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Zhang Y, Zeng Z, Mirian MS, Yen K, Park KW, Doo M, Ji J, Shen Z, McKeown MJ. Investigating the efficacy and importance of mobile-based assessments for Parkinson's disease: uncovering the potential of novel digital tests. Sci Rep 2024; 14:5307. [PMID: 38438438 PMCID: PMC10912749 DOI: 10.1038/s41598-024-55077-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 02/19/2024] [Indexed: 03/06/2024] Open
Abstract
This study introduces PDMotion, a mobile application comprising 11 digital tests, including those adapted from the MDS-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III and novel assessments, for remote Parkinson's Disease (PD) motor symptoms evaluation. Employing machine learning techniques on data from 50 PD patients and 29 healthy controls, PDMotion achieves accuracies of 0.878 for PD status prediction and 0.715 for severity assessment. A post-hoc explanation model is employed to assess the importance of features and tasks in diagnosis and severity evaluation. Notably, novel tasks that are not adapted from MDS-UPDRS Part III like the circle drawing, coordination test, and alternative tapping test are found to be highly important, suggesting digital assessments for PD can go beyond digitizing existing tests. The alternative tapping test emerges as the most significant task. Using its features alone achieves prediction accuracies comparable to the full task set, underscoring its potential as an independent screening tool. This study addresses a notable research gap by digitalizing a wide array of tests, including novel ones, and conducting a comparative analysis of their feature and task importance. These insights provide guidance for task selection and future development in PD mobile assessments, a field previously lacking such comparative studies.
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Affiliation(s)
- Yanci Zhang
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Zhiwei Zeng
- Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University, Singapore, Singapore
| | - Maryam S Mirian
- Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, Canada
| | - Kevin Yen
- Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, Canada
| | - Kye Won Park
- Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, Canada
| | - Michelle Doo
- Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, Canada
| | - Jun Ji
- Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University, Singapore, Singapore
| | - Zhiqi Shen
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.
- Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University, Singapore, Singapore.
| | - Martin J McKeown
- Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, Canada
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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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Ferreira VR, Metting E, Schauble J, Seddighi H, Beumeler L, Gallo V. eHealth tools to assess the neurological function for research, in absence of the neurologist - a systematic review, part I (software). J Neurol 2024; 271:211-230. [PMID: 37847293 PMCID: PMC10770248 DOI: 10.1007/s00415-023-12012-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 09/15/2023] [Accepted: 09/16/2023] [Indexed: 10/18/2023]
Abstract
BACKGROUND Neurological disorders remain a worldwide concern due to their increasing prevalence and mortality, combined with the lack of available treatment, in most cases. Exploring protective and risk factors associated with the development of neurological disorders will allow for improving prevention strategies. However, ascertaining neurological outcomes in population-based studies can be both complex and costly. The application of eHealth tools in research may contribute to lowering the costs and increase accessibility. The aim of this systematic review is to map existing eHealth tools assessing neurological signs and/or symptoms for epidemiological research. METHODS Four search engines (PubMed, Web of Science, Scopus & EBSCOHost) were used to retrieve articles on the development, validation, or implementation of eHealth tools to assess neurological signs and/or symptoms. The clinical and technical properties of the software tools were summarised. Due to high numbers, only software tools are presented here. FINDINGS A total of 42 tools were retrieved. These captured signs and/or symptoms belonging to four neurological domains: cognitive function, motor function, cranial nerves, and gait and coordination. An additional fifth category of composite tools was added. Most of the tools were available in English and were developed for smartphone device, with the remaining tools being available as web-based platforms. Less than half of the captured tools were fully validated, and only approximately half were still active at the time of data collection. INTERPRETATION The identified tools often presented limitations either due to language barriers or lack of proper validation. Maintenance and durability of most tools were low. The present mapping exercise offers a detailed guide for epidemiologists to identify the most appropriate eHealth tool for their research. FUNDING The current study was funded by a PhD position at the University of Groningen. No additional funding was acquired.
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Affiliation(s)
- Vasco Ribeiro Ferreira
- Department of Sustainable Health, University of Groningen, Campus Fryslân, Wirdumerdijk 34, 8911 CE, Leeuwarden, The Netherlands.
| | - Esther Metting
- Faculty of Economics and Business, University of Groningen, Groningen, The Netherlands
- University Medical College Groningen, Groningen, The Netherlands
| | - Joshua Schauble
- Department of Knowledge Infrastructure, University of Groningen, Campus Fryslân, Leeuwarden, The Netherlands
| | - Hamed Seddighi
- Department of Sustainable Health, University of Groningen, Campus Fryslân, Wirdumerdijk 34, 8911 CE, Leeuwarden, The Netherlands
- Department of Psychology, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
| | - Lise Beumeler
- Department of Sustainable Health, University of Groningen, Campus Fryslân, Wirdumerdijk 34, 8911 CE, Leeuwarden, The Netherlands
- Department of Intensive Care, Medical Centre Leeuwarden, Leeuwarden, The Netherlands
| | - Valentina Gallo
- Department of Sustainable Health, University of Groningen, Campus Fryslân, Wirdumerdijk 34, 8911 CE, Leeuwarden, The Netherlands
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Khanna A, Jones G. Toward Personalized Medicine Approaches for Parkinson Disease Using Digital Technologies. JMIR Form Res 2023; 7:e47486. [PMID: 37756050 PMCID: PMC10568402 DOI: 10.2196/47486] [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: 03/21/2023] [Revised: 09/03/2023] [Accepted: 09/05/2023] [Indexed: 09/28/2023] Open
Abstract
Parkinson disease (PD) is a complex neurodegenerative disorder that afflicts over 10 million people worldwide, resulting in debilitating motor and cognitive impairment. In the United States alone (with approximately 1 million cases), the economic burden for treating and caring for persons with PD exceeds US $50 billion and myriad therapeutic approaches are under development, including both symptomatic- and disease-modifying agents. The challenges presented in addressing PD are compounded by observations that numerous, statistically distinct patient phenotypes present with a wide variety of motor and nonmotor symptomatic profiles, varying responses to current standard-of-care symptom-alleviating medications (L-DOPA and dopaminergic agonists), and different disease trajectories. The existence of these differing phenotypes highlights the opportunities in personalized approaches to symptom management and disease control. The prodromal period of PD can span across several decades, allowing the potential to leverage the unique array of composite symptoms presented to trigger early interventions. This may be especially beneficial as disease progression in PD (alongside Alzheimer disease and Huntington disease) may be influenced by biological processes such as oxidative stress, offering the potential for individual lifestyle factors to be tailored to delay disease onset. In this viewpoint, we offer potential scenarios where emerging diagnostic and monitoring strategies might be tailored to the individual patient under the tenets of P4 medicine (predict, prevent, personalize, and participate). These approaches may be especially relevant as the causative factors and biochemical pathways responsible for the observed neurodegeneration in patients with PD remain areas of fluid debate. The numerous observational patient cohorts established globally offer an excellent opportunity to test and refine approaches to detect, characterize, control, modify the course, and ultimately stop progression of this debilitating disease. Such approaches may also help development of parallel interventive strategies in other diseases such as Alzheimer disease and Huntington disease, which share common traits and etiologies with PD. In this overview, we highlight near-term opportunities to apply P4 medicine principles for patients with PD and introduce the concept of composite orthogonal patient monitoring.
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Affiliation(s)
- Amit Khanna
- Neuroscience Global Drug Development, Novartis Pharma AG, Basel, Switzerland
| | - Graham Jones
- GDD Connected Health and Innovation Group, Novartis Pharmaceuticals, East Hanover, NJ, United States
- Clinical and Translational Science Institute, Tufts University Medical Center, Boston, MA, United States
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Liu W, Lin X, Chen X, Wang Q, Wang X, Yang B, Cai N, Chen R, Chen G, Lin Y. Vision-based estimation of MDS-UPDRS scores for quantifying Parkinson's disease tremor severity. Med Image Anal 2023; 85:102754. [PMID: 36702036 DOI: 10.1016/j.media.2023.102754] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 01/07/2023] [Accepted: 01/18/2023] [Indexed: 01/22/2023]
Abstract
Parkinson's disease (PD) is a common neurodegenerative movement disorder among older individuals. As one of the typical symptoms of PD, tremor is a critical reference in the PD assessment. A widely accepted clinical approach to assessing tremors in PD is based on part III of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). However, expert assessment of tremor is a time-consuming and laborious process that poses considerable challenges to the medical evaluation of PD. In this paper, we proposed a novel model, Global Temporal-difference Shift Network (GTSN), to estimate the MDS-UPDRS score of PD tremors based on video. The PD tremor videos were scored according to the majority vote of multiple raters. We used Eulerian Video Magnification (EVM) pre-processing to enhance the representations of subtle PD tremors in the videos. To make the model better focus on the tremors in the video, we proposed a special temporal difference module, which stacks the current optical flow to the result of inter-frame difference. The prediction scores were obtained from the Residual Networks (ResNet) embedded with a novel module, the Global Shift Module (GSM), which allowed the features of the current segment to include the global segment features. We carried out independent experiments using PD tremor videos of different body parts based on the scoring content of the MDS-UPDRS. On a fairly large dataset, our method achieved an accuracy of 90.6% for hands with rest tremors, 85.9% for tremors in the leg, and 89.0% for the jaw. An accuracy of 84.9% was obtained for postural tremors. Our study demonstrated the effectiveness of computer-assisted assessment for PD tremors based on video analysis. The latest version of the code is available at https://github.com/199507284711/PD-GTSN.
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Affiliation(s)
- Weiping Liu
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou 350007, China; Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, China
| | - Xiaozhen Lin
- Department of Geriatrics, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - Xinghong Chen
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou 350007, China; Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, China
| | - Qing Wang
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou 350007, China; Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, China
| | - Xiumei Wang
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou 350007, China; Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, China
| | - Bin Yang
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou 350007, China; Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, China
| | - Naiqing Cai
- Department of Neurology and Institute of Neurology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - Rong Chen
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou 350007, China; Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, China
| | - Guannan Chen
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou 350007, China; Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, China.
| | - Yu Lin
- Department of Neurology and Institute of Neurology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China.
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Moreta-de-Esteban P, Martín-Casas P, Ortiz-Gutiérrez RM, Straudi S, Cano-de-la-Cuerda R. Mobile Applications for Resting Tremor Assessment in Parkinson’s Disease: A Systematic Review. J Clin Med 2023; 12:jcm12062334. [PMID: 36983334 PMCID: PMC10057335 DOI: 10.3390/jcm12062334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 03/10/2023] [Accepted: 03/15/2023] [Indexed: 03/19/2023] Open
Abstract
(1) Background: Resting tremor is a motor manifestation present in most Parkinson’s disease (PD) patients. For its assessment, several scales have been created, but mobile applications could help in objectively assessing resting tremor in PD patients in person and/or remotely in a more ecological scenario. (2) Methods: a systematic review following the PRISMA recommendations was conducted in scientific databases (PubMed, Medline, Science Direct, Academic Search Premier, and Web of Science) and in the main mobile application markets (Google Play, iOS App Store, and Windows Store) to determine the applications available for the assessment of resting tremor in patients with PD using only the measurement components of the phone itself (accelerometers and gyroscopes). (3) Results: 14 articles that used mobile apps to assess resting tremor in PD were included, and 13 apps were identified in the mobile application markets for the same purpose. The risk of bias and of applicability concerns of the articles analyzed was low. Mobile applications found in the app markets met an average of 85.09% of the recommendations for the development of medical mobile applications. (4) Conclusions: the use of mobile applications for the evaluation of resting tremor in PD patients has great potential, but validation studies for this purpose are scarce.
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Affiliation(s)
- Paloma Moreta-de-Esteban
- Radiology, Rehabilitation and Physiotherapy Department, Nursing, Physiotherapy and Podiatry Faculty, Complutense of Madrid University, Plaza Ramón y Cajal 3, 28040 Madrid, Spain
| | - Patricia Martín-Casas
- Radiology, Rehabilitation and Physiotherapy Department, Nursing, Physiotherapy and Podiatry Faculty, Complutense of Madrid University, Plaza Ramón y Cajal 3, 28040 Madrid, Spain
| | - Rosa María Ortiz-Gutiérrez
- Radiology, Rehabilitation and Physiotherapy Department, Nursing, Physiotherapy and Podiatry Faculty, Complutense of Madrid University, Plaza Ramón y Cajal 3, 28040 Madrid, Spain
- Correspondence: ; Tel.: +34-913-941-524
| | - Sofía Straudi
- Department of Neuroscience and Rehabilitation, University of Ferrara, Via Luigi Borsari 46, 44121 Ferrara, Italy
| | - Roberto Cano-de-la-Cuerda
- Department of Physiotherapy, Occupational Therapy, Rehabilitation and Physical Medicine, Health Science Faculty, Rey Juan Carlos University, Avda. Atenas S/N, 28922 Alcorcón, Spain
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Koinis L, Mobbs RJ, Fonseka RD, Natarajan P. A commentary on the potential of smartphones and other wearable devices to be used in the identification and monitoring of mental illness. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:1420. [PMID: 36660675 PMCID: PMC9843326 DOI: 10.21037/atm-21-6016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 10/22/2022] [Indexed: 11/16/2022]
Affiliation(s)
- Lianne Koinis
- Department of Psychology, University of New South Wales, Sydney, Australia
| | - Ralph Jasper Mobbs
- Faculty of Medicine, University of New South Wales, Sydney, Australia;,Wearables and Gait Analysis Research Group (WAGAR), Sydney, Australia
| | - R. Dineth Fonseka
- Faculty of Medicine, University of New South Wales, Sydney, Australia;,Wearables and Gait Analysis Research Group (WAGAR), Sydney, Australia
| | - Pragadesh Natarajan
- Faculty of Medicine, University of New South Wales, Sydney, Australia;,Wearables and Gait Analysis Research Group (WAGAR), Sydney, Australia
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Xu Z, Shen B, Tang Y, Wu J, Wang J. Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:349-361. [PMID: 36939759 PMCID: PMC9590510 DOI: 10.1007/s43657-022-00051-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 03/12/2022] [Accepted: 03/28/2022] [Indexed: 11/27/2022]
Abstract
Despite recent advances in technology, clinical phenotyping of Parkinson's disease (PD) has remained relatively limited as current assessments are mainly based on empirical observation and subjective categorical judgment at the clinic. A lack of comprehensive, objective, and quantifiable clinical phenotyping data has hindered our capacity to diagnose, assess patients' conditions, discover pathogenesis, identify preclinical stages and clinical subtypes, and evaluate new therapies. Therefore, deep clinical phenotyping of PD patients is a necessary step towards understanding PD pathology and improving clinical care. In this review, we present a growing community consensus and perspective on how to clinically phenotype this disease, that is, to phenotype the entire course of disease progression by integrating capacity, performance, and perception approaches with state-of-the-art technology. We also explore the most studied aspects of PD deep clinical phenotypes, namely, bradykinesia, tremor, dyskinesia and motor fluctuation, gait impairment, speech impairment, and non-motor phenotypes.
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Affiliation(s)
- Zhiheng Xu
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Bo Shen
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Yilin Tang
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Jianjun Wu
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Jian Wang
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
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Lee J, Yeom I, Chung ML, Kim Y, Yoo S, Kim E. Use of Mobile Apps for Self-care in People With Parkinson Disease: Systematic Review. JMIR Mhealth Uhealth 2022; 10:e33944. [PMID: 35060910 PMCID: PMC8817212 DOI: 10.2196/33944] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/19/2021] [Accepted: 11/30/2021] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Self-care is essential for people with Parkinson disease (PD) to minimize their disability and adapt to alterations in physical abilities due to this progressive neurodegenerative disorder. With rapid developments in mobile technology, many health-related mobile apps for PD have been developed and used. However, research on mobile app-based self-care in PD is insufficient. OBJECTIVE This study aimed to explore the features and characteristics of mobile apps for self-care in people with PD. METHODS This study was performed sequentially according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, Cochrane Library, Web of Science, and PsycINFO were searched in consultation with a librarian on June 8, 2021. We used keywords including "Parkinson disease" and "mobile." RESULTS A total of 17 studies were selected based on the inclusion criteria, including 3 randomized controlled trials and 14 observational studies or quasi-experimental studies. The use of mobile apps for self-care in people with PD focused on symptom monitoring, especially motor symptoms. Motor symptoms were objectively measured mainly through the sensors of smartphones or wearable devices and task performance. Nonmotor symptoms were monitored through task performance or self-reported questionnaires in mobile apps. Most existing studies have focused on clinical symptom assessment in people with PD, and there is a lack of studies focusing on symptom management. CONCLUSIONS Mobile apps for people with PD have been developed and used, but strategies for self-management are insufficient. We recommend the development of mobile apps focused on self-care that can enhance symptom management and health promotion practices. Studies should also evaluate the effects of mobile apps on symptom improvement and quality of life in people with PD. TRIAL REGISTRATION PROSPERO International Prospective Register of Systematic Reviews CRD42021267374; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021267374.
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Affiliation(s)
- JuHee Lee
- Mo-Im Kim Nursing Research Institute, Yonsei Evidence Based Nursing Centre of Korea: A JBI Affiliated Group, College of Nursing, Yonsei University, Seoul, Republic of Korea.,Brain Korea 21 FOUR Project, College of Nursing, Yonsei University, Seoul, Republic of Korea
| | - Insun Yeom
- Brain Korea 21 FOUR Project, College of Nursing, Yonsei University, Seoul, Republic of Korea
| | - Misook L Chung
- College of Nursing, University of Kentucky, Lexington, KY, United States.,College of Nursing, Yonsei University, Seoul, Republic of Korea
| | - Yielin Kim
- College of Nursing, Yonsei University, Seoul, Republic of Korea
| | - Subin Yoo
- Brain Korea 21 FOUR Project, College of Nursing, Yonsei University, Seoul, Republic of Korea
| | - Eunyoung Kim
- Brain Korea 21 FOUR Project, College of Nursing, Yonsei University, Seoul, Republic of Korea
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Martín-Ávila G, Vieira-Campos A, Labrador-Marcos S, Zheng X, Burgos AM, Thuissard I, Andreu-Vázquez C, Ordieres-Meré J, Aladro Y. Patients' self-assessment of essential tremor severity by a validated scale: A useful tool in telemedicine? Parkinsonism Relat Disord 2022; 96:22-28. [DOI: 10.1016/j.parkreldis.2022.01.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 01/16/2022] [Accepted: 01/20/2022] [Indexed: 11/25/2022]
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Vescio B, Quattrone A, Nisticò R, Crasà M, Quattrone A. Wearable Devices for Assessment of Tremor. Front Neurol 2021; 12:680011. [PMID: 34177785 PMCID: PMC8226078 DOI: 10.3389/fneur.2021.680011] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 05/05/2021] [Indexed: 12/28/2022] Open
Abstract
Tremor is an impairing symptom associated with several neurological diseases. Some of such diseases are neurodegenerative, and tremor characterization may be of help in differential diagnosis. To date, electromyography (EMG) is the gold standard for the analysis and diagnosis of tremors. In the last decade, however, several studies have been conducted for the validation of different techniques and new, non-invasive, portable, or even wearable devices have been recently proposed as complementary tools to EMG for a better characterization of tremors. Such devices have proven to be useful for monitoring the efficacy of therapies or even aiding in differential diagnosis. The aim of this review is to present systematically such new solutions, trying to highlight their potentialities and limitations, with a hint to future developments.
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Affiliation(s)
| | - Andrea Quattrone
- Department of Medical and Surgical Sciences, Institute of Neurology, Magna Græcia University, Catanzaro, Italy
| | - Rita Nisticò
- Neuroimaging Unit, Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Catanzaro, Italy
| | - Marianna Crasà
- Department of Medical and Surgical Sciences, Neuroscience Research Center, Magna Græcia University, Catanzaro, Italy
| | - Aldo Quattrone
- Neuroimaging Unit, Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Catanzaro, Italy
- Department of Medical and Surgical Sciences, Neuroscience Research Center, Magna Græcia University, Catanzaro, Italy
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