Rehabilitation Technical Aids, Beijing 102676, China
| | - Xiufeng Zhang
- The National Research Center for Rehabilitation Technical Aids, Beijing 102676, China
| | - Bin Zhang
- The College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
| | - Shuoyu Wang
- Department of Intelligent Mechanical Systems Engineering, Kochi University of Technology, Kochi 782-8502, Japan
| | - Tao Liu
- The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
| | - Jingang Yi
- Department of Mechanical and Aerospace Engineering, Rutgers University, Piscataway, NJ 08854, USA
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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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Borzì L, Sigcha L, Rodríguez-Martín D, Olmo G. Real-time detection of freezing of gait in Parkinson's disease using multi-head convolutional neural networks and a single inertial sensor. Artif Intell Med 2023; 135:102459. [PMID: 36628783 DOI: 10.1016/j.artmed.2022.102459] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Freezing of gait (FOG) is one of the most disabling symptoms of Parkinson's disease (PD), contributing to poor quality of life and increased risk of falls. Wearable sensors represent a valuable means for detecting FOG in the home environment. Moreover, real-time feedback has proven to help reduce the duration of FOG episodes. This work proposes a robust real-time FOG detection algorithm, which is easy to implement in stand-alone devices working in non-supervised conditions. METHOD Data from three different data sets were used in this study, with two employed as independent test sets. Acceleration recordings from 118 PD patients and 21 healthy elderly subjects were collected while they performed simulated daily living activities. A single inertial sensor was attached to the waist of each subject. More than 17 h of valid data and a total number of 1110 FOG episodes were analyzed in this study. The implemented algorithm consisted of a multi-head convolutional neural network, which exploited different spatial resolutions in the analysis of inertial data. The architecture and the model parameters were designed to provide optimal performance while reducing computational complexity and testing time. RESULTS The developed algorithm demonstrated good to excellent classification performance, with more than 50% (30%) of FOG episodes predicted on average 3.1 s (1.3 s) before the actual onset in the main (independent) data set. Around 50% of FOG was detected with an average delay of 0.8 s (1.1 s) in the main (independent) data set. Moreover, a specificity above 88% (93%) was obtained when testing the algorithm on the main (independent) test set, while 100% specificity was obtained on healthy elderly subjects. CONCLUSION The algorithm proved robust, with low computational complexity and processing time, thus paving the way to a real-time implementation in a stand-alone device that can be used in non-supervised environments.
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Affiliation(s)
- Luigi Borzì
- Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy.
| | - Luis Sigcha
- Instrumentation and Applied Acoustics Research Group (I2A2), Universidad Politecnica de Madrid, Ctra. Valencia, Km 7, 28031 Madrid, Spain; ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal.
| | - Daniel Rodríguez-Martín
- Sense4Care S.L., Cornellà de Llobregat, 08940 Barcelona, Spain; Technical Research Centre for Dependency Care and Autonomous Living (CETPD), Universitat Politecnica de Catalunya, 08800 Vilanova i la Geltrù, Spain.
| | - Gabriella Olmo
- Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy.
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Rastegari E, Ali H, Marmelat V. Detection of Parkinson's Disease Using Wrist Accelerometer Data and Passive Monitoring. SENSORS (BASEL, SWITZERLAND) 2022; 22:9122. [PMID: 36501823 PMCID: PMC9738242 DOI: 10.3390/s22239122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 11/11/2022] [Accepted: 11/18/2022] [Indexed: 06/17/2023]
Abstract
Parkinson's disease is a neurodegenerative disorder impacting patients' movement, causing a variety of movement abnormalities. It has been the focus of research studies for early detection based on wearable technologies. The benefit of wearable technologies in the domain rises by continuous monitoring of this population's movement patterns over time. The ubiquity of wrist-worn accelerometry and the fact that the wrist is the most common and acceptable body location to wear the accelerometer for continuous monitoring suggests that wrist-worn accelerometers are the best choice for early detection of the disease and also tracking the severity of it over time. In this study, we use a dataset consisting of one-week wrist-worn accelerometry data collected from individuals with Parkinson's disease and healthy elderlies for early detection of the disease. Two feature engineering methods, including epoch-based statistical feature engineering and the document-of-words method, were used. Using various machine learning classifiers, the impact of different windowing strategies, using the document-of-words method versus the statistical method, and the amount of data in terms of number of days were investigated. Based on our results, PD was detected with the highest average accuracy value (85% ± 15%) across 100 runs of SVM classifier using a set of features containing features from every and all windowing strategies. We also found that the document-of-words method significantly improves the classification performance compared to the statistical feature engineering model. Although the best performance of the classification task between PD and healthy elderlies was obtained using seven days of data collection, the results indicated that with three days of data collection, we can reach a classification performance that is not significantly different from a model built using seven days of data collection.
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Affiliation(s)
- Elham Rastegari
- Department of Business Intelligence and Analytics, Business College, Creighton University, Omaha, NE 68178, USA
| | - Hesham Ali
- Department of Biomedical Informatics, College of Information Systems and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA
| | - Vivien Marmelat
- Department of Biomechanics, College of Education, Health and Human Sciences, University of Nebraska at Omaha, Omaha, NE 68182, USA
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Guo CC, Chiesa PA, de Moor C, Fazeli MS, Schofield T, Hofer K, Belachew S, Scotland A. Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review. J Med Internet Res 2022; 24:e37683. [DOI: 10.2196/37683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 07/18/2022] [Accepted: 10/11/2022] [Indexed: 11/22/2022] Open
Abstract
Background
With the advent of smart sensing technology, mobile and wearable devices can provide continuous and objective monitoring and assessment of motor function outcomes.
Objective
We aimed to describe the existing scientific literature on wearable and mobile technologies that are being used or tested for assessing motor functions in mobility-impaired and healthy adults and to evaluate the degree to which these devices provide clinically valid measures of motor function in these populations.
Methods
A systematic literature review was conducted by searching Embase, MEDLINE, CENTRAL (January 1, 2015, to June 24, 2020), the United States and European Union clinical trial registries, and the United States Food and Drug Administration website using predefined study selection criteria. Study selection, data extraction, and quality assessment were performed by 2 independent reviewers.
Results
A total of 91 publications representing 87 unique studies were included. The most represented clinical conditions were Parkinson disease (n=51 studies), followed by stroke (n=5), Huntington disease (n=5), and multiple sclerosis (n=2). A total of 42 motion-detecting devices were identified, and the majority (n=27, 64%) were created for the purpose of health care–related data collection, although approximately 25% were personal electronic devices (eg, smartphones and watches) and 11% were entertainment consoles (eg, Microsoft Kinect or Xbox and Nintendo Wii). The primary motion outcomes were related to gait (n=30), gross motor movements (n=25), and fine motor movements (n=23). As a group, sensor-derived motion data showed a mean sensitivity of 0.83 (SD 7.27), a mean specificity of 0.84 (SD 15.40), a mean accuracy of 0.90 (SD 5.87) in discriminating between diseased individuals and healthy controls, and a mean Pearson r validity coefficient of 0.52 (SD 0.22) relative to clinical measures. We did not find significant differences in the degree of validity between in-laboratory and at-home sensor-based assessments nor between device class (ie, health care–related device, personal electronic devices, and entertainment consoles).
Conclusions
Sensor-derived motion data can be leveraged to classify and quantify disease status for a variety of neurological conditions. However, most of the recent research on digital clinical measures is derived from proof-of-concept studies with considerable variation in methodological approaches, and much of the reviewed literature has focused on clinical validation, with less than one-quarter of the studies performing analytical validation. Overall, future research is crucially needed to further consolidate that sensor-derived motion data may lead to the development of robust and transformative digital measurements intended to predict, diagnose, and quantify neurological disease state and its longitudinal change.
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Thomsen TH, Jørgensen LB, Kjær TW, Haahr A, Vogel A, Larsen IU, Winge K. Clinical Markers of 6 Pre-dominant Coping Behaviors in Living With Parkinson Disease: A Convergent Mixed Methods Study. INQUIRY : A JOURNAL OF MEDICAL CARE ORGANIZATION, PROVISION AND FINANCING 2022; 59:469580221129929. [PMID: 36314596 PMCID: PMC9629560 DOI: 10.1177/00469580221129929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
People with Parkinson's disease (PwP) experience a variety of symptoms and fluctuations in these, which they have to cope with every day. In tailoring a person-centered treatment to PwP there is a lack of knowledge about the association between pre-dominant coping behaviors and clinical markers among PwP. To describe and compare specific clinical markers between 6 suggested coping behaviors. Thirty-four PwP, who previously had been classified into 6 different pre-dominant coping behaviors, were included in this mixed methods study. Six primary variables were included in the descriptive analysis; motor function (UPDRS-III), non-motor symptoms score (NMS-Quest), change in bradykinesia score, apathy score (LARS), personality traits (NEO-FFI), and cognitive status (evaluated by a neuropsychologist). The merged results of this mixed methods study indicate that clinical markers as apathy, burden of non-motor symptoms, cognitive impairments and personality traits, have the potential to impact the coping behavior in PwP. In a clinical setting the markers; NMS-burden, degree of apathy, cognition, and personality traits may indicate specific coping behavior. Three of the six suggested typologies of coping behaviors differed from the other groups when comparing descriptive data. In order to improve patient care and guide the development of person-centered therapies, each PwP should be approached based on those typologies.
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Affiliation(s)
- Trine Hørmann Thomsen
- Rigshospitalet Glostrup, Glostrup, Capital Region, Denmark,Trine Hørmann Thomsen, Department of Neurology, Movement disorder Clinic, Rigshospitalet Glostrup, Valdemar Hansens Vej 6, opgang 7, Glostrup, Capital Region 2600, Denmark.
| | - Lene Bastrup Jørgensen
- Knowledge Centre for Neurorehabilitation of Western Denmark, Regional Hospital Viborg, Denmark,Department of Clinical Medicine, University of Aarhus, Aarhus, Denmark
| | - Troels Wesenberg Kjær
- Zealand University Hospital, Roskilde, Denmark,University of Copenhagen, København, Denmark
| | | | - Asmus Vogel
- Copenhagen University Hospital, Rigshospitalet, Denmark
| | | | - Kristian Winge
- Odense University Hospital, Odense, Denmark,University of Southern Denmark, Denmark
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Pérez-López C, Hernández-Vara J, Caballol N, Bayes À, Buongiorno M, Lopez-Ariztegui N, Gironell A, López-Sánchez J, Martínez-Castrillo JC, Sauco M A, López-Manzanares L, Escalante-Arroyo S, Pérez-Martínez DA, Rodríguez-Molinero A. Comparison of the Results of a Parkinson's Holter Monitor With Patient Diaries, in Real Conditions of Use: A Sub-analysis of the MoMoPa-EC Clinical Trial. Front Neurol 2022; 13:835249. [PMID: 35651347 PMCID: PMC9149269 DOI: 10.3389/fneur.2022.835249] [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: 12/14/2021] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
Background For specialists in charge of Parkinson's disease (PD), one of the most time-consuming tasks of the consultations is the assessment of symptoms and motor fluctuations. This task is complex and is usually based on the information provided by the patients themselves, which in most cases is complex and biased. In recent times, different tools have appeared on the market that allow automatic ambulatory monitoring. The MoMoPa-EC clinical trial (NCT04176302) investigates the effect of one of these tools—Sense4Care's STAT-ON—can have on routine clinical practice. In this sub-analysis the agreement between the Hauser diaries and the STAT-ON sensor is analyzed. Methods Eighty four patients from MoMoPa-EC cohort were included in this sub-analysis. The intraclass correlation coefficient was calculated between the patient diary entries and the sensor data. Results The intraclass correlation coefficient of both methods was 0.57 (95% CI: 0.3–0.73) for the OFF time (%), 0.48 (95% CI: 0.17–0.68) for the time in ON (%), and 0.65 (95% CI%: 0.44–0.78) for the time with dyskinesias (%). Furthermore, the Spearman correlations with the UPDRS scale have been analyzed for different parameters of the two methods. The maximum correlation found was −0.63 (p < 0.001) between Mean Fluidity (one of the variables offered by the STAT-dON) and factor 1 of the UPDRS. Conclusion This sub-analysis shows a moderate concordance between the two tools, it is clearly appreciated that the correlation between the different UPDRS indices is better with the STAT-ON than with the Hauser diary. Trial Registration https://clinicaltrials.gov/show/NCT04176302 (NCT04176302).
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Affiliation(s)
- Carlos Pérez-López
- Department of Investigation, Consorci Sanitari de l'Alt Penedès i Garraf, Sant Pere de Ribes, Spain
| | - Jorge Hernández-Vara
- Neurology Department, Hospital Universitari Vall D Hebron Neurodegenerative Diseases Research Group, Vall D Hebron Institut de Recerca Universidad Autónoma de Barcelona, Biomedical Research Network Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Nuria Caballol
- Department of Neurology, Hospital de Sant Joan Despí Moisès Broggi, Sant Joan Despi, Spain.,Parkinson's and Movement Disorders Unit, Hospital Quirón-Teknon, Barcelona, Spain
| | - Àngels Bayes
- Parkinson's and Movement Disorders Unit, Hospital Quirón-Teknon, Barcelona, Spain
| | | | | | - Alexandre Gironell
- Department of Neurology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - José López-Sánchez
- Department of Neurology, Hospital Virgen de la Arrixaca de Murcia, Murcia, Spain
| | | | - Alvarez Sauco M
- Department of Neurology, Hospital General de Elche, Elche, Spain
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Rodríguez-Martín D, Cabestany J, Pérez-López C, Pie M, Calvet J, Samà A, Capra C, Català A, Rodríguez-Molinero A. A New Paradigm in Parkinson's Disease Evaluation With Wearable Medical Devices: A Review of STAT-ON TM. Front Neurol 2022; 13:912343. [PMID: 35720090 PMCID: PMC9202426 DOI: 10.3389/fneur.2022.912343] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
In the past decade, the use of wearable medical devices has been a great breakthrough in clinical practice, trials, and research. In the Parkinson's disease field, clinical evaluation is time limited, and healthcare professionals need to rely on retrospective data collected through patients' self-filled diaries and administered questionnaires. As this often leads to inaccurate evaluations, a more objective system for symptom monitoring in a patient's daily life is claimed. In this regard, the use of wearable medical devices is crucial. This study aims at presenting a review on STAT-ONTM, a wearable medical device Class IIa, which provides objective information on the distribution and severity of PD motor symptoms in home environments. The sensor analyzes inertial signals, with a set of validated machine learning algorithms running in real time. The device was developed for 12 years, and this review aims at gathering all the results achieved within this time frame. First, a compendium of the complete journey of STAT-ONTM since 2009 is presented, encompassing different studies and developments in funded European and Spanish national projects. Subsequently, the methodology of database construction and machine learning algorithms design and development is described. Finally, clinical validation and external studies of STAT-ONTM are presented.
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Affiliation(s)
| | - Joan Cabestany
- Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Carlos Pérez-López
- Department of Investigation, Consorci Sanitari Alt Penedès - Garraf, Vilanova i la Geltrú, Spain
| | - Marti Pie
- Sense4Care S.L., Cornellà de Llobregat, Spain
| | - Joan Calvet
- Sense4Care S.L., Cornellà de Llobregat, Spain
| | - Albert Samà
- Sense4Care S.L., Cornellà de Llobregat, Spain
| | | | - Andreu Català
- Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politecnica de Catalunya, Barcelona, Spain
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Body-Worn Sensors for Parkinson’s disease: A qualitative approach with patients and healthcare professionals. PLoS One 2022; 17:e0265438. [PMID: 35511812 PMCID: PMC9070870 DOI: 10.1371/journal.pone.0265438] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 03/01/2022] [Indexed: 11/29/2022] Open
Abstract
Body-Worn Sensors (BWS) provide reliable objective and continuous assessment of Parkinson’s disease (PD) motor symptoms, but their implementation in clinical routine has not yet become widespread. Users’ perceptions of BWS have not been explored. This study intended to evaluate the usability, user experience (UX), patients’ perceptions of BWS, and health professionals’ (HP) opinions on BWS monitoring. A qualitative analysis was performed from semi-structured interviews conducted with 22 patients and 9 HP experts in PD. Patients completed two interviews before and after the BWS one-week experiment, and they answered two questionnaires assessing the usability and UX. Patients rated the three BWS usability with high scores (SUS median [range]: 87.5 [72.5–100]). The UX across all dimensions of their interaction with the BWS was positive. During interviews, all patients and HP expressed interest in BWS monitoring. Patients’ hopes and expectations increased the more they learned about BWS. They manifested enthusiasm to wear BWS, which they imagined could improve their PD symptoms. HP highlighted needs for logistical support in the implementation of BWS in their practice. Both patients and HP suggested possible uses of BWS monitoring in clinical practice, for treatment adjustments for example, or for research purposes. Patients and HP shared ideas about the use of BWS monitoring, although patients may be more likely to integrate BWS into their disease follow-up compared to HP in their practice. This study highlights gaps that need to be fulfilled to facilitate BWS adoption and promote their potential.
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17
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Smid A, Elting JWJ, van Dijk JMC, Otten B, Oterdoom DLM, Tamasi K, Heida T, van Laar T, Drost G. Intraoperative Quantification of MDS-UPDRS Tremor Measurements Using 3D Accelerometry: A Pilot Study. J Clin Med 2022; 11:jcm11092275. [PMID: 35566401 PMCID: PMC9104023 DOI: 10.3390/jcm11092275] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/10/2022] [Accepted: 04/16/2022] [Indexed: 02/05/2023] Open
Abstract
The most frequently used method for evaluating tremor in Parkinson’s disease (PD) is currently the internationally standardized Movement Disorder Society—Unified PD Rating Scale (MDS-UPDRS). However, the MDS-UPDRS is associated with limitations, such as its inherent subjectivity and reliance on experienced raters. Objective motor measurements using accelerometry may overcome the shortcomings of visually scored scales. Therefore, the current study focuses on translating the MDS-UPDRS tremor tests into an objective scoring method using 3D accelerometry. An algorithm to measure and classify tremor according to MDS-UPDRS criteria is proposed. For this study, 28 PD patients undergoing neurosurgical treatment and 26 healthy control subjects were included. Both groups underwent MDS-UPDRS tests to rate tremor severity, while accelerometric measurements were performed at the index fingers. All measurements were performed in an off-medication state. Quantitative measures were calculated from the 3D acceleration data, such as tremor amplitude and area-under-the-curve of power in the 4−6 Hz range. Agreement between MDS-UPDRS tremor scores and objective accelerometric scores was investigated. The trends were consistent with the logarithmic relationship between tremor amplitude and MDS-UPDRS score reported in previous studies. The accelerometric scores showed a substantial concordance (>69.6%) with the MDS-UPDRS ratings. However, accelerometric kinetic tremor measures poorly associated with the given MDS-UPDRS scores (R2 < 0.3), mainly due to the noise between 4 and 6 Hz found in the healthy controls. This study shows that MDS-UDPRS tremor tests can be translated to objective accelerometric measurements. However, discrepancies were found between accelerometric kinetic tremor measures and MDS-UDPRS ratings. This technology has the potential to reduce rater dependency of MDS-UPDRS measurements and allow more objective intraoperative monitoring of tremor.
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Affiliation(s)
- Annemarie Smid
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.M.C.v.D.); (D.L.M.O.); (K.T.); (G.D.)
- Correspondence:
| | - Jan Willem J. Elting
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.W.J.E.); (T.v.L.)
- Expertise Center Movement Disorders Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - J. Marc C. van Dijk
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.M.C.v.D.); (D.L.M.O.); (K.T.); (G.D.)
| | - Bert Otten
- Center for Human Movement Sciences, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
| | - D. L. Marinus Oterdoom
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.M.C.v.D.); (D.L.M.O.); (K.T.); (G.D.)
| | - Katalin Tamasi
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.M.C.v.D.); (D.L.M.O.); (K.T.); (G.D.)
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Tjitske Heida
- Department of Biomedical Signals and Systems, Faculty EEMCS, TechMed Centre, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands;
| | - Teus van Laar
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.W.J.E.); (T.v.L.)
- Expertise Center Movement Disorders Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Gea Drost
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.M.C.v.D.); (D.L.M.O.); (K.T.); (G.D.)
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.W.J.E.); (T.v.L.)
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Predicting Axial Impairment in Parkinson's Disease through a Single Inertial Sensor. SENSORS 2022; 22:s22020412. [PMID: 35062375 PMCID: PMC8778464 DOI: 10.3390/s22020412] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 12/23/2021] [Accepted: 01/05/2022] [Indexed: 02/06/2023]
Abstract
Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson’s disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. Methods: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. Results: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). Conclusions: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients.
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Barrachina-Fernández M, Maitín AM, Sánchez-Ávila C, Romero JP. Wearable Technology to Detect Motor Fluctuations in Parkinson's Disease Patients: Current State and Challenges. SENSORS (BASEL, SWITZERLAND) 2021; 21:4188. [PMID: 34207198 PMCID: PMC8234127 DOI: 10.3390/s21124188] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/07/2021] [Accepted: 06/16/2021] [Indexed: 01/30/2023]
Abstract
Monitoring of motor symptom fluctuations in Parkinson's disease (PD) patients is currently performed through the subjective self-assessment of patients. Clinicians require reliable information about a fluctuation's occurrence to enable a precise treatment rescheduling and dosing adjustment. In this review, we analyzed the utilization of sensors for identifying motor fluctuations in PD patients and the application of machine learning techniques to detect fluctuations. The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Ten studies were included between January 2010 and March 2021, and their main characteristics and results were assessed and documented. Five studies utilized daily activities to collect the data, four used concrete scenarios executing specific activities to gather the data, and only one utilized a combination of both situations. The accuracy for classification was 83.56-96.77%. In the studies evaluated, it was not possible to find a standard cleaning protocol for the signal captured, and there is significant heterogeneity in the models utilized and in the different features introduced in the models (using spatiotemporal characteristics, frequential characteristics, or both). The two most influential factors in the good performance of the classification problem are the type of features utilized and the type of model.
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Affiliation(s)
- Mercedes Barrachina-Fernández
- Programa en Ingeniería Biomédica (PhD), ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM), Avenida Complutense, 30, 28040 Madrid, Spain;
| | - Ana María Maitín
- Centro de Estudios e Innovación en Gestión del Conocimiento (CEIEC), Universidad Francisco de Vitoria, 28223 Pozuelo de Alarcón, Spain;
| | - Carmen Sánchez-Ávila
- Department de Matemática Aplicada a las TICs, ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM), Avenida Complutense, 30, 28040 Madrid, Spain
| | - Juan Pablo Romero
- Facultad de Ciencias Experimentales, Universidad Francisco de Vitoria, 28223 Pozuelo de Alarcón, Spain
- Brain Damage Unit, Hospital Beata María Ana, 28007 Madrid, Spain
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20
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Corrà MF, Atrsaei A, Sardoreira A, Hansen C, Aminian K, Correia M, Vila-Chã N, Maetzler W, Maia L. Comparison of Laboratory and Daily-Life Gait Speed Assessment during ON and OFF States in Parkinson's Disease. SENSORS 2021; 21:s21123974. [PMID: 34207565 PMCID: PMC8229328 DOI: 10.3390/s21123974] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 05/31/2021] [Accepted: 06/07/2021] [Indexed: 11/16/2022]
Abstract
Accurate assessment of Parkinson's disease (PD) ON and OFF states in the usual environment is essential for tailoring optimal treatments. Wearables facilitate measurements of gait in novel and unsupervised environments; however, differences between unsupervised and in-laboratory measures have been reported in PD. We aimed to investigate whether unsupervised gait speed discriminates medication states and which supervised tests most accurately represent home performance. In-lab gait speeds from different gait tasks were compared to home speeds of 27 PD patients at ON and OFF states using inertial sensors. Daily gait speed distribution was expressed in percentiles and walking bout (WB) length. Gait speeds differentiated ON and OFF states in the lab and the home. When comparing lab with home performance, ON assessments in the lab showed moderate-to-high correlations with faster gait speeds in unsupervised environment (r = 0.69; p < 0.001), associated with long WB. OFF gait assessments in the lab showed moderate correlation values with slow gait speeds during OFF state at home (r = 0.56; p = 0.004), associated with short WB. In-lab and daily assessments of gait speed with wearables capture additional integrative aspects of PD, reflecting different aspects of mobility. Unsupervised assessment using wearables adds complementary information to the clinical assessment of motor fluctuations in PD.
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Affiliation(s)
- Marta Francisca Corrà
- Abel Salazar Biomedical Sciences Institute (ICBAS), University of Porto, 4050-313 Porto, Portugal; (M.C.); (L.M.)
- University Hospital Santo Antonio of Porto (CHUP), 4099-001 Porto, Portugal; (A.S.); (N.V.-C.)
- Correspondence:
| | - Arash Atrsaei
- Laboratory of Movement Analysis and Measurement, Swiss Federal Institute of Technology in Lausanne (EPFL), 1015 Lausanne, Switzerland; (A.A.); (K.A.)
| | - Ana Sardoreira
- University Hospital Santo Antonio of Porto (CHUP), 4099-001 Porto, Portugal; (A.S.); (N.V.-C.)
| | - Clint Hansen
- Department of Neurology, Christian-Albrechts-University, 24118 Kiel, Germany; (C.H.); (W.M.)
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Swiss Federal Institute of Technology in Lausanne (EPFL), 1015 Lausanne, Switzerland; (A.A.); (K.A.)
| | - Manuel Correia
- Abel Salazar Biomedical Sciences Institute (ICBAS), University of Porto, 4050-313 Porto, Portugal; (M.C.); (L.M.)
- University Hospital Santo Antonio of Porto (CHUP), 4099-001 Porto, Portugal; (A.S.); (N.V.-C.)
| | - Nuno Vila-Chã
- University Hospital Santo Antonio of Porto (CHUP), 4099-001 Porto, Portugal; (A.S.); (N.V.-C.)
| | - Walter Maetzler
- Department of Neurology, Christian-Albrechts-University, 24118 Kiel, Germany; (C.H.); (W.M.)
| | - Luís Maia
- Abel Salazar Biomedical Sciences Institute (ICBAS), University of Porto, 4050-313 Porto, Portugal; (M.C.); (L.M.)
- University Hospital Santo Antonio of Porto (CHUP), 4099-001 Porto, Portugal; (A.S.); (N.V.-C.)
- Institute for Research and Innovation in Health (i3s), University of Porto, 4200-135 Porto, Portugal
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21
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Hssayeni MD, Jimenez-Shahed J, Burack MA, Ghoraani B. Ensemble deep model for continuous estimation of Unified Parkinson's Disease Rating Scale III. Biomed Eng Online 2021; 20:32. [PMID: 33789666 PMCID: PMC8010504 DOI: 10.1186/s12938-021-00872-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 03/18/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Unified Parkinson Disease Rating Scale-part III (UPDRS III) is part of the standard clinical examination performed to track the severity of Parkinson's disease (PD) motor complications. Wearable technologies could be used to reduce the need for on-site clinical examinations of people with Parkinson's disease (PwP) and provide a reliable and continuous estimation of the severity of PD at home. The reported estimation can be used to successfully adjust the dose and interval of PD medications. METHODS We developed a novel algorithm for unobtrusive and continuous UPDRS-III estimation at home using two wearable inertial sensors mounted on the wrist and ankle. We used the ensemble of three deep-learning models to detect UPDRS-III-related patterns from a combination of hand-crafted features, raw temporal signals, and their time-frequency representation. Specifically, we used a dual-channel, Long Short-Term Memory (LSTM) for hand-crafted features, 1D Convolutional Neural Network (CNN)-LSTM for raw signals, and 2D CNN-LSTM for time-frequency data. We utilized transfer learning from activity recognition data and proposed a two-stage training for the CNN-LSTM networks to cope with the limited amount of data. RESULTS The algorithm was evaluated on gyroscope data from 24 PwP as they performed different daily living activities. The estimated UPDRS-III scores had a correlation of [Formula: see text] and a mean absolute error of 5.95 with the clinical examination scores without requiring the patients to perform any specific tasks. CONCLUSION Our analysis demonstrates the potential of our algorithm for estimating PD severity scores unobtrusively at home. Such an algorithm could provide the required motor-complication measurements without unnecessary clinical visits and help the treating physician provide effective management of the disease.
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Affiliation(s)
- Murtadha D Hssayeni
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA
| | | | - Michelle A Burack
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Behnaz Ghoraani
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA.
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22
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Williamson JR, Telfer B, Mullany R, Friedl KE. Detecting Parkinson's Disease from Wrist-Worn Accelerometry in the U.K. Biobank. SENSORS (BASEL, SWITZERLAND) 2021; 21:2047. [PMID: 33799420 PMCID: PMC7999802 DOI: 10.3390/s21062047] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/09/2021] [Accepted: 03/10/2021] [Indexed: 02/06/2023]
Abstract
Parkinson's disease (PD) is a chronic movement disorder that produces a variety of characteristic movement abnormalities. The ubiquity of wrist-worn accelerometry suggests a possible sensor modality for early detection of PD symptoms and subsequent tracking of PD symptom severity. As an initial proof of concept for this technological approach, we analyzed the U.K. Biobank data set, consisting of one week of wrist-worn accelerometry from a population with a PD primary diagnosis and an age-matched healthy control population. Measures of movement dispersion were extracted from automatically segmented gait data, and measures of movement dimensionality were extracted from automatically segmented low-movement data. Using machine learning classifiers applied to one week of data, PD was detected with an area under the curve (AUC) of 0.69 on gait data, AUC = 0.84 on low-movement data, and AUC = 0.85 on a fusion of both activities. It was also found that classification accuracy steadily improved across the one-week data collection, suggesting that higher accuracy could be achievable from a longer data collection. These results suggest the viability of using a low-cost and easy-to-use activity sensor for detecting movement abnormalities due to PD and motivate further research on early PD detection and tracking of PD symptom severity.
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Affiliation(s)
- James R. Williamson
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA 02421, USA; (B.T.); (R.M.)
| | - Brian Telfer
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA 02421, USA; (B.T.); (R.M.)
| | - Riley Mullany
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA 02421, USA; (B.T.); (R.M.)
| | - Karl E. Friedl
- U.S. Army Research Institute of Environmental Medicine, Natick, MA 01760, USA;
- Department of Neurology, University of California, San Francisco, CA 94143, USA
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23
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Ghoraani B, Galvin JE, Jimenez-Shahed J. Point of view: Wearable systems for at-home monitoring of motor complications in Parkinson's disease should deliver clinically actionable information. Parkinsonism Relat Disord 2021; 84:35-39. [PMID: 33549914 PMCID: PMC8324321 DOI: 10.1016/j.parkreldis.2021.01.022] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 12/18/2020] [Accepted: 01/26/2021] [Indexed: 01/05/2023]
Affiliation(s)
- Behnaz Ghoraani
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA.
| | - James E Galvin
- Comprehensive Center for Brain Health, Department of Neurology, University of Miami, Miami, FL, 33136, USA
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Agurto C, Heisig S, Abrami A, Ho BK, Caggiano V. Parkinson's disease medication state and severity assessment based on coordination during walking. PLoS One 2021; 16:e0244842. [PMID: 33596202 PMCID: PMC7888646 DOI: 10.1371/journal.pone.0244842] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 12/18/2020] [Indexed: 12/31/2022] Open
Abstract
Walking is a complex motor function requiring coordination of all body parts. Parkinson's disease (PD) motor signs such as rigidity, bradykinesia, and impaired balance affect movements including walking. Here, we propose a computational method to objectively assess the effects of Parkinson's disease pathology on coordination between trunk, shoulder and limbs during the gait cycle to assess medication state and disease severity. Movements during a scripted walking task were extracted from wearable devices placed at six different body locations in participants with PD and healthy participants. Three-axis accelerometer data from each device was synchronized at the beginning of either left or right steps. Canonical templates of movements were then extracted from each body location. Movements projected on those templates created a reduced dimensionality space, where complex movements are represented as discrete values. These projections enabled us to relate the body coordination in people with PD to disease severity. Our results show that the velocity profile of the right wrist and right foot during right steps correlated with the participant's total score on the gold standard Unified Parkinson's Disease Rating Scale (UPRDS) with an r2 up to 0.46. Left-right symmetry of feet, trunk and wrists also correlated with the total UPDRS score with an r2 up to 0.3. In addition, we demonstrate that binary dopamine replacement therapy medication states (self-reported 'ON' or 'OFF') can be discriminated in PD participants. In conclusion, we showed that during walking, the movement of body parts individually and in coordination with one another changes in predictable ways that vary with disease severity and medication state.
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Affiliation(s)
- Carla Agurto
- IBM Research - Healthcare and Life Sciences, Yorktown Heights, Yorktown, New York, United States of America
| | - Stephen Heisig
- IBM Research - Healthcare and Life Sciences, Yorktown Heights, Yorktown, New York, United States of America
| | - Avner Abrami
- IBM Research - Healthcare and Life Sciences, Yorktown Heights, Yorktown, New York, United States of America
| | - Bryan K. Ho
- Department of Neurology, Boston, Massachusetts, United States of America
| | - Vittorio Caggiano
- IBM Research - Healthcare and Life Sciences, Yorktown Heights, Yorktown, New York, United States of America
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25
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Thomsen TH, Jørgensen LB, Kjær TW, Winge K, Haahr A. Identification of Pre-Dominant Coping Types in Patients with Parkinson’s Disease: An Abductive Content Analysis of Video-Based Narratives. JOURNAL OF PARKINSONS DISEASE 2021; 11:349-361. [DOI: 10.3233/jpd-202217] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background: People with Parkinson’s disease suffer from a range of various symptoms. Altered movement patterns frequently represent the prevailing symptom experience and influence the everyday life of the affected persons. Objective: This qualitative study explores how persons with Parkinson‘s disease experience everyday life with a complex symptom profile and how they manage the consequential challenges in their daily life, as well as the motivation and consequences of these coping behaviours. Methods: Thirty-four patients with Parkinson’s disease were interviewed as an integrated part of the method Video-based Narrative. The interviews were analysed by means of qualitative content analysis according to Graneheim & Lundman. Results: The analysis identified six predominant coping types with different behavioural traits: The convincing behaviour, The economizing behaviour, The encapsulating behaviour, The evasive behaviour, The adaptable behaviour, and The dynamic behaviour. The strategies embedded in each of the six types are diverse, but all participants seek to maintain their integrity in different ways leading to the main motivation “To stay the same person”. Conclusion: Healthcare professionals should be aware of the patients‘ various coping behaviour in order to offer a person-centred approach. Psychoeducational interventions to promote coping skills may be essential in incorporating disease-related changes in the conduct of everyday life with Parkinson’s disease to maintain integrity.
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Affiliation(s)
- Trine Hørmann Thomsen
- Department of Clinical Neurophysiology and Neurology, Zealand University Hospital, Roskilde, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Lene Bastrup Jørgensen
- Knowledge Centre for Neurorehabilitation, Hammel, Western Denmark
- Department of Clinical Medicine, University of Aarhus, Aarhus, Denmark
| | - Troels Wesenberg Kjær
- Department of Clinical Neurophysiology and Neurology, Zealand University Hospital, Roskilde, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Kristian Winge
- Department of Neurology, Odense University Hospital, Odense, Denmark
| | - Anita Haahr
- Centre for Health promotion and Rehabilitation, VIA University College, Aarhus N, Denmark
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26
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Del Din S, Kirk C, Yarnall AJ, Rochester L, Hausdorff JM. Body-Worn Sensors for Remote Monitoring of Parkinson's Disease Motor Symptoms: Vision, State of the Art, and Challenges Ahead. JOURNAL OF PARKINSON'S DISEASE 2021; 11:S35-S47. [PMID: 33523020 PMCID: PMC8385520 DOI: 10.3233/jpd-202471] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/05/2021] [Indexed: 12/15/2022]
Abstract
The increasing prevalence of neurodegenerative conditions such as Parkinson's disease (PD) and related mobility issues places a serious burden on healthcare systems. The COVID-19 pandemic has reinforced the urgent need for better tools to manage chronic conditions remotely, as regular access to clinics may be problematic. Digital health technology in the form of remote monitoring with body-worn sensors offers significant opportunities for transforming research and revolutionizing the clinical management of PD. Significant efforts are being invested in the development and validation of digital outcomes to support diagnosis and track motor and mobility impairments "off-line". Imagine being able to remotely assess your patient, understand how well they are functioning, evaluate the impact of any recent medication/intervention, and identify the need for urgent follow-up before overt, irreparable change takes place? This could offer new pragmatic solutions for personalized care and clinical research. So the question remains: how close are we to achieving this? Here, we describe the state-of-the-art based on representative papers published between 2017 and 2020. We focus on remote (i.e., real-world, daily-living) monitoring of PD using body-worn sensors (e.g., accelerometers, inertial measurement units) for assessing motor symptoms and their complications. Despite the tremendous potential, existing challenges exist (e.g., validity, regulatory) that are preventing the widespread clinical adoption of body-worn sensors as a digital outcome. We propose a roadmap with clear recommendations for addressing these challenges and future directions to bring us closer to the implementation and widespread adoption of this important way of improving the clinical care, evaluation, and monitoring of PD.
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Affiliation(s)
- Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Alison J. Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv Israel
- Department of Physical Therapy, Sackler School of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Rush Alzheimer’s Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, 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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Czech M, Demanuele C, Erb MK, Ramos V, Zhang H, Ho B, Patel S. The Impact of Reducing the Number of Wearable Devices on Measuring Gait in Parkinson Disease: Noninterventional Exploratory Study. JMIR Rehabil Assist Technol 2020; 7:e17986. [PMID: 33084585 PMCID: PMC7641789 DOI: 10.2196/17986] [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: 01/27/2020] [Revised: 08/07/2020] [Accepted: 08/16/2020] [Indexed: 12/18/2022] Open
Abstract
Background Measuring free-living gait using wearable devices may offer higher granularity and temporal resolution than the current clinical assessments for patients with Parkinson disease (PD). However, increasing the number of devices worn on the body adds to the patient burden and impacts the compliance. Objective This study aimed to investigate the impact of reducing the number of wearable devices on the ability to assess gait impairments in patients with PD. Methods A total of 35 volunteers with PD and 60 healthy volunteers performed a gait task during 2 clinic visits. Participants with PD were assessed in the On and Off medication state using the Movement Disorder Society version of the Unified Parkinson Disease Rating Scale (MDS-UPDRS). Gait features derived from a single lumbar-mounted accelerometer were compared with those derived using 3 and 6 wearable devices for both participants with PD and healthy participants. Results A comparable performance was observed for predicting the MDS-UPDRS gait score using longitudinal mixed-effects model fit with gait features derived from a single (root mean square error [RMSE]=0.64; R2=0.53), 3 (RMSE=0.64; R2=0.54), and 6 devices (RMSE=0.54; R2=0.65). In addition, MDS-UPDRS gait scores predicted using all 3 models differed significantly between On and Off motor states (single device, P=.004; 3 devices, P=.004; 6 devices, P=.045). Conclusions We observed a marginal benefit in using multiple devices for assessing gait impairments in patients with PD when compared with gait features derived using a single lumbar-mounted accelerometer. The wearability burden associated with the use of multiple devices may offset gains in accuracy for monitoring gait under free-living conditions.
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Affiliation(s)
- Matthew Czech
- Digital Medicine & Translational Imaging, Early Clinical Development, Pfizer Inc, Cambridge, MA, United States
| | - Charmaine Demanuele
- Digital Medicine & Translational Imaging, Early Clinical Development, Pfizer Inc, Cambridge, MA, United States
| | - Michael Kelley Erb
- Digital Medicine & Translational Imaging, Early Clinical Development, Pfizer Inc, Cambridge, MA, United States
| | - Vesper Ramos
- Digital Medicine & Translational Imaging, Early Clinical Development, Pfizer Inc, Cambridge, MA, United States
| | - Hao Zhang
- Digital Medicine & Translational Imaging, Early Clinical Development, Pfizer Inc, Cambridge, MA, United States
| | - Bryan Ho
- Tufts Medical Center, Boston, MA, United States
| | - Shyamal Patel
- Digital Medicine & Translational Imaging, Early Clinical Development, Pfizer Inc, Cambridge, MA, United States
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29
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"Does the Response to Morning Medication Predict the ADL-Level of the Day in Parkinson's Disease?". PARKINSONS DISEASE 2020; 2020:7140984. [PMID: 32802307 PMCID: PMC7403929 DOI: 10.1155/2020/7140984] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 05/24/2020] [Accepted: 06/25/2020] [Indexed: 01/01/2023]
Abstract
Background Individuals with Parkinson's Disease (PD) have bradykinesia during mobility tasks in the morning before intake of dopaminergic treatment and have difficulties managing Activities of Daily Living (ADLs). Early morning off (EMO) refers to off-states in the morning where the severity of bradykinesia is increased and causes a decrease in mobility related to wearing off of effects of medication. Measurements from devices capable of continuously recording motor symptoms may provide insight into the patient's response to medication and possible impact on ADLs. Objectives To test whether poor or slow response to medication in the morning predicts the overall ADL-level and to assess the association between change in bradykinesia score (BKS) and the risk of having disabilities within three selected ADL-items. Methods In this cross-sectional study, the sample consists of 34 patients with light to moderate PD. Data collection encompasses measurements from the Parkinson KinetiGraph, and the ADL-limitations are assessed by the Movement Disorder Society Unified Parkinson Disease Rating Scale (MDS-UPDRS) Part II. Results The association between UPDRS- II and BKS from the algorithm was -0.082 (p < 0.01), 95% CL:-0.113; -0.042). The individuals experienced disabilities in performing "Speech" (p=0.004) and "Doing hobbies" (p=0.038) when being slow or poor responders to dopaminergic therapy. The PD patients' L-dopa equivalent dose seems to be a strong predictor of the ADL-level in the morning. Conclusion Slow response to the medication dosages in the morning is correlated with disabilities in the overall ADL-level in PD. The combination of PD-drugs and precise, timely dosages must be considered in the improvement of the ADL-level in PD patients.
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Abrami A, Heisig S, Ramos V, Thomas KC, Ho BK, Caggiano V. Using an unbiased symbolic movement representation to characterize Parkinson's disease states. Sci Rep 2020; 10:7377. [PMID: 32355166 PMCID: PMC7193555 DOI: 10.1038/s41598-020-64181-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 04/09/2020] [Indexed: 11/16/2022] Open
Abstract
Unconstrained human movement can be broken down into a series of stereotyped motifs or 'syllables' in an unsupervised fashion. Sequences of these syllables can be represented by symbols and characterized by a statistical grammar which varies with external situational context and internal neurological state. By first constructing a Markov chain from the transitions between these syllables then calculating the stationary distribution of this chain, we estimate the overall severity of Parkinson's symptoms by capturing the increasingly disorganized transitions between syllables as motor impairment increases. Comparing stationary distributions of movement syllables has several advantages over traditional neurologist administered in-clinic assessments. This technique can be used on unconstrained at-home behavior as well as scripted in-clinic exercises, it avoids differences across human evaluators, and can be used continuously without requiring scripted tasks be performed. We demonstrate the effectiveness of this technique using movement data captured with commercially available wrist worn sensors in 35 participants with Parkinson's disease in-clinic and 25 participants monitored at home.
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Affiliation(s)
- Avner Abrami
- IBM Research - Healthcare and Life Sciences - 1101 Kitchawan Rd, Yorktown Heights, NY, 10598, USA
| | - Stephen Heisig
- IBM Research - Healthcare and Life Sciences - 1101 Kitchawan Rd, Yorktown Heights, NY, 10598, USA
| | - Vesper Ramos
- Digital Medicine and the Pfizer Innovation Research Lab, Pfizer, 610 Main Street, Cambridge, MA, 02139, USA
| | - Kevin C Thomas
- Laboratory for Human Neurobiology, Spivack Center for Clinical and Translational Neuroscience, 650 Albany Street, X-140, Boston, MA, 02118, USA
| | - Bryan K Ho
- Department of Neurology Tufts Medical Center 800 Washington Street, Box 314, Boston, MA, 02111-1800, USA
| | - Vittorio Caggiano
- IBM Research - Healthcare and Life Sciences - 1101 Kitchawan Rd, Yorktown Heights, NY, 10598, USA.
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Taghizadeh G, Martinez-Martin P, Meimandi M, Habibi SAH, Jamali S, Dehmiyani A, Rostami S, Mahmuodi A, Mehdizadeh M, Fereshtehnejad SM. Barthel Index and modified Rankin Scale: Psychometric properties during medication phases in idiopathic Parkinson disease. Ann Phys Rehabil Med 2019; 63:500-504. [PMID: 31816448 DOI: 10.1016/j.rehab.2019.08.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 08/04/2019] [Accepted: 08/23/2019] [Indexed: 02/08/2023]
Abstract
BACKGROUND Independence in activities of daily living (ADL) is one of the most important aspects in planning treatment for people with Parkinson disease (PD). The Barthel Index (BI) and modified Rankin Scale (mRS) are commonly used in neurological diseases. OBJECTIVE This study was conducted to confirm the validity and reliability of the BI and mRS in PD during ON and OFF medication phases. METHODS We included 260 individuals with a diagnosis of idiopathic PD. The disability in ADL was measured by the BI, mRS, Parkinson's Disease Questionnaire-39 (PDQ-39), Unified Parkinson Disease Rating Scale-Activities of Daily Living (UPDRS-ADL), and Schwab and England ADL scale (SE). Test-retest, inter-rater reliability, and internal consistency were assessed by the intra-class correlation (ICC) and Cronbach α coefficients. Dimensionality was evaluated by factor analysis. Convergent validity was assessed by the SE, Berg Balance Scale (BBS), PDQ-39 and UPDRS-ADL. RESULTS For the 260 participants (187 [71.9%] males; mean [SD] age 60.3 [12.3] years), both the BI and mRS achieved an acceptable level of test-retest and inter-rater reliability (ICC=0.77 to 0.91) in ON and OFF medication phases. The Cronbach α for BI was 0.85 and 0.88, respectively. We found 1 and 2 factors for BI in ON and OFF phases, respectively. Investigation of convergent validity showed moderate to high correlation for the BI with the UPDRS-ADL, SE, PDQ-39 (ADL), BBS and mRS scores in ON and OFF phases (ρ=0.51-0.74) and mRS with SE, UPDRS-ADL, PDQ-39 (ADL) and BBS scores (ρ=0.48-0.82). CONCLUSION The BI and mRS showed acceptable validity and reliability to measure the degree of disability in patients with PD in daily activities in both ON and OFF medication phases.
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Affiliation(s)
- Ghorban Taghizadeh
- Rehabilitation Research Center, Department of Occupational Therapy, School of Rehabilitation Sciences, Iran University of Medical Science, Tehran, Iran
| | - Pablo Martinez-Martin
- National Center of Epidemiology and CIBERNED, Carlos III Institute of Health, Madrid, Spain
| | - Mahsa Meimandi
- Rehabilitation Research Center, Department of Occupational Therapy, School of Rehabilitation Sciences, Iran University of Medical Science, Tehran, Iran
| | - Sayed Amir Hasan Habibi
- Department of Neurology, Rasoul Akram Hospital, Iran University of Medical Science, Tehran, Iran
| | - Shamsi Jamali
- Rehabilitation Research Center, Department of Occupational Therapy, School of Rehabilitation Sciences, Iran University of Medical Science, Tehran, Iran
| | - Arian Dehmiyani
- Rehabilitation Research Center, Department of Occupational Therapy, School of Rehabilitation Sciences, Iran University of Medical Science, Tehran, Iran
| | - Siavash Rostami
- Rehabilitation Research Center, Department of Occupational Therapy, School of Rehabilitation Sciences, Iran University of Medical Science, Tehran, Iran
| | - Alieh Mahmuodi
- Department of Aging, University of Social Welfare Rehabilitation Sciences, Tehran, Iran
| | - Maryam Mehdizadeh
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran, Iran; Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran; Student Research Committee, Iran University of Medical Sciences, Tehran, Iran.
| | - Seyed-Mohammad Fereshtehnejad
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, Stockholm, Sweden; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; Division of Neurology, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
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Pinto C, Schuch CP, Balbinot G, Salazar AP, Hennig EM, Kleiner AFR, Pagnussat AS. Movement smoothness during a functional mobility task in subjects with Parkinson's disease and freezing of gait - an analysis using inertial measurement units. J Neuroeng Rehabil 2019; 16:110. [PMID: 31488184 PMCID: PMC6729092 DOI: 10.1186/s12984-019-0579-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 08/19/2019] [Indexed: 11/25/2022] Open
Abstract
Background Impairments of functional mobility may affect locomotion and quality of life in subjects with Parkinson’s disease (PD). Movement smoothness measurements, such as the spectral arc length (SPARC), are novel approaches to quantify movement quality. Previous studies analyzed SPARC in simple walking conditions. However, SPARC outcomes during functional mobility tasks in subjects with PD and freezing of gait (FOG) were never investigated. This study aimed to analyze SPARC during the Timed Up and Go (TUG) test in individuals with PD and FOG. Methods Thirty-one participants with PD and FOG and six healthy controls were included. SPARC during TUG test was calculated for linear and angular accelerations using an inertial measurement unit system. SPARC data were correlated with clinical parameters: motor section of the Unified Parkinson’s Disease Rating Scale, Hoehn & Yahr scale, Freezing of Gait Questionnaire, and TUG test. Results We reported lower SPARC values (reduced smoothness) during the entire TUG test, turn and stand to sit in subjects with PD and FOG, compared to healthy controls. Unlike healthy controls, individuals with PD and FOG displayed a broad spectral range that encompassed several dominant frequencies. SPARC metrics also correlated with all the above-mentioned clinical parameters. Conclusion SPARC values provide valid and relevant clinical data about movement quality (e.g., smoothness) of subjects with PD and FOG during a functional mobility test. Electronic supplementary material The online version of this article (10.1186/s12984-019-0579-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Camila Pinto
- Rehabilitation Sciences Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), 245 Sarmento Leite Street, Porto Alegre, RS, 90050170, Brazil.,Movement Analysis and Rehabilitation Laboratory, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brazil
| | - Clarissa Pedrini Schuch
- Rehabilitation Sciences Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), 245 Sarmento Leite Street, Porto Alegre, RS, 90050170, Brazil
| | - Gustavo Balbinot
- Brain Institute, Universidade Federal do Rio Grande do Norte (UFRN), Natal, RN, Brazil
| | - Ana Paula Salazar
- Rehabilitation Sciences Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), 245 Sarmento Leite Street, Porto Alegre, RS, 90050170, Brazil.,Movement Analysis and Rehabilitation Laboratory, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brazil
| | - Ewald Max Hennig
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | | | - Aline Souza Pagnussat
- Rehabilitation Sciences Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), 245 Sarmento Leite Street, Porto Alegre, RS, 90050170, Brazil. .,Movement Analysis and Rehabilitation Laboratory, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brazil.
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Mehdizadeh M, Martinez-Martin P, Habibi SA, Nikbakht N, Alvandi F, Bazipoor P, Panahi A, Taghizadeh G. The Association of Balance, Fear of Falling, and Daily Activities With Drug Phases and Severity of Disease in Patients With Parkinson. Basic Clin Neurosci 2019; 10:355-362. [PMID: 32231772 PMCID: PMC7101520 DOI: 10.32598/bcn.9.10.295] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 05/23/2018] [Accepted: 11/05/2018] [Indexed: 11/20/2022] Open
Abstract
Introduction: In the elderly, functional balance, fear of falling, and independence in daily living activities are interrelated; however, this relationship may change under the influence of drug phase and the severity of disease in individuals with idiopathic Parkinson disease. This study aimed to investigate the association of functional balance, fear of falling, and independence in the Activities of Daily Living (ADL) with the drug on- and drug off-phases. Methods: A total of 140 patients with Parkinson disease (age: Mean±SD; 60.51±12.32 y) were evaluated in terms of their functional balance, fear of falling, and independence in their daily activities by the Berg Balance Scale (BBS), Fall Efficacy Scale-International (FES-I), and Unified Parkinson Disease Rating Scale-ADL (UPDRS-ADL), respectively, in drug on- and drug off-phases. The Hoehn and Yahr scale recorded global disease rating. The Spearman coefficient, Kruskal-Wallis, and Mann-Whitney tests were used to find out whether the distribution of scale scores differs with regard to functional balance or disease severity. Results: A strong correlation was found between the functional balance, fear of falling, and independence in ADL with both drug phases. The results also showed the significant difference in the distribution of the FES-I and UPDRS-ADL scores with regard to functional balance (except independence in ADL in drug off-phase). Also, the distribution of the scores of BBS, FES-I, and UPDRS-ADL showed significant differences with regard to disease severity. Conclusion: The study showed a strong correlation between functional balance, fear of falling, and independence in ADL that can be affected by the drug phase and severity of the disease. However, more studies are needed to understand this relationship precisely.
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Affiliation(s)
- Maryam Mehdizadeh
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran, Iran.,Department of Neuroscience, School of Advance Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Pablo Martinez-Martin
- National Center of Epidemiology and CIBERNED, Carlos III Institute of Health, Madrid, Spain
| | - Seyed Amirhasan Habibi
- Department of Neurology, School of Medicine, Rasoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Negar Nikbakht
- Department of Occupational Therapy, School of Rehabilitation, Shahid Beheshti University of Medical Sciences and Health Services, Tehran, Iran
| | - Faeze Alvandi
- Department of Occupational Therapy, School of Paramedical and Health, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Parvane Bazipoor
- Department of Pathology and Sport Biomechanics, Faculty of Literature and Humanities, Bu-Ali Sina University, Hamadan, Iran
| | - Ailin Panahi
- Department of Occupational Therapy, School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Ghorban Taghizadeh
- Department of Occupational Therapy, School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran.,Rehabilitation Research Center, School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran
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Huang H, Xu H, Luo Q, He J, Li M, Chen H, Tang W, Nie Y, Zhou Y. Fecal microbiota transplantation to treat Parkinson's disease with constipation: A case report. Medicine (Baltimore) 2019; 98:e16163. [PMID: 31261545 PMCID: PMC6616439 DOI: 10.1097/md.0000000000016163] [Citation(s) in RCA: 105] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
RATIONALE Fecal microbiota transplantation (FMT) is recognized as an emerging treatment through reconstruction of gut microbiota. Parkinson's disease is a neurodegenerative disorder, which is accompanied by constipation. Here we first reported a patient with Parkinson's disease and constipation that were obviously relieved after FMT. PATIENT CONCERNS A 71-year-old male patient presented with 7 years of resting tremor, bradykinesia (first inflicted the upper limbs and subsequently spread to lower limbs), and intractable constipation (defecation needing more than 30 minutes). DIAGNOSES Parkinson's disease for 7 years; constipation >3 years. INTERVENTIONS The patient had used madopar, pramipexole, and amantadine for anti-Parkinson and showed partially mitigation while laxative therapy for constipation failed. Finally FMT was performed. OUTCOMES The patient successfully defecated within 5 minutes and maintained daily unobstructed defecation until the end of follow-up. The patient's tremor in legs almost disappeared at 1 week after FMT but recurred in the right lower extremity at 2 months after FMT. LESSONS Gut microbiota reconstruction may have therapeutic effects for Parkinson's disease patients, especially those who have gastrointestinal symptoms and limited treatment choices.
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Affiliation(s)
- Hongli Huang
- Department of Gastroenterology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong
- Department of Gastroenterology, Guangzhou Digestive Disease Center
| | - Haoming Xu
- Department of Gastroenterology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong
- Department of Gastroenterology, Guangzhou Digestive Disease Center
| | - Qingling Luo
- Department of Gastroenterology, Guangzhou Digestive Disease Center
| | - Jie He
- Department of Gastroenterology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong
- Department of Gastroenterology, Guangzhou Digestive Disease Center
| | - Mengyan Li
- Department of Neurology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Huiting Chen
- Department of Gastroenterology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong
- Department of Gastroenterology, Guangzhou Digestive Disease Center
| | - Wenjuan Tang
- Department of Gastroenterology, Guangzhou Digestive Disease Center
| | - Yuqiang Nie
- Department of Gastroenterology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong
- Department of Gastroenterology, Guangzhou Digestive Disease Center
| | - Yongjian Zhou
- Department of Gastroenterology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong
- Department of Gastroenterology, Guangzhou Digestive Disease Center
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35
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Teshuva I, Hillel I, Gazit E, Giladi N, Mirelman A, Hausdorff JM. Using wearables to assess bradykinesia and rigidity in patients with Parkinson's disease: a focused, narrative review of the literature. J Neural Transm (Vienna) 2019; 126:699-710. [PMID: 31115669 DOI: 10.1007/s00702-019-02017-9] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 05/14/2019] [Indexed: 10/26/2022]
Abstract
The potential of using wearable technologies for the objective assessment of motor symptoms in Parkinson's disease (PD) has gained prominence recently. Nonetheless, compared to tremor and gait impairment, less emphasis has been placed on the quantification of bradykinesia and rigidity. This review aimed to consolidate the existing research on objective measurement of bradykinesia and rigidity in PD through the use of wearables, focusing on the continuous monitoring of these two symptoms in free-living environments. A search of PubMed was conducted through a combination of keyword and MeSH searches. We also searched the IEEE, Google Scholar, Embase, and Scopus databases to ensure thorough results and to minimize the chances of missing relevant studies. Papers published after the year 2000 with sample sizes greater than five were included. Studies were assessed for quality and information was extracted regarding the devices used and their location on the body, the setting and duration of the study, the "gold standard" used as a reference for validation, the metrics used, and the results of each paper. Thirty-one and eight studies met the search criteria and evaluated bradykinesia and rigidity, respectively. Several studies reported strong associations between wearable-based measures and the gold-standard references for bradykinesia, and, to a lesser extent, rigidity. Only a few, pilot studies investigated the measurement of bradykinesia and rigidity in the home and free-living settings. While the current results are promising for the future of wearables, additional work is needed on their validation and adaptation in ecological, free-living settings. Doing so has the potential to improve the assessment and treatment of motor fluctuations and symptoms of PD more generally through real-time objective monitoring of bradykinesia and rigidity.
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Affiliation(s)
- Itay Teshuva
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Inbar Hillel
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Nir Giladi
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Department of Neurology and Neurosurgery, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Anat Mirelman
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Department of Neurology and Neurosurgery, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel. .,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel. .,Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv, Israel. .,Rush Alzheimer's Disease Center, Chicago, USA. .,Department of Orthopedic Surgery, Rush University Medical Center, Chicago, USA.
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Associations between daily-living physical activity and laboratory-based assessments of motor severity in patients with falls and Parkinson's disease. Parkinsonism Relat Disord 2019; 62:85-90. [DOI: 10.1016/j.parkreldis.2019.01.022] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Revised: 01/22/2019] [Accepted: 01/23/2019] [Indexed: 11/23/2022]
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Mehdizadeh M, Martinez-Martin P, Habibi SA, Fereshtehnejad SM, Abasi A, Niazi Khatoon J, Saneii SH, Taghizadeh G. Reliability and Validity of Fall Efficacy Scale-International in People with Parkinson's Disease during On- and Off-Drug Phases. PARKINSON'S DISEASE 2019; 2019:6505232. [PMID: 30719277 PMCID: PMC6334372 DOI: 10.1155/2019/6505232] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Revised: 11/27/2018] [Accepted: 12/05/2018] [Indexed: 11/27/2022]
Abstract
PURPOSE Since fear of falling may be one of the main problems in people with Parkinson's disease (PD), its assessment with valid tools is necessary in both drug phases. This study was carried out to investigate the psychometric attributes of the Fall Efficacy Scale-International (FES-I) in people with PD, both in On and Off phases. METHODS One hundred twenty-four patients with PD (mean age ± standard deviation, 60.33 ± 12.59 years) were assessed with the FES-I, both in On- and Off-drug phases. Dimensionality, internal consistency, and test-retest reliability were, respectively, explored by means of factor analysis, Cronbach's alpha, and Intraclass Correlation Coefficient. Convergent validity of FES-I was established with Visual Analog Scale-Fear of Falling, Berg Balance Scale, and Functional Reach Test. Parkinson's Disease Questionnaire-39 and Unified Parkinson Disease Rating Scale-Activities of Daily Living were also applied. Discriminative validity was tested between patients with and without a history of falling. RESULTS Factor analysis showed two factors for On- and one factor for Off-drug phase. Internal consistency (α = 0.96, On phase; 0.98, Off phase) and test-retest reliability (0.94; 0.91) were satisfactory in both drug phases. There was a moderate/high correlation (r S = |0.50-0.70|) between FES-I and Visual Analog Scale-Fear of Falling, Berg Balance Scale, and Functional Reach Test. Parkinson's Disease Questionnaire-39 and Unified Parkinson Disease Rating Scale-Activities of Daily Living were achieved in both drug phases too. The sensitivity of FES-I to discriminate Parkinson's disease with and without falls showed moderate effect size in both phases. CONCLUSION This study verified that FES-I is unidimensional, reliable, and valid to measure the Fear of Falling during On- and Off-drug phases in people with PD.
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Affiliation(s)
- Maryam Mehdizadeh
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran, Iran
- Department Neuroscience, Faculty of Advance Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
- Student Research Committee, Iran University of Medical Sciences, Tehran, Iran
| | - Pablo Martinez-Martin
- National Center of Epidemiology and CIBERNED, Carlos III Institute of Health, Madrid, Spain
| | - Seyed-Amirhasan Habibi
- Department of Neurology, Rasoul Akram Hospital, Iran University of Medical Science, Tehran, Iran
| | - Seyed-Mohammad Fereshtehnejad
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, Stockholm, Sweden
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Division of Neurology, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Amirabas Abasi
- Department of Occupational Therapy, School of Rehabilitation Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Javad Niazi Khatoon
- Department of Occupational Therapy, Faculty of Paramedicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Seyed Hassan Saneii
- Department of Basic Sciences, School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Ghorban Taghizadeh
- Department of Occupational Therapy, School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran
- Rehabilitation Research Center, School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran
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Psychometric properties of the Berg balance scale in idiopathic Parkinson’ disease in the drug off-phase. Neurol Sci 2018; 39:2175-2181. [DOI: 10.1007/s10072-018-3570-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 09/10/2018] [Indexed: 10/28/2022]
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39
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Rovini E, Maremmani C, Cavallo F. Automated Systems Based on Wearable Sensors for the Management of Parkinson's Disease at Home: A Systematic Review. Telemed J E Health 2018; 25:167-183. [PMID: 29969384 DOI: 10.1089/tmj.2018.0035] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Parkinson's disease is a common neurodegenerative pathology that significantly influences quality of life (QoL) of people affected. The increasing interest and development in telemedicine services and internet of things technologies aim to implement automated smart systems for remote assistance of patients. The wide variability of Parkinson's disease in the clinical expression, as well as in the symptom progression, seems to address the patients' care toward a personalized therapy. OBJECTIVES This review addresses automated systems based on wearable/portable devices for the remote treatment and management of Parkinson's disease. The idea is to obtain an overview of the telehealth and automated systems currently developed to address the impairments due to the pathology to allow clinicians to improve the quality of care for Parkinson's disease with benefits for patients in QoL. DATA SOURCES The research was conducted within three databases: IEEE Xplore®, Web of Science®, and PubMed Central®, between January 2008 and September 2017. STUDY ELIGIBILITY CRITERIA Accurate exclusion criteria and selection strategy were applied to screen the 173 articles found. RESULTS Ultimately, 55 articles were fully evaluated and included in this review. Divided into three categories, they were automated systems actually tested at home, implemented mobile applications for Parkinson's disease assessment, or described a telehealth system architecture. CONCLUSION This review would provide an exhaustive overview of wearable systems for the remote management and automated assessment of Parkinson's disease, taking into account the reliability and acceptability of the implemented technologies.
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Affiliation(s)
- Erika Rovini
- 1 The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera (PI), Italy
| | - Carlo Maremmani
- 2 U.O. Neurologia, Ospedale delle Apuane (AUSL Toscana Nord Ovest), Massa (MS), Italy
| | - Filippo Cavallo
- 1 The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera (PI), Italy
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40
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Rodríguez-Molinero A, Pérez-López C, Samà A, de Mingo E, Rodríguez-Martín D, Hernández-Vara J, Bayés À, Moral A, Álvarez R, Pérez-Martínez DA, Català A. A Kinematic Sensor and Algorithm to Detect Motor Fluctuations in Parkinson Disease: Validation Study Under Real Conditions of Use. JMIR Rehabil Assist Technol 2018; 5:e8. [PMID: 29695377 PMCID: PMC5943625 DOI: 10.2196/rehab.8335] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Revised: 01/29/2018] [Accepted: 01/31/2018] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND A new algorithm has been developed, which combines information on gait bradykinesia and dyskinesia provided by a single kinematic sensor located on the waist of Parkinson disease (PD) patients to detect motor fluctuations (On- and Off-periods). OBJECTIVE The goal of this study was to analyze the accuracy of this algorithm under real conditions of use. METHODS This validation study of a motor-fluctuation detection algorithm was conducted on a sample of 23 patients with advanced PD. Patients were asked to wear the kinematic sensor for 1 to 3 days at home, while simultaneously keeping a diary of their On- and Off-periods. During this testing, researchers were not present, and patients continued to carry on their usual daily activities in their natural environment. The algorithm's outputs were compared with the patients' records, which were used as the gold standard. RESULTS The algorithm produced 37% more results than the patients' records (671 vs 489). The positive predictive value of the algorithm to detect Off-periods, as compared with the patients' records, was 92% (95% CI 87.33%-97.3%) and the negative predictive value was 94% (95% CI 90.71%-97.1%); the overall classification accuracy was 92.20%. CONCLUSIONS The kinematic sensor and the algorithm for detection of motor-fluctuations validated in this study are an accurate and useful tool for monitoring PD patients with difficult-to-control motor fluctuations in the outpatient setting.
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Affiliation(s)
| | - Carlos Pérez-López
- Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politcnica de Catalunya, Vilanova i la Geltru, Spain.,Sense4Care, Barcelona, Spain
| | - Albert Samà
- Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politcnica de Catalunya, Vilanova i la Geltru, Spain.,Sense4Care, Barcelona, Spain
| | - Eva de Mingo
- Geriatrics Department, Consorci Sanitari del Garraf, Sant Pere de Ribes, Spain
| | - Daniel Rodríguez-Martín
- Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politcnica de Catalunya, Vilanova i la Geltru, Spain
| | | | - Àngels Bayés
- Unidad de Parkinson y trastornos del movimiento, Hospital Quirón-Teknon, Barcelona, Spain
| | - Alfons Moral
- Department of Neurology, Consorci Sanitari del Garraf, Sant Pere de Ribes, Spain
| | - Ramiro Álvarez
- Department of Neurology, Hospital Universitari Germans Trias i Pujol, Barcelona, Spain
| | | | - Andreu Català
- Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politcnica de Catalunya, Vilanova i la Geltru, Spain.,Sense4Care, Barcelona, Spain
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