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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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Feldmann LK, Roudini J, Kühn AA, Habets JGV. Improving naturalistic neuroscience with patient engagement strategies. Front Hum Neurosci 2024; 17:1325154. [PMID: 38259336 PMCID: PMC10800538 DOI: 10.3389/fnhum.2023.1325154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 12/13/2023] [Indexed: 01/24/2024] Open
Abstract
Introduction The clinical implementation of chronic electrophysiology-driven adaptive deep brain stimulation (DBS) algorithms in movement disorders requires reliable representation of motor and non-motor symptoms in electrophysiological biomarkers, throughout normal life (naturalistic). To achieve this, there is the need for high-resolution and -quality chronic objective and subjective symptom monitoring in parallel to biomarker recordings. To realize these recordings, an active participation and engagement of the investigated patients is necessary. To date, there has been little research into patient engagement strategies for DBS patients or chronic electrophysiological recordings. Concepts and results We here present our concept and the first results of a patient engagement strategy for a chronic DBS study. After discussing the current state of literature, we present objectives, methodology and consequences of the patient engagement regarding study design, data acquisition, and study infrastructure. Nine patients with Parkinson's disease and their caregivers participated in the meeting, and their input led to changes to our study design. Especially, the patient input helped us designing study-set-up meetings and support structures. Conclusion We believe that patient engagement increases compliance and study motivation through scientific empowerment of patients. While considering patient opinion on sensors or questionnaire questions may lead to more precise and reliable data acquisition, there was also a high demand for study support and engagement structures. Hence, we recommend the implementation of patient engagement in planning of chronic studies with complex designs, long recording durations or high demand for individual active study participation.
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Affiliation(s)
- Lucia K. Feldmann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Juliet Roudini
- QUEST Center for Responsible Research, Berlin Institute of Health at Charité, Berlin, Germany
- Patient and Stakeholder Engagement, Cluster of Excellence, NeuroCure, Berlin, Germany
| | - Andrea A. Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Charité University Medicine, Berlin, Germany
- NeuroCure Clinical Research Center, Charité University Medicine, Berlin, Germany
- DZNE, German Center for Neurodegenerative Diseases, Berlin, Germany
| | - Jeroen G. V. Habets
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
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Löhle M, Timpka J, Bremer A, Khodakarami H, Gandor F, Horne M, Ebersbach G, Odin P, Storch A. Application of single wrist-wearable accelerometry for objective motor diary assessment in fluctuating Parkinson's disease. NPJ Digit Med 2023; 6:194. [PMID: 37848531 PMCID: PMC10582031 DOI: 10.1038/s41746-023-00937-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 09/29/2023] [Indexed: 10/19/2023] Open
Abstract
Advanced Parkinson's disease (PD) is characterized by motor fluctuations including unpredictable oscillations remarkably impairing quality of life. Effective management and development of novel therapies for these response fluctuations largely depend on clinical rating instruments such as the widely-used PD home diary, which are associated with biases and errors. Recent advancements in digital health technologies provide user-friendly wearables that can be tailored for continuous monitoring of motor fluctuations. Their criterion validity under real-world conditions using clinical examination as the gold standard remains to be determined. We prospectively examined this validity of a wearable accelerometer-based digital Parkinson's Motor Diary (adPMD) using the Parkinson's Kinetigraph (PKG®) in an alternative application by converting its continuous data into one of the three motor categories of the PD home diary (Off, On and Dyskinetic state). Sixty-three out of 91 eligible participants with fluctuating PD (46% men, average age 66) had predefined sufficient adPMD datasets (>70% of half-hour periods) from 2 consecutive days. 92% of per-protocol assessments were completed. adPMD monitoring of daily times in motor states showed moderate validity for Off and Dyskinetic state (ICC = 0.43-0.51), while inter-rating methods agreements on half-hour-level can be characterized as poor (median Cohen's κ = 0.13-0.21). Individualization of adPMD thresholds for transferring accelerometer data into diary categories improved temporal agreements up to moderate level for Dyskinetic state detection (median Cohen's κ = 0.25-0.41). Here we report that adPMD real-world-monitoring captures daily times in Off and Dyskinetic state in advanced PD with moderate validities, while temporal agreement of adPMD and clinical observer diary data is limited.
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Affiliation(s)
- Matthias Löhle
- Department of Neurology, University Medical Center Rostock, Rostock, Germany.
- German Center for Neurodegenerative Diseases (DZNE) Rostock-Greifswald, Rostock, Germany.
| | - Jonathan Timpka
- Division of Neurology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Alexander Bremer
- Department of Neurology, University Medical Center Rostock, Rostock, Germany
| | | | - Florin Gandor
- Movement Disorders Hospital, Beelitz-Heilstätten, Beelitz, Germany
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
| | - Malcom Horne
- Bionics Institute, Melbourne, VIC, Australia
- The Department of Medicine, The University of Melbourne, St Vincent's Hospital, Fitzroy, VIC, 3010, Australia
| | - Georg Ebersbach
- Movement Disorders Hospital, Beelitz-Heilstätten, Beelitz, Germany
| | - Per Odin
- Division of Neurology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Alexander Storch
- Department of Neurology, University Medical Center Rostock, Rostock, Germany.
- German Center for Neurodegenerative Diseases (DZNE) Rostock-Greifswald, Rostock, Germany.
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Sotirakis C, Su Z, Brzezicki MA, Conway N, Tarassenko L, FitzGerald JJ, Antoniades CA. Identification of motor progression in Parkinson's disease using wearable sensors and machine learning. NPJ Parkinsons Dis 2023; 9:142. [PMID: 37805655 PMCID: PMC10560243 DOI: 10.1038/s41531-023-00581-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 09/20/2023] [Indexed: 10/09/2023] Open
Abstract
Wearable devices offer the potential to track motor symptoms in neurological disorders. Kinematic data used together with machine learning algorithms can accurately identify people living with movement disorders and the severity of their motor symptoms. In this study we aimed to establish whether a combination of wearable sensor data and machine learning algorithms with automatic feature selection can estimate the clinical rating scale and whether it is possible to monitor the motor symptom progression longitudinally, for people with Parkinson's Disease. Seventy-four patients visited the lab seven times at 3-month intervals. Their walking (2-minutes) and postural sway (30-seconds,eyes-closed) were recorded using six Inertial Measurement Unit sensors. Simple linear regression and Random Forest algorithms were utilised together with different routines of automatic feature selection or factorisation, resulting in seven different machine learning algorithms to estimate the clinical rating scale (Movement Disorder Society- Unified Parkinson's Disease Rating Scale part III; MDS-UPDRS-III). Twenty-nine features were found to significantly progress with time at group level. The Random Forest model revealed the most accurate estimation of the MDS-UPDRS-III among the seven models. The model estimations detected a statistically significant progression of the motor symptoms within 15 months when compared to the first visit, whereas the MDS-UPDRS-III did not capture any change. Wearable sensors and machine learning can track the motor symptom progression in people with PD better than the conventionally used clinical rating scales. The methods described in this study can be utilised complimentary to the clinical rating scales to improve the diagnostic and prognostic accuracy.
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Affiliation(s)
- Charalampos Sotirakis
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Zi Su
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Maksymilian A Brzezicki
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Niall Conway
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Lionel Tarassenko
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - James J FitzGerald
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Chrystalina A Antoniades
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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Guerra A, D'Onofrio V, Ferreri F, Bologna M, Antonini A. Objective measurement versus clinician-based assessment for Parkinson's disease. Expert Rev Neurother 2023; 23:689-702. [PMID: 37366316 DOI: 10.1080/14737175.2023.2229954] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/18/2023] [Accepted: 06/22/2023] [Indexed: 06/28/2023]
Abstract
INTRODUCTION Although clinician-based assessment through standardized clinical rating scales is currently the gold standard for quantifying motor impairment in Parkinson's disease (PD), it is not without limitations, including intra- and inter-rater variability and a degree of approximation. There is increasing evidence supporting the use of objective motion analyses to complement clinician-based assessment. Objective measurement tools hold significant potential for improving the accuracy of clinical and research-based evaluations of patients. AREAS COVERED The authors provide several examples from the literature demonstrating how different motion measurement tools, including optoelectronics, contactless and wearable systems allow for both the objective quantification and monitoring of key motor symptoms (such as bradykinesia, rigidity, tremor, and gait disturbances), and the identification of motor fluctuations in PD patients. Furthermore, they discuss how, from a clinician's perspective, objective measurements can help in various stages of PD management. EXPERT OPINION In our opinion, sufficient evidence supports the assertion that objective monitoring systems enable accurate evaluation of motor symptoms and complications in PD. A range of devices can be utilized not only to support diagnosis but also to monitor motor symptom during the disease progression and can become relevant in the therapeutic decision-making process.
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Affiliation(s)
- Andrea Guerra
- Parkinson and Movement Disorder Unit, Study Center on Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | | | - Florinda Ferreri
- Unit of Neurology, Unit of Clinical Neurophysiology, Study Center of Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
- Department of Clinical Neurophysiology, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Matteo Bologna
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed, Pozzilli, Italy
| | - Angelo Antonini
- Parkinson and Movement Disorder Unit, Study Center on Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
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de Villers-Sidani É, Voss P, Guitton D, Cisneros-Franco JM, Koch NA, Ducharme S. A novel tablet-based software for the acquisition and analysis of gaze and eye movement parameters: a preliminary validation study in Parkinson's disease. Front Neurol 2023; 14:1204733. [PMID: 37396780 PMCID: PMC10310943 DOI: 10.3389/fneur.2023.1204733] [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: 04/13/2023] [Accepted: 05/25/2023] [Indexed: 07/04/2023] Open
Abstract
The idea that eye movements can reflect certain aspects of brain function and inform on the presence of neurodegeneration is not a new one. Indeed, a growing body of research has shown that several neurodegenerative disorders, such as Alzheimer's and Parkinson's Disease, present characteristic eye movement anomalies and that specific gaze and eye movement parameters correlate with disease severity. The use of detailed eye movement recordings in research and clinical settings, however, has been limited due to the expensive nature and limited scalability of the required equipment. Here we test a novel technology that can track and measure eye movement parameters using the embedded camera of a mobile tablet. We show that using this technology can replicate several well-known findings regarding oculomotor anomalies in Parkinson's disease (PD), and furthermore show that several parameters significantly correlate with disease severity as assessed with the MDS-UPDRS motor subscale. A logistic regression classifier was able to accurately distinguish PD patients from healthy controls on the basis of six eye movement parameters with a sensitivity of 0.93 and specificity of 0.86. This tablet-based tool has the potential to accelerate eye movement research via affordable and scalable eye-tracking and aid with the identification of disease status and monitoring of disease progression in clinical settings.
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Affiliation(s)
- Étienne de Villers-Sidani
- Innodem Neurosciences, Montreal, QC, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Patrice Voss
- Innodem Neurosciences, Montreal, QC, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Daniel Guitton
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - J. Miguel Cisneros-Franco
- Innodem Neurosciences, Montreal, QC, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Nils A. Koch
- Innodem Neurosciences, Montreal, QC, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Simon Ducharme
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Douglas Mental Health University Institute, Montreal, QC, Canada
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Sotirakis C, Conway N, Su Z, Villarroel M, Tarassenko L, FitzGerald JJ, Antoniades CA. Longitudinal Monitoring of Progressive Supranuclear Palsy using Body-Worn Movement Sensors. Mov Disord 2022; 37:2263-2271. [PMID: 36054142 DOI: 10.1002/mds.29194] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 07/15/2022] [Accepted: 07/25/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND We have previously shown that wearable technology and machine learning techniques can accurately discriminate between progressive supranuclear palsy (PSP), Parkinson's disease, and healthy controls. To date these techniques have not been applied in longitudinal studies of disease progression in PSP. OBJECTIVES We aimed to establish whether data collected by a body-worn inertial measurement unit (IMU) network could predict clinical rating scale scores in PSP and whether it could be used to track disease progression. METHODS We studied gait and postural stability in 17 participants with PSP over five visits at 3-month intervals. Participants performed a 2-minute walk and an assessment of postural stability by standing for 30 seconds with their eyes closed, while wearing an array of six IMUs. RESULTS Thirty-two gait and posture features were identified, which progressed significantly with time. A simple linear regression model incorporating the three features with the clearest progression pattern was able to detect statistically significant progression 3 months in advance of the clinical scores. A more complex linear regression and a random forest approach did not improve on this. CONCLUSIONS The reduced variability of the models, in comparison to clinical rating scales, allows a significant change in disease status from baseline to be observed at an earlier stage. The current study sheds light on the individual features that are important in tracking disease progression. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Charalampos Sotirakis
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Niall Conway
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Zi Su
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Mauricio Villarroel
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Lionel Tarassenko
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - James J FitzGerald
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.,Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
| | - Chrystalina A Antoniades
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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Thomsen TH, Jørgensen LB, Kjær TW, Haahr A, Vogel A, Larsen IU, Winge K. Clinical Markers of 6 Pre-dominant Coping Behaviors in Living With Parkinson Disease: A Convergent Mixed Methods Study. INQUIRY : A JOURNAL OF MEDICAL CARE ORGANIZATION, PROVISION AND FINANCING 2022; 59:469580221129929. [PMID: 36314596 PMCID: PMC9629560 DOI: 10.1177/00469580221129929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
People with Parkinson's disease (PwP) experience a variety of symptoms and fluctuations in these, which they have to cope with every day. In tailoring a person-centered treatment to PwP there is a lack of knowledge about the association between pre-dominant coping behaviors and clinical markers among PwP. To describe and compare specific clinical markers between 6 suggested coping behaviors. Thirty-four PwP, who previously had been classified into 6 different pre-dominant coping behaviors, were included in this mixed methods study. Six primary variables were included in the descriptive analysis; motor function (UPDRS-III), non-motor symptoms score (NMS-Quest), change in bradykinesia score, apathy score (LARS), personality traits (NEO-FFI), and cognitive status (evaluated by a neuropsychologist). The merged results of this mixed methods study indicate that clinical markers as apathy, burden of non-motor symptoms, cognitive impairments and personality traits, have the potential to impact the coping behavior in PwP. In a clinical setting the markers; NMS-burden, degree of apathy, cognition, and personality traits may indicate specific coping behavior. Three of the six suggested typologies of coping behaviors differed from the other groups when comparing descriptive data. In order to improve patient care and guide the development of person-centered therapies, each PwP should be approached based on those typologies.
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Affiliation(s)
- Trine Hørmann Thomsen
- Rigshospitalet Glostrup, Glostrup, Capital Region, Denmark,Trine Hørmann Thomsen, Department of Neurology, Movement disorder Clinic, Rigshospitalet Glostrup, Valdemar Hansens Vej 6, opgang 7, Glostrup, Capital Region 2600, Denmark.
| | - Lene Bastrup Jørgensen
- Knowledge Centre for Neurorehabilitation of Western Denmark, Regional Hospital Viborg, Denmark,Department of Clinical Medicine, University of Aarhus, Aarhus, Denmark
| | - Troels Wesenberg Kjær
- Zealand University Hospital, Roskilde, Denmark,University of Copenhagen, København, Denmark
| | | | - Asmus Vogel
- Copenhagen University Hospital, Rigshospitalet, Denmark
| | | | - Kristian Winge
- Odense University Hospital, Odense, Denmark,University of Southern Denmark, Denmark
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Pérez-López C, Hernández-Vara J, Caballol N, Bayes À, Buongiorno M, Lopez-Ariztegui N, Gironell A, López-Sánchez J, Martínez-Castrillo JC, Sauco M A, López-Manzanares L, Escalante-Arroyo S, Pérez-Martínez DA, Rodríguez-Molinero A. Comparison of the Results of a Parkinson's Holter Monitor With Patient Diaries, in Real Conditions of Use: A Sub-analysis of the MoMoPa-EC Clinical Trial. Front Neurol 2022; 13:835249. [PMID: 35651347 PMCID: PMC9149269 DOI: 10.3389/fneur.2022.835249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
Background For specialists in charge of Parkinson's disease (PD), one of the most time-consuming tasks of the consultations is the assessment of symptoms and motor fluctuations. This task is complex and is usually based on the information provided by the patients themselves, which in most cases is complex and biased. In recent times, different tools have appeared on the market that allow automatic ambulatory monitoring. The MoMoPa-EC clinical trial (NCT04176302) investigates the effect of one of these tools—Sense4Care's STAT-ON—can have on routine clinical practice. In this sub-analysis the agreement between the Hauser diaries and the STAT-ON sensor is analyzed. Methods Eighty four patients from MoMoPa-EC cohort were included in this sub-analysis. The intraclass correlation coefficient was calculated between the patient diary entries and the sensor data. Results The intraclass correlation coefficient of both methods was 0.57 (95% CI: 0.3–0.73) for the OFF time (%), 0.48 (95% CI: 0.17–0.68) for the time in ON (%), and 0.65 (95% CI%: 0.44–0.78) for the time with dyskinesias (%). Furthermore, the Spearman correlations with the UPDRS scale have been analyzed for different parameters of the two methods. The maximum correlation found was −0.63 (p < 0.001) between Mean Fluidity (one of the variables offered by the STAT-dON) and factor 1 of the UPDRS. Conclusion This sub-analysis shows a moderate concordance between the two tools, it is clearly appreciated that the correlation between the different UPDRS indices is better with the STAT-ON than with the Hauser diary. Trial Registration https://clinicaltrials.gov/show/NCT04176302 (NCT04176302).
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Affiliation(s)
- Carlos Pérez-López
- Department of Investigation, Consorci Sanitari de l'Alt Penedès i Garraf, Sant Pere de Ribes, Spain
| | - Jorge Hernández-Vara
- Neurology Department, Hospital Universitari Vall D Hebron Neurodegenerative Diseases Research Group, Vall D Hebron Institut de Recerca Universidad Autónoma de Barcelona, Biomedical Research Network Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Nuria Caballol
- Department of Neurology, Hospital de Sant Joan Despí Moisès Broggi, Sant Joan Despi, Spain.,Parkinson's and Movement Disorders Unit, Hospital Quirón-Teknon, Barcelona, Spain
| | - Àngels Bayes
- Parkinson's and Movement Disorders Unit, Hospital Quirón-Teknon, Barcelona, Spain
| | | | | | - Alexandre Gironell
- Department of Neurology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - José López-Sánchez
- Department of Neurology, Hospital Virgen de la Arrixaca de Murcia, Murcia, Spain
| | | | - Alvarez Sauco M
- Department of Neurology, Hospital General de Elche, Elche, Spain
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Rodríguez-Martín D, Cabestany J, Pérez-López C, Pie M, Calvet J, Samà A, Capra C, Català A, Rodríguez-Molinero A. A New Paradigm in Parkinson's Disease Evaluation With Wearable Medical Devices: A Review of STAT-ON TM. Front Neurol 2022; 13:912343. [PMID: 35720090 PMCID: PMC9202426 DOI: 10.3389/fneur.2022.912343] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
In the past decade, the use of wearable medical devices has been a great breakthrough in clinical practice, trials, and research. In the Parkinson's disease field, clinical evaluation is time limited, and healthcare professionals need to rely on retrospective data collected through patients' self-filled diaries and administered questionnaires. As this often leads to inaccurate evaluations, a more objective system for symptom monitoring in a patient's daily life is claimed. In this regard, the use of wearable medical devices is crucial. This study aims at presenting a review on STAT-ONTM, a wearable medical device Class IIa, which provides objective information on the distribution and severity of PD motor symptoms in home environments. The sensor analyzes inertial signals, with a set of validated machine learning algorithms running in real time. The device was developed for 12 years, and this review aims at gathering all the results achieved within this time frame. First, a compendium of the complete journey of STAT-ONTM since 2009 is presented, encompassing different studies and developments in funded European and Spanish national projects. Subsequently, the methodology of database construction and machine learning algorithms design and development is described. Finally, clinical validation and external studies of STAT-ONTM are presented.
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Affiliation(s)
| | - Joan Cabestany
- Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Carlos Pérez-López
- Department of Investigation, Consorci Sanitari Alt Penedès - Garraf, Vilanova i la Geltrú, Spain
| | - Marti Pie
- Sense4Care S.L., Cornellà de Llobregat, Spain
| | - Joan Calvet
- Sense4Care S.L., Cornellà de Llobregat, Spain
| | - Albert Samà
- Sense4Care S.L., Cornellà de Llobregat, Spain
| | | | - Andreu Català
- Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politecnica de Catalunya, Barcelona, Spain
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A Single Wearable Sensor for Gait Analysis in Parkinson’s Disease: A Preliminary Study. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Movement monitoring in patients with Parkinson’s disease (PD) is critical for quantifying disease progression and assessing how a subject responds to medication administration over time. In this work, we propose a continuous monitoring system based on a single wearable sensor placed on the lower back and an algorithm for gait parameters evaluation. In order to preliminarily validate the proposed system, seven PD subjects took part in an experimental protocol in preparation for a larger randomized controlled study. We validated the feasibility of our algorithm in a constrained environment through a laboratory scenario. Successively, it was tested in an unsupervised environment, such as the home scenario, for a total of almost 12 h of daily living activity data. During all phases of the experimental protocol, videos were shot to document the tasks. The obtained results showed a good accuracy of the proposed algorithm. For all PD subjects in the laboratory scenario, the algorithm for step identification reached a percentage error low of 2%, 99.13% of sensitivity and 100% of specificity. In the home scenario the Bland–Altman plot showed a mean difference of −3.29 and −1 between the algorithm and the video recording for walking bout detection and steps identification, respectively.
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Kenny L, Moore K, O' Riordan C, Fox S, Barton J, Tedesco S, Sica M, Crowe C, Alamäki A, Condell J, Nordström A, Timmons S. The Views and Needs of People With Parkinson Disease Regarding Wearable Devices for Disease Monitoring: Mixed Methods Exploration. JMIR Form Res 2022; 6:e27418. [PMID: 34989693 PMCID: PMC8778562 DOI: 10.2196/27418] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 06/23/2021] [Accepted: 08/01/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Wearable devices can diagnose, monitor, and manage neurological disorders such as Parkinson disease. With a growing number of wearable devices, it is no longer a case of whether a wearable device can measure Parkinson disease motor symptoms, but rather which features suit the user. Concurrent with continued device development, it is important to generate insights on the nuanced needs of the user in the modern era of wearable device capabilities. OBJECTIVE This study aims to understand the views and needs of people with Parkinson disease regarding wearable devices for disease monitoring and management. METHODS This study used a mixed method parallel design, wherein survey and focus groups were concurrently conducted with people living with Parkinson disease in Munster, Ireland. Surveys and focus group schedules were developed with input from people with Parkinson disease. The survey included questions about technology use, wearable device knowledge, and Likert items about potential device features and capabilities. The focus group participants were purposively sampled for variation in age (all were aged >50 years) and sex. The discussions concerned user priorities, perceived benefits of wearable devices, and preferred features. Simple descriptive statistics represented the survey data. The focus groups analyzed common themes using a qualitative thematic approach. The survey and focus group analyses occurred separately, and results were evaluated using a narrative approach. RESULTS Overall, 32 surveys were completed by individuals with Parkinson disease. Four semistructured focus groups were held with 24 people with Parkinson disease. Overall, the participants were positive about wearable devices and their perceived benefits in the management of symptoms, especially those of motor dexterity. Wearable devices should demonstrate clinical usefulness and be user-friendly and comfortable. Participants tended to see wearable devices mainly in providing data for health care professionals rather than providing feedback for themselves, although this was also important. Barriers to use included poor hand function, average technology confidence, and potential costs. It was felt that wearable device design that considered the user would ensure better compliance and adoption. CONCLUSIONS Wearable devices that allow remote monitoring and assessment could improve health care access for patients living remotely or are unable to travel. COVID-19 has increased the use of remotely delivered health care; therefore, future integration of technology with health care will be crucial. Wearable device designers should be aware of the variability in Parkinson disease symptoms and the unique needs of users. Special consideration should be given to Parkinson disease-related health barriers and the users' confidence with technology. In this context, a user-centered design approach that includes people with Parkinson disease in the design of technology will likely be rewarded with improved user engagement and the adoption of and compliance with wearable devices, potentially leading to more accurate disease management, including self-management.
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Affiliation(s)
- Lorna Kenny
- Centre for Gerontology and Rehabilitation, School of Medicine, University College Cork, Cork, Ireland
| | - Kevin Moore
- Centre for Gerontology and Rehabilitation, School of Medicine, University College Cork, Cork, Ireland
| | - Clíona O' Riordan
- Centre for Gerontology and Rehabilitation, School of Medicine, University College Cork, Cork, Ireland
| | - Siobhan Fox
- Centre for Gerontology and Rehabilitation, School of Medicine, University College Cork, Cork, Ireland
| | - John Barton
- Tyndall National Institute, University College Cork, Cork, Ireland
| | | | - Marco Sica
- Tyndall National Institute, University College Cork, Cork, Ireland
| | - Colum Crowe
- Tyndall National Institute, University College Cork, Cork, Ireland
| | - Antti Alamäki
- Research, Development and Innovation Activities & Physiotherapy Education, Karelia University of Applied Sciences, Joensuu, Finland
| | - Joan Condell
- School of Computing, Engineering and Intelligent Systems, Faculty of Computing, Engineering and the Built Environment, Ulster University, Magee, United Kingdom
| | - Anna Nordström
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Suzanne Timmons
- Centre for Gerontology and Rehabilitation, School of Medicine, University College Cork, Cork, Ireland
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Rodríguez-Molinero A, Hernández-Vara J, Miñarro A, Pérez-López C, Bayes-Rusiñol À, Martínez-Castrillo JC, Pérez-Martínez DA. Multicentre, randomised, single-blind, parallel group trial to compare the effectiveness of a Holter for Parkinson's symptoms against other clinical monitoring methods: study protocol. BMJ Open 2021; 11:e045272. [PMID: 34281918 PMCID: PMC8291311 DOI: 10.1136/bmjopen-2020-045272] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
INTRODUCTION In recent years, multiple studies have aimed to develop and validate portable technological devices capable of monitoring the motor complications of Parkinson's disease patients (Parkinson's Holter). The effectiveness of these monitoring devices for improving clinical control is not known. METHODS AND ANALYSIS This is a single-blind, cluster-randomised controlled clinical trial. Neurologists from Spanish health centres will be randomly assigned to one of three study arms (1:1:1): (a) therapeutic adjustment using information from a Parkinson's Holter that will be worn by their patients for 7 days, (b) therapeutic adjustment using information from a diary of motor fluctuations that will be completed by their patients for 7 days and (c) therapeutic adjustment using clinical information collected during consultation. It is expected that 162 consecutive patients will be included over a period of 6 months.The primary outcome is the efficiency of the Parkinson's Holter compared with traditional clinical practice in terms of Off time reduction with respect to the baseline (recorded through a diary of motor fluctuations, which will be completed by all patients). As secondary outcomes, changes in variables related to other motor complications (dyskinesia and freezing of gait), quality of life, autonomy in activities of daily living, adherence to the monitoring system and number of doctor-patient contacts will be analysed. The noninferiority of the Parkinson's Holter against the diary of motor fluctuations in terms of Off time reduction will be studied as the exploratory objective.Ethics and dissemination approval for this study has been obtained from the Hospital Universitari de Bellvitge Ethics Committee. The results of this study will inform the practical utility of the objective information provided by a Parkinson's Holter and, therefore, the convenience of adopting this technology in clinical practice and in future clinical trials. We expect public dissemination of the results in 2022. TRIAL REGISTRATION NCT04176302; https://clinicaltrials.gov/show/NCT04176302.
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Affiliation(s)
| | - Jorge Hernández-Vara
- Department of Neurology, Hospital Universitari Vall d'Hebron and Neurodegenerative Diseases Research Group, Barcelona, Spain
| | - Antonio Miñarro
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
| | - Carlos Pérez-López
- Àrea de Recerca, Consorci Sanitari de l'Alt Penedès i Garraf, Vilafranca del Pendès, Spain
| | - Àngels Bayes-Rusiñol
- Parkinson's and Movement Disorders Unit, Hospital Quirón Teknon, Barcelona, Spain
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Parkinson's Disease Patient Monitoring: A Real-Time Tracking and Tremor Detection System Based on Magnetic Measurements. SENSORS 2021; 21:s21124196. [PMID: 34207306 PMCID: PMC8235095 DOI: 10.3390/s21124196] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/14/2021] [Accepted: 06/16/2021] [Indexed: 11/17/2022]
Abstract
Reliable diagnosis of early-stage Parkinson’s disease is an important task, since it permits the administration of a timely treatment, slowing the progression of the disease. Together with non-motor symptoms, other important signs of disease can be retrieved from the measurement of the movement trajectory and from tremor appearances. To measure these signs, the paper proposes a magnetic tracking system able to collect information about translational and vibrational movements in a spatial cubic domain, using a low-cost, low-power and highly accurate solution. These features allow the usage of the proposed technology to realize a portable monitoring system, that may be operated at home or in general practices, enabling telemedicine and preventing saturation of large neurological centers. Validation is based on three tests: movement trajectory tracking, a rest tremor test and a finger tapping test. These tests are considered in the Unified Parkinson’s Disease Rating Scale and are provided as case studies to prove the system’s capabilities to track and detect tremor frequencies. In the case of the tapping test, a preliminary classification scheme is also proposed to discriminate between healthy and ill patients. No human patients are involved in the tests, and most cases are emulated by means of a robotic arm, suitably driven to perform required tasks. Tapping test results show a classification accuracy of about 93% using a k-NN classification algorithm, while imposed tremor frequencies have been correctly detected by the system in the other two tests.
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15
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Barrachina-Fernández M, Maitín AM, Sánchez-Ávila C, Romero JP. Wearable Technology to Detect Motor Fluctuations in Parkinson's Disease Patients: Current State and Challenges. SENSORS (BASEL, SWITZERLAND) 2021; 21:4188. [PMID: 34207198 PMCID: PMC8234127 DOI: 10.3390/s21124188] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/07/2021] [Accepted: 06/16/2021] [Indexed: 01/30/2023]
Abstract
Monitoring of motor symptom fluctuations in Parkinson's disease (PD) patients is currently performed through the subjective self-assessment of patients. Clinicians require reliable information about a fluctuation's occurrence to enable a precise treatment rescheduling and dosing adjustment. In this review, we analyzed the utilization of sensors for identifying motor fluctuations in PD patients and the application of machine learning techniques to detect fluctuations. The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Ten studies were included between January 2010 and March 2021, and their main characteristics and results were assessed and documented. Five studies utilized daily activities to collect the data, four used concrete scenarios executing specific activities to gather the data, and only one utilized a combination of both situations. The accuracy for classification was 83.56-96.77%. In the studies evaluated, it was not possible to find a standard cleaning protocol for the signal captured, and there is significant heterogeneity in the models utilized and in the different features introduced in the models (using spatiotemporal characteristics, frequential characteristics, or both). The two most influential factors in the good performance of the classification problem are the type of features utilized and the type of model.
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Affiliation(s)
- Mercedes Barrachina-Fernández
- Programa en Ingeniería Biomédica (PhD), ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM), Avenida Complutense, 30, 28040 Madrid, Spain;
| | - Ana María Maitín
- Centro de Estudios e Innovación en Gestión del Conocimiento (CEIEC), Universidad Francisco de Vitoria, 28223 Pozuelo de Alarcón, Spain;
| | - Carmen Sánchez-Ávila
- Department de Matemática Aplicada a las TICs, ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM), Avenida Complutense, 30, 28040 Madrid, Spain
| | - Juan Pablo Romero
- Facultad de Ciencias Experimentales, Universidad Francisco de Vitoria, 28223 Pozuelo de Alarcón, Spain
- Brain Damage Unit, Hospital Beata María Ana, 28007 Madrid, Spain
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16
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Multimodal Classification of Parkinson's Disease in Home Environments with Resiliency to Missing Modalities. SENSORS 2021; 21:s21124133. [PMID: 34208690 PMCID: PMC8235443 DOI: 10.3390/s21124133] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/09/2021] [Accepted: 06/10/2021] [Indexed: 11/17/2022]
Abstract
Parkinson’s disease (PD) is a chronic neurodegenerative condition that affects a patient’s everyday life. Authors have proposed that a machine learning and sensor-based approach that continuously monitors patients in naturalistic settings can provide constant evaluation of PD and objectively analyse its progression. In this paper, we make progress toward such PD evaluation by presenting a multimodal deep learning approach for discriminating between people with PD and without PD. Specifically, our proposed architecture, named MCPD-Net, uses two data modalities, acquired from vision and accelerometer sensors in a home environment to train variational autoencoder (VAE) models. These are modality-specific VAEs that predict effective representations of human movements to be fused and given to a classification module. During our end-to-end training, we minimise the difference between the latent spaces corresponding to the two data modalities. This makes our method capable of dealing with missing modalities during inference. We show that our proposed multimodal method outperforms unimodal and other multimodal approaches by an average increase in F1-score of 0.25 and 0.09, respectively, on a data set with real patients. We also show that our method still outperforms other approaches by an average increase in F1-score of 0.17 when a modality is missing during inference, demonstrating the benefit of training on multiple modalities.
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A Long-Term, Real-Life Parkinson Monitoring Database Combining Unscripted Objective and Subjective Recordings. DATA 2021. [DOI: 10.3390/data6020022] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Accurate real-life monitoring of motor and non-motor symptoms is a challenge in Parkinson’s disease (PD). The unobtrusive capturing of symptoms and their naturalistic fluctuations within or between days can improve evaluation and titration of therapy. First-generation commercial PD motion sensors are promising to augment clinical decision-making in general neurological consultation, but concerns remain regarding their short-term validity, and long-term real-life usability. In addition, tools monitoring real-life subjective experiences of motor and non-motor symptoms are lacking. The dataset presented in this paper constitutes a combination of objective kinematic data and subjective experiential data, recorded parallel to each other in a naturalistic, long-term real-life setting. The objective data consists of accelerometer and gyroscope data, and the subjective data consists of data from ecological momentary assessments. Twenty PD patients were monitored without daily life restrictions for fourteen consecutive days. The two types of data can be used to address hypotheses on naturalistic motor and/or non-motor symptomatology in PD.
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Sica M, Tedesco S, Crowe C, Kenny L, Moore K, Timmons S, Barton J, O’Flynn B, Komaris DS. Continuous home monitoring of Parkinson's disease using inertial sensors: A systematic review. PLoS One 2021; 16:e0246528. [PMID: 33539481 PMCID: PMC7861548 DOI: 10.1371/journal.pone.0246528] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 01/20/2021] [Indexed: 02/01/2023] Open
Abstract
Parkinson’s disease (PD) is a progressive neurological disorder of the central nervous system that deteriorates motor functions, while it is also accompanied by a large diversity of non-motor symptoms such as cognitive impairment and mood changes, hallucinations, and sleep disturbance. Parkinsonism is evaluated during clinical examinations and appropriate medical treatments are directed towards alleviating symptoms. Tri-axial accelerometers, gyroscopes, and magnetometers could be adopted to support clinicians in the decision-making process by objectively quantifying the patient’s condition. In this context, at-home data collections aim to capture motor function during daily living and unobstructedly assess the patients’ status and the disease’s symptoms for prolonged time periods. This review aims to collate existing literature on PD monitoring using inertial sensors while it focuses on papers with at least one free-living data capture unsupervised either directly or via videotapes. Twenty-four papers were selected at the end of the process: fourteen investigated gait impairments, eight of which focused on walking, three on turning, two on falls, and one on physical activity; ten articles on the other hand examined symptoms, including bradykinesia, tremor, dyskinesia, and motor state fluctuations in the on/off phenomenon. In summary, inertial sensors are capable of gathering data over a long period of time and have the potential to facilitate the monitoring of people with Parkinson’s, providing relevant information about their motor status. Concerning gait impairments, kinematic parameters (such as duration of gait cycle, step length, and velocity) were typically used to discern PD from healthy subjects, whereas for symptoms’ assessment, researchers were capable of achieving accuracies of over 90% in a free-living environment. Further investigations should be focused on the development of ad-hoc hardware and software capable of providing real-time feedback to clinicians and patients. In addition, features such as the wearability of the system and user comfort, set-up process, and instructions for use, need to be strongly considered in the development of wearable sensors for PD monitoring.
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Affiliation(s)
- Marco Sica
- Tyndall National Institute, University College Cork, Cork, Ireland
- * E-mail:
| | | | - Colum Crowe
- Tyndall National Institute, University College Cork, Cork, Ireland
| | - Lorna Kenny
- Centre for Gerontology and Rehabilitation, University College Cork, Cork, Ireland
| | - Kevin Moore
- Centre for Gerontology and Rehabilitation, University College Cork, Cork, Ireland
| | - Suzanne Timmons
- Centre for Gerontology and Rehabilitation, University College Cork, Cork, Ireland
| | - John Barton
- Tyndall National Institute, University College Cork, Cork, Ireland
| | - Brendan O’Flynn
- Tyndall National Institute, University College Cork, Cork, Ireland
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Harte R, Ó Laighin G, Quinlan L. Validation, verification, and reliability. Digit Health 2021. [DOI: 10.1016/b978-0-12-818914-6.00009-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Del Din S, Kirk C, Yarnall AJ, Rochester L, Hausdorff JM. Body-Worn Sensors for Remote Monitoring of Parkinson's Disease Motor Symptoms: Vision, State of the Art, and Challenges Ahead. JOURNAL OF PARKINSON'S DISEASE 2021; 11:S35-S47. [PMID: 33523020 PMCID: PMC8385520 DOI: 10.3233/jpd-202471] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/05/2021] [Indexed: 12/15/2022]
Abstract
The increasing prevalence of neurodegenerative conditions such as Parkinson's disease (PD) and related mobility issues places a serious burden on healthcare systems. The COVID-19 pandemic has reinforced the urgent need for better tools to manage chronic conditions remotely, as regular access to clinics may be problematic. Digital health technology in the form of remote monitoring with body-worn sensors offers significant opportunities for transforming research and revolutionizing the clinical management of PD. Significant efforts are being invested in the development and validation of digital outcomes to support diagnosis and track motor and mobility impairments "off-line". Imagine being able to remotely assess your patient, understand how well they are functioning, evaluate the impact of any recent medication/intervention, and identify the need for urgent follow-up before overt, irreparable change takes place? This could offer new pragmatic solutions for personalized care and clinical research. So the question remains: how close are we to achieving this? Here, we describe the state-of-the-art based on representative papers published between 2017 and 2020. We focus on remote (i.e., real-world, daily-living) monitoring of PD using body-worn sensors (e.g., accelerometers, inertial measurement units) for assessing motor symptoms and their complications. Despite the tremendous potential, existing challenges exist (e.g., validity, regulatory) that are preventing the widespread clinical adoption of body-worn sensors as a digital outcome. We propose a roadmap with clear recommendations for addressing these challenges and future directions to bring us closer to the implementation and widespread adoption of this important way of improving the clinical care, evaluation, and monitoring of PD.
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Affiliation(s)
- Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Alison J. Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv Israel
- Department of Physical Therapy, Sackler School of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Rush Alzheimer’s Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
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21
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Evers LJ, Raykov YP, Krijthe JH, Silva de Lima AL, Badawy R, Claes K, Heskes TM, Little MA, Meinders MJ, Bloem BR. Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study. J Med Internet Res 2020; 22:e19068. [PMID: 33034562 PMCID: PMC7584982 DOI: 10.2196/19068] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 08/10/2020] [Accepted: 08/21/2020] [Indexed: 12/16/2022] Open
Abstract
Background Wearable sensors have been used successfully to characterize bradykinetic gait in patients with Parkinson disease (PD), but most studies to date have been conducted in highly controlled laboratory environments. Objective This paper aims to assess whether sensor-based analysis of real-life gait can be used to objectively and remotely monitor motor fluctuations in PD. Methods The Parkinson@Home validation study provides a new reference data set for the development of digital biomarkers to monitor persons with PD in daily life. Specifically, a group of 25 patients with PD with motor fluctuations and 25 age-matched controls performed unscripted daily activities in and around their homes for at least one hour while being recorded on video. Patients with PD did this twice: once after overnight withdrawal of dopaminergic medication and again 1 hour after medication intake. Participants wore sensors on both wrists and ankles, on the lower back, and in the front pants pocket, capturing movement and contextual data. Gait segments of 25 seconds were extracted from accelerometer signals based on manual video annotations. The power spectral density of each segment and device was estimated using Welch’s method, from which the total power in the 0.5- to 10-Hz band, width of the dominant frequency, and cadence were derived. The ability to discriminate between before and after medication intake and between patients with PD and controls was evaluated using leave-one-subject-out nested cross-validation. Results From 18 patients with PD (11 men; median age 65 years) and 24 controls (13 men; median age 68 years), ≥10 gait segments were available. Using logistic LASSO (least absolute shrinkage and selection operator) regression, we classified whether the unscripted gait segments occurred before or after medication intake, with mean area under the receiver operator curves (AUCs) varying between 0.70 (ankle of least affected side, 95% CI 0.60-0.81) and 0.82 (ankle of most affected side, 95% CI 0.72-0.92) across sensor locations. Combining all sensor locations did not significantly improve classification (AUC 0.84, 95% CI 0.75-0.93). Of all signal properties, the total power in the 0.5- to 10-Hz band was most responsive to dopaminergic medication. Discriminating between patients with PD and controls was generally more difficult (AUC of all sensor locations combined: 0.76, 95% CI 0.62-0.90). The video recordings revealed that the positioning of the hands during real-life gait had a substantial impact on the power spectral density of both the wrist and pants pocket sensor. Conclusions We present a new video-referenced data set that includes unscripted activities in and around the participants’ homes. Using this data set, we show the feasibility of using sensor-based analysis of real-life gait to monitor motor fluctuations with a single sensor location. Future work may assess the value of contextual sensors to control for real-world confounders.
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Affiliation(s)
- Luc Jw Evers
- Center of Expertise for Parkinson and Movement Disorders, department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands.,Institute for Computing and Information Sciences, Radboud University, Nijmegen, Netherlands
| | - Yordan P Raykov
- Department of Mathematics, School of Engineering and Applied Sciences, Aston University, Birmingham, United Kingdom
| | - Jesse H Krijthe
- Institute for Computing and Information Sciences, Radboud University, Nijmegen, Netherlands
| | - Ana Lígia Silva de Lima
- Center of Expertise for Parkinson and Movement Disorders, department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Reham Badawy
- School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | | | - Tom M Heskes
- Institute for Computing and Information Sciences, Radboud University, Nijmegen, Netherlands
| | - Max A Little
- School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Marjan J Meinders
- Scientific Center for Quality of Healthcare (IQ healthcare), Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Bastiaan R Bloem
- Center of Expertise for Parkinson and Movement Disorders, department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
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"Does the Response to Morning Medication Predict the ADL-Level of the Day in Parkinson's Disease?". PARKINSONS DISEASE 2020; 2020:7140984. [PMID: 32802307 PMCID: PMC7403929 DOI: 10.1155/2020/7140984] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 05/24/2020] [Accepted: 06/25/2020] [Indexed: 01/01/2023]
Abstract
Background Individuals with Parkinson's Disease (PD) have bradykinesia during mobility tasks in the morning before intake of dopaminergic treatment and have difficulties managing Activities of Daily Living (ADLs). Early morning off (EMO) refers to off-states in the morning where the severity of bradykinesia is increased and causes a decrease in mobility related to wearing off of effects of medication. Measurements from devices capable of continuously recording motor symptoms may provide insight into the patient's response to medication and possible impact on ADLs. Objectives To test whether poor or slow response to medication in the morning predicts the overall ADL-level and to assess the association between change in bradykinesia score (BKS) and the risk of having disabilities within three selected ADL-items. Methods In this cross-sectional study, the sample consists of 34 patients with light to moderate PD. Data collection encompasses measurements from the Parkinson KinetiGraph, and the ADL-limitations are assessed by the Movement Disorder Society Unified Parkinson Disease Rating Scale (MDS-UPDRS) Part II. Results The association between UPDRS- II and BKS from the algorithm was -0.082 (p < 0.01), 95% CL:-0.113; -0.042). The individuals experienced disabilities in performing "Speech" (p=0.004) and "Doing hobbies" (p=0.038) when being slow or poor responders to dopaminergic therapy. The PD patients' L-dopa equivalent dose seems to be a strong predictor of the ADL-level in the morning. Conclusion Slow response to the medication dosages in the morning is correlated with disabilities in the overall ADL-level in PD. The combination of PD-drugs and precise, timely dosages must be considered in the improvement of the ADL-level in PD patients.
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Habets J, Heijmans M, Herff C, Simons C, Leentjens AF, Temel Y, Kuijf M, Kubben P. Mobile Health Daily Life Monitoring for Parkinson Disease: Development and Validation of Ecological Momentary Assessments. JMIR Mhealth Uhealth 2020; 8:e15628. [PMID: 32339999 PMCID: PMC7248801 DOI: 10.2196/15628] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 11/25/2019] [Accepted: 12/15/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Parkinson disease monitoring is currently transitioning from periodic clinical assessments to continuous daily life monitoring in free-living conditions. Traditional Parkinson disease monitoring methods lack intraday fluctuation detection. Electronic diaries (eDiaries) hold the potential to collect subjective experiences on the severity and burden of motor and nonmotor symptoms in free-living conditions. OBJECTIVE This study aimed to develop a Parkinson disease-specific eDiary based on ecological momentary assessments (EMAs) and to explore its validation. METHODS An observational cohort of 20 patients with Parkinson disease used the smartphone-based EMA eDiary for 14 consecutive days without adjusting free-living routines. The eDiary app presented an identical questionnaire consisting of questions regarding affect, context, motor and nonmotor symptoms, and motor performance 7 times daily at semirandomized moments. In addition, patients were asked to complete a morning and an evening questionnaire. RESULTS Mean affect correlated moderate-to-strong and moderate with motor performance (R=0.38 to 0.75; P<.001) and motor symptom (R=0.34 to 0.50; P<.001) items, respectively. The motor performance showed a weak-to-moderate negative correlation with motor symptoms (R=-0.31 to -0.48; P<.001). Mean group answers given for on-medication conditions vs wearing-off-medication conditions differed significantly (P<.05); however, not enough questionnaires were completed for the wearing-off-medication condition to reproduce these findings on individual levels. CONCLUSIONS We presented a Parkinson disease-specific EMA eDiary. Correlations between given answers support the internal validity of the eDiary and underline EMA's potential in free-living Parkinson disease monitoring. Careful patient selection and EMA design adjustment to this targeted population and their fluctuations are necessary to generate robust proof of EMA validation in future work. Combining clinical Parkinson disease knowledge with practical EMA experience is inevitable to design and perform studies, which will lead to the successful integration of eDiaries in free-living Parkinson disease monitoring.
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Affiliation(s)
- Jeroen Habets
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Margot Heijmans
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Christian Herff
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Claudia Simons
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands.,GGzE, Institute for Mental Health Care Eindhoven, Eindhoven, Netherlands
| | - Albert Fg Leentjens
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Yasin Temel
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Mark Kuijf
- Department of Neurology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Pieter Kubben
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands.,Department of Neurosurgery, Radboud University Medical Center, Nijmegen, Netherlands
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24
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Heijmans M, Habets J, Kuijf M, Kubben P, Herff C. Evaluation of Parkinson's Disease at Home: Predicting Tremor from Wearable Sensors. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:584-587. [PMID: 31945966 DOI: 10.1109/embc.2019.8857717] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The continuous monitoring of Parkinsons's disease (PD) symptoms would allow to automatically adjust medication or deep brain stimulation parameters to a patient's momentary condition. Wearable sensors have been proposed to monitor PD symptoms and have been validated in a number of lab and hospital settings. However, taking these sensors into the daily life of patients introduces a number of difficulties, most notably the absence of an observable ground truth of what the user is currently doing. In this pilot study, we investigate PD symptoms by combining wearable sensors on both wrist and the chest with a questionnaire based evaluation of PD symptoms, in the form of experience sampling method. For a tremor dominant patient, we show that experienced tremor severity can be predicted from the sensor data with correlations of up to r = 0.43. We evaluated different window lengths to calculate the features in and see better results for longer window lengths. Our results show that continuous monitoring of PD symptoms in daily life is feasible using wearable sensors.
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Morgan C, Rolinski M, McNaney R, Jones B, Rochester L, Maetzler W, Craddock I, Whone AL. Systematic Review Looking at the Use of Technology to Measure Free-Living Symptom and Activity Outcomes in Parkinson's Disease in the Home or a Home-like Environment. JOURNAL OF PARKINSON'S DISEASE 2020; 10:429-454. [PMID: 32250314 PMCID: PMC7242826 DOI: 10.3233/jpd-191781] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/31/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND The emergence of new technologies measuring outcomes in Parkinson's disease (PD) to complement the existing clinical rating scales has introduced the possibility of measurement occurring in patients' own homes whilst they freely live and carry out normal day-to-day activities. OBJECTIVE This systematic review seeks to provide an overview of what technology is being used to test which outcomes in PD from free-living participant activity in the setting of the home environment. Additionally, this review seeks to form an impression of the nature of validation and clinimetric testing carried out on the technological device(s) being used. METHODS Five databases (Medline, Embase, PsycInfo, Cochrane and Web of Science) were systematically searched for papers dating from 2000. Study eligibility criteria included: adults with a PD diagnosis; the use of technology; the setting of a home or home-like environment; outcomes measuring any motor and non-motor aspect relevant to PD, as well as activities of daily living; unrestricted/unscripted activities undertaken by participants. RESULTS 65 studies were selected for data extraction. There were wide varieties of participant sample sizes (<10 up to hundreds) and study durations (<2 weeks up to a year). The metrics evaluated by technology, largely using inertial measurement units in wearable devices, included gait, tremor, physical activity, bradykinesia, dyskinesia and motor fluctuations, posture, falls, typing, sleep and activities of daily living. CONCLUSIONS Home-based free-living testing in PD is being conducted by multiple groups with diverse approaches, focussing mainly on motor symptoms and sleep.
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Affiliation(s)
- Catherine Morgan
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, Faculty of Engineering, University of Bristol, Bristol, UK
- Movement Disorders Group, Bristol Brain Centre, Southmead Hospital, North Bristol National Health Service Trust, Bristol, UK
| | - Michal Rolinski
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Movement Disorders Group, Bristol Brain Centre, Southmead Hospital, North Bristol National Health Service Trust, Bristol, UK
| | - Roisin McNaney
- School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, Faculty of Engineering, University of Bristol, Bristol, UK
| | - Bennet Jones
- Library and Knowledge Service, Learning and Research, Southmead Hospital, North Bristol National Health Service Trust, Bristol, UK
| | - Lynn Rochester
- Institute of Neuroscience, Newcastle University, Newcastle Upon Tyne, UK
- Newcastle Upon Tyne Hospitals National Health Service Foundation Trust, Newcastle Upon Tyne, UK
| | - Walter Maetzler
- Department of Neurology, Christian-Albrechts University, Kiel, Germany
| | - Ian Craddock
- School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, Faculty of Engineering, University of Bristol, Bristol, UK
| | - Alan L. Whone
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Movement Disorders Group, Bristol Brain Centre, Southmead Hospital, North Bristol National Health Service Trust, Bristol, UK
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Open-Source Remote Gait Analysis: A Post-Surgery Patient Monitoring Application. Sci Rep 2019; 9:17966. [PMID: 31784691 PMCID: PMC6884492 DOI: 10.1038/s41598-019-54399-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 11/14/2019] [Indexed: 12/13/2022] Open
Abstract
Critical to digital medicine is the promise of improved patient monitoring to allow assessment and personalized intervention to occur in real-time. Wearable sensor-enabled observation of physiological data in free-living conditions is integral to this vision. However, few open-source algorithms have been developed for analyzing and interpreting these data which slows development and the realization of digital medicine. There is clear need for open-source tools that analyze free-living wearable sensor data and particularly for gait analysis, which provides important biomarkers in multiple clinical populations. We present an open-source analytical platform for automated free-living gait analysis and use it to investigate a novel, multi-domain (accelerometer and electromyography) asymmetry measure for quantifying rehabilitation progress in patients recovering from surgical reconstruction of the anterior cruciate ligament (ACL). Asymmetry indices extracted from 41,893 strides were more strongly correlated (r = −0.87, p < 0.01) with recovery time than standard step counts (r = 0.25, p = 0.52) and significantly differed between patients 2- and 17-weeks post-op (p < 0.01, effect size: 2.20–2.96), and controls (p < 0.01, effect size: 1.74–4.20). Results point toward future use of this open-source platform for capturing rehabilitation progress and, more broadly, for free-living gait analysis.
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Rodríguez-Molinero A, Pérez-López C, Samà A, Rodríguez-Martín D, Alcaine S, Mestre B, Quispe P, Giuliani B, Vainstein G, Browne P, Sweeney D, Quinlan LR, Arostegui JMM, Bayes À, Lewy H, Costa A, Annicchiarico R, Counihan T, Laighin GÒ, Cabestany J. Estimating dyskinesia severity in Parkinson's disease by using a waist-worn sensor: concurrent validity study. Sci Rep 2019; 9:13434. [PMID: 31530855 PMCID: PMC6748910 DOI: 10.1038/s41598-019-49798-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 08/21/2019] [Indexed: 11/29/2022] Open
Abstract
Our research team previously developed an accelerometry-based device, which can be worn on the waist during daily life activities and detects the occurrence of dyskinesia in patients with Parkinson’s disease. The goal of this study was to analyze the magnitude of correlation between the numeric output of the device algorithm and the results of the Unified Dyskinesia Rating Scale (UDysRS), administered by a physician. In this study, 13 Parkinson’s patients, who were symptomatic with dyskinesias, were monitored with the device at home, for an average period of 30 minutes, while performing normal daily life activities. Each patient’s activity was simultaneously video-recorded. A physician was in charge of reviewing the recorded videos and determining the severity of dyskinesia through the UDysRS for every patient. The sensor device yielded only one value for dyskinesia severity, which was calculated by averaging the recorded device readings. Correlation between the results of physician’s assessment and the sensor output was analyzed with the Spearman’s correlation coefficient. The correlation coefficient between the sensor output and UDysRS result was 0.70 (CI 95%: 0.33–0.88; p = 0.01). Since the sensor was located on the waist, the correlation between the sensor output and the results of the trunk and legs scale sub-items was calculated: 0.91 (CI 95% 0.76–0.97: p < 0.001). The conclusion is that the magnitude of dyskinesia, as measured by the tested device, presented good correlation with that observed by a physician.
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Affiliation(s)
- Alejandro Rodríguez-Molinero
- Consorci Sanitari de l'Alt Penedès i Garraf, Vilafranca del Pendès, Spain. .,Electrical & Electronic Engineering Department, NUI Galway, Galway, Ireland.
| | - Carlos Pérez-López
- Technical Research Centre for Dependency Care and Autonomous Living(CETpD), Universitat Politècnica de Catalunya, Vilanova i la Geltru, Spain.,Sense4Care, Cornellà de Llobregat, Spain
| | - Albert Samà
- Technical Research Centre for Dependency Care and Autonomous Living(CETpD), Universitat Politècnica de Catalunya, Vilanova i la Geltru, Spain.,Sense4Care, Cornellà de Llobregat, Spain
| | - Daniel Rodríguez-Martín
- Technical Research Centre for Dependency Care and Autonomous Living(CETpD), Universitat Politècnica de Catalunya, Vilanova i la Geltru, Spain.,Sense4Care, Cornellà de Llobregat, Spain
| | - Sheila Alcaine
- Unidad de Parkinson y trastornos del movimiento (UParkinson), Centro Médico Teknon, Barcelona, Spain
| | - Berta Mestre
- Unidad de Parkinson y trastornos del movimiento (UParkinson), Centro Médico Teknon, Barcelona, Spain
| | - Paola Quispe
- Unidad de Parkinson y trastornos del movimiento (UParkinson), Centro Médico Teknon, Barcelona, Spain
| | | | | | - Patrick Browne
- School of Medicine, NUI Galway, Galway, Ireland.,Neurology Department, University Hospital Galway, Galway, Ireland.,School of Nursing and Midwifery, NUI Galway, Galway, Ireland
| | - Dean Sweeney
- Electrical & Electronic Engineering Department, NUI Galway, Galway, Ireland
| | | | - J Manuel Moreno Arostegui
- Technical Research Centre for Dependency Care and Autonomous Living(CETpD), Universitat Politècnica de Catalunya, Vilanova i la Geltru, Spain.,Sense4Care, Cornellà de Llobregat, Spain
| | - Àngels Bayes
- Unidad de Parkinson y trastornos del movimiento (UParkinson), Centro Médico Teknon, Barcelona, Spain
| | - Hadas Lewy
- Maccabi Healthcare Services, Tel Aviv, Israel
| | - Alberto Costa
- IRCCS Fondazione Santa Lucia, Rome, Italy.,Niccolò Cusano University of Rome, Rome, Italy
| | | | | | - Gearòid Ò Laighin
- Electrical & Electronic Engineering Department, NUI Galway, Galway, Ireland
| | - Joan Cabestany
- Technical Research Centre for Dependency Care and Autonomous Living(CETpD), Universitat Politècnica de Catalunya, Vilanova i la Geltru, Spain.,Sense4Care, Cornellà de Llobregat, Spain
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29
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Teshuva I, Hillel I, Gazit E, Giladi N, Mirelman A, Hausdorff JM. Using wearables to assess bradykinesia and rigidity in patients with Parkinson's disease: a focused, narrative review of the literature. J Neural Transm (Vienna) 2019; 126:699-710. [PMID: 31115669 DOI: 10.1007/s00702-019-02017-9] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 05/14/2019] [Indexed: 10/26/2022]
Abstract
The potential of using wearable technologies for the objective assessment of motor symptoms in Parkinson's disease (PD) has gained prominence recently. Nonetheless, compared to tremor and gait impairment, less emphasis has been placed on the quantification of bradykinesia and rigidity. This review aimed to consolidate the existing research on objective measurement of bradykinesia and rigidity in PD through the use of wearables, focusing on the continuous monitoring of these two symptoms in free-living environments. A search of PubMed was conducted through a combination of keyword and MeSH searches. We also searched the IEEE, Google Scholar, Embase, and Scopus databases to ensure thorough results and to minimize the chances of missing relevant studies. Papers published after the year 2000 with sample sizes greater than five were included. Studies were assessed for quality and information was extracted regarding the devices used and their location on the body, the setting and duration of the study, the "gold standard" used as a reference for validation, the metrics used, and the results of each paper. Thirty-one and eight studies met the search criteria and evaluated bradykinesia and rigidity, respectively. Several studies reported strong associations between wearable-based measures and the gold-standard references for bradykinesia, and, to a lesser extent, rigidity. Only a few, pilot studies investigated the measurement of bradykinesia and rigidity in the home and free-living settings. While the current results are promising for the future of wearables, additional work is needed on their validation and adaptation in ecological, free-living settings. Doing so has the potential to improve the assessment and treatment of motor fluctuations and symptoms of PD more generally through real-time objective monitoring of bradykinesia and rigidity.
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Affiliation(s)
- Itay Teshuva
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Inbar Hillel
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Nir Giladi
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Department of Neurology and Neurosurgery, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Anat Mirelman
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Department of Neurology and Neurosurgery, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel. .,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel. .,Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv, Israel. .,Rush Alzheimer's Disease Center, Chicago, USA. .,Department of Orthopedic Surgery, Rush University Medical Center, Chicago, USA.
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30
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Iwaki H, Sogo H, Morita H, Nishikawa N, Ando R, Miyaue N, Tada S, Yabe H, Nagai M, Nomoto M. Using Spontaneous Eye-blink Rates to Predict the Motor Status of Patients with Parkinson's Disease. Intern Med 2019; 58:1417-1421. [PMID: 31092772 PMCID: PMC6548932 DOI: 10.2169/internalmedicine.1960-18] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Objective Assessing daily motor fluctuations is an important part of the disease management for patients with Parkinson's disease (PD). However, the frequent recording of subjective and/or objective assessments is not always feasible, and easier monitoring methods have been sought. Previous studies have reported that the spontaneous eye-blink rate (EBR) is correlated with the dopamine levels in the brain. Thus, the continuous monitoring of the EBR may be useful for predicting the motor status in patients with PD. Methods Electrooculograms (EOGs) were recorded for up to 7.5 hours from three PD patients using a wearable device that resembled ordinary glasses. An receiver operating characteristic (ROC) analysis was performed to compare the ability of the EBR estimates at each time-point (Blink Index) and the plasma levodopa levels to predict the motor status. Results The Blink Index was correlated with the plasma levodopa levels. When an indicator for the first hour of the observation period was included in the model, the Blink Index discerned wearing-off and dyskinesia as accurately as the plasma levodopa level. Conclusion Our study provides preliminary evidence regarding the utility of continuous EBR monitoring for the non-invasive evaluation of the motor status in patients with PD.
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Affiliation(s)
- Hirotaka Iwaki
- Department of Neurology and Clinical Pharmacology, Ehime University Graduate School of Medicine, Japan
| | | | | | - Noriko Nishikawa
- Department of Neurology and Clinical Pharmacology, Ehime University Graduate School of Medicine, Japan
| | - Rina Ando
- Department of Neurology and Clinical Pharmacology, Ehime University Graduate School of Medicine, Japan
| | - Noriyuki Miyaue
- Department of Neurology and Clinical Pharmacology, Ehime University Graduate School of Medicine, Japan
| | - Satoshi Tada
- Department of Neurology and Clinical Pharmacology, Ehime University Graduate School of Medicine, Japan
| | - Hayato Yabe
- Department of Neurology and Clinical Pharmacology, Ehime University Graduate School of Medicine, Japan
| | - Masahiro Nagai
- Department of Neurology and Clinical Pharmacology, Ehime University Graduate School of Medicine, Japan
| | - Masahiro Nomoto
- Department of Neurology and Clinical Pharmacology, Ehime University Graduate School of Medicine, Japan
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Thorp JE, Adamczyk PG, Ploeg HL, Pickett KA. Monitoring Motor Symptoms During Activities of Daily Living in Individuals With Parkinson's Disease. Front Neurol 2018; 9:1036. [PMID: 30619024 PMCID: PMC6299017 DOI: 10.3389/fneur.2018.01036] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2018] [Accepted: 11/16/2018] [Indexed: 01/23/2023] Open
Abstract
This literature review addressed wearable sensor systems to monitor motor symptoms in individuals with Parkinson's disease (PD) during activities of daily living (ADLs). Specifically, progress in monitoring tremor, freezing of gait, dyskinesia, bradykinesia, and hypokinesia was reviewed. Twenty-seven studies were found that met the criteria of measuring symptoms in a home or home-like setting, with some studies examining multiple motor disorders. Accelerometers, gyroscopes, and electromyography sensors were included, with some studies using more than one type of sensor. Five studies measured tremor, five studies examined bradykinesia or hypokinesia, thirteen studies included devices to measure dyskinesia or motor fluctuations, and ten studies measured akinesia or freezing of gait. Current sensor technology can detect the presence and severity of each of these symptoms; however, most systems require sensors on multiple body parts, which is challenging for remote or ecologically valid observation. Different symptoms are detected by different sensor placement, suggesting that the goal of detecting all symptoms with a reduced set of sensors may not be achievable. For the goal of monitoring motor symptoms during ADLs in a home setting, the measurement system should be simple to use, unobtrusive to the wearer and easy for an individual with PD to put on and take off. Machine learning algorithms such as neural networks appear to be the most promising way to detect symptoms using a small number of sensors. More work should be done validating the systems during unscripted and unconstrained ADLs rather than in scripted motions.
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Affiliation(s)
- Jenna E. Thorp
- Department of Mechanical Engineering, College of Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Peter Gabriel Adamczyk
- Department of Mechanical Engineering, College of Engineering, University of Wisconsin-Madison, Madison, WI, United States
- Department of Biomedical Engineering, College of Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Heidi-Lynn Ploeg
- Department of Mechanical Engineering, College of Engineering, University of Wisconsin-Madison, Madison, WI, United States
- Department of Biomedical Engineering, College of Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Kristen A. Pickett
- Department of Biomedical Engineering, College of Engineering, University of Wisconsin-Madison, Madison, WI, United States
- Occupational Therapy Program, Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, United States
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