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Paredes-Acuna N, Utpadel-Fischler D, Ding K, Thakor NV, Cheng G. Upper limb intention tremor assessment: opportunities and challenges in wearable technology. J Neuroeng Rehabil 2024; 21:8. [PMID: 38218890 PMCID: PMC10787996 DOI: 10.1186/s12984-023-01302-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 12/26/2023] [Indexed: 01/15/2024] Open
Abstract
BACKGROUND Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature. METHODOLOGY & RESULTS This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table. CONCLUSIONS Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.
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Affiliation(s)
- Natalia Paredes-Acuna
- Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany.
| | - Daniel Utpadel-Fischler
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Keqin Ding
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Nitish V Thakor
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Gordon Cheng
- Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany
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2
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Moulaei K, Moulaei R, Bahaadinbeigy K. The most used questionnaires for evaluating the usability of robots and smart wearables: A scoping review. Digit Health 2024; 10:20552076241237384. [PMID: 38601185 PMCID: PMC11005511 DOI: 10.1177/20552076241237384] [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: 05/15/2023] [Accepted: 02/27/2024] [Indexed: 04/12/2024] Open
Abstract
Background As the field of robotics and smart wearables continues to advance rapidly, the evaluation of their usability becomes paramount. Researchers may encounter difficulty in finding a suitable questionnaire for evaluating the usability of robotics and smart wearables. Therefore, the aim of this study is to identify the most commonly utilized questionnaires for assessing the usability of robots and smart wearables. Methods A comprehensive search of databases, including PubMed, Web of Science, and Scopus, was conducted for this scoping review. Two authors performed the selection of articles and data extraction using a 10-field data extraction form. In cases of disagreements, a third author was consulted to reach a consensus. The inclusions were English-language original research articles that utilized validated questionnaires to assess the usability of healthcare robots and smart wearables. The exclusions comprised review articles, non-English publications, studies not focused on usability, those assessing clinical outcomes, articles lacking questionnaire details, and those using non-validated or researcher-made questionnaires. Descriptive statistics methods (frequency and percentage), were employed to analyze the data. Results A total of 314 articles were obtained, and after eliminating irrelevant and duplicate articles, a final selection of 50 articles was included in this review. A total of 17 questionnaires were identified to evaluate the usability of robots and smart wearables, with 10 questionnaires specifically for wearables and 7 questionnaires for robots. The System Usability Scale (50%) and Post-Study System Usability Questionnaire (19.44%) were the predominant questionnaires utilized to assess the usability of smart wearables. Moreover, the most commonly used questionnaires for evaluating the usability of robots were the System Usability Scale (56.66%), User Experience Questionnaire (16.66%), and Quebec User Evaluation of Satisfaction with Assistive Technology (10%). Conclusion Commonly employed questionnaires serve as valuable tools in assessing the usability of robots and smart wearables, aiding in the refinement and optimization of these technologies for enhanced user experiences. By incorporating user feedback and insights, designers can strive towards creating more intuitive and effective robotic and wearable solutions.
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Affiliation(s)
- Khadijeh Moulaei
- Department of Health Information Technology, Faculty of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Reza Moulaei
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Kambiz Bahaadinbeigy
- Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
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3
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Sjaelland NS, Gramkow MH, Hasselbalch SG, Frederiksen KS. Digital Biomarkers for the Assessment of Non-Cognitive Symptoms in Patients with Dementia with Lewy Bodies: A Systematic Review. J Alzheimers Dis 2024; 100:431-451. [PMID: 38943394 PMCID: PMC11307079 DOI: 10.3233/jad-240327] [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] [Accepted: 05/14/2024] [Indexed: 07/01/2024]
Abstract
Background Portable digital health technologies (DHTs) could help evaluate non-cognitive symptoms, but evidence to support their use in patients with dementia with Lewy bodies (DLB) is uncertain. Objective 1) To describe portable or wearable DHTs used to obtain digital biomarkers in patients with DLB, 2) to assess the digital biomarkers' ability to evaluate non-cognitive symptoms, and 3) to assess the feasibility of applying digital biomarkers in patients with DLB. Methods We systematically searched databases MEDLINE, Embase, and Web of Science from inception through February 28, 2023. Studies assessing digital biomarkers obtained by portable or wearable DHTs and related to non-cognitive symptoms were eligible if including patients with DLB. The quality of studies was assessed using a modified check list based on the NIH Quality assessment tool for Observational Cohort and Cross-sectional Studies. A narrative synthesis of data was carried out. Results We screened 4,295 records and included 20 studies. Seventeen different DHTs were identified for assessment of most non-cognitive symptoms related to DLB. No thorough validation of digital biomarkers for measurement of non-cognitive symptoms in DLB was reported. Studies did not report on aspects of feasibility in a systematic way. Conclusions Knowledge about feasibility and validity of individual digital biomarkers remains extremely limited. Study heterogeneity is a barrier for establishing a broad evidence base for application of digital biomarkers in DLB. Researchers should conform to recommended standards for systematic evaluation of digital biomarkers.
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Affiliation(s)
- Nikolai S. Sjaelland
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Mathias H. Gramkow
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Steen G. Hasselbalch
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kristian Steen Frederiksen
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Fereshtehnejad SM, Lökk J. Challenges of Teleneurology in the Care of Complex Neurodegenerative Disorders: The Case of Parkinson's Disease with Possible Solutions. Healthcare (Basel) 2023; 11:3187. [PMID: 38132077 PMCID: PMC10742857 DOI: 10.3390/healthcare11243187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 12/23/2023] Open
Abstract
Teleneurology is a specialist field within the realm of telemedicine, which is dedicated to delivering neurological care and consultations through virtual encounters. Teleneurology has been successfully used in acute care (e.g., stroke) and outpatient evaluation for chronic neurological conditions such as epilepsy and headaches. However, for some neurologic entities like Parkinson's disease, in which an in-depth physical examination by palpating muscles and performing neurologic maneuvers is the mainstay of monitoring the effects of medication, the yield and feasibility of a virtual encounter are low. Therefore, in this prospective review, we discuss two promising teleneurology approaches and propose adjustments to enhance the value of virtual encounters by improving the validity of neurological examination: 'hybrid teleneurology', which involves revising the workflow of virtual encounters; and 'artificial intelligence (AI)-assisted teleneurology', namely the use of biosensors and wearables and data processing using AI.
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Affiliation(s)
- Seyed-Mohammad Fereshtehnejad
- Edmond J. Safra Program in Parkinsonߣs Disease and Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, Toronto, ON M5T 2S8, Canada
- Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5S 1A1, Canada
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, 171 77 Stockholm, Sweden;
| | - Johan Lökk
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, 171 77 Stockholm, Sweden;
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Goffredo M, Baglio F, DE Icco R, Proietti S, Maggioni G, Turolla A, Pournajaf S, Jonsdottir J, Zeni F, Federico S, Cacciante L, Cioeta M, Tassorelli C, Franceschini M, Calabrò RS. Efficacy of non-immersive virtual reality-based telerehabilitation on postural stability in Parkinson's disease: a multicenter randomized controlled trial. Eur J Phys Rehabil Med 2023; 59:689-696. [PMID: 37847247 PMCID: PMC10795069 DOI: 10.23736/s1973-9087.23.07954-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/19/2023] [Accepted: 09/28/2023] [Indexed: 10/18/2023]
Abstract
BACKGROUND The implementation of regular prolonged, and effective rehabilitation in people with Parkinson's disease is essential for ensuring a good quality of life. However, the continuity of rehabilitation care may find barriers related to economic, geographic, and social issues. In these scenarios, telerehabilitation could be a possible solution to guarantee the continuity of care. AIM To investigate the efficacy of non-immersive virtual reality-based telerehabilitation on postural stability in people with Parkinson's disease, compared to at-home self-administered structured conventional motor activities. DESIGN Multicenter randomized controlled trial. SETTING Five rehabilitation hospitals of the Italian Neuroscience and Rehabilitation Network. POPULATION Individuals diagnosed with Parkinson's disease. METHODS Ninety-seven participants were randomized into two groups: 49 in the telerehabilitation group (non-immersive virtual reality-based telerehabilitation) and 48 in the control group (at-home self-administered structured conventional motor activities). Both treatments lasted 30 sessions (3-5 days/week for, 6-10 weeks). Static and dynamic balance, gait, and functional motor outcomes were registered before and after the treatments. RESULTS All participants improved the outcomes at the end of the treatments. The primary outcome (mini-Balance Evaluation Systems Test) registered a greater significant improvement in the telerehabilitation group than in the control group. The gait and endurance significantly improved in the telerehabilitation group only, with significant within-group and between-group differences. CONCLUSIONS Our results showed that non-immersive virtual reality-based telerehabilitation is feasible, improves static and dynamic balance, and is a reasonably valuable alternative for reducing postural instability in people with Parkinson's disease. CLINICAL REHABILITATION IMPACT Non-immersive virtual reality-based telerehabilitation is an effective and well-tolerated modality of rehabilitation which may help to improve access and scale up rehabilitation services as suggested by the World Health Organization's Rehabilitation 2030 agenda.
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Affiliation(s)
- Michela Goffredo
- Neurorehabilitation Research Laboratory, Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy
| | | | - Roberto DE Icco
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Movement Analysis Research Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Stefania Proietti
- Neurorehabilitation Research Laboratory, Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy
- Department of Human Sciences and Promotion of the Quality of Life, San Raffaele University, Rome, Italy
| | - Giorgio Maggioni
- Unità di Neuroriabilitazione, ICS Maugeri SB IRCCS Veruno, Veruno, Novara, Italy
| | - Andrea Turolla
- Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater University of Bologna, Bologna, Italy
- Unit of Occupational Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Sanaz Pournajaf
- Neurorehabilitation Research Laboratory, Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy
| | | | - Federica Zeni
- Unità di Neuroriabilitazione, ICS Maugeri SB IRCCS Veruno, Veruno, Novara, Italy
| | - Sara Federico
- Laboratory of Healthcare Innovation Technology, IRCCS San Camillo Hospital, Venice, Italy
| | - Luisa Cacciante
- Laboratory of Healthcare Innovation Technology, IRCCS San Camillo Hospital, Venice, Italy
| | - Matteo Cioeta
- Neurorehabilitation Research Laboratory, Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy
| | - Cristina Tassorelli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Movement Analysis Research Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Marco Franceschini
- Neurorehabilitation Research Laboratory, Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy
- Department of Human Sciences and Promotion of the Quality of Life, San Raffaele University, Rome, Italy
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Antonini A, Reichmann H, Gentile G, Garon M, Tedesco C, Frank A, Falkenburger B, Konitsiotis S, Tsamis K, Rigas G, Kostikis N, Ntanis A, Pattichis C. Toward objective monitoring of Parkinson's disease motor symptoms using a wearable device: wearability and performance evaluation of PDMonitor ®. Front Neurol 2023; 14:1080752. [PMID: 37260606 PMCID: PMC10228366 DOI: 10.3389/fneur.2023.1080752] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 02/27/2023] [Indexed: 06/02/2023] Open
Abstract
Parkinson's disease (PD) is characterized by a variety of motor and non-motor symptoms. As disease progresses, fluctuations in the response to levodopa treatment may develop, along with emergence of freezing of gait (FoG) and levodopa induced dyskinesia (LiD). The optimal management of the motor symptoms and their complications, depends, principally, on the consistent detection of their course, leading to improved treatment decisions. During the last few years, wearable devices have started to be used in the clinical practice for monitoring patients' PD-related motor symptoms, during their daily activities. This work describes the results of 2 multi-site clinical studies (PDNST001 and PDNST002) designed to validate the performance and the wearability of a new wearable monitoring device, the PDMonitor®, in the detection of PD-related motor symptoms. For the studies, 65 patients with Parkinson's disease and 28 healthy individuals (controls) were recruited. Specifically, during the Phase I of the first study, participants used the monitoring device for 2-6 h in a clinic while neurologists assessed the exhibited parkinsonian symptoms every half hour using the Unified Parkinson's Disease Rating Scale (UPDRS) Part III, as well as the Abnormal Involuntary Movement Scale (AIMS) for dyskinesia severity assessment. The goal of Phase I was data gathering. On the other hand, during the Phase II of the first study, as well as during the second study (PDNST002), day-to-day variability was evaluated, with patients in the former and with control subjects in the latter. In both cases, the device was used for a number of days, with the subjects being unsupervised and free to perform any kind of daily activities. The monitoring device produced estimations of the severity of the majority of PD-related motor symptoms and their fluctuations. Statistical analysis demonstrated that the accuracy in the detection of symptoms and the correlation between their severity and the expert evaluations were high. As a result, the studies confirmed the effectiveness of the system as a continuous telemonitoring solution, easy to be used to facilitate decision-making for the treatment of patients with Parkinson's disease.
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Affiliation(s)
- Angelo Antonini
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | - Heinz Reichmann
- Department of Neurology, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universitat Dresden, Dresden, Germany
| | - Giovanni Gentile
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | - Michela Garon
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | - Chiara Tedesco
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | - Anika Frank
- Department of Neurology, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universitat Dresden, Dresden, Germany
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Bjoern Falkenburger
- Department of Neurology, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universitat Dresden, Dresden, Germany
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Spyridon Konitsiotis
- Department of Neurology, University Hospital of Ioannina and Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
| | - Konstantinos Tsamis
- Department of Neurology, University Hospital of Ioannina and Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
| | | | | | | | - Constantinos Pattichis
- Department of Computer Science and Biomedical Engineering Research Centre, University of Cyprus, Nicosia, Cyprus
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Gait Analysis to Monitor Fracture Healing of the Lower Leg. Bioengineering (Basel) 2023; 10:bioengineering10020255. [PMID: 36829749 PMCID: PMC9952799 DOI: 10.3390/bioengineering10020255] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/07/2023] [Accepted: 02/13/2023] [Indexed: 02/17/2023] Open
Abstract
Fracture healing is typically monitored by infrequent radiographs. Radiographs come at the cost of radiation exposure and reflect fracture healing with a time lag due to delayed fracture mineralization following increases in stiffness. Since union problems frequently occur after fractures, better and timelier methods to monitor the healing process are required. In this review, we provide an overview of the changes in gait parameters following lower leg fractures to investigate whether gait analysis can be used to monitor fracture healing. Studies assessing gait after lower leg fractures that were treated either surgically or conservatively were included. Spatiotemporal gait parameters, kinematics, kinetics, and pedography showed improvements in the gait pattern throughout the healing process of lower leg fractures. Especially gait speed and asymmetry measures have a high potential to monitor fracture healing. Pedographic measurements showed differences in gait between patients with and without union. No literature was available for other gait measures, but it is expected that further parameters reflect progress in bone healing. In conclusion, gait analysis seems to be a valuable tool for monitoring the healing process and predicting the occurrence of non-union of lower leg fractures.
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Feasibility of a wearable inertial sensor to assess motor complications and treatment in Parkinson's disease. PLoS One 2023; 18:e0279910. [PMID: 36730238 PMCID: PMC9894418 DOI: 10.1371/journal.pone.0279910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 12/18/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Wearable sensors-based systems have emerged as a potential tool to continuously monitor Parkinson's Disease (PD) motor features in free-living environments. OBJECTIVES To analyse the responsivity of wearable inertial sensor (WIS) measures (On/Off-Time, dyskinesia, freezing of gait (FoG) and gait parameters) after treatment adjustments. We also aim to study the ability of the sensor in the detection of MF, dyskinesia, FoG and the percentage of Off-Time, under ambulatory conditions of use. METHODS We conducted an observational, open-label study. PD patients wore a validated WIS (STAT-ONTM) for one week (before treatment), and one week, three months after therapeutic changes. The patients were analyzed into two groups according to whether treatment changes had been indicated or not. RESULTS Thirty-nine PD patients were included in the study (PD duration 8 ± 3.5 years). Treatment changes were made in 29 patients (85%). When comparing the two groups (treatment intervention vs no intervention), the WIS detected significant changes in the mean percentage of Off-Time (p = 0.007), the mean percentage of On-Time (p = 0.002), the number of steps (p = 0.008) and the gait fluidity (p = 0.004). The mean percentage of Off-Time among the patients who decreased their Off-Time (79% of patients) was -7.54 ± 5.26. The mean percentage of On-Time among the patients that increased their On-Time (59% of patients) was 8.9 ± 6.46. The Spearman correlation between the mean fluidity of the stride and the UPDRS-III- Factor I was 0.6 (p = <0.001). The system detected motor fluctuations (MF) in thirty-seven patients (95%), whilst dyskinesia and FoG were detected in fifteen (41%), and nine PD patients (23%), respectively. However, the kappa agreement analysis between the UPDRS-IV/clinical interview and the sensor was 0.089 for MF, 0.318 for dyskinesia and 0.481 for FoG. CONCLUSIONS It's feasible to use this sensor for monitoring PD treatment under ambulatory conditions. This system could serve as a complementary tool to assess PD motor complications and treatment adjustments, although more studies are required.
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Das J, Morris R, Barry G, Vitorio R, Oman P, McDonald C, Walker R, Stuart S. Exploring the feasibility of technological visuo-cognitive training in Parkinson's: Study protocol for a pilot randomised controlled trial. PLoS One 2022; 17:e0275738. [PMID: 36206239 PMCID: PMC9543984 DOI: 10.1371/journal.pone.0275738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 08/19/2022] [Indexed: 11/12/2022] Open
Abstract
Visual and cognitive dysfunction are common in Parkinson's disease and relate to balance and gait impairment, as well as increased falls risk and reduced quality of life. Vision and cognition are interrelated (termed visuo-cognition) which makes intervention complex in people with Parkinson's (PwP). Non-pharmacological interventions for visuo-cognitive deficits are possible with modern technology, such as combined mobile applications and stroboscopic glasses, but evidence for their effectiveness in PwP is lacking. We aim to investigate whether technological visuo-cognitive training (TVT) can improve visuo-cognitive function in PwP. We will use a parallel group randomised controlled trial to evaluate the feasibility and acceptability of TVT versus standard care in PwP. Forty PwP who meet our inclusion criteria will be randomly assigned to one of two visuo-cognitive training interventions. Both interventions will be carried out by a qualified physiotherapist in participants own homes (1-hour sessions, twice a week, for 4 weeks). Outcome measures will be assessed on anti-parkinsonian medication at baseline and at the end of the 4-week intervention. Feasibility of the TVT intervention will be assessed in relation to safety and acceptability of the technological intervention, compliance and adherence to the intervention and usability of equipment in participants homes. Additionally, semi structured interviews will be conducted to explore participants' experience of the technology. Exploratory efficacy outcomes will include change in visual attention measured using the Trail Making Test as well as changes in balance, gait, quality of life, fear of falling and levels of activity. This pilot study will focus on the feasibility and acceptability of TVT in PwP and provide preliminary data to support the design of a larger, multi-centre randomised controlled trial. This trial is registered at isrctn.com (ISRCTN46164906).
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Affiliation(s)
- Julia Das
- Department of Sport, Exercise & Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
- Northumbria Healthcare NHS Foundation Trust, North Tyneside General Hospital, North Shields, United Kingdom
| | - Rosie Morris
- Department of Sport, Exercise & Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
- Northumbria Healthcare NHS Foundation Trust, North Tyneside General Hospital, North Shields, United Kingdom
| | - Gill Barry
- Department of Sport, Exercise & Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Rodrigo Vitorio
- Department of Sport, Exercise & Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Paul Oman
- Department of Mathematics, Physics & Electrical Engineering, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Claire McDonald
- Gateshead Health NHS Foundation Trust, Gateshead, United Kingdom
| | - Richard Walker
- Northumbria Healthcare NHS Foundation Trust, North Tyneside General Hospital, North Shields, United Kingdom
| | - Samuel Stuart
- Department of Sport, Exercise & Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
- Northumbria Healthcare NHS Foundation Trust, North Tyneside General Hospital, North Shields, United Kingdom
- * E-mail:
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10
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Tripathi S, Malhotra A, Qazi M, Chou J, Wang F, Barkan S, Hellmers N, Henchcliffe C, Sarva H. Clinical Review of Smartphone Applications in Parkinson's Disease. Neurologist 2022; 27:183-193. [PMID: 35051970 DOI: 10.1097/nrl.0000000000000413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Parkinson's disease (PD) is the second leading neurodegenerative disease worldwide. Important advances in monitoring and treatment have been made in recent years. This article reviews literature on utility of smartphone applications in monitoring PD symptoms that may ultimately facilitate improved patient care, and on movement modulation as a potential therapeutic. REVIEW SUMMARY Novel mobile phone applications can provide one-time and/or continuous data to monitor PD motor symptoms in person or remotely, that may support precise therapeutic adjustments and management decisions. Apps have also been developed for medication management and treatment. CONCLUSIONS Smartphone applications provide a wide array of platforms allowing for meaningful short-term and long-term data collection and are also being tested for intervention. However, the variability of the applications and the need to translate complicated sensor data may hinder immediate clinical applicability. Future studies should involve stake-holders early in the design process to promote usability and streamline the interface between patients, clinicians, and PD apps.
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Affiliation(s)
- Susmit Tripathi
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Ashwin Malhotra
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Murtaza Qazi
- Weill Cornell Medicine Qatar, Education City, Qatar
| | - Jingyuan Chou
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
| | - Fei Wang
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
| | - Samantha Barkan
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
| | - Natalie Hellmers
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
| | - Claire Henchcliffe
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
- Department of Neurology, University of California, Irvine, Irvine, CA
| | - Harini Sarva
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
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11
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Body-Worn Sensors for Parkinson’s disease: A qualitative approach with patients and healthcare professionals. PLoS One 2022; 17:e0265438. [PMID: 35511812 PMCID: PMC9070870 DOI: 10.1371/journal.pone.0265438] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 03/01/2022] [Indexed: 11/29/2022] Open
Abstract
Body-Worn Sensors (BWS) provide reliable objective and continuous assessment of Parkinson’s disease (PD) motor symptoms, but their implementation in clinical routine has not yet become widespread. Users’ perceptions of BWS have not been explored. This study intended to evaluate the usability, user experience (UX), patients’ perceptions of BWS, and health professionals’ (HP) opinions on BWS monitoring. A qualitative analysis was performed from semi-structured interviews conducted with 22 patients and 9 HP experts in PD. Patients completed two interviews before and after the BWS one-week experiment, and they answered two questionnaires assessing the usability and UX. Patients rated the three BWS usability with high scores (SUS median [range]: 87.5 [72.5–100]). The UX across all dimensions of their interaction with the BWS was positive. During interviews, all patients and HP expressed interest in BWS monitoring. Patients’ hopes and expectations increased the more they learned about BWS. They manifested enthusiasm to wear BWS, which they imagined could improve their PD symptoms. HP highlighted needs for logistical support in the implementation of BWS in their practice. Both patients and HP suggested possible uses of BWS monitoring in clinical practice, for treatment adjustments for example, or for research purposes. Patients and HP shared ideas about the use of BWS monitoring, although patients may be more likely to integrate BWS into their disease follow-up compared to HP in their practice. This study highlights gaps that need to be fulfilled to facilitate BWS adoption and promote their potential.
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Chandrabhatla AS, Pomeraniec IJ, Ksendzovsky A. Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms. NPJ Digit Med 2022; 5:32. [PMID: 35304579 PMCID: PMC8933519 DOI: 10.1038/s41746-022-00568-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 01/21/2022] [Indexed: 11/09/2022] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor impairments such as tremor, bradykinesia, dyskinesia, and gait abnormalities. Current protocols assess PD symptoms during clinic visits and can be subjective. Patient diaries can help clinicians evaluate at-home symptoms, but can be incomplete or inaccurate. Therefore, researchers have developed in-home automated methods to monitor PD symptoms to enable data-driven PD diagnosis and management. We queried the US National Library of Medicine PubMed database to analyze the progression of the technologies and computational/machine learning methods used to monitor common motor PD symptoms. A sub-set of roughly 12,000 papers was reviewed that best characterized the machine learning and technology timelines that manifested from reviewing the literature. The technology used to monitor PD motor symptoms has advanced significantly in the past five decades. Early monitoring began with in-lab devices such as needle-based EMG, transitioned to in-lab accelerometers/gyroscopes, then to wearable accelerometers/gyroscopes, and finally to phone and mobile & web application-based in-home monitoring. Significant progress has also been made with respect to the use of machine learning algorithms to classify PD patients. Using data from different devices (e.g., video cameras, phone-based accelerometers), researchers have designed neural network and non-neural network-based machine learning algorithms to categorize PD patients across tremor, gait, bradykinesia, and dyskinesia. The five-decade co-evolution of technology and computational techniques used to monitor PD motor symptoms has driven significant progress that is enabling the shift from in-lab/clinic to in-home monitoring of PD symptoms.
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Affiliation(s)
- Anirudha S Chandrabhatla
- School of Medicine, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA
| | - I Jonathan Pomeraniec
- Surgical Neurology Branch, National Institutes of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA.
- Department of Neurosurgery, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA.
| | - Alexander Ksendzovsky
- Department of Neurosurgery, University of Maryland Medical System, Baltimore, MD, 21201, USA
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Gopal A, Hsu WY, Allen DD, Bove R. Remote Assessments of Hand Function in Neurological Disorders: Systematic Review. JMIR Rehabil Assist Technol 2022; 9:e33157. [PMID: 35262502 PMCID: PMC8943610 DOI: 10.2196/33157] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 01/17/2022] [Accepted: 01/26/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Loss of fine motor skills is observed in many neurological diseases, and remote monitoring assessments can aid in early diagnosis and intervention. Hand function can be regularly assessed to monitor loss of fine motor skills in people with central nervous system disorders; however, there are challenges to in-clinic assessments. Remotely assessing hand function could facilitate monitoring and supporting of early diagnosis and intervention when warranted. OBJECTIVE Remote assessments can facilitate the tracking of limitations, aiding in early diagnosis and intervention. This study aims to systematically review existing evidence regarding the remote assessment of hand function in populations with chronic neurological dysfunction. METHODS PubMed and MEDLINE, CINAHL, Web of Science, and Embase were searched for studies that reported remote assessment of hand function (ie, outside of traditional in-person clinical settings) in adults with chronic central nervous system disorders. We excluded studies that included participants with orthopedic upper limb dysfunction or used tools for intervention and treatment. We extracted data on the evaluated hand function domains, validity and reliability, feasibility, and stage of development. RESULTS In total, 74 studies met the inclusion criteria for Parkinson disease (n=57, 77% studies), stroke (n=9, 12%), multiple sclerosis (n=6, 8%), spinal cord injury (n=1, 1%), and amyotrophic lateral sclerosis (n=1, 1%). Three assessment modalities were identified: external device (eg, wrist-worn accelerometer), smartphone or tablet, and telerehabilitation. The feasibility and overall participant acceptability were high. The most common hand function domains assessed included finger tapping speed (fine motor control and rigidity), hand tremor (pharmacological and rehabilitation efficacy), and finger dexterity (manipulation of small objects required for daily tasks) and handwriting (coordination). Although validity and reliability data were heterogeneous across studies, statistically significant correlations with traditional in-clinic metrics were most commonly reported for telerehabilitation and smartphone or tablet apps. The most readily implementable assessments were smartphone or tablet-based. CONCLUSIONS The findings show that remote assessment of hand function is feasible in neurological disorders. Although varied, the assessments allow clinicians to objectively record performance in multiple hand function domains, improving the reliability of traditional in-clinic assessments. Remote assessments, particularly via telerehabilitation and smartphone- or tablet-based apps that align with in-clinic metrics, facilitate clinic to home transitions, have few barriers to implementation, and prompt remote identification and treatment of hand function impairments.
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Affiliation(s)
- Arpita Gopal
- Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Wan-Yu Hsu
- Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Diane D Allen
- Department of Physical Therapy and Rehabilitation Science, University of California San Francisco/San Francisco State University, San Francisco, CA, United States
| | - Riley Bove
- Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States
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Simonet C, Noyce AJ. Domotics, Smart Homes, and Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2022; 11:S55-S63. [PMID: 33612494 PMCID: PMC8385512 DOI: 10.3233/jpd-202398] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Technology has an increasing presence and role in the management of Parkinson’s disease. Whether embraced or rebuffed by patients and clinicians, this is an undoubtedly growing area. Wearable sensors have received most of the attention so far. This review will focus on technology integrated into the home setting; from fixed sensors to automated appliances, which are able to capture information and have the potential to respond in an unsupervised manner. Domotics also have the potential to provide ‘real world’ context to kinematic data and therapeutic opportunities to tackle challenging motor and non-motor symptoms. Together with wearable technology, domotics have the ability to gather long-term data and record discrete events, changing the model of the cross-sectional outpatient assessment. As clinicians, our ultimate goal is to maximise quality of life, promote autonomy, and personalise care. In these respects, domotics may play an essential role in the coming years.
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Affiliation(s)
- Cristina Simonet
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
| | - Alastair J Noyce
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK.,Department of Clinical and Movement Neurosciences, Institute of Neurology, University College London, London, UK
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Rhoden PA, Bonilha H, Harvey J. Patient Satisfaction of Telemedicine Remote Patient Monitoring: A Systematic Review. Telemed J E Health 2022; 28:1332-1341. [PMID: 35041549 DOI: 10.1089/tmj.2021.0434] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Introduction: To examine the variety of patient satisfaction instruments (i.e., measures, methods, and scales) used within telemedicine remote patient monitoring (RPM) services; and to assess the quality of RPM patient satisfaction instruments. Methods: Three databases were searched for articles that used survey instrumentation to assess patient satisfaction of RPM services: (1) Healthcare Administration Database (PROQUEST), (2) Cumulative Index to Nursing and Allied Health Literature (CINAHL), and (3) PubMed (MEDLINE). The quality of survey instrumentation methods was assessed based on validity and reliability using the Terwee et al. framework. Results: Nine studies were included in the final review. For internal consistency, seven out of nine studies received an "indeterminant" quality rating; six out of nine of the studies received a "positive" quality rating for measurement error. For content validity, seven out of nine studies received a "positive" quality rating. Discussion: There are several RPM surveys that are used to assess patient satisfaction. This review suggests wide variation among the quality, reliability, and validity of the surveys currently used in practice. Assessing patient satisfaction of RPM services by organizations, researchers, and practitioners should be done through use of reliable instrumentation.
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Affiliation(s)
- Parker A Rhoden
- Department of Healthcare Leadership & Management and College of Health Professions, Medical University of South Carolina, Charleston, South Carolina, USA.,Department of Health Sciences & Research, College of Health Professions, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Heather Bonilha
- Department of Health Sciences & Research, College of Health Professions, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Jillian Harvey
- Department of Healthcare Leadership & Management and College of Health Professions, Medical University of South Carolina, Charleston, South Carolina, USA
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Morgan C, Tonkin EL, Craddock I, Whone AL. Acceptability of an In-Home Multimodal Sensor Platform in Parkinson’s Disease: A Qualitative Study (Preprint). JMIR Hum Factors 2022; 9:e36370. [PMID: 35797101 PMCID: PMC9305404 DOI: 10.2196/36370] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/07/2022] [Accepted: 05/23/2022] [Indexed: 12/28/2022] Open
Abstract
Background Parkinson disease (PD) symptoms are complex, gradually progressive, and fluctuate hour by hour. Home-based technological sensors are being investigated to measure symptoms and track disease progression. A smart home sensor platform, with cameras and wearable devices, could be a useful tool to use to get a fuller picture of what someone’s symptoms are like. High-resolution video can capture the ground truth of symptoms and activities. There is a paucity of information about the acceptability of such sensors in PD. Objective The primary objective of our study was to explore the acceptability of living with a multimodal sensor platform in a naturalistic setting in PD. Two subobjectives are to identify any suggested limitations and to explore the sensors’ impact on participant behaviors. Methods A qualitative study was conducted with an inductive approach using semistructured interviews with a cohort of PD and control participants who lived freely for several days in a home-like environment while continuously being sensed. Results This study of 24 participants (12 with PD) found that it is broadly acceptable to use multimodal sensors including wrist-worn wearables, cameras, and other ambient sensors passively in free-living in PD. The sensor that was found to be the least acceptable was the wearable device. Suggested limitations on the platform for home deployment included camera-free time and space. Behavior changes were noted by the study participants, which may have related to being passively sensed. Recording high-resolution video in the home setting for limited periods of time was felt to be acceptable to all participants. Conclusions The results broaden the knowledge of what types of sensors are acceptable for use in research in PD and what potential limitations on these sensors should be considered in future work. The participants’ reported behavior change in this study should inform future similar research design to take this factor into account. Collaborative research study design, involving people living with PD at every stage, is important to ensure that the technology is acceptable and that the data outcomes produced are ecologically valid and accurate. International Registered Report Identifier (IRRID) RR2-10.1136/bmjopen-2020-041303
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Affiliation(s)
- Catherine Morgan
- Translational Health Sciences, University of Bristol Medical School, Bristol, United Kingdom
- Movement Disorders Group, Bristol Brain Centre, North Bristol NHS Trust, Bristol, United Kingdom
| | - Emma L Tonkin
- School of Computer Science, Electrical and Electronic Engineering, University of Bristol, Bristol, United Kingdom
| | - Ian Craddock
- School of Computer Science, Electrical and Electronic Engineering, University of Bristol, Bristol, United Kingdom
| | - Alan L Whone
- Translational Health Sciences, University of Bristol Medical School, Bristol, United Kingdom
- Movement Disorders Group, Bristol Brain Centre, North Bristol NHS Trust, Bristol, United Kingdom
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LoBuono DL, Shea KS, Tovar A, Leedahl SN, Mahler L, Xu F, Lofgren IE. Acceptance and perception of digital health for managing nutrition in people with Parkinson's disease and their caregivers and their digital competence in the United States: A mixed-methods study. Health Sci Rep 2021; 4:e412. [PMID: 34796282 PMCID: PMC8581626 DOI: 10.1002/hsr2.412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 08/27/2021] [Accepted: 08/31/2021] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND AND AIMS This mixed-methods study examined participants' acceptance and perception of using digital health for managing nutrition and participants' digital competence. The results will be formative for making digital nutrition education more effective and acceptable for people with Parkinson's disease (PwPD) and their informal caregivers. METHODS Qualitative data were collected through in-person semi-structured, dyadic interviews, and questionnaires from 20 dyads (20 PwPD and their caregivers) in the Northeastern United States and analyzed throughout the 2018 to 2019 academic year. Interview transcripts were deductively coded using the framework analysis method. Phrases related to acceptance of digital health were sub-coded into accept, neutral, or reject and those related to perceptions of digital health were sub-coded into perceived usefulness, perceived ease of use, and awareness of digital health. Quantitative data were analyzed using independent samples t tests and Fisher's exact tests. Qualitative codes were transformed into variables and compared to digital competence scores to integrate the data. An average acceptance rate for digital health was calculated through examining the mean percent of phrases coded as accept from interview transcripts. RESULTS Twenty-five of 40 (62.5%) participants used the internet for at least 5 health-related purposes and the average acceptance rate was 54.4%. Dyads rejected digital health devices if they did not see the added benefit. The majority of participants reported digital health to be useful, but hard to use, and about half felt they needed education about existing digital health platforms. There was no difference in digital competence scores between PwPD and their caregivers (28.6 ± 12.6). CONCLUSION Findings suggest that dyads accept and use technology but not to its full potential as technology can be perceived as hard to use. This finding, combined with digital competence scores, revealed that education is warranted prior to providing a digital nutrition intervention.
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Affiliation(s)
- Dara L. LoBuono
- Department of Health and Exercise ScienceRowan UniversityGlassboroNew JerseyUSA
| | - Kyla S. Shea
- Johnson and Wales University in the College of Food Innovation and Technology in Providence, RI
| | - Alison Tovar
- Johnson and Wales University in the College of Food Innovation and Technology in Providence, RI
| | - Skye N. Leedahl
- Department of Human Development and Family ScienceUniversity of Rhode IslandKingstonRhode IslandUSA
| | - Leslie Mahler
- Department of Communicative DisordersUniversity of Rhode IslandKingstonRhode IslandUSA
| | - Furong Xu
- School of EducationUniversity of Rhode IslandKingstonRhode IslandUSA
| | - Ingrid E. Lofgren
- Johnson and Wales University in the College of Food Innovation and Technology in Providence, RI
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Keogh A, Argent R, Anderson A, Caulfield B, Johnston W. Assessing the usability of wearable devices to measure gait and physical activity in chronic conditions: a systematic review. J Neuroeng Rehabil 2021; 18:138. [PMID: 34526053 PMCID: PMC8444467 DOI: 10.1186/s12984-021-00931-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 09/01/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The World Health Organisation's global strategy for digital health emphasises the importance of patient involvement. Understanding the usability and acceptability of wearable devices is a core component of this. However, usability assessments to date have focused predominantly on healthy adults. There is a need to understand the patient perspective of wearable devices in participants with chronic health conditions. METHODS A systematic review was conducted to identify any study design that included a usability assessment of wearable devices to measure mobility, through gait and physical activity, within five cohorts with chronic conditions (Parkinson's disease [PD], multiple sclerosis [MS], congestive heart failure, [CHF], chronic obstructive pulmonary disorder [COPD], and proximal femoral fracture [PFF]). RESULTS Thirty-seven studies were identified. Substantial heterogeneity in the quality of reporting, the methods used to assess usability, the devices used, and the aims of the studies precluded any meaningful comparisons. Questionnaires were used in the majority of studies (70.3%; n = 26) with a reliance on intervention specific measures (n = 16; 61.5%). For those who used interviews (n = 17; 45.9%), no topic guides were provided, while methods of analysis were not reported in over a third of studies (n = 6; 35.3%). CONCLUSION Usability of wearable devices is a poorly measured and reported variable in chronic health conditions. Although the heterogeneity in how these devices are implemented implies acceptance, the patient voice should not be assumed. In the absence of being able to make specific usability conclusions, the results of this review instead recommends that future research needs to: (1) Conduct usability assessments as standard, irrespective of the cohort under investigation or the type of study undertaken. (2) Adhere to basic reporting standards (e.g. COREQ) including the basic details of the study. Full copies of any questionnaires and interview guides should be supplied through supplemental files. (3) Utilise mixed methods research to gather a more comprehensive understanding of usability than either qualitative or quantitative research alone will provide. (4) Use previously validated questionnaires alongside any intervention specific measures.
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Affiliation(s)
- Alison Keogh
- UCD School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland.
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland.
| | - Rob Argent
- UCD School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | | | - Brian Caulfield
- UCD School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | - William Johnston
- UCD School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
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Vellata C, Belli S, Balsamo F, Giordano A, Colombo R, Maggioni G. Effectiveness of Telerehabilitation on Motor Impairments, Non-motor Symptoms and Compliance in Patients With Parkinson's Disease: A Systematic Review. Front Neurol 2021; 12:627999. [PMID: 34512495 PMCID: PMC8427282 DOI: 10.3389/fneur.2021.627999] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 07/19/2021] [Indexed: 12/17/2022] Open
Abstract
Introduction: Parkinson's disease (PD) is a chronic neurodegenerative disease involving a progressive alteration of the motor and non-motor function. PD influences the patient's daily living and reduces participation and quality of life in all phases of the disease. Early physical exercise can mitigate the effects of symptoms but access to specialist care is difficult. With current technological progress, telemedicine, and telerehabilitation is now a viable option for managing patients, although few studies have investigated the use of telerehabilitation in PD. In this systematic review, was investigated whether telerehabilitation leads to improvements in global or specific motor tasks (gait and balance, hand function) and non-motor dysfunction (motor speech disorder, dysphagia). The impact of TR on quality of life and patient satisfaction, were also assessed. The usage of telerehabilitation technologies in the management of cognitive impairment was not addressed. Method: An electronic database search was performed using the following databases: PubMed/MEDLINE, COCHRANE Library, PEDro, and SCOPUS for data published between January 2005 and December 2019 on the effects of telerehabilitation systems in managing motor and non-motor symptoms. This systematic review was conducted in accordance with the PRISMA guideline and was registered in the PROSPERO database (CRD42020141300). Results: A total of 15 articles involving 421 patients affected by PD were analyzed. The articles were divided into two categories based on their topic of interest or outcome. The first category consisted of the effects of telerehabilitation on gait and balance (3), dexterity of the upper limbs (3), and bradykinesia (0); the second category regarded non-motor symptoms such as speech disorders (8) and dysphagia (0). Quality of life (7) and patient satisfaction (8) following telerehabilitation programs were also analyzed, as well as feasibility and costs. Conclusion: Telerehabilitation is feasible in people affected by PD. Our analysis of the available data highlighted that telerehabilitation systems are effective in maintaining and/or improving some clinical and non-clinical aspects of PD (balance and gait, speech and voice, quality of life, patient satisfaction). Systematic Review Registration:https://www.crd.york.ac.uk/prospero/, identifier: CRD42020141300.
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Affiliation(s)
- Chiara Vellata
- Istituti Clinici Scientifici Maugeri Spa - Società Benefit, Neurologic Rehabilitation Unit of Veruno Institute, Veruno, Italy
| | - Stefano Belli
- Istituti Clinici Scientifici Maugeri Spa - Società Benefit, Neurologic Rehabilitation Unit of Veruno Institute, Veruno, Italy
| | - Francesca Balsamo
- Istituti Clinici Scientifici Maugeri Spa - Società Benefit, Neurologic Rehabilitation Unit of Veruno Institute, Veruno, Italy
| | - Andrea Giordano
- Istituti Clinici Scientifici Maugeri Spa - Società Benefit, Bioengineering Service, Veruno, Italy
| | - Roberto Colombo
- Istituti Clinici Scientifici Maugeri Spa - Società Benefit, Bioengineering Service, Veruno, Italy
| | - Giorgio Maggioni
- Istituti Clinici Scientifici Maugeri Spa - Società Benefit, Neurologic Rehabilitation Unit of Veruno Institute, Veruno, Italy
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Abou L, Peters J, Wong E, Akers R, Dossou MS, Sosnoff JJ, Rice LA. Gait and Balance Assessments using Smartphone Applications in Parkinson's Disease: A Systematic Review. J Med Syst 2021; 45:87. [PMID: 34392429 PMCID: PMC8364438 DOI: 10.1007/s10916-021-01760-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 08/04/2021] [Indexed: 01/21/2023]
Abstract
Gait dysfunctions and balance impairments are key fall risk factors and associated with reduced quality of life in individuals with Parkinson's Disease (PD). Smartphone-based assessments show potential to increase remote monitoring of the disease. This review aimed to summarize the validity, reliability, and discriminative abilities of smartphone applications to assess gait, balance, and falls in PD. Two independent reviewers screened articles systematically identified through PubMed, Web of Science, Scopus, CINAHL, and SportDiscuss. Studies that used smartphone-based gait, balance, or fall applications in PD were retrieved. The validity, reliability, and discriminative abilities of the smartphone applications were summarized and qualitatively discussed. Methodological quality appraisal of the studies was performed using the quality assessment tool for observational cohort and cross-sectional studies. Thirty-one articles were included in this review. The studies present mostly with low risk of bias. In total, 52% of the studies reported validity, 22% reported reliability, and 55% reported discriminative abilities of smartphone applications to evaluate gait, balance, and falls in PD. Those studies reported strong validity, good to excellent reliability, and good discriminative properties of smartphone applications. Only 19% of the studies formally evaluated the usability of their smartphone applications. The current evidence supports the use of smartphone to assess gait and balance, and detect freezing of gait in PD. More studies are needed to explore the use of smartphone to predict falls in this population. Further studies are also warranted to evaluate the usability of smartphone applications to improve remote monitoring in this population.Registration: PROSPERO CRD 42020198510.
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Affiliation(s)
- Libak Abou
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Joseph Peters
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Ellyce Wong
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Rebecca Akers
- Department of Rehabilitation Science, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Mauricette Sènan Dossou
- Centre National Hospitalier et Universitaire de Pneumo-Phtisiologie, Cotonou, Littoral, Benin
| | - Jacob J Sosnoff
- Department of Physical Therapy and Rehabilitation Science, School of Health Professions, Medical Center, University of Kansas, Kansas City, KS, USA
| | - Laura A Rice
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
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van Munster M, Stümpel J, Thieken F, J. Pedrosa D, Antonini A, Côté D, Fabbri M, Ferreira JJ, Růžička E, Grimes D, Mestre TA. Moving towards Integrated and Personalized Care in Parkinson's Disease: A Framework Proposal for Training Parkinson Nurses. J Pers Med 2021; 11:623. [PMID: 34209024 PMCID: PMC8304750 DOI: 10.3390/jpm11070623] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/17/2021] [Accepted: 06/25/2021] [Indexed: 12/28/2022] Open
Abstract
Delivering healthcare to people living with Parkinson's disease (PD) may be challenging in face of differentiated care needs during a PD journey and a growing complexity. In this regard, integrative care models may foster flexible solutions on patients' care needs whereas Parkinson Nurses (PN) may be pivotal facilitators. However, at present hardly any training opportunities tailored to the care priorities of PD-patients are to be found for nurses. Following a conceptual approach, this article aims at setting a framework for training PN by reviewing existing literature on care priorities for PD. As a result, six prerequisites were formulated concerning a framework for training PN. The proposed training framework consist of three modules covering topics of PD: (i) comprehensive care, (ii) self-management support and (iii) health coaching. A fourth module on telemedicine may be added if applicable. The framework streamlines important theoretical concepts of professional PD management and may enable the development of novel, personalized care approaches.
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Affiliation(s)
- Marlena van Munster
- Department of Neurology, University Hospital Marburg, 35033 Marburg, Germany; (F.T.); (D.J.P.)
| | - Johanne Stümpel
- Cologne Center for Ethics, Rights, Economics, and Social Sciences of Health (CERES), University of Cologne, 50931 Cologne, Germany;
- Research Unit Ethics, University Hospital Cologne, 50931 Cologne, Germany
| | - Franziska Thieken
- Department of Neurology, University Hospital Marburg, 35033 Marburg, Germany; (F.T.); (D.J.P.)
| | - David J. Pedrosa
- Department of Neurology, University Hospital Marburg, 35033 Marburg, Germany; (F.T.); (D.J.P.)
| | - Angelo Antonini
- Parkinson and Movement Disorders Unit, University of Padua, 35122 Padua, Italy;
| | - Diane Côté
- The Ottawa Hospital Research Institute, Ottawa, ON K1Y 4E9, Canada;
| | - Margherita Fabbri
- Department of Neurosciences, Clinical Investigation Center CIC 1436, Parkinson Toulouse Expert Center, NS-Park/FCRIN Network and NeuroToul COEN Center, TOULOUSE University Hospital, INSERM, University of Toulouse 3, 31062 Toulouse, France;
| | - Joaquim J. Ferreira
- Laboratory of Clinical Pharmacology and Therapeutics, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal;
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal
- CNS—Campus Neurológico Sénior Torres Vedras, 2560-280 Torres Vedras, Portugal
| | - Evžen Růžička
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University, General University Hospital in Prague, CZ-121 08 Prague, Czech Republic;
| | - David Grimes
- Parkinson Disease and Movement Disorders Centre, Division of Neurology, Department of Medicine, The Ottawa Hospital Research Institute, University of Ottawa Brain and Mind Research Institute, Ottawa, ON K1Y 4E9, Canada; (D.G.); (T.A.M.)
| | - Tiago A. Mestre
- Parkinson Disease and Movement Disorders Centre, Division of Neurology, Department of Medicine, The Ottawa Hospital Research Institute, University of Ottawa Brain and Mind Research Institute, Ottawa, ON K1Y 4E9, Canada; (D.G.); (T.A.M.)
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22
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Hssayeni MD, Jimenez-Shahed J, Burack MA, Ghoraani B. Dyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks. Sci Rep 2021; 11:7865. [PMID: 33846387 PMCID: PMC8041801 DOI: 10.1038/s41598-021-86705-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 03/09/2021] [Indexed: 02/01/2023] Open
Abstract
Levodopa-induced dyskinesias are abnormal involuntary movements experienced by the majority of persons with Parkinson's disease (PwP) at some point over the course of the disease. Choreiform as the most common phenomenology of levodopa-induced dyskinesias can be managed by adjusting the dose/frequency of PD medication(s) based on a PwP's motor fluctuations over a typical day. We developed a sensor-based assessment system to provide such information. We used movement data collected from the upper and lower extremities of 15 PwPs along with a deep recurrent model to estimate dyskinesia severity as they perform different activities of daily living (ADL). Subjects performed a variety of ADLs during a 4-h period while their dyskinesia severity was rated by the movement disorder experts. The estimated dyskinesia severity scores from our model correlated highly with the expert-rated scores (r = 0.87 (p < 0.001)), which was higher than the performance of linear regression that is commonly used for dyskinesia estimation (r = 0.81 (p < 0.001)). Our model provided consistent performance at different ADLs with minimum r = 0.70 (during walking) to maximum r = 0.84 (drinking) in comparison to linear regression with r = 0.00 (walking) to r = 0.76 (cutting food). These findings suggest that when our model is applied to at-home sensor data, it can provide an accurate picture of changes of dyskinesia severity facilitating effective medication adjustments.
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Affiliation(s)
- Murtadha D Hssayeni
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA
| | | | - Michelle A Burack
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Behnaz Ghoraani
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA.
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23
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Ghoraani B, Galvin JE, Jimenez-Shahed J. Point of view: Wearable systems for at-home monitoring of motor complications in Parkinson's disease should deliver clinically actionable information. Parkinsonism Relat Disord 2021; 84:35-39. [PMID: 33549914 PMCID: PMC8324321 DOI: 10.1016/j.parkreldis.2021.01.022] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 12/18/2020] [Accepted: 01/26/2021] [Indexed: 01/05/2023]
Affiliation(s)
- Behnaz Ghoraani
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA.
| | - James E Galvin
- Comprehensive Center for Brain Health, Department of Neurology, University of Miami, Miami, FL, 33136, USA
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24
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Telehealth in Neurodegenerative Diseases: Opportunities and Challenges for Patients and Physicians. Brain Sci 2021; 11:brainsci11020237. [PMID: 33668641 PMCID: PMC7917616 DOI: 10.3390/brainsci11020237] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 02/06/2021] [Accepted: 02/11/2021] [Indexed: 02/06/2023] Open
Abstract
Telehealth, by definition, is distributing health-related services while using electronic technologies. This narrative Review describes the technological health services (telemedicine and telemonitoring) for delivering care in neurodegenerative diseases, Alzheimer's disease, Parkinson's Disease, and amyotrophic lateral Sclerosis, among others. This paper aims to illustrate this approach's primary experience and application, highlighting the strengths and weaknesses, with the goal of understanding which could be the most useful application for each one, in order to facilitate telehealth improvement and use in standard clinical practice. We also described the potential role of the COVID-19 pandemic to speed up this service's use, avoiding a sudden interruption of medical care.
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25
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AlMahadin G, Lotfi A, Zysk E, Siena FL, Carthy MM, Breedon P. Parkinson's disease: current assessment methods and wearable devices for evaluation of movement disorder motor symptoms - a patient and healthcare professional perspective. BMC Neurol 2020; 20:419. [PMID: 33208135 PMCID: PMC7677815 DOI: 10.1186/s12883-020-01996-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 11/09/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Parkinson's disease is the second most common long-term chronic, progressive, neurodegenerative disease, affecting more than 10 million people worldwide. There has been a rising interest in wearable devices for evaluation of movement disorder diseases such as Parkinson's disease due to the limitations in current clinic assessment methods such as Unified Parkinson's Disease Rating Scale (UPDRS) and the Hoehn and Yahr (HY) scale. However, there are only a few commercial wearable devices available, which, in addition, have had very limited adoption and implementation. This inconsistency may be due to a lack of users' perspectives in terms of device design and implementation. This study aims to identify the perspectives of healthcare professionals and patients linked to current assessment methods and to identify preferences, and requirements of wearable devices. METHODS This was a qualitative study using semi-structured interviews followed by focus groups. Transcripts from sessions were analysed using an inductive thematic approach. RESULTS It was noted that the well-known assessment process such as Unified Parkinson's Disease Rating Scale (UPDRS) was not used routinely in clinics since it is time consuming, subjective, inaccurate, infrequent and dependent on patients' memories. Participants suggested that objective assessment methods are needed to increase the chance of effective treatment. The participants' perspectives were positive toward using wearable devices, particularly if they were involved in early design stages. Patients emphasized that the devices should be comfortable, but they did not have any concerns regarding device visibility or data privacy transmitted over the internet when it comes to their health. In terms of wearing a monitor, the preferable part of the body for all participants was the wrist. Healthcare professionals stated a need for an economical solution that is easy to interpret. Some design aspects identified by patients included clasps, material choice, and form factor. CONCLUSION The study concluded that current assessment methods are limited. Patients' and healthcare professionals' involvement in wearable devices design process has a pivotal role in terms of ultimate user acceptance. This includes the provision of additional functions to the wearable device, such as fall detection and medication reminders, which could be attractive features for patients.
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Affiliation(s)
- Ghayth AlMahadin
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS UK
| | - Ahmad Lotfi
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS UK
| | - Eva Zysk
- Department of Psychology, University of British Columbia in Vancouver, West Mall, Vancouver, V6T 1Z4 Canada
| | - Francesco Luke Siena
- School Of Architecture Design & Built Environment, Nottingham Trent University, Goldsmith Street, Nottingham, NG1 4FQ UK
| | | | - Philip Breedon
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS UK
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26
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Garg D, Dhamija RK. Teleneurorehabilitation for Parkinson's Disease: A Panacea for the Times to Come? Ann Indian Acad Neurol 2020; 23:592-597. [PMID: 33623256 PMCID: PMC7887501 DOI: 10.4103/aian.aian_566_20] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 07/28/2020] [Accepted: 08/04/2020] [Indexed: 12/18/2022] Open
Abstract
Telemedicine is witnessing a rebirth due to the COVID-19 pandemic and the continuing need for limited-contact or contactless care in medicine. Telerehabilitation, an offshoot of telemedicine, is a valuable yet underexplored tool in the therapeutic armamentarium of patients with neurological conditions, particularly Parkinson's disease (PD). Although there is evidence in literature reporting the use of telerehabilitation and virtual reality-based services in providing rehabilitation to improve speech, swallowing, gait, and postural instability among persons with PD, the evidence is limited due to small patient numbers. Teleneurorehabilitation (TNR) is an underutilized strategy that may be as effective and perhaps more feasible and affordable among Indian PD patients and also allows sustained rehabilitation. In this article, we encapsulate the evidence on the utility and efficacy of TNR among persons with PD and call upon the neurology community to recognize and utilize the valuable asset that TNR may be for PD patients.
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Affiliation(s)
- Divyani Garg
- Department of Neurology, Lady Hardinge Medical College, New Delhi, India
| | - Rajinder K Dhamija
- Department of Neurology, Lady Hardinge Medical College, New Delhi, India
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27
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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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28
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Matamala-Gomez M, Maisto M, Montana JI, Mavrodiev PA, Baglio F, Rossetto F, Mantovani F, Riva G, Realdon O. The Role of Engagement in Teleneurorehabilitation: A Systematic Review. Front Neurol 2020; 11:354. [PMID: 32435227 PMCID: PMC7218051 DOI: 10.3389/fneur.2020.00354] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 04/09/2020] [Indexed: 01/04/2023] Open
Abstract
The growing understanding of the importance of involving patients with neurological diseases in their healthcare routine either for at-home management of their chronic conditions or after the hospitalization period has opened the research for new rehabilitation strategies to enhance patient engagement in neurorehabilitation. In addition, the use of new digital technologies in the neurorehabilitation field enables the implementation of telerehabilitation systems such as virtual reality interventions, video games, web-based interventions, mobile applications, web-based or telephonic telecoach programs, in order to facilitate the relationship between clinicians and patients, and to motivate and activate patients to continue with the rehabilitation process at home. Here we present a systematic review that aims at reviewing the effectiveness of different engagement strategies and the different engagement assessments while using telerehabilitation systems in patients with neurological disorders. We used PICO's format to define the question of the review, and the systematic review protocol was designed following the Preferred Reported Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Bibliographical data was collected by using the following bibliographic databases: PubMed, EMBASE, Scopus, and Web of Science. Eighteen studies were included in this systematic review for full-text analyses. Overall, the reviewed studies using engagement strategies through telerehabilitation systems in patients with neurological disorders were mainly focused on patient self-management and self-awareness, patient motivation, and patient adherence subcomponents of engagement, that are involved in by the behavioral, cognitive, and emotional dimensions of engagement. Conclusion: The studies commented throughout this systematic review pave the way for the design of new telerehabilitation protocols, not only focusing on measuring quantitative or qualitative measures but measuring both of them through a mixed model intervention design (1). The future clinical studies with a mixed model design will provide more abundant data regarding the role of engagement in telerehabilitation, leading to a possibly greater understanding of its underlying components.
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Affiliation(s)
- Marta Matamala-Gomez
- "Riccardo Massa" Department of Human Sciences for Education, University of Milano-Bicocca, Milan, Italy
| | - Marta Maisto
- "Riccardo Massa" Department of Human Sciences for Education, University of Milano-Bicocca, Milan, Italy
| | - Jessica Isbely Montana
- "Riccardo Massa" Department of Human Sciences for Education, University of Milano-Bicocca, Milan, Italy
| | | | | | | | - Fabrizia Mantovani
- "Riccardo Massa" Department of Human Sciences for Education, University of Milano-Bicocca, Milan, Italy
| | - Giuseppe Riva
- Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy.,Applied Technology for Neuro-Psychology Laboratory, Istituto Auxologico Italiano, IRCCS, Milan, Italy
| | - Olivia Realdon
- "Riccardo Massa" Department of Human Sciences for Education, University of Milano-Bicocca, Milan, Italy
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29
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Timotijevic L, Hodgkins CE, Banks A, Rusconi P, Egan B, Peacock M, Seiss E, Touray MML, Gage H, Pellicano C, Spalletta G, Assogna F, Giglio M, Marcante A, Gentile G, Cikajlo I, Gatsios D, Konitsiotis S, Fotiadis D. Designing a mHealth clinical decision support system for Parkinson's disease: a theoretically grounded user needs approach. BMC Med Inform Decis Mak 2020; 20:34. [PMID: 32075633 PMCID: PMC7031960 DOI: 10.1186/s12911-020-1027-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 01/20/2020] [Indexed: 11/13/2022] Open
Abstract
Background Despite the established evidence and theoretical advances explaining human judgments under uncertainty, developments of mobile health (mHealth) Clinical Decision Support Systems (CDSS) have not explicitly applied the psychology of decision making to the study of user needs. We report on a user needs approach to develop a prototype of a mHealth CDSS for Parkinson’s disease (PD), which is theoretically grounded in the psychological literature about expert decision making and judgement under uncertainty. Methods A suite of user needs studies was conducted in 4 European countries (Greece, Italy, Slovenia, the UK) prior to the development of PD_Manager, a mHealth-based CDSS designed for Parkinson’s disease, using wireless technology. Study 1 undertook Hierarchical Task Analysis (HTA) including elicitation of user needs, cognitive demands and perceived risks/benefits (ethical considerations) associated with the proposed CDSS, through structured interviews of prescribing clinicians (N = 47). Study 2 carried out computational modelling of prescribing clinicians’ (N = 12) decision strategies based on social judgment theory. Study 3 was a vignette study of prescribing clinicians’ (N = 18) willingness to change treatment based on either self-reported symptoms data, devices-generated symptoms data or combinations of both. Results Study 1 indicated that system development should move away from the traditional silos of ‘motor’ and ‘non-motor’ symptom evaluations and suggest that presenting data on symptoms according to goal-based domains would be the most beneficial approach, the most important being patients’ overall Quality of Life (QoL). The computational modelling in Study 2 extrapolated different factor combinations when making judgements about different questions. Study 3 indicated that the clinicians were equally likely to change the care plan based on information about the change in the patient’s condition from the patient’s self-report and the wearable devices. Conclusions Based on our approach, we could formulate the following principles of mHealth design: 1) enabling shared decision making between the clinician, patient and the carer; 2) flexibility that accounts for diagnostic and treatment variation among clinicians; 3) monitoring of information integration from multiple sources. Our approach highlighted the central importance of the patient-clinician relationship in clinical decision making and the relevance of theoretical as opposed to algorithm (technology)-based modelling of human judgment.
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Affiliation(s)
- L Timotijevic
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
| | - C E Hodgkins
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - A Banks
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - P Rusconi
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - B Egan
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - M Peacock
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - E Seiss
- Department of Psychology, University of Bournemouth, Bournemouth, UK
| | - M M L Touray
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - H Gage
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - C Pellicano
- Department of Neurorehabilitation, Fondanzione Santa Lucia, Rome, Italy
| | - G Spalletta
- Department of Neurorehabilitation, Fondanzione Santa Lucia, Rome, Italy
| | - F Assogna
- Department of Neurorehabilitation, Fondanzione Santa Lucia, Rome, Italy
| | - M Giglio
- Fondanzione Ospedale San Camillo (I.R.C.C.S.), Parkinson's Department Institute of Neurology, Venice, Italy
| | - A Marcante
- Fondanzione Ospedale San Camillo (I.R.C.C.S.), Parkinson's Department Institute of Neurology, Venice, Italy
| | - G Gentile
- Fondanzione Ospedale San Camillo (I.R.C.C.S.), Parkinson's Department Institute of Neurology, Venice, Italy
| | - I Cikajlo
- University Rehabilitation Institute, Republic of Slovenia, Soča, Ljubljana, Slovenia
| | - D Gatsios
- Department of Material Sciences and Engineering, University of Ioannina, Ioannina, Greece
| | - S Konitsiotis
- Nurology, Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
| | - D Fotiadis
- Department of Material Sciences and Engineering, University of Ioannina, Ioannina, Greece
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30
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Godoi BB, Amorim GD, Quiroga DG, Holanda VM, Júlio T, Tournier MB. Parkinson's disease and wearable devices, new perspectives for a public health issue: an integrative literature review. ACTA ACUST UNITED AC 2020; 65:1413-1420. [PMID: 31800906 DOI: 10.1590/1806-9282.65.11.1413] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2019] [Accepted: 03/31/2019] [Indexed: 11/22/2022]
Abstract
Parkinson's disease is the second most common neurodegenerative disease, with an estimated prevalence of 41/100,000 individuals affected aged between 40 and 49 years old and 1,900/100,000 aged 80 and over. Based on the essentiality of ascertaining which wearable devices have clinical literary evidence and with the purpose of analyzing the information revealed by such technologies, we conducted this scientific article of integrative review. It is an integrative review, whose main objective is to carry out a summary of the state of the art of wearable devices used in patients with Parkinson's disease. After the review, we retrieved 8 papers. Of the selected articles, only 3 were not systematic reviews; one was a series of cases and two prospective longitudinal studies. These technologies have a very rich field of application; however, research is still necessary to make such evaluations reliable and crucial to the well-being of these patients.
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Affiliation(s)
- Bruno Bastos Godoi
- Universidade Federal dos Vales do Jequitinhonha e Mucuri; Diamantina, MG, Brasil
| | - Gabriel Donato Amorim
- Escola Superior de Ciências da Santa Casa de Misericórdia de Vitória, Vitória, ES. Brasil
| | | | - Vanessa Milanesi Holanda
- Centro de Neurologia e Neurocirurgia Associados (NeuroCenna), BP - A Beneficência Portuguesa de São Paulo, São Paulo, SP, Brasil
| | - Thiago Júlio
- Dasa - Diagnósticos da América, Barueri, SP, Brasil
| | - Marcelo Benedet Tournier
- Hult International Business School. Campus & Enrollment Office. Hult International Business School, Cambridge, MA, USA
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31
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Mahadevan N, Demanuele C, Zhang H, Volfson D, Ho B, Erb MK, Patel S. Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device. NPJ Digit Med 2020; 3:5. [PMID: 31970290 PMCID: PMC6962225 DOI: 10.1038/s41746-019-0217-7] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 12/16/2019] [Indexed: 01/09/2023] Open
Abstract
Objective assessment of Parkinson's disease symptoms during daily life can help improve disease management and accelerate the development of new therapies. However, many current approaches require the use of multiple devices, or performance of prescribed motor activities, which makes them ill-suited for free-living conditions. Furthermore, there is a lack of open methods that have demonstrated both criterion and discriminative validity for continuous objective assessment of motor symptoms in this population. Hence, there is a need for systems that can reduce patient burden by using a minimal sensor setup while continuously capturing clinically meaningful measures of motor symptom severity under free-living conditions. We propose a method that sequentially processes epochs of raw sensor data from a single wrist-worn accelerometer by using heuristic and machine learning models in a hierarchical framework to provide continuous monitoring of tremor and bradykinesia. Results show that sensor derived continuous measures of resting tremor and bradykinesia achieve good to strong agreement with clinical assessment of symptom severity and are able to discriminate between treatment-related changes in motor states.
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Affiliation(s)
| | | | - Hao Zhang
- Pfizer, Inc., Cambridge, MA 02139 USA
| | | | - Bryan Ho
- Tufts Medical Center, Boston, MA 02111 USA
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32
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Dorsey ER, Omberg L, Waddell E, Adams JL, Adams R, Ali MR, Amodeo K, Arky A, Augustine EF, Dinesh K, Hoque ME, Glidden AM, Jensen-Roberts S, Kabelac Z, Katabi D, Kieburtz K, Kinel DR, Little MA, Lizarraga KJ, Myers T, Riggare S, Rosero SZ, Saria S, Schifitto G, Schneider RB, Sharma G, Shoulson I, Stevenson EA, Tarolli CG, Luo J, McDermott MP. Deep Phenotyping of Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2020; 10:855-873. [PMID: 32444562 PMCID: PMC7458535 DOI: 10.3233/jpd-202006] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/01/2020] [Indexed: 12/13/2022]
Abstract
Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have left gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.
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Affiliation(s)
- E. Ray Dorsey
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | | | - Emma Waddell
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Jamie L. Adams
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Roy Adams
- Machine Learning, AI and Healthcare Lab, Johns Hopkins University, Baltimore, MD, USA
| | | | - Katherine Amodeo
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Abigail Arky
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Erika F. Augustine
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Karthik Dinesh
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
| | | | - Alistair M. Glidden
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Stella Jensen-Roberts
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Zachary Kabelac
- Department of Computer Science and Artificial Intelligence, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Dina Katabi
- Department of Computer Science and Artificial Intelligence, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Karl Kieburtz
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Daniel R. Kinel
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Max A. Little
- School of Computer Science, University of Birmingham, UK
- Massachusetts Institute of Technology, MA, USA
| | - Karlo J. Lizarraga
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Taylor Myers
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Sara Riggare
- Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden
| | | | - Suchi Saria
- Machine Learning, AI and Healthcare Lab, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Statistics, and Health Policy, Johns Hopkins University, MD, USA
| | - Giovanni Schifitto
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Ruth B. Schneider
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Gaurav Sharma
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Ira Shoulson
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
- Grey Matter Technologies, Sarasota, FL, USA
| | - E. Anna Stevenson
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Christopher G. Tarolli
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Jiebo Luo
- Department of Computer Science, University of Rochester, Rochester, NY, USA
| | - Michael P. McDermott
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
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Riggare S, Hägglund M. Precision Medicine in Parkinson's Disease - Exploring Patient-Initiated Self-Tracking. JOURNAL OF PARKINSONS DISEASE 2019; 8:441-446. [PMID: 30124453 PMCID: PMC6130409 DOI: 10.3233/jpd-181314] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Background: Individually tailored healthcare, in the form of precision medicine, holds substantial potential for the future of medicine, especially for a complex disorder like Parkinson’s disease (PD). Patient self-tracking is an under-researched area in PD. Objective: This study aimed to explore patient-initiated self-tracking in PD and discuss it in the context of precision medicine. Methods: The first author used a smartphone app to capture finger-tapping data and also noted times for medication intakes. Results: Data were collected during four subsequent days. Only data from the first two days were complete enough to analyze, leading to the realization that the collection of data over a period of time can pose a significant burden to patients. From the first two days of data, a dip in finger function was observed around the time for the second medication dose of the day. Conclusions: Patient-initiated self-tracking enabled the first author to glean important insights about how her PD symptoms varied over the course of the day. Symptom tracking holds great potential in precision medicine and can, if shared in a clinical encounter, contribute to the learning of both patient and clinician. More work is needed to develop this field and extra focus needs to be given to balancing the burden of tracking for the patient against any expected benefit.
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Affiliation(s)
- Sara Riggare
- Department for Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden
| | - Maria Hägglund
- Department for Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden
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Riggare S, Scott Duncan T, Hvitfeldt H, Hägglund M. "You have to know why you're doing this": a mixed methods study of the benefits and burdens of self-tracking in Parkinson's disease. BMC Med Inform Decis Mak 2019; 19:175. [PMID: 31470832 PMCID: PMC6716928 DOI: 10.1186/s12911-019-0896-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 08/14/2019] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND This study explores opinions and experiences of people with Parkinson's disease (PwP) in Sweden of using self-tracking. Parkinson's disease (PD) is a neurodegenerative condition entailing varied and changing symptoms and side effects that can be a challenge to manage optimally. Patients' self-tracking has demonstrated potential in other diseases, but we know little about PD self-tracking. The aim of this study was therefore to explore the opinions and experiences of PwP in Sweden of using self-tracking for PD. METHOD A mixed methods approach was used, combining qualitative data from seven interviews with quantitative data from a survey to formulate a model for self-tracking in PD. In total 280 PwP responded to the survey, 64% (n = 180) of which had experience from self-tracking. RESULT We propose a model for self-tracking in PD which share distinctive characteristics with the Plan-Do-Study-Act (PDSA) cycle for healthcare improvement. PwP think that tracking takes a lot of work and the right individual balance between burdens and benefits needs to be found. Some strategies have here been identified; to focus on positive aspects rather than negative, to find better solutions for their selfcare, and to increase the benefits through improved tools and increased use of self-tracking results in the dialogue with healthcare. CONCLUSION The main identified benefits are that self-tracking gives PwP a deeper understanding of their own specific manifestations of PD and contributes to a more effective decision making regarding their own selfcare. The process of self-tracking also enables PwP to be more active in communicating with healthcare. Tracking takes a lot of work and there is a need to find the right balance between burdens and benefits.
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Affiliation(s)
- Sara Riggare
- LIME, Health Informatics Centre, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Therese Scott Duncan
- LIME, Health Informatics Centre, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Helena Hvitfeldt
- Karolinska Institutet, LIME, Medical Management Centre, 171 77 Stockholm, Sweden
- Norrtälje Hospital, FoUU, 761 29 Norrtälje, Sweden
| | - Maria Hägglund
- LIME, Health Informatics Centre, Karolinska Institutet, 171 77 Stockholm, Sweden
- Department of Women’s and Children’s Health, Uppsala University, 752 37 Uppsala, Sweden
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Son H, Kim H. A Pilot Study to Test the Feasibility of a Home Mobility Monitoring System in Community-Dwelling Older Adults. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E1512. [PMID: 31035678 PMCID: PMC6539780 DOI: 10.3390/ijerph16091512] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 04/16/2019] [Accepted: 04/18/2019] [Indexed: 12/24/2022]
Abstract
Technology enables home-based personalized care through continuous, automated, real-time monitoring of a participant's health condition and remote communication between health care providers and participants. Technology has been implemented in a variety of nursing practices. However, little is known about the use of home mobility monitoring systems in visiting nursing practice. Therefore, the current study tested the feasibility of a home mobility monitoring system as a supportive tool for monitoring daily activities in community-dwelling older adults. Daily mobility data were collected for 15 months via home-based mobility monitoring sensors among eight older adults living alone. Indoor sensor outputs were categorized into sleeping, indoor activities, and going out. Atypical patterns were identified with reference to baseline activity. Daily indoor activities were clearly differentiated by sensor outputs and sensor outputs discriminated atypical activity patterns. During the year of monitoring, a health-related issue was identified in a participant. Our findings indicate the feasibility of a home mobility monitoring system for remote, continuous, and automated assessment of a participant's health-related mobility patterns. Such a system could be used as a supportive tool to detect and intervene in the case of problematic health issues.
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Affiliation(s)
- Heesook Son
- Red Cross College of Nursing, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea.
| | - Hyerang Kim
- Red Cross College of Nursing, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea.
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36
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Linares-del Rey M, Vela-Desojo L, Cano-de la Cuerda R. Mobile phone applications in Parkinson's disease: a systematic review. NEUROLOGÍA (ENGLISH EDITION) 2019. [DOI: 10.1016/j.nrleng.2018.12.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
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37
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Linares-del Rey M, Vela-Desojo L, Cano-de la Cuerda R. Aplicaciones móviles en la enfermedad de Parkinson: una revisión sistemática. Neurologia 2019; 34:38-54. [DOI: 10.1016/j.nrl.2017.03.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2016] [Revised: 02/20/2017] [Accepted: 03/02/2017] [Indexed: 02/07/2023] Open
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38
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Cohen S, Waks Z, Elm JJ, Gordon MF, Grachev ID, Navon-Perry L, Fine S, Grossman I, Papapetropoulos S, Savola JM. Characterizing patient compliance over six months in remote digital trials of Parkinson's and Huntington disease. BMC Med Inform Decis Mak 2018; 18:138. [PMID: 30572891 PMCID: PMC6302308 DOI: 10.1186/s12911-018-0714-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2018] [Accepted: 11/23/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A growing number of clinical trials use various sensors and smartphone applications to collect data outside of the clinic or hospital, raising the question to what extent patients comply with the unique requirements of remote study protocols. Compliance is particularly important in conditions where patients are motorically and cognitively impaired. Here, we sought to understand patient compliance in digital trials of two such pathologies, Parkinson's disease (PD) and Huntington disease (HD). METHODS Patient compliance was assessed in two remote, six-month clinical trials of PD (n = 51, Clinician Input Study funded by the Michael J. Fox Foundation for Parkinson's Research) and HD (n = 17, sponsored by Teva Pharmaceuticals). We monitored four compliance metrics specific to remote studies: smartphone app-based medication reporting, app-based symptoms reporting, the duration of smartwatch data streaming except while charging, and the performance of structured motor tasks at home. RESULTS While compliance over time differed between the PD and HD studies, both studies maintained high compliance levels for their entire six month duration. None (- 1%) to a 30% reduction in compliance rate was registered for HD patients, and a reduction of 34 to 53% was registered for the PD study. Both studies exhibited marked changes in compliance rates during the initial days of enrollment. Interestingly, daily smartwatch data streaming patterns were similar, peaking around noon, dropping sharply in the late evening hours around 8 pm, and having a mean of 8.6 daily streaming hours for the PD study and 10.5 h for the HD study. Individual patients tended to have either high or low compliance across all compliance metrics as measured by pairwise correlation. Encouragingly, predefined schedules and app-based reminders fulfilled their intended effect on the timing of medication intake reporting and performance of structured motor tasks at home. CONCLUSIONS Our findings suggest that maintaining compliance over long durations is feasible, promote the use of predefined app-based reminders, and highlight the importance of patient selection as highly compliant patients typically have a higher adherence rate across the different aspects of the protocol. Overall, these data can serve as a reference point for the design of upcoming remote digital studies. TRIAL REGISTRATION Trials described in this study include a sub-study of the Open PRIDE-HD Huntington's disease study (TV7820-CNS-20016), which was registered on July 7th, 2015, sponsored by Teva Pharmaceuticals Ltd., and registered on Clinicaltrials.gov as NCT02494778 and EudraCT as 2015-000904-24 .
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Affiliation(s)
- Shani Cohen
- Advanced Analytics Department, Intel, 94 Em Hamoshavot Road, Petah Tikva, Israel
| | - Zeev Waks
- Advanced Analytics Department, Intel, 94 Em Hamoshavot Road, Petah Tikva, Israel.
| | - Jordan J Elm
- Department of Public Health Sciences, Medical University of South Carolina, 135 Cannon St., Suite 303, PO Box 250835, Charleston, SC, 29425, USA
| | - Mark Forrest Gordon
- Teva Branded Pharmaceutical Products R&D, Inc, 41 Moores Rd., Frazer, Petah Tikva, PA, 19355, USA
| | - Igor D Grachev
- Guide Pharmaceutical Consulting, LLC, Millstone Township, NJ, 08535, USA
| | - Leehee Navon-Perry
- Teva Pharmaceutical Industries Ltd, 12 Hatrufa St, 4250483, Netanya, Israel
| | - Shai Fine
- Data Science Institute, Interdisciplinary Center, 1 Kanfei Nesharim St, 4610101, Herzliya, Israel
| | - Iris Grossman
- CAMP4 Therapeutics, One Kendall Square, Bldg 1400 West, 3rd Floor, Cambridge, MA, 02139, USA
| | | | - Juha-Matti Savola
- Teva Pharmaceuticals International GmbH, Elisabethenstrasse 15, 4051, Basel, Switzerland
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Habets JGV, Heijmans M, Kuijf ML, Janssen MLF, Temel Y, Kubben PL. An update on adaptive deep brain stimulation in Parkinson's disease. Mov Disord 2018; 33:1834-1843. [PMID: 30357911 PMCID: PMC6587997 DOI: 10.1002/mds.115] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 06/26/2018] [Accepted: 07/08/2018] [Indexed: 12/24/2022] Open
Abstract
Advancing conventional open‐loop DBS as a therapy for PD is crucial for overcoming important issues such as the delicate balance between beneficial and adverse effects and limited battery longevity that are currently associated with treatment. Closed‐loop or adaptive DBS aims to overcome these limitations by real‐time adjustment of stimulation parameters based on continuous feedback input signals that are representative of the patient's clinical state. The focus of this update is to discuss the most recent developments regarding potential input signals and possible stimulation parameter modulation for adaptive DBS in PD. Potential input signals for adaptive DBS include basal ganglia local field potentials, cortical recordings (electrocorticography), wearable sensors, and eHealth and mHealth devices. Furthermore, adaptive DBS can be applied with different approaches of stimulation parameter modulation, the feasibility of which can be adapted depending on specific PD phenotypes. Implementation of technological developments like machine learning show potential in the design of such approaches; however, energy consumption deserves further attention. Furthermore, we discuss future considerations regarding the clinical implementation of adaptive DBS in PD. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Jeroen G V Habets
- Departments of Neurosurgery, Maastricht University Medical Center, Maastricht, The Netherlands.,School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Margot Heijmans
- Departments of Neurosurgery, Maastricht University Medical Center, Maastricht, The Netherlands.,School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Mark L Kuijf
- Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Marcus L F Janssen
- Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands.,Department of Clinical Neurophysiology, Maastricht University Medical Center, Maastricht, The Netherlands.,School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Yasin Temel
- Departments of Neurosurgery, Maastricht University Medical Center, Maastricht, The Netherlands.,School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Pieter L Kubben
- Departments of Neurosurgery, Maastricht University Medical Center, Maastricht, The Netherlands.,School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
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40
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Son H, Park WS, Kim H. Mobility monitoring using smart technologies for Parkinson’s disease in free-living environment. Collegian 2018. [DOI: 10.1016/j.colegn.2017.11.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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41
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Pham MH, Warmerdam E, Elshehabi M, Schlenstedt C, Bergeest LM, Heller M, Haertner L, Ferreira JJ, Berg D, Schmidt G, Hansen C, Maetzler W. Validation of a Lower Back "Wearable"-Based Sit-to-Stand and Stand-to-Sit Algorithm for Patients With Parkinson's Disease and Older Adults in a Home-Like Environment. Front Neurol 2018; 9:652. [PMID: 30158894 PMCID: PMC6104484 DOI: 10.3389/fneur.2018.00652] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Accepted: 07/20/2018] [Indexed: 01/17/2023] Open
Abstract
Introduction: Impaired sit-to-stand and stand-to-sit movements (postural transitions, PTs) in patients with Parkinson's disease (PD) and older adults (OA) are associated with risk of falling and reduced quality of life. Inertial measurement units (IMUs, also called "wearables") are powerful tools to monitor PT kinematics. The purpose of this study was to develop and validate an algorithm, based on a single IMU positioned at the lower back, for PT detection and description in the above-mentioned groups in a home-like environment. Methods: Four PD patients (two with dyskinesia) and one OA served as algorithm training group, and 21 PD patients (16 without and 5 with dyskinesia) and 11 OA served as test group. All wore an IMU on the lower back and were videotaped while performing everyday activities for 90-180 min in a non-standardized home-like environment. Accelerometer and gyroscope signals were analyzed using discrete wavelet transformation (DWT), a six degrees-of-freedom (DOF) fusion algorithm and vertical displacement estimation. Results: From the test group, 1,001 PTs, defined by video reference, were analyzed. The accuracy of the algorithm for the detection of PTs against video observation was 82% for PD patients without dyskinesia, 47% for PD patients with dyskinesia and 85% for OA. The overall accuracy of the PT direction detection was comparable across groups and yielded 98%. Mean PT duration values were 1.96 s for PD patients and 1.74 s for OA based on the algorithm (p < 0.001) and 1.77 s for PD patients and 1.51 s for OA based on clinical observation (p < 0.001). Conclusion: Validation of the PT detection algorithm in a home-like environment shows acceptable accuracy against the video reference in PD patients without dyskinesia and controls. Current limitations are the PT detection in PD patients with dyskinesia and the use of video observation as the video reference. Potential reasons are discussed.
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Affiliation(s)
- Minh H Pham
- Department of Neurology, University Hospital Schleswig-Holstein, Kiel University, Kiel, Germany.,Digital Signal Processing and System Theory, Faculty of Engineering, Kiel University, Kiel, Germany
| | - Elke Warmerdam
- Department of Neurology, University Hospital Schleswig-Holstein, Kiel University, Kiel, Germany.,Digital Signal Processing and System Theory, Faculty of Engineering, Kiel University, Kiel, Germany
| | - Morad Elshehabi
- Department of Neurology, University Hospital Schleswig-Holstein, Kiel University, Kiel, Germany.,Department of Neurodegeneration, Center for Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Christian Schlenstedt
- Department of Neurology, University Hospital Schleswig-Holstein, Kiel University, Kiel, Germany
| | - Lu-Marie Bergeest
- Department of Neurology, University Hospital Schleswig-Holstein, Kiel University, Kiel, Germany
| | - Maren Heller
- Department of Neurology, University Hospital Schleswig-Holstein, Kiel University, Kiel, Germany
| | - Linda Haertner
- Department of Neurodegeneration, Center for Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,DZNE, German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Joaquim J Ferreira
- Clinical Pharmacology Unit, Instituto de Medicina Molecular, Lisbon, Portugal.,Laboratory of Clinical Pharmacology and Therapeutics, Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Daniela Berg
- Department of Neurology, University Hospital Schleswig-Holstein, Kiel University, Kiel, Germany.,Department of Neurodegeneration, Center for Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Gerhard Schmidt
- Digital Signal Processing and System Theory, Faculty of Engineering, Kiel University, Kiel, Germany
| | - Clint Hansen
- Department of Neurology, University Hospital Schleswig-Holstein, Kiel University, Kiel, Germany
| | - Walter Maetzler
- Department of Neurology, University Hospital Schleswig-Holstein, Kiel University, Kiel, Germany.,Department of Neurodegeneration, Center for Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
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Rovini E, Maremmani C, Cavallo F. Automated Systems Based on Wearable Sensors for the Management of Parkinson's Disease at Home: A Systematic Review. Telemed J E Health 2018; 25:167-183. [PMID: 29969384 DOI: 10.1089/tmj.2018.0035] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Parkinson's disease is a common neurodegenerative pathology that significantly influences quality of life (QoL) of people affected. The increasing interest and development in telemedicine services and internet of things technologies aim to implement automated smart systems for remote assistance of patients. The wide variability of Parkinson's disease in the clinical expression, as well as in the symptom progression, seems to address the patients' care toward a personalized therapy. OBJECTIVES This review addresses automated systems based on wearable/portable devices for the remote treatment and management of Parkinson's disease. The idea is to obtain an overview of the telehealth and automated systems currently developed to address the impairments due to the pathology to allow clinicians to improve the quality of care for Parkinson's disease with benefits for patients in QoL. DATA SOURCES The research was conducted within three databases: IEEE Xplore®, Web of Science®, and PubMed Central®, between January 2008 and September 2017. STUDY ELIGIBILITY CRITERIA Accurate exclusion criteria and selection strategy were applied to screen the 173 articles found. RESULTS Ultimately, 55 articles were fully evaluated and included in this review. Divided into three categories, they were automated systems actually tested at home, implemented mobile applications for Parkinson's disease assessment, or described a telehealth system architecture. CONCLUSION This review would provide an exhaustive overview of wearable systems for the remote management and automated assessment of Parkinson's disease, taking into account the reliability and acceptability of the implemented technologies.
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Affiliation(s)
- Erika Rovini
- 1 The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera (PI), Italy
| | - Carlo Maremmani
- 2 U.O. Neurologia, Ospedale delle Apuane (AUSL Toscana Nord Ovest), Massa (MS), Italy
| | - Filippo Cavallo
- 1 The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera (PI), Italy
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43
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Daneault JF. Could Wearable and Mobile Technology Improve the Management of Essential Tremor? Front Neurol 2018; 9:257. [PMID: 29725318 PMCID: PMC5916972 DOI: 10.3389/fneur.2018.00257] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Accepted: 04/03/2018] [Indexed: 11/13/2022] Open
Abstract
Essential tremor (ET) is the most common movement disorder. Individuals exhibit postural and kinetic tremor that worsens over time and patients may also exhibit other motor and non-motor symptoms. While millions of people are affected by this disorder worldwide, several barriers impede an optimal clinical management of symptoms. In this paper, we discuss the impact of ET on patients and review major issues to the optimal management of ET; from the side-effects and limited efficacy of current medical treatments to the limited number of people who seek treatment for their tremor. Then, we propose seven different areas within which mobile and wearable technology may improve the clinical management of ET and review the current state of research in these areas.
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Affiliation(s)
- Jean-Francois Daneault
- Motor Behavior Laboratory, Department of Rehabilitation and Movement Sciences, School of Health Professions, Rutgers, The State University of New Jersey, Newark, NJ, United States
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44
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Haertner L, Elshehabi M, Zaunbrecher L, Pham MH, Maetzler C, van Uem JMT, Hobert MA, Hucker S, Nussbaum S, Berg D, Liepelt-Scarfone I, Maetzler W. Effect of Fear of Falling on Turning Performance in Parkinson's Disease in the Lab and at Home. Front Aging Neurosci 2018; 10:78. [PMID: 29636676 PMCID: PMC5880950 DOI: 10.3389/fnagi.2018.00078] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Accepted: 03/08/2018] [Indexed: 12/26/2022] Open
Abstract
Background: Parkinson’s disease (PD) is a neurodegenerative movement disorder associated with gait and balance problems and a substantially increased risk of falling. Falls occur often during complex movements, such as turns. Both fear of falling (FOF) and previous falls are relevant risk factors for future falls. Based on recent studies indicating that lab-based and home assessment of similar movements show different results, we hypothesized that FOF and a positive fall history would influence the quantitative turning parameters differently in the laboratory and home. Methods: Fifty-five PD patients (43 underwent a standardized lab assessment; 40 were assessed over a mean of 12 days at home with approximately 10,000 turns per participant; and 28 contributed to both assessments) were classified regarding FOF and previous falls as “vigorous” (no FOF, negative fall history), “anxious” (FOF, negative fall history), “stoic” (no FOF, positive fall history) and “aware” (FOF, positive fall history). During the assessments, each participant wore a sensor on the lower back. Results: In the lab assessment, FOF was associated with a longer turning duration and lowered maximum and middle angular velocities of turns. In the home evaluations, a lack of FOF was associated with lowered maximum and average angular velocities of turns. Positive falls history was not significantly associated with turning parameters, neither in the lab nor in the home. Conclusion: FOF but not a positive fall history influences turning metrics in PD patients in both supervised and unsupervised environments, and this association is different between lab and home assessments. Our findings underline the relevance of comprehensive assessments including home-based data collection strategies for fall risk evaluation.
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Affiliation(s)
- Linda Haertner
- Center for Neurology and Hertie-Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Morad Elshehabi
- Center for Neurology and Hertie-Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases, Tübingen, Germany.,Department of Neurology, Universitätsklinikum Schleswig-Holstein, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Laura Zaunbrecher
- Center for Neurology and Hertie-Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Minh H Pham
- Center for Neurology and Hertie-Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases, Tübingen, Germany.,Department of Neurology, Universitätsklinikum Schleswig-Holstein, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Corina Maetzler
- Center for Neurology and Hertie-Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases, Tübingen, Germany.,Department of Neurology, Universitätsklinikum Schleswig-Holstein, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Janet M T van Uem
- Center for Neurology and Hertie-Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Markus A Hobert
- Center for Neurology and Hertie-Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tübingen, Tübingen, Germany.,Department of Neurology, Universitätsklinikum Schleswig-Holstein, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Svenja Hucker
- Center for Neurology and Hertie-Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Susanne Nussbaum
- Center for Neurology and Hertie-Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Daniela Berg
- Center for Neurology and Hertie-Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases, Tübingen, Germany.,Department of Neurology, Universitätsklinikum Schleswig-Holstein, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Inga Liepelt-Scarfone
- Center for Neurology and Hertie-Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Walter Maetzler
- Center for Neurology and Hertie-Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases, Tübingen, Germany.,Department of Neurology, Universitätsklinikum Schleswig-Holstein, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
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Silva de Lima AL, Hahn T, Evers LJW, de Vries NM, Cohen E, Afek M, Bataille L, Daeschler M, Claes K, Boroojerdi B, Terricabras D, Little MA, Baldus H, Bloem BR, Faber MJ. Feasibility of large-scale deployment of multiple wearable sensors in Parkinson's disease. PLoS One 2017; 12:e0189161. [PMID: 29261709 PMCID: PMC5738046 DOI: 10.1371/journal.pone.0189161] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 11/20/2017] [Indexed: 02/02/2023] Open
Abstract
Wearable devices can capture objective day-to-day data about Parkinson's Disease (PD). This study aims to assess the feasibility of implementing wearable technology to collect data from multiple sensors during the daily lives of PD patients. The Parkinson@home study is an observational, two-cohort (North America, NAM; The Netherlands, NL) study. To recruit participants, different strategies were used between sites. Main enrolment criteria were self-reported diagnosis of PD, possession of a smartphone and age≥18 years. Participants used the Fox Wearable Companion app on a smartwatch and smartphone for a minimum of 6 weeks (NAM) or 13 weeks (NL). Sensor-derived measures estimated information about movement. Additionally, medication intake and symptoms were collected via self-reports in the app. A total of 953 participants were included (NL: 304, NAM: 649). Enrolment rate was 88% in the NL (n = 304) and 51% (n = 649) in NAM. Overall, 84% (n = 805) of participants contributed sensor data. Participants were compliant for 68% (16.3 hours/participant/day) of the study period in NL and for 62% (14.8 hours/participant/day) in NAM. Daily accelerometer data collection decreased 23% in the NL after 13 weeks, and 27% in NAM after 6 weeks. Data contribution was not affected by demographics, clinical characteristics or attitude towards technology, but was by the platform usability score in the NL (χ2 (2) = 32.014, p<0.001), and self-reported depression in NAM (χ2(2) = 6.397, p = .04). The Parkinson@home study shows that it is feasible to collect objective data using multiple wearable sensors in PD during daily life in a large cohort.
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Affiliation(s)
- Ana Lígia Silva de Lima
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- CAPES Foundation, Ministry of Education of Brazil, Brasília/DF, Brazil
| | - Tim Hahn
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Luc J. W. Evers
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Nienke M. de Vries
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Eli Cohen
- Intel, Advanced Analytics, Tel Aviv, Israel
| | | | - Lauren Bataille
- The Michael J Fox Foundation for Parkinson’s Research, New York, United States of America
| | - Margaret Daeschler
- The Michael J Fox Foundation for Parkinson’s Research, New York, United States of America
| | | | | | | | - Max A. Little
- Aston University, Birmingham, United Kingdom
- Media Lab, Massachusetts Institute of Technology, Cambridge, United States of America
| | - Heribert Baldus
- Philips Research, Department Personal Health, Eindhoven, the Netherlands
| | - Bastiaan R. Bloem
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marjan J. Faber
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Radboud University Medical Center, Radboud Institute for Health Sciences, Scientific Center for Quality of Healthcare, Nijmegen, the Netherlands
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Kuhner A, Schubert T, Cenciarini M, Wiesmeier IK, Coenen VA, Burgard W, Weiller C, Maurer C. Correlations between Motor Symptoms across Different Motor Tasks, Quantified via Random Forest Feature Classification in Parkinson's Disease. Front Neurol 2017; 8:607. [PMID: 29184533 PMCID: PMC5694559 DOI: 10.3389/fneur.2017.00607] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 10/31/2017] [Indexed: 01/02/2023] Open
Abstract
Background Objective assessments of Parkinson’s disease (PD) patients’ motor state using motion capture techniques are still rarely used in clinical practice, even though they may improve clinical management. One major obstacle relates to the large dimensionality of motor abnormalities in PD. We aimed to extract global motor performance measures covering different everyday motor tasks, as a function of a clinical intervention, i.e., deep brain stimulation (DBS) of the subthalamic nucleus. Methods We followed a data-driven, machine-learning approach and propose performance measures that employ Random Forests with probability distributions. We applied this method to 14 PD patients with DBS switched-off or -on, and 26 healthy control subjects performing the Timed Up and Go Test (TUG), the Functional Reach Test (FRT), a hand coordination task, walking 10-m straight, and a 90° curve. Results For each motor task, a Random Forest identified a specific set of metrics that optimally separated PD off DBS from healthy subjects. We noted the highest accuracy (94.6%) for standing up. This corresponded to a sensitivity of 91.5% to detect a PD patient off DBS, and a specificity of 97.2% representing the rate of correctly identified healthy subjects. We then calculated performance measures based on these sets of metrics and applied those results to characterize symptom severity in different motor tasks. Task-specific symptom severity measures correlated significantly with each other and with the Unified Parkinson’s Disease Rating Scale (UPDRS, part III, correlation of r2 = 0.79). Agreement rates between different measures ranged from 79.8 to 89.3%. Conclusion The close correlation of PD patients’ various motor abnormalities quantified by different, task-specific severity measures suggests that these abnormalities are only facets of the underlying one-dimensional severity of motor deficits. The identification and characterization of this underlying motor deficit may help to optimize therapeutic interventions, e.g., to “automatically” adapt DBS settings in PD patients.
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Affiliation(s)
- Andreas Kuhner
- Department of Computer Science, University of Freiburg, Freiburg, Germany.,BrainLinks BrainTools, Cluster of Excellence, University of Freiburg, Freiburg, Germany
| | - Tobias Schubert
- Department of Computer Science, University of Freiburg, Freiburg, Germany.,BrainLinks BrainTools, Cluster of Excellence, University of Freiburg, Freiburg, Germany
| | - Massimo Cenciarini
- BrainLinks BrainTools, Cluster of Excellence, University of Freiburg, Freiburg, Germany.,Department of Neurology and Neuroscience, Medical Center, University of Freiburg, Freiburg, Germany.,Medical Faculty, University of Freiburg, Freiburg, Germany
| | - Isabella Katharina Wiesmeier
- BrainLinks BrainTools, Cluster of Excellence, University of Freiburg, Freiburg, Germany.,Department of Neurology and Neuroscience, Medical Center, University of Freiburg, Freiburg, Germany.,Medical Faculty, University of Freiburg, Freiburg, Germany
| | - Volker Arnd Coenen
- BrainLinks BrainTools, Cluster of Excellence, University of Freiburg, Freiburg, Germany.,Medical Faculty, University of Freiburg, Freiburg, Germany.,Department of Stereotactic and Functional Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany
| | - Wolfram Burgard
- Department of Computer Science, University of Freiburg, Freiburg, Germany.,BrainLinks BrainTools, Cluster of Excellence, University of Freiburg, Freiburg, Germany
| | - Cornelius Weiller
- BrainLinks BrainTools, Cluster of Excellence, University of Freiburg, Freiburg, Germany.,Department of Neurology and Neuroscience, Medical Center, University of Freiburg, Freiburg, Germany.,Medical Faculty, University of Freiburg, Freiburg, Germany
| | - Christoph Maurer
- BrainLinks BrainTools, Cluster of Excellence, University of Freiburg, Freiburg, Germany.,Department of Neurology and Neuroscience, Medical Center, University of Freiburg, Freiburg, Germany.,Medical Faculty, University of Freiburg, Freiburg, Germany
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Hasan H, Athauda DS, Foltynie T, Noyce AJ. Technologies Assessing Limb Bradykinesia in Parkinson's Disease. JOURNAL OF PARKINSONS DISEASE 2017; 7:65-77. [PMID: 28222539 PMCID: PMC5302048 DOI: 10.3233/jpd-160878] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Background: The MDS-UPDRS (Movement Disorders Society – Unified Parkinson’s Disease Rating Scale) is the most widely used scale for rating impairment in PD. Subscores measuring bradykinesia have low reliability that can be subject to rater variability. Novel technological tools can be used to overcome such issues. Objective: To systematically explore and describe the available technologies for measuring limb bradykinesia in PD that were published between 2006 and 2016. Methods: A systematic literature search using PubMed (MEDLINE), IEEE Xplore, Web of Science, Scopus and Engineering Village (Compendex and Inspec) databases was performed to identify relevant technologies published until 18 October 2016. Results: 47 technologies assessing bradykinesia in PD were identified, 17 of which offered home and clinic-based assessment whilst 30 provided clinic-based assessment only. Of the eligible studies, 7 were validated in a PD patient population only, whilst 40 were tested in both PD and healthy control groups. 19 of the 47 technologies assessed bradykinesia only, whereas 28 assessed other parkinsonian features as well. 33 technologies have been described in additional PD-related studies, whereas 14 are not known to have been tested beyond the pilot phase. Conclusion: Technology based tools offer advantages including objective motor assessment and home monitoring of symptoms, and can be used to assess response to intervention in clinical trials or routine care. This review provides an up-to-date repository and synthesis of the current literature regarding technology used for assessing limb bradykinesia in PD. The review also discusses the current trends with regards to technology and discusses future directions in development.
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Affiliation(s)
- Hasan Hasan
- UCL Institute of Neurology, Queen Square, London, UK
| | - Dilan S Athauda
- UCL Institute of Neurology, Queen Square, London, UK.,Sobell Department of Motor Neuroscience and Movement Disorders, The National Hospital for Neurology and Neurosurgery, London, UK
| | - Thomas Foltynie
- UCL Institute of Neurology, Queen Square, London, UK.,Sobell Department of Motor Neuroscience and Movement Disorders, The National Hospital for Neurology and Neurosurgery, London, UK
| | - Alastair J Noyce
- UCL Institute of Neurology, Queen Square, London, UK.,Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University London, London, UK.,Reta Lila Weston Institute of Neurological studies, UCL Institute of Neurology, London, UK
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48
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Schneider RB, Biglan KM. The promise of telemedicine for chronic neurological disorders: the example of Parkinson's disease. Lancet Neurol 2017; 16:541-551. [DOI: 10.1016/s1474-4422(17)30167-9] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 04/02/2017] [Accepted: 05/03/2017] [Indexed: 10/19/2022]
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Heldman DA, Harris DA, Felong T, Andrzejewski KL, Dorsey ER, Giuffrida JP, Goldberg B, Burack MA. Telehealth Management of Parkinson's Disease Using Wearable Sensors: An Exploratory Study. Digit Biomark 2017; 1:43-51. [PMID: 29725667 DOI: 10.1159/000475801] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Background Parkinson's disease (PD) motor symptoms can fluctuate and may not be accurately reflected during a clinical evaluation. In addition, access to movement disorder specialists is limited for many with PD. The objective was to assess the impact of motion sensor-based telehealth diagnostics on PD clinical care and management. Methods Eighteen adults with PD were randomized to control or experimental groups. All participants were instructed to use a motion sensor-based monitoring system at home one day per week, for seven months. The system included a finger-worn motion sensor and tablet-based software interface that guided patients through tasks to quantify tremor, bradykinesia, and dyskinesia. Data were processed into motor symptom severity reports, which were reviewed by a movement disorders neurologist for experimental group participants. After three months and six months, control group participants visited the clinic for a routine appointment, while experimental group participants had a videoconference or phone call instead. Results Home based assessments were completed with median compliance of 95.7%. For a subset of participants, the neurologist successfully used information in the reports such as quantified response to treatment or progression over time to make therapy adjustments. Changes in clinical characteristics from study start to end were not significantly different between groups. Discussion Individuals with PD were able and willing to use remote monitoring technology. Patient management aided by telehealth diagnostics provided comparable outcomes to standard care. Telehealth technologies combined with wearable sensors have the potential to improve care for disparate PD populations or those unable to travel.
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Affiliation(s)
| | - Denzil A Harris
- School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, NY, USA.,Center for Human Experimental Therapeutics, University of Rochester Medical Center, Rochester, NY, USA
| | - Timothy Felong
- Center for Human Experimental Therapeutics, University of Rochester Medical Center, Rochester, NY, USA
| | - Kelly L Andrzejewski
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.,Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - E Ray Dorsey
- Center for Human Experimental Therapeutics, University of Rochester Medical Center, Rochester, NY, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | | | | | - Michelle A Burack
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
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50
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Pulliam CL, Heldman DA, Brokaw EB, Mera TO, Mari ZK, Burack MA. Continuous Assessment of Levodopa Response in Parkinson's Disease Using Wearable Motion Sensors. IEEE Trans Biomed Eng 2017; 65:159-164. [PMID: 28459677 DOI: 10.1109/tbme.2017.2697764] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Fluctuations in response to levodopa in Parkinson's disease (PD) are difficult to treat as tools to monitor temporal patterns of symptoms are hampered by several challenges. The objective was to use wearable sensors to quantify the dose response of tremor, bradykinesia, and dyskinesia in individuals with PD. METHODS Thirteen individuals with PD and fluctuating motor benefit were instrumented with wrist and ankle motion sensors and recorded by video. Kinematic data were recorded as subjects completed a series of activities in a simulated home environment through transition from off to on medication. Subjects were evaluated using the unified Parkinson disease rating scale motor exam (UPDRS-III) at the start and end of data collection. Algorithms were applied to the kinematic data to score tremor, bradykinesia, and dyskinesia. A blinded clinician rated severity observed on video. Accuracy of algorithms was evaluated by comparing scores with clinician ratings using a receiver operating characteristic (ROC) analysis. RESULTS Algorithm scores for tremor, bradykinesia, and dyskinesia agreed with clinician ratings of video recordings (ROC area > 0.8). Summary metrics extracted from time intervals before and after taking medication provided quantitative measures of therapeutic response (p < 0.01). Radar charts provided intuitive visualization, with graphical features correlated with UPDRS-III scores (R = 0.81). CONCLUSION A system with wrist and ankle motion sensors can provide accurate measures of tremor, bradykinesia, and dyskinesia as patients complete routine activities. SIGNIFICANCE This technology could provide insight on motor fluctuations in the context of daily life to guide clinical management and aid in development of new therapies.
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