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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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Smid A, Dominguez-Vega ZT, van Laar T, Oterdoom DLM, Absalom AR, van Egmond ME, Drost G, van Dijk JMC. Objective clinical registration of tremor, bradykinesia, and rigidity during awake stereotactic neurosurgery: a scoping review. Neurosurg Rev 2024; 47:81. [PMID: 38355824 PMCID: PMC10866747 DOI: 10.1007/s10143-024-02312-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/19/2024] [Accepted: 01/28/2024] [Indexed: 02/16/2024]
Abstract
Tremor, bradykinesia, and rigidity are incapacitating motor symptoms that can be suppressed with stereotactic neurosurgical treatment like deep brain stimulation (DBS) and ablative surgery (e.g., thalamotomy, pallidotomy). Traditionally, clinicians rely on clinical rating scales for intraoperative evaluation of these motor symptoms during awake stereotactic neurosurgery. However, these clinical scales have a relatively high inter-rater variability and rely on experienced raters. Therefore, objective registration (e.g., using movement sensors) is a reasonable extension for intraoperative assessment of tremor, bradykinesia, and rigidity. The main goal of this scoping review is to provide an overview of electronic motor measurements during awake stereotactic neurosurgery. The protocol was based on the PRISMA extension for scoping reviews. After a systematic database search (PubMed, Embase, and Web of Science), articles were screened for relevance. Hundred-and-three articles were subject to detailed screening. Key clinical and technical information was extracted. The inclusion criteria encompassed use of electronic motor measurements during stereotactic neurosurgery performed under local anesthesia. Twenty-three articles were included. These studies had various objectives, including correlating sensor-based outcome measures to clinical scores, identifying optimal DBS electrode positions, and translating clinical assessments to objective assessments. The studies were highly heterogeneous in device choice, sensor location, measurement protocol, design, outcome measures, and data analysis. This review shows that intraoperative quantification of motor symptoms is still limited by variable signal analysis techniques and lacking standardized measurement protocols. However, electronic motor measurements can complement visual evaluations and provide objective confirmation of correct placement of the DBS electrode and/or lesioning. On the long term, this might benefit patient outcomes and provide reliable outcome measures in scientific research.
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Affiliation(s)
- Annemarie Smid
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands.
| | - Zeus T Dominguez-Vega
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - Teus van Laar
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - D L Marinus Oterdoom
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - Anthony R Absalom
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - Martje E van Egmond
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - Gea Drost
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - J Marc C van Dijk
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
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Paredes-Acuna N, Utpadel-Fischler D, Ding K, Thakor NV, Cheng G. Upper limb intention tremor assessment: opportunities and challenges in wearable technology. J Neuroeng Rehabil 2024; 21:8. [PMID: 38218890 PMCID: PMC10787996 DOI: 10.1186/s12984-023-01302-9] [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: 08/02/2023] [Accepted: 12/26/2023] [Indexed: 01/15/2024] Open
Abstract
BACKGROUND Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature. METHODOLOGY & RESULTS This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table. CONCLUSIONS Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.
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Affiliation(s)
- Natalia Paredes-Acuna
- Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany.
| | - Daniel Utpadel-Fischler
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Keqin Ding
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Nitish V Thakor
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Gordon Cheng
- Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, 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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Geraedts VJ, van Vugt JPP, Marinus J, Kuiper R, Middelkoop HAM, Zutt R, van der Gaag NA, Hoffmann CFE, Dorresteijn LDA, van Hilten JJ, Contarino MF. Predicting Motor Outcome and Quality of Life After Subthalamic Deep Brain Stimulation for Parkinson's Disease: The Role of Standard Screening Measures and Wearable-Data. JOURNAL OF PARKINSON'S DISEASE 2023:JPD225101. [PMID: 37182900 DOI: 10.3233/jpd-225101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
BACKGROUND Standardized screening for subthalamic deep brain stimulation (STN DBS) in Parkinson's disease (PD) patients is crucial to determine eligibility, but its utility to predict postoperative outcomes in eligible patients is inconclusive. It is unknown whether wearable data can contribute to this aim. OBJECTIVE To evaluate the utility of universal components incorporated in the DBS screening, complemented by a wearable sensor, to predict motor outcomes and Quality of life (QoL) one year after STN DBS surgery. METHODS Consecutive patients were included in the OPTIMIST cohort study from two DBS centers. Standardized assessments included a preoperative Levodopa Challenge Test (LCT), and questionnaires on QoL and non-motor symptoms including cognition, psychiatric symptoms, impulsiveness, autonomic symptoms, and sleeping problems. Moreover, an ambulatory wearable sensor (Parkinson Kinetigraph (PKG)) was used. Postoperative assessments were similar and also included a Stimulation Challenge Test to determine DBS effects on motor function. RESULTS Eighty-three patients were included (median (interquartile range) age 63 (56-68) years, 36% female). Med-OFF (Stim-OFF) motor severity deteriorated indicating disease progression, but patients significantly improved in terms of Med-ON (Stim-ON) motor function, motor fluctuations, QoL, and most non-motor domains. Motor outcomes were not predicted by preoperative tests, including covariates of either LCT or PKG. Postoperative QoL was predicted by better preoperative QoL, lower age, and more preoperative impulsiveness scores in multivariate models. CONCLUSION Data from the DBS screening including wearable data do not predict postoperative motor outcome at one year. Post-DBS QoL appears primarily driven by non-motor symptoms, rather than by motor improvement.
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Affiliation(s)
- Victor J Geraedts
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Johan Marinus
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Roy Kuiper
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Neurology, HAGA Teaching Hospital, Den Haag, the Netherlands
| | - Huub A M Middelkoop
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Rodi Zutt
- Department of Neurology, HAGA Teaching Hospital, Den Haag, the Netherlands
| | - Niels A van der Gaag
- Department of Neurosurgery, HAGA Teaching Hospital, Den Haag, the Netherlands
- Department of Neurosurgery, Leiden University Medical Center, Leiden, the Netherlands
| | - Carel F E Hoffmann
- Department of Neurosurgery, HAGA Teaching Hospital, Den Haag, the Netherlands
| | | | - Jacobus J van Hilten
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Maria Fiorella Contarino
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Neurology, HAGA Teaching Hospital, Den Haag, the Netherlands
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Virbel-Fleischman C, Mousin F, Liu S, Hardy S, Corvol JC, Benatru I, Bendetowicz D, Béreau M, De Cock VC, Drapier S, Frismand S, Giordana C, Devos D, Rétory Y, Grabli D. Symptoms assessment and decision to treat patients with advanced Parkinson's disease based on wearables data. NPJ Parkinsons Dis 2023; 9:45. [PMID: 36973302 PMCID: PMC10042860 DOI: 10.1038/s41531-023-00489-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 03/08/2023] [Indexed: 03/29/2023] Open
Abstract
Body-worn sensors (BWS) could provide valuable information in the management of Parkinson's disease and support therapeutic decisions based on objective monitoring. To study this pivotal step and better understand how relevant information is extracted from BWS results and translated into treatment adaptation, eight neurologists examined eight virtual cases composed of basic patient profiles and their BWS monitoring results. Sixty-four interpretations of monitoring results and the subsequent therapeutic decisions were collected. Relationship between interrater agreements in the BWS reading and the severity of symptoms were analyzed via correlation studies. Logistic regression was used to identify associations between the BWS parameters and suggested treatment modifications. Interrater agreements were high and significantly associated with the BWS scores. Summarized BWS scores reflecting bradykinesia, dyskinesia, and tremor predicted the direction of treatment modifications. Our results suggest that monitoring information is robustly linked to treatment adaptation and pave the way to loop systems able to automatically propose treatment modifications from BWS recordings information.
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Affiliation(s)
- Clara Virbel-Fleischman
- Sorbonne University, Brain Institute - ICM, Inserm, CNRS, Paris, France.
- Centre EXPLOR!, Air Liquide Healthcare, Gentilly, France.
| | - Flavien Mousin
- Centre EXPLOR!, Air Liquide Healthcare, Gentilly, France
| | - Shuo Liu
- Centre EXPLOR!, Air Liquide Healthcare, Gentilly, France
| | | | - Jean-Christophe Corvol
- Sorbonne University, Brain Institute - ICM, Inserm, CNRS, Paris, France
- APHP, Pitié-Salpêtrière Hospital, Neurology Department, Paris, France
| | - Isabelle Benatru
- Poitiers University Hospital, Neurology Department, Poitiers, France
- INSERM, Poitiers CHU, Poitiers University, Centre d'Investigation Clinique CIC, 1402, Poitiers, France
| | - David Bendetowicz
- Sorbonne University, Brain Institute - ICM, Inserm, CNRS, Paris, France
- APHP, Pitié-Salpêtrière Hospital, Neurology Department, Paris, France
| | | | - Valérie Cochen De Cock
- Beau Soleil Clinic, Sleep et Neurology Department, Montpellier, France
- EuroMov Digital Health in Motion, Montpellier University, IMT Mines Ales, Montpellier, France
| | | | | | | | - David Devos
- Lille University, Lille CHU, Inserm, U1172, Lille Neuroscience & Cognition, NS-park F-CRIN network, LICEND, Lille, France
| | - Yann Rétory
- Centre EXPLOR!, Air Liquide Healthcare, Gentilly, France
- Paris-Saclay University, Laboratoire Complexité, innovations, activités motrices et sportives (CIAMS), 91405, Orsay, France
- Orléans University, CIAMS, 45067, Orléans, France
| | - David Grabli
- Sorbonne University, Brain Institute - ICM, Inserm, CNRS, Paris, France
- APHP, Pitié-Salpêtrière Hospital, Neurology Department, Paris, France
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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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Greenland JC, Camacho M, Williams-Gray CH. The dilemma between milestones of progression versus clinical scales in Parkinson's disease. HANDBOOK OF CLINICAL NEUROLOGY 2023; 192:169-185. [PMID: 36796941 DOI: 10.1016/b978-0-323-85538-9.00010-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
There are significant challenges in accurately documenting the progression of Parkinson's disease (PD). The disease course is highly heterogeneous, there are no validated biomarkers, and we are reliant on repeated clinical measures to assess disease state over time. Yet, the ability to chart disease progression accurately is vital in both observational and interventional study designs, where reliable measures are critical to determine whether an outcome has been met. In this chapter, we first discuss the natural history of PD, including the spectrum of clinical presentation and expected developments through the course of the disease. We then explore in detail the current strategies for measuring disease progression, which can be broadly divided into: (i) the use of quantitative clinical scales; and (ii) determination of the onset time of key milestones. We discuss the strengths and limitations of these approaches for use in clinical trials, with a particular focus on disease modification trials. The selection of outcome measures for a particular study will depend on multiple factors, but trial duration is an important determinant. Milestones are reached over a course of years rather than months, and hence clinical scales with sensitivity to change are needed for short-term studies. However, milestones represent important markers of disease stage which are not confounded by symptomatic therapies and are of critical relevance to the patient. Prolonged but low intensity follow-up beyond a limited period of treatment with a putative disease-modifying agent may allow milestones to be incorporated into evaluation of efficacy in a practical and cost-effective way.
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Affiliation(s)
- Julia C Greenland
- Department of Clinical Neuroscience, University of Cambridge, Cambridge, United Kingdom
| | - Marta Camacho
- Department of Clinical Neuroscience, University of Cambridge, Cambridge, United Kingdom
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Xu Z, Shen B, Tang Y, Wu J, Wang J. Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:349-361. [PMID: 36939759 PMCID: PMC9590510 DOI: 10.1007/s43657-022-00051-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 03/12/2022] [Accepted: 03/28/2022] [Indexed: 11/27/2022]
Abstract
Despite recent advances in technology, clinical phenotyping of Parkinson's disease (PD) has remained relatively limited as current assessments are mainly based on empirical observation and subjective categorical judgment at the clinic. A lack of comprehensive, objective, and quantifiable clinical phenotyping data has hindered our capacity to diagnose, assess patients' conditions, discover pathogenesis, identify preclinical stages and clinical subtypes, and evaluate new therapies. Therefore, deep clinical phenotyping of PD patients is a necessary step towards understanding PD pathology and improving clinical care. In this review, we present a growing community consensus and perspective on how to clinically phenotype this disease, that is, to phenotype the entire course of disease progression by integrating capacity, performance, and perception approaches with state-of-the-art technology. We also explore the most studied aspects of PD deep clinical phenotypes, namely, bradykinesia, tremor, dyskinesia and motor fluctuation, gait impairment, speech impairment, and non-motor phenotypes.
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Affiliation(s)
- Zhiheng Xu
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Bo Shen
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Yilin Tang
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Jianjun Wu
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Jian Wang
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
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10
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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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11
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Ma C, Zhang P, Wang J, Zhang J, Pan L, Li X, Yin C, Li A, Zong R, Zhang Z. Objective quantification of the severity of postural tremor based on kinematic parameters: A multi-sensory fusion study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 219:106741. [PMID: 35338882 DOI: 10.1016/j.cmpb.2022.106741] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 02/27/2022] [Accepted: 03/08/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Current clinical assessments of essential tremor (ET) are primarily based on expert consultation combined with reviewing patient complaints, physician expertise, and diagnostic experience. Thus, traditional evaluation methods often lead to biased diagnostic results. There is a clinical demand for a method that can objectively quantify the severity of the patient's disease. METHODS This study aims to develop an artificial intelligence-aided diagnosis method based on multi-sensory fusion wearables. The experiment relies on a rigorous clinical trial paradigm to collect multi-modal fusion of signals from 98 ET patients. At the same time, three clinicians scored independently, and the consensus score obtained was used as the ground truth for the machine learning models. RESULTS Sixty kinematic parameters were extracted from the signals recorded by the nine-axis inertial measurement unit (IMU). The results showed that most of the features obtained by IMU could effectively characterize the severity of the tremors. The accuracy of the optimal model for three tasks classifying five severity levels was 97.71%, 97.54%, and 97.72%, respectively. CONCLUSIONS This paper reports the first attempt to combine multiple feature selection and machine learning algorithms for fine-grained automatic quantification of postural tremor in ET patients. The promising results showed the potential of the proposed approach to quantify the severity of ET objectively.
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Affiliation(s)
- Chenbin Ma
- Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, PLA General Hospital, 100853, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, 100191, Beijing, China; School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China; Shenyuan Honors College, Beihang University, 100191, Beijing, China
| | - Peng Zhang
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, 100191, Beijing, China; School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China
| | - Jiachen Wang
- Medical School of Chinese PLA, 100853, Beijing, China
| | - Jian Zhang
- Medical School of Chinese PLA, 100853, Beijing, China
| | - Longsheng Pan
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, 100853, Beijing, China
| | - Xuemei Li
- Clinics of Cadre, Department of Outpatient, First Medical Center of Chinese PLA General Hospital, 100853, Beijing, China
| | - Chunyu Yin
- Clinics of Cadre, Department of Outpatient, First Medical Center of Chinese PLA General Hospital, 100853, Beijing, China
| | - Ailing Li
- Pusheng Yixin (Beijing) Hospital Management Co., Ltd, 100020, Beijing, China
| | - Rui Zong
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, 100853, Beijing, China.
| | - Zhengbo Zhang
- Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, PLA General Hospital, 100853, Beijing, China.
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12
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Ma C, Li D, Pan L, Li X, Yin C, Li A, Zhang Z, Zong R. Quantitative assessment of essential tremor based on machine learning methods using wearable device. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103244] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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13
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Habets JGV, Herff C, Kubben PL, Kuijf ML, Temel Y, Evers LJW, Bloem BR, Starr PA, Gilron R, Little S. Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson's Disease Using a Wrist-Worn Accelerometer. SENSORS 2021; 21:s21237876. [PMID: 34883886 PMCID: PMC8659489 DOI: 10.3390/s21237876] [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] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 01/07/2023]
Abstract
Motor fluctuations in Parkinson’s disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson’s patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson’s patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.
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Affiliation(s)
- Jeroen G. V. Habets
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
- Correspondence: ; Tel.: +31-433-876-052
| | - Christian Herff
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
| | - Pieter L. Kubben
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
| | - Mark L. Kuijf
- Department of Neurology, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands;
| | - Yasin Temel
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
| | - Luc J. W. Evers
- Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 GC Nijmegen, The Netherlands; (L.J.W.E.); (B.R.B.)
| | - Bastiaan R. Bloem
- Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 GC Nijmegen, The Netherlands; (L.J.W.E.); (B.R.B.)
| | - Philip A. Starr
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, CA 94143, USA; (P.A.S.); (R.G.); (S.L.)
| | - Ro’ee Gilron
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, CA 94143, USA; (P.A.S.); (R.G.); (S.L.)
| | - Simon Little
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, CA 94143, USA; (P.A.S.); (R.G.); (S.L.)
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14
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Could New Generations of Sensors Reshape the Management of Parkinson’s Disease? CLINICAL AND TRANSLATIONAL NEUROSCIENCE 2021. [DOI: 10.3390/ctn5020018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Parkinson's disease (PD) is a chronic neurologic disease that has a great impact on the patient’s quality of life. The natural course of the disease is characterized by an insidious onset of symptoms, such as rest tremor, shuffling gait, bradykinesia, followed by improvement with the initiation of dopaminergic therapy. However, this “honeymoon period” gradually comes to an end with the emergence of motor fluctuations and dyskinesia. PD patients need long-term treatments and monitoring throughout the day; however, clinical examinations in hospitals are often not sufficient for optimal management of the disease. Technology-based devices are a new comprehensive assessment method of PD patient’s symptoms that are easy to use and give unbiased measurements. This review article provides an exhaustive overview of motor complications of advanced PD and new approaches to the management of the disease using sensors.
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15
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Abstract
PURPOSE OF REVIEW The COVID-pandemic has facilitated the implementation of telemedicine in both clinical practice and research. We highlight recent developments in three promising areas of telemedicine: teleconsultation, telemonitoring, and teletreatment. We illustrate this using Parkinson's disease as a model for other chronic neurological disorders. RECENT FINDINGS Teleconsultations can reliably administer parts of the neurological examination remotely, but are typically not useful for establishing a reliable diagnosis. For follow-ups, teleconsultations can provide enhanced comfort and convenience to patients, and provide opportunities for blended and proactive care models. Barriers include technological challenges, limited clinician confidence, and a suboptimal clinician-patient relationship. Telemonitoring using wearable sensors and smartphone-based apps can support clinical decision-making, but we lack large-scale randomized controlled trials to prove effectiveness on clinical outcomes. Increasingly many trials are now incorporating telemonitoring as an exploratory outcome, but more work remains needed to demonstrate its clinical meaningfulness. Finding a balance between benefits and burdens for individual patients remains vital. Recent work emphasised the promise of various teletreatment solutions, such as remotely adjustable deep brain stimulation parameters, virtual reality enhanced exercise programs, and telephone-based cognitive behavioural therapy. Personal contact remains essential to ascertain adherence to teletreatment. SUMMARY The availability of different telemedicine tools for remote consultation, monitoring, and treatment is increasing. Future research should establish whether telemedicine improves outcomes in routine clinical care, and further underpin its merits both as intervention and outcome in research settings.
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Affiliation(s)
- Robin van den Bergh
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders
| | - Bastiaan R. Bloem
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders
| | - Marjan J. Meinders
- Radboud University Medical Center, Radboud Institute for Health Sciences, Scientific Center for Quality of Healthcare, Nijmegen, The Netherlands
| | - Luc J.W. Evers
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders
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16
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Sundgren M, Andréasson M, Svenningsson P, Noori RM, Johansson A. Does Information from the Parkinson KinetiGraph™ (PKG) Influence the Neurologist's Treatment Decisions?-An Observational Study in Routine Clinical Care of People with Parkinson's Disease. J Pers Med 2021; 11:jpm11060519. [PMID: 34198780 PMCID: PMC8227056 DOI: 10.3390/jpm11060519] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 05/27/2021] [Accepted: 06/03/2021] [Indexed: 11/16/2022] Open
Abstract
Management of Parkinson's disease traditionally relies solely on clinical assessment. The PKG objectively measures affected persons' movements in daily life. The present study evaluated how often PKG data changed treatment decisions in routine clinical care and to what extent the clinical assessment and the PKG interpretation differed. PKG recordings were performed before routine visits. The neurologist first made a clinical assessment without reviewing the PKG. Signs and symptoms were recorded, and a treatment plan was documented. Afterward, the PKG was evaluated. Then, the neurologist decided whether to change the initial treatment plan or not. PKG review resulted in a change in the initial treatment plan in 21 of 66 participants (31.8%). The clinical assessment and the PKG review differed frequently, mainly regarding individual overall presence of motor problems (67%), profile of bradykinesia/wearing off (79%), dyskinesia (35%) and sleep (55%). PKG improved the dialogue with the participant in 88% of cases. PKG and clinical variables were stable when they were repeated after 3-6 months. In conclusion, PKG information changes treatment decisions in nearly a third of people with Parkinson's disease in routine care. Standard clinical assessment and PKG evaluation are often non-identical. Objective measurements in people living with Parkinson's disease can add therapeutically relevant information.
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Affiliation(s)
- Mathias Sundgren
- Department of Neurology, Karolinska University Hospital, 171 64 Stockholm, Sweden; (M.A.); (P.S.); (R.-M.N.); (A.J.)
- Center for Neurology, Akademiskt Specialistcentrum, Stockholms Läns Sjukvårdsområde (SLSO), 113 65 Stockholm, Sweden
- Correspondence:
| | - Mattias Andréasson
- Department of Neurology, Karolinska University Hospital, 171 64 Stockholm, Sweden; (M.A.); (P.S.); (R.-M.N.); (A.J.)
- Center for Neurology, Akademiskt Specialistcentrum, Stockholms Läns Sjukvårdsområde (SLSO), 113 65 Stockholm, Sweden
| | - Per Svenningsson
- Department of Neurology, Karolinska University Hospital, 171 64 Stockholm, Sweden; (M.A.); (P.S.); (R.-M.N.); (A.J.)
- Center for Neurology, Akademiskt Specialistcentrum, Stockholms Läns Sjukvårdsområde (SLSO), 113 65 Stockholm, Sweden
| | - Rose-Marie Noori
- Department of Neurology, Karolinska University Hospital, 171 64 Stockholm, Sweden; (M.A.); (P.S.); (R.-M.N.); (A.J.)
| | - Anders Johansson
- Department of Neurology, Karolinska University Hospital, 171 64 Stockholm, Sweden; (M.A.); (P.S.); (R.-M.N.); (A.J.)
- Center for Neurology, Akademiskt Specialistcentrum, Stockholms Läns Sjukvårdsområde (SLSO), 113 65 Stockholm, Sweden
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17
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Krause E, Randhawa J, Mehanna R. Comparing subjective and objective response to medications in Parkinson's disease patients using the Personal KinetiGraph™. Parkinsonism Relat Disord 2021; 87:105-110. [PMID: 34020301 DOI: 10.1016/j.parkreldis.2021.05.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/04/2021] [Accepted: 05/06/2021] [Indexed: 11/24/2022]
Abstract
BACKGROUND Management of motor symptoms in Parkinson's Disease(PD) relies on subjective information provided by patients, the quality of which can be affected by many factors. RATIONALE Objective data collected during daily life could complement this information and improve management of motor symptoms. OBJECTIVES To assess the usefulness of the Personal KinetiGraph (PKG) in characterizing the intensity and timing of motor symptoms in PD patients. METHODS Retrospective study of all PD patients followed at a tertiary academic movement disorders center assessed by PKG between December 1, 2016 and October 30, 2018. PKG was worn for 7 days prior to the clinical visit. We compared the information obtained from the interview and the clinical visit, and assessed the impact of the PKG on treatment decision making. RESULTS 170 PKG results were reviewed. PKG complemented patient input in 82.9%(141/170) and led to medication changes in 71%(100/141) of the complemented inputs. PKG contributed the least to correcting or complementing patients' input when patients self-reported as undertreated (22%) and the most when patient were unable to answer all questions regarding motor response to individual doses (100%) (Fisher, p < 0.0001). The majority of patient undergoing 3 or 4 PKG encounters did not reach a controlled state as defined by PKG until the 3rd or 4th encounter, suggesting that repeated use of the PKG might be needed to help optimize motor control as therapy changes done after one encounter might not be enough. CONCLUSIONS PKG might be useful in supplementing patient-provided information for accurate assessment and treatment plan.
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Affiliation(s)
- Erik Krause
- UT MOVE, University of Texas Health Science Center at Houston Mc Govern Medical School, Houston, TX, USA
| | - Jaskaren Randhawa
- UT MOVE, University of Texas Health Science Center at Houston Mc Govern Medical School, Houston, TX, USA
| | - Raja Mehanna
- UT MOVE, University of Texas Health Science Center at Houston Mc Govern Medical School, Houston, TX, USA.
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18
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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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19
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Powers R, Etezadi-Amoli M, Arnold EM, Kianian S, Mance I, Gibiansky M, Trietsch D, Alvarado AS, Kretlow JD, Herrington TM, Brillman S, Huang N, Lin PT, Pham HA, Ullal AV. Smartwatch inertial sensors continuously monitor real-world motor fluctuations in Parkinson's disease. Sci Transl Med 2021; 13:13/579/eabd7865. [PMID: 33536284 DOI: 10.1126/scitranslmed.abd7865] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 01/11/2021] [Indexed: 12/19/2022]
Abstract
Longitudinal, remote monitoring of motor symptoms in Parkinson's disease (PD) could enable more precise treatment decisions. We developed the Motor fluctuations Monitor for Parkinson's Disease (MM4PD), an ambulatory monitoring system that used smartwatch inertial sensors to continuously track fluctuations in resting tremor and dyskinesia. We designed and validated MM4PD in 343 participants with PD, including a longitudinal study of up to 6 months in a 225-subject cohort. MM4PD measurements correlated to clinical evaluations of tremor severity (ρ = 0.80) and mapped to expert ratings of dyskinesia presence (P < 0.001) during in-clinic tasks. MM4PD captured symptom changes in response to treatment that matched the clinician's expectations in 94% of evaluated subjects. In the remaining 6% of cases, symptom data from MM4PD identified opportunities to make improvements in pharmacologic strategy. These results demonstrate the promise of MM4PD as a tool to support patient-clinician communication, medication titration, and clinical trial design.
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Affiliation(s)
| | | | | | - Sara Kianian
- Apple Inc., Cupertino, CA 95014, USA.,Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | | | | | | | | | | | - Todd M Herrington
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.,Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
| | - Salima Brillman
- Parkinson's Disease and Movement Center of Silicon Valley, Menlo Park, CA 94025, USA
| | - Nengchun Huang
- Silicon Valley Parkinson's Center, Los Gatos, CA 95032, USA
| | - Peter T Lin
- Silicon Valley Parkinson's Center, Los Gatos, CA 95032, USA
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20
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Knudson M, Thomsen TH, Kjaer TW. Comparing Objective and Subjective Measures of Parkinson's Disease Using the Parkinson's KinetiGraph. Front Neurol 2020; 11:570833. [PMID: 33250843 PMCID: PMC7674832 DOI: 10.3389/fneur.2020.570833] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 10/01/2020] [Indexed: 11/25/2022] Open
Abstract
Background: Parkinson's disease (PD) is a neurodegenerative disease that can lead to impaired motor function and execution of activities of daily living (ADL). Since clinicians typically can only observe patients' symptoms during visits, prescribed medication schedules may not reflect the full range of symptoms experienced throughout the day. Therefore, objective tools are needed to provide comprehensive symptom data to optimize treatment. One such tool is the Parkinson's KinetiGraph® (PKG), a wearable sensor that measures motor symptoms of Parkinson's disease. Objective: To build a mathematical model to determine if PKG data measuring Parkinson's patients' motor symptoms can predict patients' ADL impairment. Methods: Thirty-four patients with PD wore the PKG device for 6 days while performing their ADL. Patients' PKG scores for bradykinesia and dyskinesia, as well as their responses to a questionnaire asking if their ADL-level had been impacted by various motor symptoms, were used to build a multiple regression model predicting the patients' Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part II scores. Results: Calculation of bradykinesia score response to medication showed that using a dosage response time of 30 min yielded a greater bradykinesia response than when the response time was set to 40, 50, 60, 70, 80, or 90 min. The overall multiple regression model predicting MDS-UPDRS part II score was significant (R2 = 0.546, p < 0.001). Conclusion: The PKG's ability to provide motor symptom data that correlates with clinical measures of ADL impairment suggests that it has strong potential as a tool for the assessment and management of Parkinson's disease motor symptoms.
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Affiliation(s)
- Mei Knudson
- Department of Mathematics and Statistics, Carleton College, Northfield, MN, United States.,DIS Copenhagen, Copenhagen, Denmark.,Department of Clinical Neurophysiology and Neurology, Zealand University Hospital, Roskilde, Denmark
| | - Trine Hoermann Thomsen
- Department of Clinical Neurophysiology and Neurology, Zealand University Hospital, Roskilde, Denmark.,Department of Clinical Medicine, Faculty of Health, University of Copenhagen, Copenhagen, Denmark
| | - Troels Wesenberg Kjaer
- DIS Copenhagen, Copenhagen, Denmark.,Department of Clinical Neurophysiology and Neurology, Zealand University Hospital, Roskilde, Denmark.,Department of Clinical Medicine, Faculty of Health, University of Copenhagen, Copenhagen, Denmark
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21
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AlMahadin G, Lotfi A, Zysk E, Siena FL, Carthy MM, Breedon P. Parkinson's disease: current assessment methods and wearable devices for evaluation of movement disorder motor symptoms - a patient and healthcare professional perspective. BMC Neurol 2020; 20:419. [PMID: 33208135 PMCID: PMC7677815 DOI: 10.1186/s12883-020-01996-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 11/09/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Parkinson's disease is the second most common long-term chronic, progressive, neurodegenerative disease, affecting more than 10 million people worldwide. There has been a rising interest in wearable devices for evaluation of movement disorder diseases such as Parkinson's disease due to the limitations in current clinic assessment methods such as Unified Parkinson's Disease Rating Scale (UPDRS) and the Hoehn and Yahr (HY) scale. However, there are only a few commercial wearable devices available, which, in addition, have had very limited adoption and implementation. This inconsistency may be due to a lack of users' perspectives in terms of device design and implementation. This study aims to identify the perspectives of healthcare professionals and patients linked to current assessment methods and to identify preferences, and requirements of wearable devices. METHODS This was a qualitative study using semi-structured interviews followed by focus groups. Transcripts from sessions were analysed using an inductive thematic approach. RESULTS It was noted that the well-known assessment process such as Unified Parkinson's Disease Rating Scale (UPDRS) was not used routinely in clinics since it is time consuming, subjective, inaccurate, infrequent and dependent on patients' memories. Participants suggested that objective assessment methods are needed to increase the chance of effective treatment. The participants' perspectives were positive toward using wearable devices, particularly if they were involved in early design stages. Patients emphasized that the devices should be comfortable, but they did not have any concerns regarding device visibility or data privacy transmitted over the internet when it comes to their health. In terms of wearing a monitor, the preferable part of the body for all participants was the wrist. Healthcare professionals stated a need for an economical solution that is easy to interpret. Some design aspects identified by patients included clasps, material choice, and form factor. CONCLUSION The study concluded that current assessment methods are limited. Patients' and healthcare professionals' involvement in wearable devices design process has a pivotal role in terms of ultimate user acceptance. This includes the provision of additional functions to the wearable device, such as fall detection and medication reminders, which could be attractive features for patients.
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Affiliation(s)
- Ghayth AlMahadin
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS UK
| | - Ahmad Lotfi
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS UK
| | - Eva Zysk
- Department of Psychology, University of British Columbia in Vancouver, West Mall, Vancouver, V6T 1Z4 Canada
| | - Francesco Luke Siena
- School Of Architecture Design & Built Environment, Nottingham Trent University, Goldsmith Street, Nottingham, NG1 4FQ UK
| | | | - Philip Breedon
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS UK
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22
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Chen L, Cai G, Weng H, Yu J, Yang Y, Huang X, Chen X, Ye Q. More Sensitive Identification for Bradykinesia Compared to Tremors in Parkinson's Disease Based on Parkinson's KinetiGraph (PKG). Front Aging Neurosci 2020; 12:594701. [PMID: 33240078 PMCID: PMC7670912 DOI: 10.3389/fnagi.2020.594701] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 09/29/2020] [Indexed: 11/18/2022] Open
Abstract
The effective management and therapies for Parkinson's disease (PD) require appropriate clinical evaluation. The Parkinson's KinetiGraph (PKG) is a wearable sensor system that can monitor the motion characteristics of PD objectively and continuously. This study was aimed to assess the correlations between PKG data and clinical scores of bradykinesia, rigidity, tremor, and fluctuation. It also aims to explore the application value of identifying early motor symptoms. An observational study of 100 PD patients wearing the PKG for ≥ 6 days was performed. It provides a series of data, such as the bradykinesia score (BKS), percent time tremor (PTT), dyskinesia score (DKS), and fluctuation and dyskinesia score (FDS). PKG data and UPDRS scores were analyzed, including UPDRS III total scores, UPDRS III-bradykinesia scores (UPDRS III-B: items 23-26, 31), UPDRS III-rigidity scores (UPDRS III-R: item 22), and scores from the Wearing-off Questionnaire (WOQ-9). This study shows that there was significant correlation between BKS and UPDRS III scores, including UPDRS III total scores, UPDRS III-B, and UPDRS III-R scores (r = 0.479-0.588, p ≤ 0.001), especially in the early-stage group (r = 0.682, p < 0.001). Furthermore, we found that BKS in patients with left-sided onset (33.57 ± 5.14, n = 37) is more serious than in patients with right-sided onset (29.87 ± 6.86, n = 26). Our findings support the feasibility of using the PKG to detect abnormal movements, especially bradykinesia in PD. It is suitable for the early detection, remote monitoring, and timely treatment of PD symptoms.
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Affiliation(s)
- Lina Chen
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, China
| | - Guoen Cai
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, China
| | - Huidan Weng
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jiao Yu
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, China
| | - Yu Yang
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, China
| | - Xuanyu Huang
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, China
| | - Xiaochun Chen
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, China
- Institute of Neuroscience, Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, Fuzhou, China
| | - Qinyong Ye
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, China
- Institute of Neuroscience, Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, Fuzhou, China
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23
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Dominey T, Kehagia AA, Gorst T, Pearson E, Murphy F, King E, Carroll C. Introducing the Parkinson's KinetiGraph into Routine Parkinson's Disease Care: A 3-Year Single Centre Experience. JOURNAL OF PARKINSONS DISEASE 2020; 10:1827-1832. [PMID: 33016893 PMCID: PMC7683053 DOI: 10.3233/jpd-202101] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
In an effort to provide timely clinical input for people with Parkinson's disease (PD) in the face of increasing demand and resource limitation in our UK based service, we introduced remote management in place of clinic appointment, including the use of the Parkinson's KinetiGraph (PKG™), a wrist-worn device that provides a continuous measure of movement. We evaluated our reporting methods and findings, the nature of unmet need we identified, our treatment recommendations and the degree of their implementation in our patients whose feedback guided our service developments. Our evaluation highlighted opportunities and challenges associated with incorporating digital data into care traditionally delivered via in-person contact.
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Affiliation(s)
- Thea Dominey
- Applied Parkinson's Research Group, University of Plymouth, Faculty of Health, Plymouth, Devon, United Kingdom
| | - Angie A Kehagia
- Barts and the London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom
| | - Terry Gorst
- Applied Parkinson's Research Group, University of Plymouth, Faculty of Health, Plymouth, Devon, United Kingdom
| | - Emma Pearson
- University Hospitals Plymouth NHS Trust, Plymouth, Devon, United Kingdom
| | - Fiona Murphy
- University Hospitals Plymouth NHS Trust, Plymouth, Devon, United Kingdom
| | - Emma King
- Applied Parkinson's Research Group, University of Plymouth, Faculty of Health, Plymouth, Devon, United Kingdom
| | - Camille Carroll
- Applied Parkinson's Research Group, University of Plymouth, Faculty of Health, Plymouth, Devon, United Kingdom.,University Hospitals Plymouth NHS Trust, Plymouth, Devon, United Kingdom
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24
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Mahadevan N, Demanuele C, Zhang H, Volfson D, Ho B, Erb MK, Patel S. Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device. NPJ Digit Med 2020; 3:5. [PMID: 31970290 PMCID: PMC6962225 DOI: 10.1038/s41746-019-0217-7] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 12/16/2019] [Indexed: 01/09/2023] Open
Abstract
Objective assessment of Parkinson's disease symptoms during daily life can help improve disease management and accelerate the development of new therapies. However, many current approaches require the use of multiple devices, or performance of prescribed motor activities, which makes them ill-suited for free-living conditions. Furthermore, there is a lack of open methods that have demonstrated both criterion and discriminative validity for continuous objective assessment of motor symptoms in this population. Hence, there is a need for systems that can reduce patient burden by using a minimal sensor setup while continuously capturing clinically meaningful measures of motor symptom severity under free-living conditions. We propose a method that sequentially processes epochs of raw sensor data from a single wrist-worn accelerometer by using heuristic and machine learning models in a hierarchical framework to provide continuous monitoring of tremor and bradykinesia. Results show that sensor derived continuous measures of resting tremor and bradykinesia achieve good to strong agreement with clinical assessment of symptom severity and are able to discriminate between treatment-related changes in motor states.
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Affiliation(s)
| | | | - Hao Zhang
- Pfizer, Inc., Cambridge, MA 02139 USA
| | | | - Bryan Ho
- Tufts Medical Center, Boston, MA 02111 USA
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25
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Dorsey ER, Omberg L, Waddell E, Adams JL, Adams R, Ali MR, Amodeo K, Arky A, Augustine EF, Dinesh K, Hoque ME, Glidden AM, Jensen-Roberts S, Kabelac Z, Katabi D, Kieburtz K, Kinel DR, Little MA, Lizarraga KJ, Myers T, Riggare S, Rosero SZ, Saria S, Schifitto G, Schneider RB, Sharma G, Shoulson I, Stevenson EA, Tarolli CG, Luo J, McDermott MP. Deep Phenotyping of Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2020; 10:855-873. [PMID: 32444562 PMCID: PMC7458535 DOI: 10.3233/jpd-202006] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/01/2020] [Indexed: 12/13/2022]
Abstract
Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have left gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.
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Affiliation(s)
- E. Ray Dorsey
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | | | - Emma Waddell
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Jamie L. Adams
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Roy Adams
- Machine Learning, AI and Healthcare Lab, Johns Hopkins University, Baltimore, MD, USA
| | | | - Katherine Amodeo
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Abigail Arky
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Erika F. Augustine
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Karthik Dinesh
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
| | | | - Alistair M. Glidden
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Stella Jensen-Roberts
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Zachary Kabelac
- Department of Computer Science and Artificial Intelligence, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Dina Katabi
- Department of Computer Science and Artificial Intelligence, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Karl Kieburtz
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Daniel R. Kinel
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Max A. Little
- School of Computer Science, University of Birmingham, UK
- Massachusetts Institute of Technology, MA, USA
| | - Karlo J. Lizarraga
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Taylor Myers
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Sara Riggare
- Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden
| | | | - Suchi Saria
- Machine Learning, AI and Healthcare Lab, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Statistics, and Health Policy, Johns Hopkins University, MD, USA
| | - Giovanni Schifitto
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Ruth B. Schneider
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Gaurav Sharma
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Ira Shoulson
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
- Grey Matter Technologies, Sarasota, FL, USA
| | - E. Anna Stevenson
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Christopher G. Tarolli
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Jiebo Luo
- Department of Computer Science, University of Rochester, Rochester, NY, USA
| | - Michael P. McDermott
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
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26
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Joshi R, Bronstein JM, Keener A, Alcazar J, Yang DD, Joshi M, Hermanowicz N. PKG Movement Recording System Use Shows Promise in Routine Clinical Care of Patients With Parkinson's Disease. Front Neurol 2019; 10:1027. [PMID: 31632333 PMCID: PMC6779790 DOI: 10.3389/fneur.2019.01027] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 09/10/2019] [Indexed: 01/03/2023] Open
Abstract
Parkinson's disease (PD) is a debilitating, neurodegenerative disorder that affects nearly one million people. It's hallmark signs and symptoms include slow movements, rigidity, tremor, and unstable posture. Additionally, non-motor symptoms such as sleeplessness, depression, cognitive impairment, impulse control behaviors (ICB) have been reported. Today, treatment regimens to modify disease progression do not exist and as such, treatment is focused on symptom relief. Additionally, physicians are challenged to base their diagnoses and treatment plans on unreliable self-reported symptoms, even when used in conjunction to validated assessments such as the Unified Parkinson's Disease Rating Scale (UPDRS) and clinical exams. Wearable technology may provide clinicians objective measures of motor problems to supplement current subjective methods. Global Kinetics Corporation (GKC) has developed a watch-device called the Personal KinetiGraph (PKG) that records movements and provides patients medication dosing reminders. A separate clinician-use report supplies longitudinal motor and event data. The PKG was FDA-cleared in September 2016. We studied 63 PD patients during 85 routine care visits in 2 US academic institutions, evaluating the clinical utility of the PKG. Patients wore a PKG for 6 continuous days before their visit. Next, PKG data was uploaded to produce a report. In clinic, physicians discussed PD symptoms with patients and conducted a motor examination prior to reviewing the PKG report and comparing it to their initial assessments. Lastly, patient, caregiver and physician satisfaction surveys were conducted by each user. Across all visits when patients did not report bradykinesia or dyskinesia, the PKG reported these symptoms (50 and 33% of the time, respectively). The PKG provided insights for treatment plans in 50 (79%) patients across 71 (84%) visits. Physicians found improved patient dialogue in 50 (59%) visits, improved ability to assess treatment impact in 32 (38%) visits, and improved motor assessment in 28 (33%) visits. Patients stated in 82% of responses that they agreed or strongly agreed in PKG training, usability, performance, and satisfaction. In 39% of responses, they also reported a very valuable impact on their care. PKG use in 63 PD patients within our clinical practice showed clinically relevant utility in many areas.
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Affiliation(s)
| | - Jeffrey M Bronstein
- Department of Neurology, University of California, Los Angeles School of Medicine, Los Angeles, CA, United States
| | - A Keener
- Department of Neurology, University of California, Los Angeles School of Medicine, Los Angeles, CA, United States
| | - Jaclyn Alcazar
- Department of Neurology, University of California, Irvine, Irvine, CA, United States
| | - Diane D Yang
- Department of Neurology, University of California, Los Angeles School of Medicine, Los Angeles, CA, United States
| | - Maya Joshi
- Clinical Partners Group, Santa Monica, CA, United States
| | - Neal Hermanowicz
- Department of Neurology, University of California, Irvine, Irvine, CA, United States
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27
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Tang P, Hou C, Liu Y, Liu P, Zhang X, Zhang L, Chong L, Li R. Quantitative Assessment of Finger Movement Profile in a Visual-Motor Task Based on a Tablet Computer: The Application in Parkinson's Disease. JOURNAL OF PARKINSONS DISEASE 2019; 9:811-819. [PMID: 31450513 DOI: 10.3233/jpd-191695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND/OBJECTIVE Easily applicable, quantitative assessment of movement is widely needed in various clinical settings, especially in the evaluation of Parkinson's disease (PD). METHODS We developed a highly repeatable tablet computer-based finger movement assessment system (FMAS) to record finger movement profile in a visual-motor task both in PD (n = 217) and healthy participants (n = 221). RESULTS We found age-related declines in finger movement performance among the healthy participants but not in PD patients with the FMAS. Significant differences in movement time (MT) and latency/MT ratio but not in latency were observed in PD patients as compared with healthy subjects (P < 0.000). Meanwhile, we identified the latency/MT ratio as the optimal parameter to differentiate PD from age-matched healthy subjects in an age-independent manner (cut-off 1.08 with corresponding AUC = 0.861). In addition, a significant correlation was found between finger movement parameters and the Hoehn and Yahr scale (H-Y scale), UPDRS III score and the duration of the disease in PD patients (P < 0.01). CONCLUSION It was suggested that the tablet computer-based evaluation of finger movement provided an easily applicable quantitative method to assess the conditions of PD patients.
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Affiliation(s)
- Peng Tang
- Department of Neurology, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Chen Hou
- Department of Neurology, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Yue Liu
- Department of Neurology, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Peng Liu
- Department of Neurology, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Xin Zhang
- Department of Neurology, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Lina Zhang
- Department of Neurology, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Li Chong
- Department of Neurology, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Rui Li
- Department of Neurology, Shaanxi Provincial People's Hospital, Xi'an, China
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28
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Carroll C, Kobylecki C, Silverdale M, Thomas C. Impact of Quantitative Assessment of Parkinson's Disease-Associated Symptoms Using Wearable Technology on Treatment Decisions. JOURNAL OF PARKINSON'S DISEASE 2019; 9:601. [PMID: 31081794 PMCID: PMC6700649 DOI: 10.3233/jpd-191623] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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