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Ozeloglu IG, Akman Aydin E. Combining features on vertical ground reaction force signal analysis for multiclass diagnosing neurodegenerative diseases. Int J Med Inform 2024; 191:105542. [PMID: 39096593 DOI: 10.1016/j.ijmedinf.2024.105542] [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: 02/26/2024] [Revised: 06/29/2024] [Accepted: 07/05/2024] [Indexed: 08/05/2024]
Abstract
Neurodegenerative diseases (NDDs), which are caused by the degeneration of neurons and their functions, affect a significant part of the world's population. Although gait disorders are one of the critical and common markers to determine the presence of NDDs, diagnosing which NDD the patients have among a group of NDDs using gait data is still a significant challenge to be addressed. In this study, we addressed the multi-class classification of NDDs and aim to diagnose Parkinson's disease (PD), Amyotrophic lateral sclerosis disease (AD), and Huntington's disease (HD) from a group containing NDDs and healthy control subjects. We also examined the impact of disease-specific identified features derived from VGRF signals. Detrended Fluctuation Analysis (DFA), Dynamic Time Warping (DTW) and Autocorrelation (AC) were used for feature extraction on Vertical Ground Reaction Force (VGRF) signals. To compare the performance of the features, we employed Support Vector Machines, K-Nearest Neighbors, and Neural Networks as classifiers. In three-class problem addressing the classification of AD, PD and HD 93.3% accuracy rate was achieved, while in the four classes case, in which NDDs and HC groups were considered together, 93.5% accuracy rate was yielded. Considering the disease-specific impact of features, it is revealed that while DFA based features diagnose patients with AD with the highest accuracy, DTW has been shown to be more successful in diagnosing PD. AC based features provided the highest accuracy in diagnosing HD. Although gait disorder is common for NDDs, each disease may have its own distinctive gait rhythms; therefore, it is important to identify disease-specific patterns and parameters for the diagnosis of each disease. To increase the diagnostic accuracy, it is necessary to use a combination of features, which were effective for each disease diagnosis. Determining a limited number of disease-specific features would provide NDD diagnostic systems suitable to be deployed in edge-computing environments.
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Affiliation(s)
- Ismihan Gul Ozeloglu
- Gazi University, Graduate School of Natural and Applied Sciences, Electrical and Electronics Engineering, Ankara, Turkey.
| | - Eda Akman Aydin
- Gazi University, Faculty of Technology, Electrical and Electronics Engineering, Ankara, Turkey.
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2
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Göttgens I, Darweesh SKL, Bloem BR, Oertelt-Prigione S. A multidimensional gender analysis of health technology self-efficacy among people with Parkinson's disease. J Neurol 2024:10.1007/s00415-024-12635-3. [PMID: 39168866 DOI: 10.1007/s00415-024-12635-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Revised: 08/07/2024] [Accepted: 08/10/2024] [Indexed: 08/23/2024]
Abstract
BACKGROUND Digital health technologies (DHT) enable self-tracking of bio-behavioral states and pharmacotherapy outcomes in various diseases. However, the role of gender, encompassing social roles, expectations, and relations, is often overlooked in their adoption and use. This study addresses this issue for persons with Parkinson's disease (PD), where DHT hold promise for remote evaluations. METHODS We conducted a cross-sectional survey study in the Netherlands, assessing the impact of gender identity, roles, and relations on health technology self-efficacy (HTSE) and attitude (HTA). An intersectional gender analysis was applied to explore how gender intersects with education, employment, disease duration, and severity in influencing HTSE and HTA. RESULTS Among 313 participants (40% women), no significant correlation was found between gender identity or relations and HTSE or HTA. However, individuals with an androgynous (non-binary) gender role orientation demonstrated better HTSE and HTA. The exploratory intersectional analysis suggested that sociodemographic and clinical factors might affect the influence of gender role orientations on HTSE and HTA, indicating complex and nuanced interactions. CONCLUSION This study highlights the importance of investigating gender as a multidimensional variable in PD research on health technology adoption and use. Considering gender as a behavioral construct, such as through gender roles and norms, shows more significant associations with HTSE and HTA, although effect sized were generally small. The impact of gender dimensions on these outcomes can be compounded by intersecting social and disease-specific factors. Future studies should consider multiple gender dimensions and intersecting factors to fully understand their combined effects on technology uptake and use among people with PD.
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Affiliation(s)
- Irene Göttgens
- Research Institute for Medical Innovation, Department of Primary and Community Care, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Sirwan K L Darweesh
- Donders Institute for Brain, Cognition and Behavior, Center of Expertise for Parkinson & Movement Disorders, Department of Neurology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Bastiaan R Bloem
- Donders Institute for Brain, Cognition and Behavior, Center of Expertise for Parkinson & Movement Disorders, Department of Neurology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Sabine Oertelt-Prigione
- Research Institute for Medical Innovation, Department of Primary and Community Care, Radboud University Medical Center, Nijmegen, The Netherlands.
- AG 10 Sex- and Gender-Sensitive Medicine, Medical Faculty OWL, University of Bielefeld, Bielefeld, Germany.
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Bhidayasiri R, Chaisongkram A, Anan C, Phuenpathom W. User-centred design, validation and clinical testing of an anti-choking mug for people with Parkinson's disease. Sci Rep 2024; 14:14165. [PMID: 38898235 PMCID: PMC11187143 DOI: 10.1038/s41598-024-65071-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Accepted: 06/17/2024] [Indexed: 06/21/2024] Open
Abstract
Oropharyngeal dysphagia, or difficulty initiating swallowing, is a frequent problem in people with Parkinson's disease (PD) and can lead to aspiration pneumonia. The efficacy of pharmacological options is limited. Postural strategies, such as a chin-down manoeuvre when drinking, have had some degree of success but may be difficult for people who have other limitations such as dementia or neck rigidity, to reproduce consistently. Using a user-centred design approach and a multidisciplinary team, we developed and tested an anti-choking mug for people with PD that helps angle the head in the optimum position for drinking. The design reflected anthropometric and ergonomic aspects of user needs with features including regulation of water flow rate and sip volume, an inner slope, a thickened handle and a wide base, which promoted a chin-down posture when used. Prototype testing using digital technology to compare neck flexion angles (the primary outcome), plus clinical outcomes assessed using standard tools (Swallowing Clinical Assessment Score in Parkinson's Disease (SCAS-PD) and Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Parts II and III), found significant improvements in a range of parameters related to efficient swallowing and safe drinking when using the anti-choking mug versus a sham mug.
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Affiliation(s)
- Roongroj Bhidayasiri
- Chulalongkorn Centre of Excellence for Parkinson's Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, 1873 Rama 4 Road, Bangkok, 10330, Thailand.
- The Academy of Science, The Royal Society of Thailand, Bangkok, 10300, Thailand.
| | - Araya Chaisongkram
- Chulalongkorn Centre of Excellence for Parkinson's Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, 1873 Rama 4 Road, Bangkok, 10330, Thailand
| | - Chanawat Anan
- Chulalongkorn Centre of Excellence for Parkinson's Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, 1873 Rama 4 Road, Bangkok, 10330, Thailand
| | - Warongporn Phuenpathom
- Chulalongkorn Centre of Excellence for Parkinson's Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, 1873 Rama 4 Road, Bangkok, 10330, Thailand
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4
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Bhidayasiri R. Old problems, new solutions: harnessing technology and innovation in Parkinson's disease-evidence and experiences from Thailand. J Neural Transm (Vienna) 2024; 131:721-738. [PMID: 38189972 DOI: 10.1007/s00702-023-02727-1] [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: 11/06/2023] [Accepted: 12/09/2023] [Indexed: 01/09/2024]
Abstract
The prevalence of Parkinson's disease (PD) is increasing rapidly worldwide, but there are notable inequalities in its distribution and in the availability of healthcare resources across different world regions. Low- and middle-income countries (LMICs), including Thailand, bear the highest burden of PD so there is an urgent need to develop effective solutions that can overcome the many regional challenges associated with delivering high-quality, and equitable care to a diverse population with limited resources. This article describes the evolution of healthcare delivery for PD in Thailand, as a case example of a LMIC. The discussions reflect the author's presentation at the Yoshikuni Mizuno Lectureship Award given during the 8th Asian and Oceanian Parkinson's Disease and Movement Disorders Congress in March 2023 for which he was the 2023 recipient. The specific challenges faced in Thailand are reviewed along with new solutions that have been implemented to improve the knowledge and skills of healthcare professionals nationally, the delivery of care, and the outcomes for PD patients. Technology and innovation have played an important role in this process with many new tools and devices being implemented in clinical practice. Without any realistic prospect of a curative therapy in the near future that could halt the current PD pandemic, it will be necessary to focus on preventative lifestyle strategies that can help reduce the risk of developing PD such as good nutrition (EAT), exercise (MOVE), good sleep hygiene (SLEEP), and minimizing environmental risks (PROTECT), which should be initiated and continued (REPEAT) as early as possible.
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Affiliation(s)
- Roongroj Bhidayasiri
- Chulalongkorn Centre of Excellence for Parkinson's Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, 1873 Rama 4 Road, Bangkok, 10330, Thailand.
- The Academy of Science, The Royal Society of Thailand, Bangkok, 10330, Thailand.
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5
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Chen M, Sun Z, Xin T, Chen Y, Su F. An Interpretable Deep Learning Optimized Wearable Daily Detection System for Parkinson's Disease. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3937-3946. [PMID: 37695969 DOI: 10.1109/tnsre.2023.3314100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
Walking detection in the daily life of patients with Parkinson's disease (PD) is of great significance for tracking the progress of the disease. This study aims to implement an accurate, objective, and passive detection algorithm optimized based on an interpretable deep learning architecture for the daily walking of patients with PD and to explore the most representative spatiotemporal motor features. Five inertial measurement units attached to the wrist, ankle, and waist are used to collect motion data from 100 subjects during a 10-meter walking test. The raw data of each sensor are subjected to the continuous wavelet transform to train the classification model of the constructed 6-channel convolutional neural network (CNN). The results show that the sensor located at the waist has the best classification performance with an accuracy of 98.01%±0.85% and the area under the receiver operating characteristic curve (AUC) of 0.9981±0.0017 under ten-fold cross-validation. The gradient-weighted class activation mapping shows that the feature points with greater contribution to PD were concentrated in the lower frequency band (0.5~3Hz) compared with healthy controls. The visual maps of the 3D CNN show that only three out of the six time series have a greater contribution, which is used as a basis to further optimize the model input, greatly reducing the raw data processing costs (50%) while ensuring its performance (AUC=0.9929±0.0019). To the best of our knowledge, this is the first study to consider the visual interpretation-based optimization of an intelligent classification model in the intelligent diagnosis of PD.
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Liu WM, Yeh CL, Chen PW, Lin CW, Liu AB. Keystroke Biometrics as a Tool for the Early Diagnosis and Clinical Assessment of Parkinson's Disease. Diagnostics (Basel) 2023; 13:3061. [PMID: 37835803 PMCID: PMC10572112 DOI: 10.3390/diagnostics13193061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/22/2023] [Accepted: 09/23/2023] [Indexed: 10/15/2023] Open
Abstract
(1) Background: Parkinson's disease (PD) is the second most common neurodegenerative disease. Early diagnosis and reliable clinical assessments are essential for appropriate therapy and improving patients' quality of life. Keystroke biometrics, which capture unique typing behavior, have shown potential for early PD diagnosis. This study aimed to evaluate keystroke biometric parameters from two datasets to identify indicators that can effectively distinguish de novo PD patients from healthy controls. (2) Methods: Data from natural typing tasks in Physionet were analyzed to estimate keystroke biometric parameters. The parameters investigated included alternating-finger tapping (afTap) and standard deviations of interkey latencies (ILSD) and release latencies (RLSD). Sensitivity rates were calculated to assess the discriminatory ability of these parameters. (3) Results: Significant differences were observed in three parameters, namely afTap, ILSD, and RLSD, between de novo PD patients and healthy controls. The sensitivity rates were high, with values of 83%, 88%, and 96% for afTap, ILSD, and RLSD, respectively. Correlation analysis revealed a significantly negative correlation between typing speed and number of words typed with the standard motor assessment for PD, UPDRS-III, in patients with early PD. (4) Conclusions: Simple algorithms utilizing keystroke biometric parameters can serve as effective screening tests in distinguishing de novo PD patients from healthy controls. Moreover, typing speed and number of words typed were identified as reliable tools for assessing clinical statuses in PD patients. These findings underscore the potential of keystroke biometrics for early PD diagnosis and clinical severity assessment.
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Affiliation(s)
- Wei-Min Liu
- Department of Computer Science and Information Engineering, Advanced Institute of Manufacturing with High-Tech Innovations, National Chung Cheng University, Chiayi 621301, Taiwan; (W.-M.L.); (C.-L.Y.)
| | - Che-Lun Yeh
- Department of Computer Science and Information Engineering, Advanced Institute of Manufacturing with High-Tech Innovations, National Chung Cheng University, Chiayi 621301, Taiwan; (W.-M.L.); (C.-L.Y.)
| | - Po-Wei Chen
- Department of Physical Medicine and Rehabilitation, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970473, Taiwan;
| | - Che-Wei Lin
- Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan 701401, Taiwan;
| | - An-Bang Liu
- Department of Medicine, School of Medicine, Tzu Chi University, Hualien 970374, Taiwan
- Department of Neurology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, Hualien 970473, Taiwan
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Alberts JL, Shuaib U, Fernandez H, Walter BL, Schindler D, Miller Koop M, Rosenfeldt AB. The Parkinson's disease waiting room of the future: measurements, not magazines. Front Neurol 2023; 14:1212113. [PMID: 37670776 PMCID: PMC10475536 DOI: 10.3389/fneur.2023.1212113] [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: 04/25/2023] [Accepted: 08/08/2023] [Indexed: 09/07/2023] Open
Abstract
Utilizing technology to precisely quantify Parkinson's disease motor symptoms has evolved over the past 50 years from single point in time assessments using traditional biomechanical approaches to continuous monitoring of performance with wearables. Despite advances in the precision, usability, availability and affordability of technology, the "gold standard" for assessing Parkinson's motor symptoms continues to be a subjective clinical assessment as none of these technologies have been fully integrated into routine clinical care of Parkinson's disease patients. To facilitate the integration of technology into routine clinical care, the Develop with Clinical Intent (DCI) model was created. The DCI model takes a unique approach to the development and integration of technology into clinical practice by focusing on the clinical problem to be solved by technology rather than focusing on the technology and then contemplating how it could be integrated into clinical care. The DCI model was successfully used to develop the Parkinson's disease Waiting Room of the Future (WROTF) within the Center for Neurological Restoration at the Cleveland Clinic. Within the WROTF, Parkinson's disease patients complete the self-directed PD-Optimize application on an iPad. The PD-Optimize platform contains cognitive and motor assessments to quantify PD symptoms that are difficult and time-consuming to evaluate clinically. PD-Optimize is completed by the patient prior to their medical appointment and the results are immediately integrated into the electronic health record for discussion with the movement disorder neurologist. Insights from the clinical use of PD-Optimize has spurred the development of a virtual reality technology to evaluate instrumental activities of daily living in PD patients. This new technology will undergo rigorous assessment and validation as dictated by the DCI model. The DCI model is intended to serve as a health enablement roadmap to formalize and accelerate the process of bringing the advantages of cutting-edge technology to those who could benefit the most: the patient.
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Affiliation(s)
- Jay L. Alberts
- Department of Biomedical Engineering, Cleveland Clinic, Lerner Research Institute, Cleveland, OH, United States
- Cleveland Clinic, Neurological Institute, Center for Neurological Restoration, Cleveland, OH, United States
| | - Umar Shuaib
- Cleveland Clinic, Neurological Institute, Center for Neurological Restoration, Cleveland, OH, United States
| | - Hubert Fernandez
- Cleveland Clinic, Neurological Institute, Center for Neurological Restoration, Cleveland, OH, United States
| | - Benjamin L. Walter
- Cleveland Clinic, Neurological Institute, Center for Neurological Restoration, Cleveland, OH, United States
| | - David Schindler
- Department of Biomedical Engineering, Cleveland Clinic, Lerner Research Institute, Cleveland, OH, United States
| | - Mandy Miller Koop
- Department of Biomedical Engineering, Cleveland Clinic, Lerner Research Institute, Cleveland, OH, United States
| | - Anson B. Rosenfeldt
- Department of Biomedical Engineering, Cleveland Clinic, Lerner Research Institute, Cleveland, OH, United States
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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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Triantafyllidis A, Segkouli S, Zygouris S, Michailidou C, Avgerinakis K, Fappa E, Vassiliades S, Bougea A, Papagiannakis N, Katakis I, Mathioudis E, Sorici A, Bajenaru L, Tageo V, Camonita F, Magga-Nteve C, Vrochidis S, Pedullà L, Brichetto G, Tsakanikas P, Votis K, Tzovaras D. Mobile App Interventions for Parkinson's Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3396. [PMID: 37050456 PMCID: PMC10098868 DOI: 10.3390/s23073396] [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: 01/30/2023] [Revised: 03/19/2023] [Accepted: 03/21/2023] [Indexed: 06/19/2023]
Abstract
Central nervous system diseases (CNSDs) lead to significant disability worldwide. Mobile app interventions have recently shown the potential to facilitate monitoring and medical management of patients with CNSDs. In this direction, the characteristics of the mobile apps used in research studies and their level of clinical effectiveness need to be explored in order to advance the multidisciplinary research required in the field of mobile app interventions for CNSDs. A systematic review of mobile app interventions for three major CNSDs, i.e., Parkinson's disease (PD), multiple sclerosis (MS), and stroke, which impose significant burden on people and health care systems around the globe, is presented. A literature search in the bibliographic databases of PubMed and Scopus was performed. Identified studies were assessed in terms of quality, and synthesized according to target disease, mobile app characteristics, study design and outcomes. Overall, 21 studies were included in the review. A total of 3 studies targeted PD (14%), 4 studies targeted MS (19%), and 14 studies targeted stroke (67%). Most studies presented a weak-to-moderate methodological quality. Study samples were small, with 15 studies (71%) including less than 50 participants, and only 4 studies (19%) reporting a study duration of 6 months or more. The majority of the mobile apps focused on exercise and physical rehabilitation. In total, 16 studies (76%) reported positive outcomes related to physical activity and motor function, cognition, quality of life, and education, whereas 5 studies (24%) clearly reported no difference compared to usual care. Mobile app interventions are promising to improve outcomes concerning patient's physical activity, motor ability, cognition, quality of life and education for patients with PD, MS, and Stroke. However, rigorous studies are required to demonstrate robust evidence of their clinical effectiveness.
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Affiliation(s)
- Andreas Triantafyllidis
- Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thermi, Greece
| | - Sofia Segkouli
- Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thermi, Greece
| | - Stelios Zygouris
- Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thermi, Greece
- Department of Psychology, University of Western Macedonia, 53100 Florina, Greece
| | | | | | | | | | - Anastasia Bougea
- Eginition Hospital, 1st Department of Neurology, Medical School, National and Kapodistrian University of Athens, 15772 Athens, Greece
| | - Nikos Papagiannakis
- Eginition Hospital, 1st Department of Neurology, Medical School, National and Kapodistrian University of Athens, 15772 Athens, Greece
| | - Ioannis Katakis
- Department of Computer Science, School of Sciences and Engineering, University of Nicosia, 2417 Nicosia, Cyprus
| | - Evangelos Mathioudis
- Department of Computer Science, School of Sciences and Engineering, University of Nicosia, 2417 Nicosia, Cyprus
| | - Alexandru Sorici
- Department of Computer Science, University Politechnica of Bucharest, 060042 Bucharest, Romania
| | - Lidia Bajenaru
- Department of Computer Science, University Politechnica of Bucharest, 060042 Bucharest, Romania
| | | | | | - Christoniki Magga-Nteve
- Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thermi, Greece
| | - Stefanos Vrochidis
- Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thermi, Greece
| | | | | | - Panagiotis Tsakanikas
- Institute of Communication and Computer Systems, National Technical University of Athens, 10682 Athens, Greece
| | - Konstantinos Votis
- Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thermi, Greece
| | - Dimitrios Tzovaras
- Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thermi, Greece
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Sushkova OS, Morozov AA, Kershner IA, Khokhlova MN, Gabova AV, Karabanov AV, Chigaleichick LA, Illarioshkin SN. Investigation of Phase Shifts Using AUC Diagrams: Application to Differential Diagnosis of Parkinson's Disease and Essential Tremor. SENSORS (BASEL, SWITZERLAND) 2023; 23:1531. [PMID: 36772568 PMCID: PMC9921843 DOI: 10.3390/s23031531] [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: 11/29/2022] [Revised: 01/20/2023] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
This study was motivated by the well-known problem of the differential diagnosis of Parkinson's disease and essential tremor using the phase shift between the tremor signals in the antagonist muscles of patients. Different phase shifts are typical for different diseases; however, it remains unclear how this parameter can be used for clinical diagnosis. Neurophysiological papers have reported different estimations of the accuracy of this parameter, which varies from insufficient to 100%. To address this issue, we developed special types of area under the ROC curve (AUC) diagrams and used them to analyze the phase shift. Different phase estimations, including the Hilbert instantaneous phase and the cross-wavelet spectrum mean phase, were applied. The results of the investigation of the clinical data revealed several regularities with opposite directions in the phase shift of the electromyographic signals in patients with Parkinson's disease and essential tremor. The detected regularities provide insights into the contradictory results reported in the literature. Moreover, the developed AUC diagrams show the potential for the investigation of neurodegenerative diseases related to the hyperkinetic movements of the extremities and the creation of high-accuracy methods of clinical diagnosis.
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Affiliation(s)
- Olga S. Sushkova
- Kotel’nikov Institute of Radio Engineering and Electronics of RAS, Mokhovaya 11-7, 125009 Moscow, Russia
| | - Alexei A. Morozov
- Kotel’nikov Institute of Radio Engineering and Electronics of RAS, Mokhovaya 11-7, 125009 Moscow, Russia
| | - Ivan A. Kershner
- Kotel’nikov Institute of Radio Engineering and Electronics of RAS, Mokhovaya 11-7, 125009 Moscow, Russia
| | - Margarita N. Khokhlova
- Kotel’nikov Institute of Radio Engineering and Electronics of RAS, Mokhovaya 11-7, 125009 Moscow, Russia
| | - Alexandra V. Gabova
- Institute of Higher Nervous Activity and Neurophysiology of RAS, Butlerova 5A, 117485 Moscow, Russia
| | - Alexei V. Karabanov
- FSBI “Research Center of Neurology”, Volokolamskoe Shosse 80, 125367 Moscow, Russia
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Meigal AY, Gerasimova-Meigal LI, Reginya SA, Soloviev AV, Moschevikin AP. Gait Characteristics Analyzed with Smartphone IMU Sensors in Subjects with Parkinsonism under the Conditions of "Dry" Immersion. SENSORS (BASEL, SWITZERLAND) 2022; 22:7915. [PMID: 36298272 PMCID: PMC9611186 DOI: 10.3390/s22207915] [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: 08/19/2022] [Revised: 09/23/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023]
Abstract
Parkinson's disease (PD) is increasingly being studied using science-intensive methods due to economic, medical, rehabilitation and social reasons. Wearable sensors and Internet of Things-enabled technologies look promising for monitoring motor activity and gait in PD patients. In this study, we sought to evaluate gait characteristics by analyzing the accelerometer signal received from a smartphone attached to the head during an extended TUG test, before and after single and repeated sessions of terrestrial microgravity modeled with the condition of "dry" immersion (DI) in five subjects with PD. The accelerometer signal from IMU during walking phases of the TUG test allowed for the recognition and characterization of up to 35 steps. In some patients with PD, unusually long steps have been identified, which could potentially have diagnostic value. It was found that after one DI session, stepping did not change, though in one subject it significantly improved (cadence, heel strike and step length). After a course of DI sessions, some characteristics of the TUG test improved significantly. In conclusion, the use of accelerometer signals received from a smartphone IMU looks promising for the creation of an IoT-enabled system to monitor gait in subjects with PD.
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Affiliation(s)
- Alexander Y. Meigal
- Medical Institute, Petrozavodsk State University, 33, Lenina pr., 185910 Petrozavodsk, Russia
| | | | - Sergey A. Reginya
- Physical-Technical Institute, Petrozavodsk State University, 33, Lenina pr., 185910 Petrozavodsk, Russia
| | - Alexey V. Soloviev
- Physical-Technical Institute, Petrozavodsk State University, 33, Lenina pr., 185910 Petrozavodsk, Russia
| | - Alex P. Moschevikin
- Physical-Technical Institute, Petrozavodsk State University, 33, Lenina pr., 185910 Petrozavodsk, Russia
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