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Islam T, Washington P. Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review. BIOSENSORS 2024; 14:183. [PMID: 38667177 PMCID: PMC11048540 DOI: 10.3390/bios14040183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 03/27/2024] [Accepted: 04/01/2024] [Indexed: 04/28/2024]
Abstract
The rapid development of biosensing technologies together with the advent of deep learning has marked an era in healthcare and biomedical research where widespread devices like smartphones, smartwatches, and health-specific technologies have the potential to facilitate remote and accessible diagnosis, monitoring, and adaptive therapy in a naturalistic environment. This systematic review focuses on the impact of combining multiple biosensing techniques with deep learning algorithms and the application of these models to healthcare. We explore the key areas that researchers and engineers must consider when developing a deep learning model for biosensing: the data modality, the model architecture, and the real-world use case for the model. We also discuss key ongoing challenges and potential future directions for research in this field. We aim to provide useful insights for researchers who seek to use intelligent biosensing to advance precision healthcare.
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Lee J, Suh Y, Kim E, Yoo S, Kim Y. A Mobile App for Comprehensive Symptom Management in People With Parkinson's Disease: A Pilot Usability Study. Comput Inform Nurs 2024; 42:289-297. [PMID: 38261451 DOI: 10.1097/cin.0000000000001089] [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: 01/25/2024]
Abstract
There is an increasing need for highly accessible health management platforms for comprehensive symptoms of Parkinson disease. Mobile apps encompassing nonmotor symptoms have been rarely developed since these symptoms are often subjective and difficult to reflect what individuals actually experience. The study developed an app for comprehensive symptom management and evaluated its usability and feasibility. A single-group repeated measurement experimental design was used. Twenty-two participants used the app for 6 weeks. Monitoring of nonmotor symptoms, games to address motor symptoms, and medication management were incorporated in the app. Quantitative outcomes were self-assessed through an online questionnaire, and one-on-one telephone interviews were conducted to understand the user's point of view. The successful experience of self-monitoring had improved participants' self-efficacy ( Z = -3.634, P < .001) and medication adherence ( Z = -3.371, P = .001). Facilitators included a simple-to-use interface, entertaining content, and medication helps. Barriers included simple forgetfulness and digital literacy, including unfamiliarity with mobile phone manipulation itself. The study suggested insight into the app use related to acceptability of mobile technology. The preliminary effects on self-efficacy and medication adherence will guide future nursing interventions using mobile health. Our approach will contribute to improving the continuum of care for Parkinson disease by promoting self-monitoring of symptoms.
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Affiliation(s)
- JuHee Lee
- Author Affiliations: Mo-Im Kim Nursing Research Institute, Yonsei Evidence Based Nursing Centre of Korea: A Joanna Briggs Institute of Excellence, College of Nursing, Yonsei University (Dr Lee), Seoul; College of Nursing, Health Science & Human Ecology, Dong-Eui University (Dr Suh), Busan; and Graduate School, Brain Korea 21 FOUR Project, College of Nursing, Yonsei University (Mss E. Kim and Yoo); and Division of Nursing, Severance Hospital, Yonsei University Health System (Dr Y. Kim), Seoul, South Korea
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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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Willemse IHJ, Schootemeijer S, van den Bergh R, Dawes H, Nonnekes JH, van de Warrenburg BPC. Smartphone applications for Movement Disorders: Towards collaboration and re-use. Parkinsonism Relat Disord 2024; 120:105988. [PMID: 38184466 DOI: 10.1016/j.parkreldis.2023.105988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/20/2023] [Accepted: 12/31/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND Numerous smartphone and tablet applications (apps) are available to monitor movement disorders, but an overview of their purpose and stage of development is missing. OBJECTIVES To systematically review published literature and classify smartphone and tablet apps with objective measurement capabilities for the diagnosis, monitoring, assessment, or treatment of movement disorders. METHODS We systematically searched for publications covering smartphone or tablet apps to monitor movement disorders until November 22nd, 2023. We reviewed the target population, measured domains, purpose, and technology readiness level (TRL) of the proposed app and checked their availability in common app stores. RESULTS We identified 113 apps. Most apps were developed for Parkinson's disease specifically (n = 82; 73%) or for movement disorders in general (n = 17; 15%). Apps were either designed to momentarily assess symptoms (n = 65; 58%), support treatment (n = 22; 19%), aid in diagnosis (n = 16; 14%), or passively track symptoms (n = 11; 10%). Commonly assessed domains across movement disorders included fine motor skills (n = 34; 30%), gait (n = 36; 32%), and tremor (n = 32; 28%) for the motor domain and cognition (n = 16; 14%) for the non-motor domain. Twenty-six (23%) apps were proof-of-concepts (TRL 1-3), while most apps were tested in a controlled setting (TRL 4-6; n = 63; 56%). Twenty-four apps were tested in their target setting (TRL 7-9) of which 10 were accessible in common app stores or as Android Package. CONCLUSIONS The development of apps strongly gravitates towards Parkinson's disease and a selection of motor symptoms. Collaboration, re-use and further development of existing apps is encouraged to avoid reinventions of the wheel.
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Affiliation(s)
- Ilse H J Willemse
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands.
| | - Sabine Schootemeijer
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Robin van den Bergh
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Helen Dawes
- NIHR Exeter BRC, Medical School, Faculty of Health and Life Sciences, University of Exeter, UK
| | - Jorik H Nonnekes
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Rehabilitation, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands; Department of Rehabilitation, Sint Maartenskliniek, Nijmegen, the Netherlands
| | - Bart P C van de Warrenburg
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
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Minaei-Moghadam S, Manzari ZS, Vaghee S, Mirhosseini S. Effectiveness of a supportive care program via a smartphone application on the quality of life and care burden among family caregivers of patients with major depressive disorder: a randomized controlled trial. BMC Public Health 2024; 24:66. [PMID: 38166907 PMCID: PMC10762964 DOI: 10.1186/s12889-023-17594-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 12/27/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND The majority of patients with major depressive disorder require care that has generally affected caregivers' lives. Providing care could cause negative experiences as a care burden and deteriorate quality of life. However, there is a lack of evidence about caregiver training-based informatics and its impact on the caregiver's life. METHODS This experimental study was carried out in Mashhad, Iran. A total of 60 primary family caregivers of patients with major depressive disorder were included in the study between February and July 2021. The quadruple block randomization method was used to allocate the participants into control and intervention groups. In the intervention group, family caregivers used the application with weekly phone calls for one month. The app contains the most important points of patient care and has the possibility of communicating with the nurse. The Novak and Guest Care Burden Inventory and the short form of the World Health Organization Quality of Life Questionnaire were completed before and after the intervention. Data analysis was performed using chi-squared tests, independent sample t tests, and analysis of covariance. RESULTS At baseline, the mean scores of care burden and quality of life were homogeneous between the two groups. After the intervention, the mean scores of care burden and quality of life were significantly reduced and improved in the intervention group compared with the control group (p < 0.001). CONCLUSIONS Using the application with the ability to communicate with the caregiver, along with educational support, helps to strengthen the relationship between the family caregiver and the nurse. Despite the effectiveness of the present intervention, before including this form of implementation of support in care programs, it is necessary to evaluate its other positive aspects in future studies. TRIAL REGISTRATION Iranian Registry of Clinical Trials (IRCT), IRCT20210202050222N1. Registered on 05/02/2022.
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Affiliation(s)
- Somaye Minaei-Moghadam
- Nursing and Midwifery Care Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zahra Sadat Manzari
- Nursing and Midwifery Care Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Saeed Vaghee
- Nursing and Midwifery Care Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Seyedmohammad Mirhosseini
- Department of Nursing, School of Nursing and Midwifery, Shahroud University of Medical Sciences, Shahroud, Iran
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Moreau C, Rouaud T, Grabli D, Benatru I, Remy P, Marques AR, Drapier S, Mariani LL, Roze E, Devos D, Dupont G, Bereau M, Fabbri M. Overview on wearable sensors for the management of Parkinson's disease. NPJ Parkinsons Dis 2023; 9:153. [PMID: 37919332 PMCID: PMC10622581 DOI: 10.1038/s41531-023-00585-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 10/02/2023] [Indexed: 11/04/2023] Open
Abstract
Parkinson's disease (PD) is affecting about 1.2 million patients in Europe with a prevalence that is expected to have an exponential increment, in the next decades. This epidemiological evolution will be challenged by the low number of neurologists able to deliver expert care for PD. As PD is better recognized, there is an increasing demand from patients for rigorous control of their symptoms and for therapeutic education. In addition, the highly variable nature of symtoms between patients and the fluctuations within the same patient requires innovative tools to help doctors and patients monitor the disease in their usual living environment and adapt treatment in a more relevant way. Nowadays, there are various body-worn sensors (BWS) proposed to monitor parkinsonian clinical features, such as motor fluctuations, dyskinesia, tremor, bradykinesia, freezing of gait (FoG) or gait disturbances. BWS have been used as add-on tool for patients' management or research purpose. Here, we propose a practical anthology, summarizing the characteristics of the most used BWS for PD patients in Europe, focusing on their role as tools to improve treatment management. Consideration regarding the use of technology to monitor non-motor features is also included. BWS obviously offer new opportunities for improving management strategy in PD but their precise scope of use in daily routine care should be clarified.
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Affiliation(s)
- Caroline Moreau
- Department of Neurology, Parkinson's disease expert Center, Lille University, INSERM UMRS_1172, University Hospital Center, Lille, France
- The French Ns-Park Network, Paris, France
| | - Tiphaine Rouaud
- The French Ns-Park Network, Paris, France
- CHU Nantes, Centre Expert Parkinson, Department of Neurology, Nantes, F-44093, France
| | - David Grabli
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - Isabelle Benatru
- The French Ns-Park Network, Paris, France
- Department of Neurology, University Hospital of Poitiers, Poitiers, France
- INSERM, CHU de Poitiers, University of Poitiers, Centre d'Investigation Clinique CIC1402, Poitiers, France
| | - Philippe Remy
- The French Ns-Park Network, Paris, France
- Centre Expert Parkinson, NS-Park/FCRIN Network, CHU Henri Mondor, AP-HP, Equipe NPI, IMRB, INSERM et Faculté de Santé UPE-C, Créteil, FranceService de neurologie, hôpital Henri-Mondor, AP-HP, Créteil, France
| | - Ana-Raquel Marques
- The French Ns-Park Network, Paris, France
- Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand University Hospital, Neurology department, Clermont-Ferrand, France
| | - Sophie Drapier
- The French Ns-Park Network, Paris, France
- Pontchaillou University Hospital, Department of Neurology, CIC INSERM 1414, Rennes, France
| | - Louise-Laure Mariani
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - Emmanuel Roze
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - David Devos
- The French Ns-Park Network, Paris, France
- Parkinson's Disease Centre of Excellence, Department of Medical Pharmacology, Univ. Lille, INSERM; CHU Lille, U1172 - Degenerative & Vascular Cognitive Disorders, LICEND, NS-Park Network, F-59000, Lille, France
| | - Gwendoline Dupont
- The French Ns-Park Network, Paris, France
- Centre hospitalier universitaire François Mitterrand, Département de Neurologie, Université de Bourgogne, Dijon, France
| | - Matthieu Bereau
- The French Ns-Park Network, Paris, France
- Service de neurologie, université de Franche-Comté, CHRU de Besançon, 25030, Besançon, France
| | - Margherita Fabbri
- The French Ns-Park Network, Paris, France.
- Department of Neurosciences, Clinical Investigation Center CIC 1436, Parkinson Toulouse Expert Centre, NS-Park/FCRIN Network and NeuroToul COEN Center, Toulouse University Hospital, INSERM, University of Toulouse 3, Toulouse, France.
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Tsamis KI, Odin P, Antonini A, Reichmann H, Konitsiotis S. A Paradigm Shift in the Management of Patients with Parkinson's Disease. NEURODEGENER DIS 2023; 23:13-19. [PMID: 37913759 PMCID: PMC10659004 DOI: 10.1159/000533798] [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: 04/08/2023] [Accepted: 08/23/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGROUND Technological evolution leads to the constant enhancement of monitoring systems and recording symptoms of diverse disorders. SUMMARY For Parkinson's disease, wearable devices empowered with machine learning analysis are the main modules for objective measurements. Software and hardware improvements have led to the development of reliable systems that can detect symptoms accurately and be implicated in the follow-up and treatment decisions. KEY MESSAGES Among many different devices developed so far, the most promising ones are those that can record symptoms from all extremities and the trunk, in the home environment during the activities of daily living, assess gait impairment accurately, and be suitable for a long-term follow-up of the patients. Such wearable systems pave the way for a paradigm shift in the management of patients with Parkinson's disease.
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Affiliation(s)
- Konstantinos I. Tsamis
- Department of Physiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
- Department of Neurology, University Hospital of Ioannina, University of Ioannina, Ioannina, Greece
| | - Per Odin
- Division of Neurology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Angelo Antonini
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration CESNE, Department of Neuroscience, University of Padova, Padova, Italy
| | - Heinz Reichmann
- Department of Neurology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Spyridon Konitsiotis
- Department of Neurology, University Hospital of Ioannina, University of Ioannina, Ioannina, Greece
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Ravara B, Giuriati W, Maccarone MC, Kern H, Masiero S, Carraro U. Optimized progression of Full-Body In-Bed Gym workout: an educational case report. Eur J Transl Myol 2023. [PMID: 37358234 PMCID: PMC10388607 DOI: 10.4081/ejtm.2023.11525] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 06/16/2023] [Indexed: 06/27/2023] Open
Abstract
People suffering from fatigue syndromes spend less time exercising each day, thus aggravating their motor difficulties. Indeed, muscles and mobility deteriorate with age, while exercising muscles is the only sure countermeasure. It is useful to offer a safe and toll-free rehabilitation training: Full-Body In-Bed Gym, easy to learn and performe at home. We suggest a 10-20 min daily routine of easy and safe physical exercises that may improve the main 200 skeletal muscles used for every-day activities. Many of the exercises can be performed in bed (Full-Body In-Bed Gym), so hospital patients can learn this light workout before leaving the hospital. The routine consists of series of repetitions of 15 bodyweight exercises to be performed one after the other without time breaks in between. Alternating sequences of arm and leg exercises are followed by moving body parts in lying and sitting positions in bed. These are followed by series of tiptoeing off the bed. Progressive improvements can be tested by a series of push-ups on the floor. Starting from 3-5, number of repetitions are increased by adding 3 more every week. To maintain or even shorten total daily time of workout each movement is weekly speeded up. The devoted time every morning (or at least five days a week) to train all the major muscles of the body can remain under 10 minutes. Because there are no breaks during and between sets, the final push-ups become very challenging: at the end of the daily workout heart rate, depth and number of ventilations and frontal perspiration increase for a few minutes. We here provide an example of how to implement the progression of the Full-Body In-Bed Gym presenting an educational Case Report of a trained 80-year old person in stable pharmacological managements. In addition to strengthening the main muscles, including the ventilatory muscles, Although performed in bed, Full-Body In-Bed Gym is a resistance training equivalent to a short jog.. Started in early winter and continued regularly throughout spring and summer, Full-Body In-Bed Gym can help maintain independence of frail people, including those younger persons suffering with the fatigue syndrome related to the viral infection of the recent COVID-19 pandemic.
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Affiliation(s)
- Barbara Ravara
- Department of Biomedical Sciences, University of Padova, Padua, Italy; CIR-Myo-Interdepartmental Research Center of Myology, University of Padova, Padua, Italy; A&C M-C Foundation for Translational Myology, Padua.
| | - Walter Giuriati
- Department of Biomedical Sciences, University of Padova, Padua.
| | - Maria Chiara Maccarone
- Physical Medicine and Rehabilitation School, University of Padova, Padua, Italy; Department of Neuroscience, Section of Rehabilitation, University of Padova, Padua.
| | - Helmut Kern
- Ludwig Boltzmann Institute for Rehabilitation Research, St. Pölten, Austria; Institute of Physical Medicine and Rehabilitation, Prim. Dr. H Kern GmbH, Amstetten.
| | - Stefano Masiero
- CIR-Myo-Interdepartmental Research Center of Myology, University of Padova, Padua, Italy; Department of Neuroscience, Section of Rehabilitation, University of Padova, Padua.
| | - Ugo Carraro
- Department of Biomedical Sciences, University of Padova, Padua, Italy; CIR-Myo-Interdepartmental Research Center of Myology, University of Padova, Padua, Italy; A&C M-C Foundation for Translational Myology, Padua.
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9
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Broeder S, Roussos G, De Vleeschhauwer J, D'Cruz N, de Xivry JJO, Nieuwboer A. A smartphone-based tapping task as a marker of medication response in Parkinson's disease: a proof of concept study. J Neural Transm (Vienna) 2023:10.1007/s00702-023-02659-w. [PMID: 37268772 DOI: 10.1007/s00702-023-02659-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 05/24/2023] [Indexed: 06/04/2023]
Abstract
Tapping tasks have the potential to distinguish between ON-OFF fluctuations in Parkinson's disease (PD) possibly aiding assessment of medication status in e-diaries and research. This proof of concept study aims to assess the feasibility and accuracy of a smartphone-based tapping task (developed as part of the cloudUPDRS-project) to discriminate between ON-OFF used in the home setting without supervision. 32 PD patients performed the task before their first medication intake, followed by two test sessions after 1 and 3 h. Testing was repeated for 7 days. Index finger tapping between two targets was performed as fast as possible with each hand. Self-reported ON-OFF status was also indicated. Reminders were sent for testing and medication intake. We studied task compliance, objective performance (frequency and inter-tap distance), classification accuracy and repeatability of tapping. Average compliance was 97.0% (± 3.3%), but 16 patients (50%) needed remote assistance. Self-reported ON-OFF scores and objective tapping were worse pre versus post medication intake (p < 0.0005). Repeated tests showed good to excellent test-retest reliability in ON (0.707 ≤ ICC ≤ 0.975). Although 7 days learning effects were apparent, ON-OFF differences remained. Discriminative accuracy for ON-OFF was particularly good for right-hand tapping (0.72 ≤ AUC ≤ 0.80). Medication dose was associated with ON-OFF tapping changes. Unsupervised tapping tests performed on a smartphone have the potential to classify ON-OFF fluctuations in the home setting, despite some learning and time effects. Replication of these results are needed in a wider sample of patients.
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Affiliation(s)
- Sanne Broeder
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Tervuursevest 101, 3001, Leuven, Belgium.
| | - George Roussos
- Department of Computer Science and Information Systems, Birkbeck College, University of London, Malet Street, London, WC1E 7HX, UK
| | - Joni De Vleeschhauwer
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Tervuursevest 101, 3001, Leuven, Belgium
| | - Nicholas D'Cruz
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Tervuursevest 101, 3001, Leuven, Belgium
| | - Jean-Jacques Orban de Xivry
- KU Leuven, Department of Kinesiology, Movement Control and Neuroplasticity Research Group, Tervuursevest 101, 3001, Leuven, Belgium
- KU Leuven, KU Leuven Brain Institute, Leuven, Belgium
| | - Alice Nieuwboer
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Tervuursevest 101, 3001, Leuven, Belgium
- KU Leuven, KU Leuven Brain Institute, Leuven, Belgium
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Tsakanikas V, Ntanis A, Rigas G, Androutsos C, Boucharas D, Tachos N, Skaramagkas V, Chatzaki C, Kefalopoulou Z, Tsiknakis M, Fotiadis D. Evaluating Gait Impairment in Parkinson's Disease from Instrumented Insole and IMU Sensor Data. SENSORS (BASEL, SWITZERLAND) 2023; 23:3902. [PMID: 37112243 PMCID: PMC10143543 DOI: 10.3390/s23083902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/06/2023] [Accepted: 04/07/2023] [Indexed: 06/19/2023]
Abstract
Parkinson's disease (PD) is characterized by a variety of motor and non-motor symptoms, some of them pertaining to gait and balance. The use of sensors for the monitoring of patients' mobility and the extraction of gait parameters, has emerged as an objective method for assessing the efficacy of their treatment and the progression of the disease. To that end, two popular solutions are pressure insoles and body-worn IMU-based devices, which have been used for precise, continuous, remote, and passive gait assessment. In this work, insole and IMU-based solutions were evaluated for assessing gait impairment, and were subsequently compared, producing evidence to support the use of instrumentation in everyday clinical practice. The evaluation was conducted using two datasets, generated during a clinical study, in which patients with PD wore, simultaneously, a pair of instrumented insoles and a set of wearable IMU-based devices. The data from the study were used to extract and compare gait features, independently, from the two aforementioned systems. Subsequently, subsets comprised of the extracted features, were used by machine learning algorithms for gait impairment assessment. The results indicated that insole gait kinematic features were highly correlated with those extracted from IMU-based devices. Moreover, both had the capacity to train accurate machine learning models for the detection of PD gait impairment.
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Affiliation(s)
- Vassilis Tsakanikas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece
| | | | - George Rigas
- PD Neurotechnology Ltd., GR 45500 Ioannina, Greece
| | - Christos Androutsos
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece
| | - Dimitrios Boucharas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece
| | - Nikolaos Tachos
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece
- Biomedical Research Institute, Foundation for Research and Technology—Hellas, GR 45500 Ioannina, Greece
| | - Vasileios Skaramagkas
- Institute of Computer Science, Foundation for Research and Technology—Hellas, GR 70013 Heraklion, Greece
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, GR 71004 Heraklion, Greece
| | - Chariklia Chatzaki
- Institute of Computer Science, Foundation for Research and Technology—Hellas, GR 70013 Heraklion, Greece
| | - Zinovia Kefalopoulou
- Department of Neurology, General University Hospital of Patras, GR 26504 Patras, Greece
| | - Manolis Tsiknakis
- Institute of Computer Science, Foundation for Research and Technology—Hellas, GR 70013 Heraklion, Greece
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, GR 71004 Heraklion, Greece
| | - Dimitrios Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece
- Biomedical Research Institute, Foundation for Research and Technology—Hellas, GR 45500 Ioannina, Greece
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Kanellos FS, Tsamis KI, Rigas G, Simos YV, Katsenos AP, Kartsakalis G, Fotiadis DI, Vezyraki P, Peschos D, Konitsiotis S. Clinical Evaluation in Parkinson's Disease: Is the Golden Standard Shiny Enough? SENSORS (BASEL, SWITZERLAND) 2023; 23:3807. [PMID: 37112154 PMCID: PMC10145765 DOI: 10.3390/s23083807] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 04/04/2023] [Accepted: 04/04/2023] [Indexed: 06/19/2023]
Abstract
Parkinson's disease (PD) has become the second most common neurodegenerative condition following Alzheimer's disease (AD), exhibiting high prevalence and incident rates. Current care strategies for PD patients include brief appointments, which are sparsely allocated, at outpatient clinics, where, in the best case scenario, expert neurologists evaluate disease progression using established rating scales and patient-reported questionnaires, which have interpretability issues and are subject to recall bias. In this context, artificial-intelligence-driven telehealth solutions, such as wearable devices, have the potential to improve patient care and support physicians to manage PD more effectively by monitoring patients in their familiar environment in an objective manner. In this study, we evaluate the validity of in-office clinical assessment using the MDS-UPDRS rating scale compared to home monitoring. Elaborating the results for 20 patients with Parkinson's disease, we observed moderate to strong correlations for most symptoms (bradykinesia, rest tremor, gait impairment, and freezing of gait), as well as for fluctuating conditions (dyskinesia and OFF). In addition, we identified for the first time the existence of an index capable of remotely measuring patients' quality of life. In summary, an in-office examination is only partially representative of most PD symptoms and cannot accurately capture daytime fluctuations and patients' quality of life.
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Affiliation(s)
- Foivos S. Kanellos
- Department of Physiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
- PD Neurotechnology Ltd., 45500 Ioannina, Greece
| | - Konstantinos I. Tsamis
- Department of Physiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
- Department of Neurology, University Hospital of Ioannina, 45110 Ioannina, Greece
| | | | - Yannis V. Simos
- Department of Physiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | - Andreas P. Katsenos
- Department of Physiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | - Gerasimos Kartsakalis
- Department of Physiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, 45110 Ioannina, Greece
| | - Patra Vezyraki
- Department of Physiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | - Dimitrios Peschos
- Department of Physiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | - Spyridon Konitsiotis
- Department of Neurology, University Hospital of Ioannina, 45110 Ioannina, Greece
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12
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Oyama G, Burq M, Hatano T, Marks WJ, Kapur R, Fernandez J, Fujikawa K, Furusawa Y, Nakatome K, Rainaldi E, Chen C, Ho KC, Ogawa T, Kamo H, Oji Y, Takeshige-Amano H, Taniguchi D, Nakamura R, Sasaki F, Ueno S, Shiina K, Hattori A, Nishikawa N, Ishiguro M, Saiki S, Hayashi A, Motohashi M, Hattori N. Analytical and clinical validity of wearable, multi-sensor technology for assessment of motor function in patients with Parkinson's disease in Japan. Sci Rep 2023; 13:3600. [PMID: 36918552 PMCID: PMC10015076 DOI: 10.1038/s41598-023-29382-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 02/03/2023] [Indexed: 03/16/2023] Open
Abstract
Continuous, objective monitoring of motor signs and symptoms may help improve tracking of disease progression and treatment response in Parkinson's disease (PD). This study assessed the analytical and clinical validity of multi-sensor smartwatch measurements in hospitalized and home-based settings (96 patients with PD; mean wear time 19 h/day) using a twice-daily virtual motor examination (VME) at times representing medication OFF/ON states. Digital measurement performance was better during inpatient clinical assessments for composite V-scores than single-sensor-derived features for bradykinesia (Spearman |r|= 0.63, reliability = 0.72), tremor (|r|= 0.41, reliability = 0.65), and overall motor features (|r|= 0.70, reliability = 0.67). Composite levodopa effect sizes during hospitalization were 0.51-1.44 for clinical assessments and 0.56-1.37 for VMEs. Reliability of digital measurements during home-based VMEs was 0.62-0.80 for scores derived from weekly averages and 0.24-0.66 for daily measurements. These results show that unsupervised digital measurements of motor features with wrist-worn sensors are sensitive to medication state and are reliable in naturalistic settings.Trial Registration: Japan Pharmaceutical Information Center Clinical Trials Information (JAPIC-CTI): JapicCTI-194825; Registered June 25, 2019.
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Affiliation(s)
- Genko Oyama
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan.
| | - Maximilien Burq
- Verily Life Sciences, 269 East Grand Avenue, South San Francisco, CA, USA
| | - Taku Hatano
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - William J Marks
- Verily Life Sciences, 269 East Grand Avenue, South San Francisco, CA, USA
| | - Ritu Kapur
- Verily Life Sciences, 269 East Grand Avenue, South San Francisco, CA, USA
| | - Jovelle Fernandez
- Takeda Pharmaceutical Company Limited, 2 Chome-1-1 Nihonbashihoncho, Chuo-Ku, Tokyo, 103-0023, Japan
| | - Keita Fujikawa
- Takeda Pharmaceutical Company Limited, 2 Chome-1-1 Nihonbashihoncho, Chuo-Ku, Tokyo, 103-0023, Japan
| | - Yoshihiko Furusawa
- Takeda Pharmaceutical Company Limited, 2 Chome-1-1 Nihonbashihoncho, Chuo-Ku, Tokyo, 103-0023, Japan
| | - Keisuke Nakatome
- Takeda Pharmaceutical Company Limited, 2 Chome-1-1 Nihonbashihoncho, Chuo-Ku, Tokyo, 103-0023, Japan
| | - Erin Rainaldi
- Verily Life Sciences, 269 East Grand Avenue, South San Francisco, CA, USA
| | - Chen Chen
- Verily Life Sciences, 269 East Grand Avenue, South San Francisco, CA, USA
| | - King Chung Ho
- Verily Life Sciences, 269 East Grand Avenue, South San Francisco, CA, USA
| | - Takashi Ogawa
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Hikaru Kamo
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Yutaka Oji
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Haruka Takeshige-Amano
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Daisuke Taniguchi
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Ryota Nakamura
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Fuyuko Sasaki
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Shinichi Ueno
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Kenta Shiina
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Anri Hattori
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Noriko Nishikawa
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Mayu Ishiguro
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Shinji Saiki
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Ayako Hayashi
- Takeda Pharmaceutical Company Limited, 2 Chome-1-1 Nihonbashihoncho, Chuo-Ku, Tokyo, 103-0023, Japan
| | - Masatoshi Motohashi
- Takeda Pharmaceutical Company Limited, 2 Chome-1-1 Nihonbashihoncho, Chuo-Ku, Tokyo, 103-0023, Japan
| | - Nobutaka Hattori
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
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13
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The effect of mobile application-based rehabilitation in patients with Parkinson's disease: A systematic review and meta-analysis. Clin Neurol Neurosurg 2023; 225:107579. [PMID: 36603336 DOI: 10.1016/j.clineuro.2022.107579] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 12/04/2022] [Accepted: 12/26/2022] [Indexed: 12/29/2022]
Abstract
OBJECTIVE Mobile app-based telerehabilitation is practical and cost-effective in neurological rehabilitation. The present systematic review aimed to investigate the effectiveness of mobile application-based rehabilitation in patients with Parkinson's Disease. METHODS Literature was searched via databases of "Web of Science (WoS), PubMed, Cochrane, Scopus and ScienceDirect". Physiotherapy Evidence Database (PEDro) and Revised Cochrane risk-of-bias tool for randomized trials (RoB2) were used to evaluate the quality analysis and risk of bias evaluation. Both narrative and quantitative synthesis were carried out. RESULTS A total of 2175 articles were screened (WoS=41, PubMed=42, Cochrane=84, Scopus=114, ScienceDirect=1894). A total of 5 studies were included in the systematic review following the screening and eligibility procedures. Two studies were enrolled in meta-analysis regarding the data homogeneity. PEDro scores of the trials ranged from 4 to 7 (median:6), indicating good quality. All studies were in the "some concerns" category. The mobile application-based intervention yielded better results on quality of life and patient adherence in two studies. Application-based rehabilitation was not superior to standard treatment on MiniBESTest (ES:0.15, 95 % CI: -0.33 to 0.26) and UPDRS III (ES:0.86, 95 % CI: -0.94 to 2.46) scores. CONCLUSION Mobile application-based rehabilitation is not superior to standard treatments in balance and disease severity. However, mobile technologies could be preferred to increase patient adherence and quality of life. The limited study and the low number of cases in the review may reduce the level of evidence for the results.
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14
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Zhang Z, Chen J. The Enterprise's Willingness to Use Remote Monitoring Technology Under the Background of Green Operation and Service-Oriented Manufacturing. J ORGAN END USER COM 2023. [DOI: 10.4018/joeuc.316165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The current development of remote monitoring technology (RMT) has become increasingly mature. The key to implementing this technology lies in the user's willingness to use it. In order to study the influencing factors of using RMT in green operation and service-oriented manufacturing enterprises, based on organizational behavior, this exploration discusses the reasons that affect the introduction of new technologies into enterprises from the perspectives of perceived risk, conformity and technology acceptance. Moreover, a series of data is obtained through the questionnaire and the results are obtained by analyzing the data. Suggestions to improve the use of RMT in enterprises are put forward. The results show that technology itself, external environment and organizational characteristics can all affect the decision-making of enterprises on new technology.
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Affiliation(s)
- Zhe Zhang
- Shandong University of Finance and Economics, China
| | - Jin Chen
- University of International Business and Economics, China
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15
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Bendig J, Spanz A, Leidig J, Frank A, Stahr M, Reichmann H, Loewenbrück KF, Falkenburger BH. Measuring the Usability of eHealth Solutions for Patients With Parkinson Disease: Observational Study. JMIR Form Res 2022; 6:e39954. [DOI: 10.2196/39954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 08/28/2022] [Accepted: 09/03/2022] [Indexed: 11/06/2022] Open
Abstract
Background
Parkinson disease (PD) is a neurodegenerative disorder with a variety of motor and nonmotor symptoms. Many of these symptoms can be monitored by eHealth solutions, including smartphone apps, wearable sensors, and camera systems. The usability of such systems is a key factor in long-term use, but not much is known about the predictors of successful use and preferable methods to assess usability in patients with PD.
Objective
This study tested methods to assess usability and determined prerequisites for successful use in patients with PD.
Methods
We performed comprehensive usability assessments with 18 patients with PD using a mixed methods usability battery containing the System Usability Scale, a rater-based evaluation of device-specific tasks, and qualitative interviews. Each patient performed the usability battery with 2 of 3 randomly assigned devices: a tablet app, wearable sensors, and a camera system. The usability battery was administered at the beginning and at the end of a 4-day testing period. Between usability batteries, the systems were used by the patients during 3 sessions of motor assessments (wearable sensors and camera system) and at the movement disorder ward (tablet app).
Results
In this study, the rater-based evaluation of tasks discriminated the best between the 3 eHealth solutions, whereas subjective modalities such as the System Usability Scale were not able to distinguish between the systems. Successful use was associated with different clinical characteristics for each system: eHealth literacy and cognitive function predicted successful use of the tablet app, and better motor function and lower age correlated with the independent use of the camera system. The successful use of the wearable sensors was independent of clinical characteristics. Unfortunately, patients who were not able to use the devices well provided few improvement suggestions in qualitative interviews.
Conclusions
eHealth solutions should be developed with a specific set of patients in mind and subsequently tested in this cohort. For a complete picture, usability assessments should include a rater-based evaluation of task performance, and there is a need to develop strategies to circumvent the underrepresentation of poorly performing patients in qualitative usability research.
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16
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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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17
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Chandrabhatla AS, Pomeraniec IJ, Ksendzovsky A. Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms. NPJ Digit Med 2022; 5:32. [PMID: 35304579 PMCID: PMC8933519 DOI: 10.1038/s41746-022-00568-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 01/21/2022] [Indexed: 11/09/2022] Open
Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor impairments such as tremor, bradykinesia, dyskinesia, and gait abnormalities. Current protocols assess PD symptoms during clinic visits and can be subjective. Patient diaries can help clinicians evaluate at-home symptoms, but can be incomplete or inaccurate. Therefore, researchers have developed in-home automated methods to monitor PD symptoms to enable data-driven PD diagnosis and management. We queried the US National Library of Medicine PubMed database to analyze the progression of the technologies and computational/machine learning methods used to monitor common motor PD symptoms. A sub-set of roughly 12,000 papers was reviewed that best characterized the machine learning and technology timelines that manifested from reviewing the literature. The technology used to monitor PD motor symptoms has advanced significantly in the past five decades. Early monitoring began with in-lab devices such as needle-based EMG, transitioned to in-lab accelerometers/gyroscopes, then to wearable accelerometers/gyroscopes, and finally to phone and mobile & web application-based in-home monitoring. Significant progress has also been made with respect to the use of machine learning algorithms to classify PD patients. Using data from different devices (e.g., video cameras, phone-based accelerometers), researchers have designed neural network and non-neural network-based machine learning algorithms to categorize PD patients across tremor, gait, bradykinesia, and dyskinesia. The five-decade co-evolution of technology and computational techniques used to monitor PD motor symptoms has driven significant progress that is enabling the shift from in-lab/clinic to in-home monitoring of PD symptoms.
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Affiliation(s)
- Anirudha S Chandrabhatla
- School of Medicine, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA
| | - I Jonathan Pomeraniec
- Surgical Neurology Branch, National Institutes of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA. .,Department of Neurosurgery, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA.
| | - Alexander Ksendzovsky
- Department of Neurosurgery, University of Maryland Medical System, Baltimore, MD, 21201, USA
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18
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Bendig J, Wolf AS, Mark T, Frank A, Mathiebe J, Scheibe M, Müller G, Stahr M, Schmitt J, Reichmann H, Loewenbrück KF, Falkenburger BH. Feasibility of a Multimodal Telemedical Intervention for Patients with Parkinson's Disease-A Pilot Study. J Clin Med 2022; 11:jcm11041074. [PMID: 35207351 PMCID: PMC8875136 DOI: 10.3390/jcm11041074] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/09/2022] [Accepted: 02/16/2022] [Indexed: 01/13/2023] Open
Abstract
Symptoms of Parkinson’s disease (PD) can be controlled well, but treatment often requires expert judgment. Telemedicine and sensor-based assessments can allow physicians to better observe the evolvement of symptoms over time, in particular with motor fluctuations. In addition, they potentially allow less frequent visits to the expert’s office and facilitate care in rural areas. A variety of systems with different strengths and shortcomings has been investigated in recent years. We designed a multimodal telehealth intervention (TelePark) to mitigate the shortcomings of individual systems and assessed the feasibility of our approach in 12 patients with PD over 12 weeks in preparation for a larger randomized controlled trial. TelePark uses video visits, a smartphone app, a camera system, and wearable sensors. Structured training included setting up the equipment in patients’ homes and group-based online training. Usability was assessed by questionnaires and semi-standardized telephone interviews. Overall, 11 out of 12 patients completed the trial (5 female, 6 male). Mean age was 65 years, mean disease duration 7 years, mean MoCA score 27. Adherence was stable throughout the study and 79% for a short questionnaire administered every second day, 62% for medication confirmation, and 33% for an electronic Hauser diary. Quality of life did not change in the course of the study, and a larger cohort will be required to determine the effect on motor symptoms. Interviews with trial participants identified motivations to use such systems and areas for improvements. These insights can be helpful in designing similar trials.
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Affiliation(s)
- Jonas Bendig
- Department of Neurology, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, 01307 Dresden, Germany; (J.B.); (A.-S.W.); (T.M.); (A.F.); (M.S.); (H.R.); (K.F.L.)
| | - Anna-Sophie Wolf
- Department of Neurology, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, 01307 Dresden, Germany; (J.B.); (A.-S.W.); (T.M.); (A.F.); (M.S.); (H.R.); (K.F.L.)
| | - Tony Mark
- Department of Neurology, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, 01307 Dresden, Germany; (J.B.); (A.-S.W.); (T.M.); (A.F.); (M.S.); (H.R.); (K.F.L.)
| | - Anika Frank
- Department of Neurology, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, 01307 Dresden, Germany; (J.B.); (A.-S.W.); (T.M.); (A.F.); (M.S.); (H.R.); (K.F.L.)
- German Center for Neurodegenerative Diseases (DZNE), 01307 Dresden, Germany
| | - Josephine Mathiebe
- Center for Evidence-Based Healthcare, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, 01307 Dresden, Germany; (J.M.); (M.S.); (G.M.); (J.S.)
| | - Madlen Scheibe
- Center for Evidence-Based Healthcare, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, 01307 Dresden, Germany; (J.M.); (M.S.); (G.M.); (J.S.)
| | - Gabriele Müller
- Center for Evidence-Based Healthcare, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, 01307 Dresden, Germany; (J.M.); (M.S.); (G.M.); (J.S.)
| | - Marcus Stahr
- Department of Neurology, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, 01307 Dresden, Germany; (J.B.); (A.-S.W.); (T.M.); (A.F.); (M.S.); (H.R.); (K.F.L.)
- Department of Psychiatry, Sächsisches Krankenhaus Arnsdorf, 01477 Arnsdorf, Germany
| | - Jochen Schmitt
- Center for Evidence-Based Healthcare, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, 01307 Dresden, Germany; (J.M.); (M.S.); (G.M.); (J.S.)
| | - Heinz Reichmann
- Department of Neurology, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, 01307 Dresden, Germany; (J.B.); (A.-S.W.); (T.M.); (A.F.); (M.S.); (H.R.); (K.F.L.)
| | - Kai F. Loewenbrück
- Department of Neurology, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, 01307 Dresden, Germany; (J.B.); (A.-S.W.); (T.M.); (A.F.); (M.S.); (H.R.); (K.F.L.)
| | - Björn H. Falkenburger
- Department of Neurology, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, 01307 Dresden, Germany; (J.B.); (A.-S.W.); (T.M.); (A.F.); (M.S.); (H.R.); (K.F.L.)
- German Center for Neurodegenerative Diseases (DZNE), 01307 Dresden, Germany
- Correspondence:
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19
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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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20
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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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21
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Hadley AJ, Riley DE, Heldman DA. Real-World Evidence for a Smartwatch-Based Parkinson's Motor Assessment App for Patients Undergoing Therapy Changes. Digit Biomark 2021; 5:206-215. [PMID: 34703975 DOI: 10.1159/000518571] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 07/19/2021] [Indexed: 01/18/2023] Open
Abstract
Introduction Parkinson's disease (PD) is poorly quantified by patients outside the clinic, and paper diaries have problems with subjective descriptions and bias. Wearable sensor platforms; however, can accurately quantify symptoms such as tremor, dyskinesia, and bradykinesia. Commercially available smartwatches are equipped with accelerometers and gyroscopes that can measure motion for objective evaluation. We sought to evaluate the clinical utility of a prescription smartwatch-based monitoring system for PD utilizing periodic task-based motor assessment. Methods Sixteen patients with PD used a smartphone- and smartwatch-based monitoring system to objectively assess motor symptoms for 1 week prior to instituting a doctor recommended change in therapy and for 4 weeks after the change. After 5 weeks the participants returned to the clinic to discuss their results with their doctor, who made therapy recommendations based on the reports and his clinical judgment. Symptom scores were synchronized with the medication diary and the temporal effects of therapy on weekly and hourly timescales were calculated. Results Thirteen participants successfully completed the study and averaged 4.9 assessments per day for 3 days per week during the study. The doctor instructed 8 participants to continue their new regimens and 5 to revert to their previous regimens. The smartwatch-based assessments successfully captured intraday fluctuations and short- and long-term responses to therapies, including detecting significant improvements (p < 0.05) in at least one symptom in 7 participants. Conclusions The smartwatch-based app successfully captured temporal trends in symptom scores following application of new therapy on hourly, daily, and weekly timescales. These results suggest that validated smartwatch-based PD monitoring can provide clinically relevant information and may reduce the need for traditional office visits for therapy adjustment.
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22
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Godkin FE, Turner E, Demnati Y, Vert A, Roberts A, Swartz RH, McLaughlin PM, Weber KS, Thai V, Beyer KB, Cornish B, Abrahao A, Black SE, Masellis M, Zinman L, Beaton D, Binns MA, Chau V, Kwan D, Lim A, Munoz DP, Strother SC, Sunderland KM, Tan B, McIlroy WE, Van Ooteghem K. Feasibility of a continuous, multi-sensor remote health monitoring approach in persons living with neurodegenerative disease. J Neurol 2021; 269:2673-2686. [PMID: 34705114 PMCID: PMC8548705 DOI: 10.1007/s00415-021-10831-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 01/02/2023]
Abstract
BACKGROUND Remote health monitoring with wearable sensor technology may positively impact patient self-management and clinical care. In individuals with complex health conditions, multi-sensor wear may yield meaningful information about health-related behaviors. Despite available technology, feasibility of device-wearing in daily life has received little attention in persons with physical or cognitive limitations. This mixed methods study assessed the feasibility of continuous, multi-sensor wear in persons with cerebrovascular (CVD) or neurodegenerative disease (NDD). METHODS Thirty-nine participants with CVD, Alzheimer's disease/amnestic mild cognitive impairment, frontotemporal dementia, Parkinson's disease, or amyotrophic lateral sclerosis (median age 68 (45-83) years, 36% female) wore five devices (bilateral ankles and wrists, chest) continuously for a 7-day period. Adherence to device wearing was quantified by examining volume and pattern of device removal (non-wear). A thematic analysis of semi-structured de-brief interviews with participants and study partners was used to examine user acceptance. RESULTS Adherence to multi-sensor wear, defined as a minimum of three devices worn concurrently, was high (median 98.2% of the study period). Non-wear rates were low across all sensor locations (median 17-22 min/day), with significant differences between some locations (p = 0.006). Multi-sensor non-wear was higher for daytime versus nighttime wear (p < 0.001) and there was a small but significant increase in non-wear over the collection period (p = 0.04). Feedback from de-brief interviews suggested that multi-sensor wear was generally well accepted by both participants and study partners. CONCLUSION A continuous, multi-sensor remote health monitoring approach is feasible in a cohort of persons with CVD or NDD.
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Affiliation(s)
- F Elizabeth Godkin
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Erin Turner
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Youness Demnati
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Adam Vert
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Angela Roberts
- School of Communication Sciences and Disorders, Elborn College, Western University, London, ON, Canada.,Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA
| | - Richard H Swartz
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | | | - Kyle S Weber
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Vanessa Thai
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Kit B Beyer
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Benjamin Cornish
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Agessandro Abrahao
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Sandra E Black
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Mario Masellis
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Lorne Zinman
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Derek Beaton
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Malcolm A Binns
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Vivian Chau
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Donna Kwan
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Andrew Lim
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Douglas P Munoz
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Kelly M Sunderland
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Brian Tan
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - William E McIlroy
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Karen Van Ooteghem
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
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23
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Wijers A, Hochstenbach L, Tissingh G. Telemonitoring via Questionnaires Reduces Outpatient Healthcare Consumption in Parkinson's Disease. Mov Disord Clin Pract 2021; 8:1075-1082. [PMID: 34631943 DOI: 10.1002/mdc3.13280] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 06/10/2021] [Accepted: 06/27/2021] [Indexed: 11/07/2022] Open
Abstract
Background Parkinson's disease (PD) is best managed by neurologists, traditionally including frequent doctor-patient contact. Because of a rise in PD prevalence and associated healthcare costs, this personnel-intensive care may not be future proof. Telemedicine tools for home monitoring have shown to reduce healthcare consumption in several chronic diseases and also seem promising for PD. Objective To explore whether telemonitoring can reduce outpatient healthcare consumption in PD. Methods We conducted a cohort study with 116 outpatients with PD who used the telemedicine tool "myParkinsoncoach." The tool involved periodic monitoring, feedback, knowledge modules, and text message functionality. Retrospective data about PD-related healthcare consumption in the year before and after introduction of the tool were retrieved from the hospital information system. Additional data about tool-related activities performed by nursing staff were logged prospectively for 3 months. Results There was a 29% reduction in the number of outpatient visits (P < 0.001) in the year after introduction of the tool compared with the year before. A 39% reduction was seen in overall PD-related healthcare costs (P = 0.001). Similar results were found for patients ≥70 years old. Nursing staff spent on average 15.5 minutes per patient a month on monitoring the tool and follow-up activities. Conclusions Study results demonstrate a significant reduction in PD-related healthcare consumption using telemonitoring. Notably, these results were also found in elderly patients. Further research is needed to confirm these findings, preferably taking a broader perspective on healthcare consumption and within a larger, multicenter and prospective setup.
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Affiliation(s)
- Anke Wijers
- Department of Neurology Zuyderland Medical Centre Heerlen The Netherlands
| | - Laura Hochstenbach
- Department of Remote Care Zuyd University of Applied Sciences Heerlen The Netherlands
| | - Gerrit Tissingh
- Department of Neurology Zuyderland Medical Centre Heerlen The Netherlands
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24
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Wasmann JW, Pragt L, Eikelboom R, Swanepoel DW. Digital approaches to automated and machine learning assessments of hearing: a scoping review (Preprint). J Med Internet Res 2021; 24:e32581. [PMID: 34919056 PMCID: PMC8851345 DOI: 10.2196/32581] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 12/01/2021] [Accepted: 12/16/2021] [Indexed: 01/24/2023] Open
Abstract
Background Hearing loss affects 1 in 5 people worldwide and is estimated to affect 1 in 4 by 2050. Treatment relies on the accurate diagnosis of hearing loss; however, this first step is out of reach for >80% of those affected. Increasingly automated approaches are being developed for self-administered digital hearing assessments without the direct involvement of professionals. Objective This study aims to provide an overview of digital approaches in automated and machine learning assessments of hearing using pure-tone audiometry and to focus on the aspects related to accuracy, reliability, and time efficiency. This review is an extension of a 2013 systematic review. Methods A search across the electronic databases of PubMed, IEEE, and Web of Science was conducted to identify relevant reports from the peer-reviewed literature. Key information about each report’s scope and details was collected to assess the commonalities among the approaches. Results A total of 56 reports from 2012 to June 2021 were included. From this selection, 27 unique automated approaches were identified. Machine learning approaches require fewer trials than conventional threshold-seeking approaches, and personal digital devices make assessments more affordable and accessible. Validity can be enhanced using digital technologies for quality surveillance, including noise monitoring and detecting inconclusive results. Conclusions In the past 10 years, an increasing number of automated approaches have reported similar accuracy, reliability, and time efficiency as manual hearing assessments. New developments, including machine learning approaches, offer features, versatility, and cost-effectiveness beyond manual audiometry. Used within identified limitations, automated assessments using digital devices can support task-shifting, self-care, telehealth, and clinical care pathways.
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Affiliation(s)
- Jan-Willem Wasmann
- Department of Otorhinolaryngology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Leontien Pragt
- Department of Otorhinolaryngology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Robert Eikelboom
- Ear Science Institute Australia, Subiaco, Australia
- Ear Sciences Centre, Medical School, The University of Western Australia, Perth, Australia
- Department of Speech-Language Pathology and Audiology, University of Pretoria, Pretoria, South Africa
| | - De Wet Swanepoel
- Ear Science Institute Australia, Subiaco, Australia
- Ear Sciences Centre, Medical School, The University of Western Australia, Perth, Australia
- Department of Speech-Language Pathology and Audiology, University of Pretoria, Pretoria, South Africa
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25
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Abstract
PURPOSE OF REVIEW The COVID-pandemic has facilitated the implementation of telemedicine in both clinical practice and research. We highlight recent developments in three promising areas of telemedicine: teleconsultation, telemonitoring, and teletreatment. We illustrate this using Parkinson's disease as a model for other chronic neurological disorders. RECENT FINDINGS Teleconsultations can reliably administer parts of the neurological examination remotely, but are typically not useful for establishing a reliable diagnosis. For follow-ups, teleconsultations can provide enhanced comfort and convenience to patients, and provide opportunities for blended and proactive care models. Barriers include technological challenges, limited clinician confidence, and a suboptimal clinician-patient relationship. Telemonitoring using wearable sensors and smartphone-based apps can support clinical decision-making, but we lack large-scale randomized controlled trials to prove effectiveness on clinical outcomes. Increasingly many trials are now incorporating telemonitoring as an exploratory outcome, but more work remains needed to demonstrate its clinical meaningfulness. Finding a balance between benefits and burdens for individual patients remains vital. Recent work emphasised the promise of various teletreatment solutions, such as remotely adjustable deep brain stimulation parameters, virtual reality enhanced exercise programs, and telephone-based cognitive behavioural therapy. Personal contact remains essential to ascertain adherence to teletreatment. SUMMARY The availability of different telemedicine tools for remote consultation, monitoring, and treatment is increasing. Future research should establish whether telemedicine improves outcomes in routine clinical care, and further underpin its merits both as intervention and outcome in research settings.
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Affiliation(s)
- Robin van den Bergh
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders
| | - Bastiaan R. Bloem
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders
| | - Marjan J. Meinders
- Radboud University Medical Center, Radboud Institute for Health Sciences, Scientific Center for Quality of Healthcare, Nijmegen, The Netherlands
| | - Luc J.W. Evers
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders
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26
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van Wamelen DJ, Sringean J, Trivedi D, Carroll CB, Schrag AE, Odin P, Antonini A, Bloem BR, Bhidayasiri R, Chaudhuri KR. Digital health technology for non-motor symptoms in people with Parkinson's disease: Futile or future? Parkinsonism Relat Disord 2021; 89:186-194. [PMID: 34362670 DOI: 10.1016/j.parkreldis.2021.07.032] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 07/26/2021] [Accepted: 07/28/2021] [Indexed: 10/20/2022]
Abstract
INTRODUCTION There is an ongoing digital revolution in the field of Parkinson's disease (PD) for the objective measurement of motor aspects, to be used in clinical trials and possibly support therapeutic choices. The focus of remote technologies is now also slowly shifting towards the broad but more "hidden" spectrum of non-motor symptoms (NMS). METHODS A narrative review of digital health technologies for measuring NMS in people with PD was conducted. These digital technologies were defined as assessment tools for NMS offered remotely in the form of a wearable, downloadable as a mobile app, or any other objective measurement of NMS in PD that did not require a hospital visit and could be performed remotely. Searches were performed using peer-reviewed literature indexed databases (MEDLINE, Embase, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane CENTRAL Register of Controlled Trials), as well as Google and Google Scholar. RESULTS Eighteen studies deploying digital health technology in PD were identified, for example for the measurement of sleep disorders, cognitive dysfunction and orthostatic hypotension. In addition, we describe promising developments in other conditions that could be translated for use in PD. CONCLUSION Unlike motor symptoms, non-motor features of PD are difficult to measure directly using remote digital technologies. Nonetheless, it is currently possible to reliably measure several NMS and further digital technology developments are underway to offer further capture of often under-reported and under-recognised NMS.
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Affiliation(s)
- Daniel J van Wamelen
- King's College London, Department of Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom; Parkinson's Foundation Centre of Excellence at King's College Hospital, Denmark Hill, London, United Kingdom; Radboud University Medical Centre; Donders Institute for Brain, Cognition and Behaviour; Department of Neurology, Nijmegen, the Netherlands.
| | - Jirada Sringean
- Chulalongkorn Centre of Excellence for Parkinson's Disease & Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Dhaval Trivedi
- King's College London, Department of Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom; Parkinson's Foundation Centre of Excellence at King's College Hospital, Denmark Hill, London, United Kingdom
| | - Camille B Carroll
- Faculty of Health, University of Plymouth, Plymouth, Devon, United Kingdom
| | - Anette E Schrag
- Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom
| | - Per Odin
- Division of Neurology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Angelo Antonini
- Movement Disorders Unit, Department of Neuroscience, University of Padua, Padua, Italy
| | - Bastiaan R Bloem
- Radboud University Medical Centre; Donders Institute for Brain, Cognition and Behaviour; Department of Neurology, Nijmegen, the Netherlands
| | - Roongroj Bhidayasiri
- Chulalongkorn Centre of Excellence for Parkinson's Disease & Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand; The Academy of Science, The Royal Society of Thailand, Bangkok, Thailand
| | - K Ray Chaudhuri
- King's College London, Department of Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom; Parkinson's Foundation Centre of Excellence at King's College Hospital, Denmark Hill, London, United Kingdom
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27
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Feasibility of a Mobile-Based System for Unsupervised Monitoring in Parkinson's Disease. SENSORS 2021; 21:s21154972. [PMID: 34372208 PMCID: PMC8347665 DOI: 10.3390/s21154972] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/09/2021] [Accepted: 07/19/2021] [Indexed: 11/23/2022]
Abstract
Mobile health (mHealth) has emerged as a potential solution to providing valuable ecological information about the severity and burden of Parkinson’s disease (PD) symptoms in real-life conditions. Objective: The objective of our study was to explore the feasibility and usability of an mHealth system for continuous and objective real-life measures of patients’ health and functional mobility, in unsupervised settings. Methods: Patients with a clinical diagnosis of PD, who were able to walk unassisted, and had an Android smartphone were included. Patients were asked to answer a daily survey, to perform three weekly active tests, and to perform a monthly in-person clinical assessment. Feasibility and usability were explored as primary and secondary outcomes. An exploratory analysis was performed to investigate the correlation between data from the mKinetikos app and clinical assessments. Results: Seventeen participants (85%) completed the study. Sixteen participants (94.1%) showed a medium-to-high level of compliance with the mKinetikos system. A 6-point drop in the total score of the Post-Study System Usability Questionnaire was observed. Conclusions: Our results support the feasibility of the mKinetikos system for continuous and objective real-life measures of a patient’s health and functional mobility. The observed correlations of mKinetikos metrics with clinical data seem to suggest that this mHealth solution is a promising tool to support clinical decisions.
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Tsamis KI, Rigas G, Nikolaos K, Fotiadis DI, Konitsiotis S. Accurate Monitoring of Parkinson's Disease Symptoms With a Wearable Device During COVID-19 Pandemic. In Vivo 2021; 35:2327-2330. [PMID: 34182513 DOI: 10.21873/invivo.12507] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 05/03/2021] [Accepted: 05/04/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Accurate assessment of symptoms in Parkinson's disease (PD) is essential for optimal treatment decisions. During the past few years, different monitoring modalities have started to be used in the everyday clinical practice, mainly for the evaluation of motor symptoms. However, monitoring technologies for PD have not yet gained wide acceptance among physicians, patients, and caregivers. The COVID-19 pandemic disrupted the patients' access to healthcare, bringing to the forefront the need for wearable sensors, which provide effective remote symptoms' evaluation and follow-up. CASE REPORT We report two cases with PD, whose symptoms were monitored with a new wearable CE-marked system (PDMonitor®), enabling appropriate treatment modifications. CONCLUSION Objective assessment of the patient's motor symptoms in his daily home environment is essential for an accurate monitoring in PD and enhances treatment decisions.
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Affiliation(s)
| | - George Rigas
- Unit of Medical Technology and Intelligent Information System, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece.,PD Neurotechnology Ltd, Ioannina, Greece
| | | | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information System, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
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Carraro U, Albertin G, Martini A, Giuriati W, Guidolin D, Masiero S, Kern H, Hofer C, Marcante A, Ravara B. To contrast and reverse skeletal muscle weakness by Full-Body In-Bed Gym in chronic COVID-19 pandemic syndrome. Eur J Transl Myol 2021; 31. [PMID: 33709653 PMCID: PMC8056156 DOI: 10.4081/ejtm.2021.9641] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 03/01/2021] [Indexed: 01/30/2023] Open
Abstract
Mobility-impaired persons, either very old or younger but suffering with systemic neuromuscular disorders or chronic organ failures, spend small amounts of time for daily physical activity, contributing to aggravate their poor mobility by resting muscle atrophy. Sooner or later the limitations to their mobility enforce them to bed and to more frequent hospitalizations. We include among these patients at risk those who are negative for the SARS-COV-2 infection, but suffering with COVID-19 pandemic syndrome. Beside managements of psychological symptoms, it is mandatory to offer to the last group physical rehabilitation approaches easy to learn and self-managed at home. Inspired by the proven capability to recover skeletal muscle contractility and strength by home-based volitional exercises and functional electrical stimulation, we suggest also for chronic COVID-19 pandemic syndrome a 10-20 min long daily routine of easy and safe physical exercises that can activate, and recover from weakness, the main 400 skeletal muscles used for every-day mobility activities. Persons can do many of them in bed (Full-Body in-Bed Gym), and hospitalized patients can learn this light training before leaving the hospital. It is, indeed, an extension of well-established cardiovascular-respiratory rehabilitation training performed after heavy surgical interventions. Blood pressure readings, monitored before and after daily routine, demonstrate a transient decrease in peripheral resistance due to increased blood flow of many muscles. Continued regularly, Full-Body in-Bed Gym may help maintaining independence of frail people, including those suffering with the COVID-19 pandemic syndrome.
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Affiliation(s)
- Ugo Carraro
- Department of Biomedical Sciences, University of Padova, Italy; CIR-Myo - Interdepartmental Research Center of Myology, University of Padova, Italy; A-C M-C Foundation for Translational Myology, Padova.
| | - Giovanna Albertin
- CIR-Myo - Interdepartmental Research Center of Myology, University of Padova, Italy; A-C M-C Foundation for Translational Myology, Padova.
| | - Alessandro Martini
- Department of Neuroscience, University of Padova, Italy; Padova University Research Center "I Approve", University of Padov.
| | | | - Diego Guidolin
- Department of Neuroscience, Section of Human Anatomy, University of Padova.
| | - Stefano Masiero
- CIR-Myo - Interdepartmental Research Center of Myology, University of Padova, Italy; Department of Neuroscience, Section of Rehabilitation, University of Padova.
| | - Helmut Kern
- Ludwig Boltzmann Institute for Rehabilitation Research, St. Pölten, Austria; Physiko- und Rheumatherapie, St. Pölten.
| | | | - Andrea Marcante
- UOC Recovery and Functional Rehabilitation, Lonigo Hospital, Azienda ULSS 8 Berica, Lonigo.
| | - Barbara Ravara
- Department of Biomedical Sciences, University of Padova, Italy; CIR-Myo - Interdepartmental Research Center of Myology, University of Padova, Italy; AC M-C Foundation for Translational Myology, Padova, Italy; Department of Neuroscience, Section of Human Anatomy, University of Padova.
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Carraro U, Albertin G, Martini A, Giuriati W, Guidolin D, Masiero S, Kern H, Hofer C, Marcante A, Ravara B. To contrast and reverse skeletal muscle weakness by Full-Body In-Bed Gym in chronic COVID-19 pandemic syndrome. Eur J Transl Myol 2021. [DOI: 10.4081/ejtm.2020.9641] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Mobility-impaired persons, either very old or younger but suffering with systemic neuromuscular disorders or chronic organ failures, spend small amounts of time for daily physical activity, contributing to aggravate their poor mobility by resting muscle atrophy. Sooner or later the limitations to their mobility enforce them to bed and to more frequent hospitalizations. We include among these patients at risk those who are negative for the SARS-COV-2 infection, but suffering with COVID-19 pandemic syndrome. Beside managements of psychological symptoms, it is mandatory to offer to the last group physical rehabilitation approaches easy to learn and self-managed at home. Inspired by the proven capability to recover skeletal muscle contractility and strength by home-based volitional exercises and functional electrical stimulation, we suggest also for chronic COVID-19 pandemic syndrome a 10–20 min long daily routine of easy and safe physical exercises that can activate, and recover from weakness, the main 400 skeletal muscles used for every-day mobility activities. Persons can do many of them in bed (Full-Body in-Bed Gym), and hospitalized patients can learn this light training before leaving the hospital. It is, indeed, an extension of well-established cardiovascular-respiratory rehabilitation training performed after heavy surgical interventions. Blood pressure readings, monitored before and after daily routine, demonstrate a transient decrease in peripheral resistance due to increased blood flow of many muscles. Continued regularly, Full-Body in-Bed Gym may help maintaining independence of frail people, including those suffering with the COVID-19 pandemic syndrome.
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