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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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Mirelman A, Volkov J, Salomon A, Gazit E, Nieuwboer A, Rochester L, Del Din S, Avanzino L, Pelosin E, Bloem BR, Della Croce U, Cereatti A, Thaler A, Roggen D, Mazza C, Shirvan J, Cedarbaum JM, Giladi N, Hausdorff JM. Digital Mobility Measures: A Window into Real-World Severity and Progression of Parkinson's Disease. Mov Disord 2024; 39:328-338. [PMID: 38151859 DOI: 10.1002/mds.29689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 12/29/2023] Open
Abstract
BACKGROUND Real-world monitoring using wearable sensors has enormous potential for assessing disease severity and symptoms among persons with Parkinson's disease (PD). Many distinct features can be extracted, reflecting multiple mobility domains. However, it is unclear which digital measures are related to PD severity and are sensitive to disease progression. OBJECTIVES The aim was to identify real-world mobility measures that reflect PD severity and show discriminant ability and sensitivity to disease progression, compared to the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scale. METHODS Multicenter real-world continuous (24/7) digital mobility data from 587 persons with PD and 68 matched healthy controls were collected using an accelerometer adhered to the lower back. Machine learning feature selection and regression algorithms evaluated associations of the digital measures using the MDS-UPDRS (I-III). Binary logistic regression assessed discriminatory value using controls, and longitudinal observational data from a subgroup (n = 33) evaluated sensitivity to change over time. RESULTS Digital measures were only moderately correlated with the MDS-UPDRS (part II-r = 0.60 and parts I and III-r = 0.50). Most associated measures reflected activity quantity and distribution patterns. A model with 14 digital measures accurately distinguished recently diagnosed persons with PD from healthy controls (81.1%, area under the curve: 0.87); digital measures showed larger effect sizes (Cohen's d: [0.19-0.66]), for change over time than any of the MDS-UPDRS parts (Cohen's d: [0.04-0.12]). CONCLUSIONS Real-world mobility measures are moderately associated with clinical assessments, suggesting that they capture different aspects of motor capacity and function. Digital mobility measures are sensitive to early-stage disease and to disease progression, to a larger degree than conventional clinical assessments, demonstrating their utility, primarily for clinical trials but ultimately also for clinical care. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Anat Mirelman
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jana Volkov
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Amit Salomon
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Eran Gazit
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Alice Nieuwboer
- Department of Rehabilitation Science, KU Leuven, Neuromotor Rehabilitation Research Group, Leuven, Belgium
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Laura Avanzino
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Genoa, Italy
| | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Policlinico San Martino Teaching Hospital, Genoa, Italy
| | - Bastiaan R Bloem
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands
| | - Ugo Della Croce
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Avner Thaler
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | | | | | | | - Jesse M Cedarbaum
- Coeruleus Clinical Sciences, Woodbridge, Connecticut, USA
- Yale University School of Medicine, New Haven, Connecticut, USA
| | - Nir Giladi
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jeffrey M Hausdorff
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Physical Therapy, Tel Aviv University, Tel Aviv, Israel
- Department of Orthopedic Surgery, Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
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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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Qu Y, Zhang T, Duo Y, Chen L, Li X. Identification and quantitative assessment of motor complications in Parkinson's disease using the Parkinson's KinetiGraph™. Front Aging Neurosci 2023; 15:1142268. [PMID: 37593376 PMCID: PMC10427502 DOI: 10.3389/fnagi.2023.1142268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 07/17/2023] [Indexed: 08/19/2023] Open
Abstract
Introduction Effective management and therapies for the motor complications of Parkinson's disease (PD) require appropriate clinical evaluation. The Parkinson's KinetiGraph™ (PKG) is a wearable biosensor system that can record the motion characteristics of PD objectively and remotely. Objective The study aims to investigate the value of PKG in identifying and quantitatively assessing motor complications including motor fluctuations and dyskinesia in the Chinese PD population, as well as the correlation with the clinical scale assessments. Methods Eighty-four subjects with PD were recruited and continuously wore the PKG for 7 days. Reports with 7-day output data were provided by the manufacturer, including the fluctuation scores (FS) and dyskinesia scores (DKS). Specialists in movement disorders used the Movement Disorder Society-Unified Parkinson's Disease Rating Scale-IV (MDS-UPDRS IV), the wearing-off questionnaire 9 (WOQ-9), and the unified dyskinesia rating scale (UDysRS) for the clinical assessment of motor complications. Spearman correlation analyses were used to evaluate the correlation between the FS and DKS recorded by the PKG and the clinical scale assessment results. Receiver operating characteristic (ROC) curves were generated to analyze the sensitivity and specificity of the FS and DKS scores in the identification of PD motor complications. Results The FS was significantly positively correlated with the MDS-UPDRS IV motor fluctuation (items 4.3-4.5) scores (r = 0.645, p < 0.001). ROC curve analysis showed a maximum FS cut-off value of 7.5 to identify motor fluctuation, with a sensitivity of 74.3% and specificity of 87.8%. The DKS was significantly positively correlated with the UDysRS total score (r = 0.629, p < 0.001) and the UDysRS III score (r = 0.634, p < 0.001). ROC curve analysis showed that the maximum DKS cut-off value for the diagnosis of dyskinesia was 0.7, with a sensitivity of 83.3% and a specificity of 83.3%. Conclusion The PKG assessment of motor complications in the PD population analyzed in this study has a significant correlation with the clinical scale assessment, high sensitivity, and high specificity. Compared with clinical evaluations, PKG can objectively, quantitatively, and remotely identify and assess motor complications in PD, providing a good objective recording for managing motor complications.
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Affiliation(s)
- Yan Qu
- Department of Neurology, Affiliated Dalian Municipal Friendship Hospital of Dalian Medical University, Dalian, China
| | - Tingting Zhang
- Department of Neurology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yunyan Duo
- Department of Neurology, Affiliated Dalian Municipal Friendship Hospital of Dalian Medical University, Dalian, China
| | - Liling Chen
- Department of Neurology, Affiliated Dalian Municipal Friendship Hospital of Dalian Medical University, Dalian, China
| | - Xiaohong Li
- Department of Neurology, Affiliated Dalian Municipal Friendship Hospital of Dalian Medical University, Dalian, China
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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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Closing the loop for patients with Parkinson disease: where are we? Nat Rev Neurol 2022; 18:497-507. [PMID: 35681103 DOI: 10.1038/s41582-022-00674-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/12/2022] [Indexed: 02/07/2023]
Abstract
Although levodopa remains the most efficacious symptomatic therapy for Parkinson disease (PD), management of levodopa treatment during the advanced stages of the disease is extremely challenging. This difficulty is a result of levodopa's short half-life, a progressive narrowing of the therapeutic window, and major inter-patient and intra-patient variations in the dose-response relationship. Therefore, a suitable alternative to repeated oral administration of levodopa is being sought. Recent research efforts have focused on the development of novel levodopa delivery strategies and wearable physical sensors that track symptoms and disease progression. However, the need for methods to monitor the levels of levodopa present in the body in real time has been overlooked. Advances in chemical sensor technology mean that the development of wearable and mobile biosensors for continuous or frequent levodopa measurements is now possible. Such levodopa monitoring could help to deliver personalized and timely medication dosing to alleviate treatment-related fluctuations in the symptoms of PD. Therefore, with the aim of optimizing therapeutic management of PD and improving the quality of life of patients, we share our vision of a future closed-loop autonomous wearable 'sense-and-act' system. This system consists of a network of physical and chemical sensors coupled with a levodopa delivery device and is guided by effective big data fusion algorithms and machine learning methods.
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Chandrabhatla AS, Pomeraniec IJ, Ksendzovsky A. Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms. NPJ Digit Med 2022; 5:32. [PMID: 35304579 PMCID: PMC8933519 DOI: 10.1038/s41746-022-00568-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 01/21/2022] [Indexed: 11/09/2022] Open
Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor impairments such as tremor, bradykinesia, dyskinesia, and gait abnormalities. Current protocols assess PD symptoms during clinic visits and can be subjective. Patient diaries can help clinicians evaluate at-home symptoms, but can be incomplete or inaccurate. Therefore, researchers have developed in-home automated methods to monitor PD symptoms to enable data-driven PD diagnosis and management. We queried the US National Library of Medicine PubMed database to analyze the progression of the technologies and computational/machine learning methods used to monitor common motor PD symptoms. A sub-set of roughly 12,000 papers was reviewed that best characterized the machine learning and technology timelines that manifested from reviewing the literature. The technology used to monitor PD motor symptoms has advanced significantly in the past five decades. Early monitoring began with in-lab devices such as needle-based EMG, transitioned to in-lab accelerometers/gyroscopes, then to wearable accelerometers/gyroscopes, and finally to phone and mobile & web application-based in-home monitoring. Significant progress has also been made with respect to the use of machine learning algorithms to classify PD patients. Using data from different devices (e.g., video cameras, phone-based accelerometers), researchers have designed neural network and non-neural network-based machine learning algorithms to categorize PD patients across tremor, gait, bradykinesia, and dyskinesia. The five-decade co-evolution of technology and computational techniques used to monitor PD motor symptoms has driven significant progress that is enabling the shift from in-lab/clinic to in-home monitoring of PD symptoms.
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Affiliation(s)
- Anirudha S Chandrabhatla
- School of Medicine, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA
| | - I Jonathan Pomeraniec
- Surgical Neurology Branch, National Institutes of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA. .,Department of Neurosurgery, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA.
| | - Alexander Ksendzovsky
- Department of Neurosurgery, University of Maryland Medical System, Baltimore, MD, 21201, USA
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Personalized Assessment of Insomnia and Sleep Quality in Patients with Parkinson's Disease. J Pers Med 2022; 12:jpm12020322. [PMID: 35207811 PMCID: PMC8875986 DOI: 10.3390/jpm12020322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 02/15/2022] [Accepted: 02/17/2022] [Indexed: 02/01/2023] Open
Abstract
Sleep disturbances are more common in patients with Parkinson’s disease (PD) than in the general population and are considered one of the most troublesome symptoms by these patients. Insomnia represents one of the most common sleep disturbances in PD, and it correlates significantly with poor quality of life. There are several known causes of insomnia in the general population, but the complex manifestations that might be associated with PD may also induce insomnia and impact the quality of sleep. The treatment of insomnia and the strategies needed to improve sleep quality may therefore represent a challenge for the neurologist. A personalized approach to the PD patient with insomnia may help the clinician to identify the factors and comorbidities that should also be considered in order to establish a better individualized therapeutic plan. This review will focus on the main characteristics and correlations of insomnia, the most common risk factors, and the main subjective and objective methods indicated for the assessment of insomnia and sleep quality in order to offer a concise guide containing the main steps needed to approach the PD patient with chronic insomnia in a personalized manner.
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9
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Optimizing Treatment of Parkinson’s Disease. J Pers Med 2022; 12:jpm12020245. [PMID: 35207734 PMCID: PMC8880050 DOI: 10.3390/jpm12020245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 02/05/2022] [Indexed: 02/04/2023] Open
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Hadley AJ, Riley DE, Heldman DA. Real-World Evidence for a Smartwatch-Based Parkinson's Motor Assessment App for Patients Undergoing Therapy Changes. Digit Biomark 2021; 5:206-215. [PMID: 34703975 DOI: 10.1159/000518571] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 07/19/2021] [Indexed: 01/18/2023] Open
Abstract
Introduction Parkinson's disease (PD) is poorly quantified by patients outside the clinic, and paper diaries have problems with subjective descriptions and bias. Wearable sensor platforms; however, can accurately quantify symptoms such as tremor, dyskinesia, and bradykinesia. Commercially available smartwatches are equipped with accelerometers and gyroscopes that can measure motion for objective evaluation. We sought to evaluate the clinical utility of a prescription smartwatch-based monitoring system for PD utilizing periodic task-based motor assessment. Methods Sixteen patients with PD used a smartphone- and smartwatch-based monitoring system to objectively assess motor symptoms for 1 week prior to instituting a doctor recommended change in therapy and for 4 weeks after the change. After 5 weeks the participants returned to the clinic to discuss their results with their doctor, who made therapy recommendations based on the reports and his clinical judgment. Symptom scores were synchronized with the medication diary and the temporal effects of therapy on weekly and hourly timescales were calculated. Results Thirteen participants successfully completed the study and averaged 4.9 assessments per day for 3 days per week during the study. The doctor instructed 8 participants to continue their new regimens and 5 to revert to their previous regimens. The smartwatch-based assessments successfully captured intraday fluctuations and short- and long-term responses to therapies, including detecting significant improvements (p < 0.05) in at least one symptom in 7 participants. Conclusions The smartwatch-based app successfully captured temporal trends in symptom scores following application of new therapy on hourly, daily, and weekly timescales. These results suggest that validated smartwatch-based PD monitoring can provide clinically relevant information and may reduce the need for traditional office visits for therapy adjustment.
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