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Eguchi K, Yaguchi H, Uwatoko H, Iida Y, Hamada S, Honma S, Takei A, Moriwaka F, Yabe I. Feasibility of differentiating gait in Parkinson's disease and spinocerebellar degeneration using a pose estimation algorithm in two-dimensional video. J Neurol Sci 2024; 464:123158. [PMID: 39096835 DOI: 10.1016/j.jns.2024.123158] [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: 05/27/2024] [Revised: 07/18/2024] [Accepted: 07/27/2024] [Indexed: 08/05/2024]
Abstract
BACKGROUND Although pose estimation algorithms have been used to analyze videos of patients with Parkinson's disease (PD) to assess symptoms, their feasibility for differentiating PD from other neurological disorders that cause gait disturbances has not been evaluated yet. We aimed to determine whether it was possible to differentiate between PD and spinocerebellar degeneration (SCD) by analyzing video recordings of patient gait using a pose estimation algorithm. METHODS We videotaped 82 patients with PD and 61 patients with SCD performing the timed up-and-go test. A pose estimation algorithm was used to extract the coordinates of 25 key points of the participants from these videos. A transformer-based deep neural network (DNN) model was trained to predict PD or SCD using the extracted coordinate data. We employed a leave-one-participant-out cross-validation method to evaluate the predictive performance of the trained model using accuracy, sensitivity, and specificity. As there were significant differences in age, weight, and body mass index between the PD and SCD groups, propensity score matching was used to perform the same experiment in a population that did not differ in these clinical characteristics. RESULTS The accuracy, sensitivity, and specificity of the trained model were 0.86, 0.94, and 0.75 for all participants and 0.83, 0.88, and 0.78 for the participants extracted by propensity score matching. CONCLUSION The differentiation of PD and SCD using key point coordinates extracted from gait videos and the DNN model was feasible and could be used as a collaborative tool in clinical practice and telemedicine.
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Affiliation(s)
- Katsuki Eguchi
- Department of Neurology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo 060-8638, Japan; Hokuyukai Neurological Hospital, 4-30, 2jo, 2cho-me, Nijuyonken, Nishi-ku, Sapporo 063-0802, Japan.
| | - Hiroaki Yaguchi
- Department of Neurology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo 060-8638, Japan
| | - Hisashi Uwatoko
- Department of Neurology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo 060-8638, Japan
| | - Yuki Iida
- Hokuyukai Neurological Hospital, 4-30, 2jo, 2cho-me, Nijuyonken, Nishi-ku, Sapporo 063-0802, Japan
| | - Shinsuke Hamada
- Hokuyukai Neurological Hospital, 4-30, 2jo, 2cho-me, Nijuyonken, Nishi-ku, Sapporo 063-0802, Japan
| | - Sanae Honma
- Hokuyukai Neurological Hospital, 4-30, 2jo, 2cho-me, Nijuyonken, Nishi-ku, Sapporo 063-0802, Japan
| | - Asako Takei
- Hokuyukai Neurological Hospital, 4-30, 2jo, 2cho-me, Nijuyonken, Nishi-ku, Sapporo 063-0802, Japan
| | - Fumio Moriwaka
- Hokuyukai Neurological Hospital, 4-30, 2jo, 2cho-me, Nijuyonken, Nishi-ku, Sapporo 063-0802, Japan
| | - Ichiro Yabe
- Department of Neurology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo 060-8638, Japan
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Lal R, Singh A, Watts S, Chopra K. Experimental models of Parkinson's disease: Challenges and Opportunities. Eur J Pharmacol 2024:176819. [PMID: 39029778 DOI: 10.1016/j.ejphar.2024.176819] [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: 09/07/2023] [Revised: 05/29/2024] [Accepted: 07/17/2024] [Indexed: 07/21/2024]
Abstract
Parkinson's disease (PD) is a widespread neurodegenerative disorder occurs due to the degradation of dopaminergic neurons present in the substantia nigra pars compacta (SNpc). Millions of people are affected by this devastating disorder globally, and the frequency of the condition increases with the increase in the elderly population. A significant amount of progress has been made in acquiring more knowledge about the etiology and the pathogenesis of PD over the past decades. Animal models have been regarded to be a vital tool for the exploration of complex molecular mechanisms involved in PD. Various animals used as models for disease monitoring include vertebrates (zebrafish, rats, mice, guinea pigs, rabbits and monkeys) and invertebrate models (Drosophila, Caenorhabditis elegans). The animal models most relevant for study of PD are neurotoxin induction-based models (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), 6-Hydroxydopamine (6-OHDA) and agricultural pesticides (rotenone, paraquat), pharmacological models (reserpine or haloperidol treated rats), genetic models (α-synuclein, Leucine-rich repeat kinase 2 (LRRK2), DJ-1, PINK-1 and Parkin). Several non-mammalian genetic models such as zebrafish, Drosophila and Caenorhabditis elegance have also gained popularity in recent years due to easy genetic manipulation, presence of genes homologous to human PD, and rapid screening of novel therapeutic molecules. In addition, in vitro models (SH-SY5Y, PC12, Lund human mesencephalic (LUHMES) cells, Human induced pluripotent stem cell (iPSC), Neural organoids, organ-on-chip) are also currently in trend providing edge in investigating molecular mechanisms involved in PD as they are derived from PD patients. In this review, we explain the current situation and merits and demerits of the various animal models.
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Affiliation(s)
- Roshan Lal
- Pharmacology Division, University Institute of Pharmaceutical Sciences (UIPS), Panjab University, Chandigarh, 160014 India
| | - Aditi Singh
- TR(i)P for Health Laboratory, Centre for Excellence in Functional Foods, Department of Food and Nutritional Biotechnology, National Agri-Food Biotechnology Institute (NABI), Knowledge City, Sector 81, SAS Nagar, Punjab 140306, India
| | - Shivam Watts
- Pharmacology Division, University Institute of Pharmaceutical Sciences (UIPS), Panjab University, Chandigarh, 160014 India
| | - Kanwaljit Chopra
- Pharmacology Division, University Institute of Pharmaceutical Sciences (UIPS), Panjab University, Chandigarh, 160014 India.
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Roy A, Zenker S, Jain S, Afshari R, Oz Y, Zheng Y, Annabi N. A Highly Stretchable, Conductive, and Transparent Bioadhesive Hydrogel as a Flexible Sensor for Enhanced Real-Time Human Health Monitoring. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2404225. [PMID: 38970527 DOI: 10.1002/adma.202404225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/05/2024] [Indexed: 07/08/2024]
Abstract
Real-time continuous monitoring of non-cognitive markers is crucial for the early detection and management of chronic conditions. Current diagnostic methods are often invasive and not suitable for at-home monitoring. An elastic, adhesive, and biodegradable hydrogel-based wearable sensor with superior accuracy and durability for monitoring real-time human health is developed. Employing a supramolecular engineering strategy, a pseudo-slide-ring hydrogel is synthesized by combining polyacrylamide (pAAm), β-cyclodextrin (β-CD), and poly 2-(acryloyloxy)ethyltrimethylammonium chloride (AETAc) bio ionic liquid (Bio-IL). This novel approach decouples conflicting mechano-chemical effects arising from different molecular building blocks and provides a balance of mechanical toughness (1.1 × 106 Jm-3), flexibility, conductivity (≈0.29 S m-1), and tissue adhesion (≈27 kPa), along with rapid self-healing and remarkable stretchability (≈3000%). Unlike traditional hydrogels, the one-pot synthesis avoids chemical crosslinkers and metallic nanofillers, reducing cytotoxicity. While the pAAm provides mechanical strength, the formation of the pseudo-slide-ring structure ensures high stretchability and flexibility. Combining pAAm with β-CD and pAETAc enhances biocompatibility and biodegradability, as confirmed by in vitro and in vivo studies. The hydrogel also offers transparency, passive-cooling, ultraviolet (UV)-shielding, and 3D printability, enhancing its practicality for everyday use. The engineered sensor demonstratesimproved efficiency, stability, and sensitivity in motion/haptic sensing, advancing real-time human healthcare monitoring.
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Affiliation(s)
- Arpita Roy
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Shea Zenker
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Saumya Jain
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Ronak Afshari
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Yavuz Oz
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Yuting Zheng
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Nasim Annabi
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
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Parisi F, Corniani G, Bonato P, Balkwill D, Acuna P, Go C, Sharma N, Stephen CD. Motor assessment of X-linked dystonia parkinsonism via machine-learning-based analysis of wearable sensor data. Sci Rep 2024; 14:13229. [PMID: 38853162 PMCID: PMC11162996 DOI: 10.1038/s41598-024-63946-4] [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: 11/07/2023] [Accepted: 06/03/2024] [Indexed: 06/11/2024] Open
Abstract
X-linked dystonia parkinsonism (XDP) is a neurogenetic combined movement disorder involving both parkinsonism and dystonia. Complex, overlapping phenotypes result in difficulties in clinical rating scale assessment. We performed wearable sensor-based analyses in XDP participants to quantitatively characterize disease phenomenology as a potential clinical trial endpoint. Wearable sensor data was collected from 10 symptomatic XDP patients and 3 healthy controls during a standardized examination. Disease severity was assessed with the Unified Parkinson's Disease Rating Scale Part 3 (MDS-UPDRS) and Burke-Fahn-Marsden dystonia scale (BFM). We collected sensor data during the performance of specific MDS-UPDRS/BFM upper- and lower-limb motor tasks, and derived data features suitable to estimate clinical scores using machine learning (ML). XDP patients were at varying stages of disease and clinical severity. ML-based algorithms estimated MDS-UPDRS scores (parkinsonism) and dystonia-specific data features with a high degree of accuracy. Gait spatio-temporal parameters had high discriminatory power in differentiating XDP patients with different MDS-UPDRS scores from controls, XDP freezing of gait, and dystonic/non-dystonic gait. These analyses suggest the feasibility of using wearable sensor data for deriving reliable clinical score estimates associated with both parkinsonian and dystonic features in a complex, combined movement disorder and the utility of motion sensors in quantifying clinical examination.
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Affiliation(s)
- Federico Parisi
- Department of Physical Medicine and Rehabilitation, Motion Analysis Laboratory, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA, 300 1st Avenue 02129, USA
| | - Giulia Corniani
- Department of Physical Medicine and Rehabilitation, Motion Analysis Laboratory, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA, 300 1st Avenue 02129, USA
| | - Paolo Bonato
- Department of Physical Medicine and Rehabilitation, Motion Analysis Laboratory, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA, 300 1st Avenue 02129, USA.
| | - David Balkwill
- Jenks Vestibular Physiology Laboratory, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA
| | - Patrick Acuna
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 100 Cambridge Street, Suite 2000, Boston, MA, 02114, USA
| | - Criscely Go
- Department of Behavioral Medicine, Jose Reyes Memorial Medical Center, Manila, Philippines
| | - Nutan Sharma
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 100 Cambridge Street, Suite 2000, Boston, MA, 02114, USA
| | - Christopher D Stephen
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 100 Cambridge Street, Suite 2000, Boston, MA, 02114, USA.
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Shigapova RR, Mukhamedshina YO. Electrophysiology Methods for Assessing of Neurodegenerative and Post-Traumatic Processes as Applied to Translational Research. Life (Basel) 2024; 14:737. [PMID: 38929721 PMCID: PMC11205106 DOI: 10.3390/life14060737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 05/30/2024] [Accepted: 05/31/2024] [Indexed: 06/28/2024] Open
Abstract
Electrophysiological studies have long established themselves as reliable methods for assessing the functional state of the brain and spinal cord, the degree of neurodegeneration, and evaluating the effectiveness of therapy. In addition, they can be used to diagnose, predict functional outcomes, and test the effectiveness of therapeutic and rehabilitation programs not only in clinical settings, but also at the preclinical level. Considering the urgent need to develop potential stimulators of neuroregeneration, it seems relevant to obtain objective data when modeling neurological diseases in animals. Thus, in the context of the application of electrophysiological methods, not only the comparison of the basic characteristics of bioelectrical activity of the brain and spinal cord in humans and animals, but also their changes against the background of neurodegenerative and post-traumatic processes are of particular importance. In light of the above, this review will contribute to a better understanding of the results of electrophysiological assessment in neurodegenerative and post-traumatic processes as well as the possibility of translating these methods from model animals to humans.
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Affiliation(s)
- Rezeda Ramilovna Shigapova
- Institute of Fundamental Medicine and Biology, Kazan (Volga Region) Federal University, Kazan 420008, Russia;
| | - Yana Olegovna Mukhamedshina
- Institute of Fundamental Medicine and Biology, Kazan (Volga Region) Federal University, Kazan 420008, Russia;
- Department of Histology, Cytology and Embryology, Kazan State Medical University, Kazan 420012, Russia
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Bridges B, Taylor J, Weber JT. Evaluation of the Parkinson's Remote Interactive Monitoring System in a Clinical Setting: Usability Study. JMIR Hum Factors 2024; 11:e54145. [PMID: 38787603 PMCID: PMC11161713 DOI: 10.2196/54145] [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: 10/31/2023] [Revised: 03/15/2024] [Accepted: 04/14/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND The fastest-growing neurological disorder is Parkinson disease (PD), a progressive neurodegenerative disease that affects 10 million people worldwide. PD is typically treated with levodopa, an oral pill taken to increase dopamine levels, and other dopaminergic agonists. As the disease advances, the efficacy of the drug diminishes, necessitating adjustments in treatment dosage according to the patient's symptoms and disease progression. Therefore, remote monitoring systems that can provide more detailed and accurate information on a patient's condition regularly are a valuable tool for clinicians and patients to manage their medication. The Parkinson's Remote Interactive Monitoring System (PRIMS), developed by PragmaClin Research Inc, was designed on the premise that it will be an easy-to-use digital system that can accurately capture motor and nonmotor symptoms of PD remotely. OBJECTIVE We performed a usability evaluation in a simulated clinical environment to assess the ease of use of the PRIMS and determine whether the product offers suitable functionality for users in a clinical setting. METHODS Participants were recruited from a user sign-up web-based database owned by PragmaClin Research Inc. A total of 11 participants were included in the study based on the following criteria: (1) being diagnosed with PD and (2) not being diagnosed with dementia or any other comorbidities that would make it difficult to complete the PRIMS assessment safely and independently. Patient users completed a questionnaire that is based on the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale. Interviews and field notes were analyzed for underlying themes and topics. RESULTS In total, 11 people with PD participated in the study (female individuals: n=5, 45%; male individuals: n=6, 55%; age: mean 66.7, SD 7.77 years). Thematic analysis of the observer's notes revealed 6 central usability issues associated with the PRIMS. These were the following: (1) the automated voice prompts are confusing, (2) the small camera is problematic, (3) the motor test exhibits excessive sensitivity to the participant's orientation and position in relation to the cameras, (4) the system poses mobility challenges, (5) navigating the system is difficult, and (6) the motor test exhibits inconsistencies and technical issues. Thematic analysis of qualitative interview responses revealed four central themes associated with participants' perspectives and opinions on the PRIMS, which were (1) admiration of purpose, (2) excessive system sensitivity, (3) video instructions preferred, and (4) written instructions disliked. The average system usability score was calculated to be 69.2 (SD 4.92), which failed to meet the acceptable system usability score of 70. CONCLUSIONS Although multiple areas of improvement were identified, most of the participants showed an affinity for the overarching objective of the PRIMS. This feedback is being used to upgrade the current PRIMS so that it aligns more with patients' needs.
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Affiliation(s)
- Bronwyn Bridges
- School of Pharmacy, Memorial University, St. John's, NL, Canada
| | - Jake Taylor
- School of Exercise Science, Physical & Health Education, University of Victoria, Victoria, BC, Canada
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de Graaf D, de Vries NM, van de Zande T, Schimmel JJP, Shin S, Kowahl N, Barman P, Kapur R, Marks WJ, van 't Hul A, Bloem B. Measuring Physical Functioning Using Wearable Sensors in Parkinson Disease and Chronic Obstructive Pulmonary Disease (the Accuracy of Digital Assessment of Performance Trial Study): Protocol for a Prospective Observational Study. JMIR Res Protoc 2024; 13:e55452. [PMID: 38713508 PMCID: PMC11109858 DOI: 10.2196/55452] [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: 12/13/2023] [Revised: 03/07/2024] [Accepted: 03/11/2024] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND Physical capacity and physical activity are important aspects of physical functioning and quality of life in people with a chronic disease such as Parkinson disease (PD) or chronic obstructive pulmonary disease (COPD). Both physical capacity and physical activity are currently measured in the clinic using standardized questionnaires and tests, such as the 6-minute walk test (6MWT) and the Timed Up and Go test (TUG). However, relying only on in-clinic tests is suboptimal since they offer limited information on how a person functions in daily life and how functioning fluctuates throughout the day. Wearable sensor technology may offer a solution that enables us to better understand true physical functioning in daily life. OBJECTIVE We aim to study whether device-assisted versions of 6MWT and TUG, such that the tests can be performed independently at home using a smartwatch, is a valid and reliable way to measure the performance compared to a supervised, in-clinic test. METHODS This is a decentralized, prospective, observational study including 100 people with PD and 100 with COPD. The inclusion criteria are broad: age ≥18 years, able to walk independently, and no co-occurrence of PD and COPD. Participants are followed for 15 weeks with 4 in-clinic visits, once every 5 weeks. Outcomes include several walking tests, cognitive tests, and disease-specific questionnaires accompanied by data collection using wearable devices (the Verily Study Watch and Modus StepWatch). Additionally, during the last 10 weeks of this study, participants will follow an aerobic exercise training program aiming to increase physical capacity, creating the opportunity to study the responsiveness of the remote 6MWT. RESULTS In total, 89 people with PD and 65 people with COPD were included in this study. Data analysis will start in April 2024. CONCLUSIONS The results of this study will provide information on the measurement properties of the device-assisted 6MWT and TUG in the clinic and at home. When reliable and valid, this can contribute to a better understanding of a person's physical capacity in real life, which makes it possible to personalize treatment options. TRIAL REGISTRATION ClinicalTrials.gov NCT05756075; https://clinicaltrials.gov/study/NCT05756075. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/55452.
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Affiliation(s)
- Debbie de Graaf
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
| | - Nienke M de Vries
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
| | - Tessa van de Zande
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
| | - Janneke J P Schimmel
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
| | - Sooyoon Shin
- Verily Life Sciences, South San Fransisco, CA, United States
| | - Nathan Kowahl
- Verily Life Sciences, South San Fransisco, CA, United States
| | - Poulami Barman
- Verily Life Sciences, South San Fransisco, CA, United States
| | - Ritu Kapur
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
- Verily Life Sciences, South San Fransisco, CA, United States
| | - William J Marks
- Verily Life Sciences, South San Fransisco, CA, United States
| | - Alex van 't Hul
- Radboud University Medical Center, Radboud Institute for Health Sciences, Department of Respiratory Diseases, Nijmegen, Netherlands
| | - Bastiaan Bloem
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
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Wu X, Ma L, Wei P, Shan Y, Chan P, Wang K, Zhao G. Wearable sensor devices can automatically identify the ON-OFF status of patients with Parkinson's disease through an interpretable machine learning model. Front Neurol 2024; 15:1387477. [PMID: 38751881 PMCID: PMC11094303 DOI: 10.3389/fneur.2024.1387477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Accepted: 04/12/2024] [Indexed: 05/18/2024] Open
Abstract
Introduction Accurately and objectively quantifying the clinical features of Parkinson's disease (PD) is crucial for assisting in diagnosis and guiding the formulation of treatment plans. Therefore, based on the data on multi-site motor features, this study aimed to develop an interpretable machine learning (ML) model for classifying the "OFF" and "ON" status of patients with PD, as well as to explore the motor features that are most associated with changes in clinical symptoms. Methods We employed a support vector machine with a recursive feature elimination (SVM-RFE) algorithm to select promising motion features. Subsequently, 12 ML models were constructed based on these features, and we identified the model with the best classification performance. Then, we used the SHapley Additive exPlanations (SHAP) and the Local Interpretable Model agnostic Explanations (LIME) methods to explain the model and rank the importance of those motor features. Results A total of 96 patients were finally included in this study. The naive Bayes (NB) model had the highest classification performance (AUC = 0.956; sensitivity = 0.8947, 95% CI 0.6686-0.9870; accuracy = 0.8421, 95% CI 0.6875-0.9398). Based on the NB model, we analyzed the importance of eight motor features toward the classification results using the SHAP algorithm. The Gait: range of motion (RoM) Shank left (L) (degrees) [Mean] might be the most important motor feature for all classification horizons. Conclusion The symptoms of PD could be objectively quantified. By utilizing suitable motor features to construct ML models, it became possible to intelligently identify whether patients with PD were in the "ON" or "OFF" status. The variations in these motor features were significantly correlated with improvement rates in patients' quality of life. In the future, they might act as objective digital biomarkers to elucidate the changes in symptoms observed in patients with PD and might be used to assist in the diagnosis and treatment of patients with PD.
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Affiliation(s)
- Xiaolong Wu
- Department of Neurosurgery, Xuanwu Hospital of Capital Medical University, Beijing, China
- International Neuroscience Institute (China-INI), Beijing, China
| | - Lin Ma
- Department of Neurorehabilitation, Rehabilitation Medicine of Capital Medical University, China Rehabilitation Research Centre, Beijing, China
| | - Penghu Wei
- Department of Neurosurgery, Xuanwu Hospital of Capital Medical University, Beijing, China
- International Neuroscience Institute (China-INI), Beijing, China
| | - Yongzhi Shan
- Department of Neurosurgery, Xuanwu Hospital of Capital Medical University, Beijing, China
- International Neuroscience Institute (China-INI), Beijing, China
| | - Piu Chan
- Department of Neurology and Neurobiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Kailiang Wang
- Department of Neurosurgery, Xuanwu Hospital of Capital Medical University, Beijing, China
- International Neuroscience Institute (China-INI), Beijing, China
| | - Guoguang Zhao
- Department of Neurosurgery, Xuanwu Hospital of Capital Medical University, Beijing, China
- International Neuroscience Institute (China-INI), Beijing, China
- Beijing Municipal Geriatric Medical Research Center, Beijing, China
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Godoy Junior CA, Miele F, Mäkitie L, Fiorenzato E, Koivu M, Bakker LJ, Groot CUD, Redekop WK, van Deen WK. Attitudes Toward the Adoption of Remote Patient Monitoring and Artificial Intelligence in Parkinson's Disease Management: Perspectives of Patients and Neurologists. THE PATIENT 2024; 17:275-285. [PMID: 38182935 DOI: 10.1007/s40271-023-00669-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/10/2023] [Indexed: 01/07/2024]
Abstract
OBJECTIVE Early detection of Parkinson's Disease (PD) progression remains a challenge. As remote patient monitoring solutions (RMS) and artificial intelligence (AI) technologies emerge as potential aids for PD management, there's a gap in understanding how end users view these technologies. This research explores patient and neurologist perspectives on AI-assisted RMS. METHODS Qualitative interviews and focus-groups were conducted with 27 persons with PD (PwPD) and six neurologists from Finland and Italy. The discussions covered traditional disease progression detection and the prospects of integrating AI and RMS. Sessions were recorded, transcribed, and underwent thematic analysis. RESULTS The study involved five individual interviews (four Italian participants and one Finnish) and six focus-groups (four Finnish and two Italian) with PwPD. Additionally, six neurologists (three from each country) were interviewed. Both cohorts voiced frustration with current monitoring methods due to their limited real-time detection capabilities. However, there was enthusiasm for AI-assisted RMS, contingent upon its value addition, user-friendliness, and preservation of the doctor-patient bond. While some PwPD had privacy and trust concerns, the anticipated advantages in symptom regulation seemed to outweigh these apprehensions. DISCUSSION The study reveals a willingness among PwPD and neurologists to integrate RMS and AI into PD management. Widespread adoption requires these technologies to provide tangible clinical benefits, remain user-friendly, and uphold trust within the physician-patient relationship. CONCLUSION This study offers insights into the potential drivers and barriers for adopting AI-assisted RMS in PD care. Recognizing these factors is pivotal for the successful integration of these digital health tools in PD management.
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Affiliation(s)
- Carlos Antonio Godoy Junior
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands.
| | - Francesco Miele
- Department of Political and Social Sciences, University of Trieste, Trieste, Italy
| | - Laura Mäkitie
- Department of Neurology, Brain Center, Helsinki University Hospital, Helsinki, Finland
- Department of Clinical Neurosciences, University of Helsinki, Helsinki, Finland
| | | | - Maija Koivu
- Department of Neurology, Brain Center, Helsinki University Hospital, Helsinki, Finland
- Department of Clinical Neurosciences, University of Helsinki, Helsinki, Finland
| | - Lytske Jantien Bakker
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands
| | - Carin Uyl-de Groot
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands
| | - William Ken Redekop
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands
| | - Welmoed Kirsten van Deen
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands
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Zampogna A, Borzì L, Rinaldi D, Artusi CA, Imbalzano G, Patera M, Lopiano L, Pontieri F, Olmo G, Suppa A. Unveiling the Unpredictable in Parkinson's Disease: Sensor-Based Monitoring of Dyskinesias and Freezing of Gait in Daily Life. Bioengineering (Basel) 2024; 11:440. [PMID: 38790307 PMCID: PMC11117481 DOI: 10.3390/bioengineering11050440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 04/23/2024] [Accepted: 04/28/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Dyskinesias and freezing of gait are episodic disorders in Parkinson's disease, characterized by a fluctuating and unpredictable nature. This cross-sectional study aims to objectively monitor Parkinsonian patients experiencing dyskinesias and/or freezing of gait during activities of daily living and assess possible changes in spatiotemporal gait parameters. METHODS Seventy-one patients with Parkinson's disease (40 with dyskinesias and 33 with freezing of gait) were continuously monitored at home for a minimum of 5 days using a single wearable sensor. Dedicated machine-learning algorithms were used to categorize patients based on the occurrence of dyskinesias and freezing of gait. Additionally, specific spatiotemporal gait parameters were compared among patients with and without dyskinesias and/or freezing of gait. RESULTS The wearable sensor algorithms accurately classified patients with and without dyskinesias as well as those with and without freezing of gait based on the recorded dyskinesias and freezing of gait episodes. Standard spatiotemporal gait parameters did not differ significantly between patients with and without dyskinesias or freezing of gait. Both the time spent with dyskinesias and the number of freezing of gait episodes positively correlated with the disease severity and medication dosage. CONCLUSIONS A single inertial wearable sensor shows promise in monitoring complex, episodic movement patterns, such as dyskinesias and freezing of gait, during daily activities. This approach may help implement targeted therapeutic and preventive strategies for Parkinson's disease.
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Affiliation(s)
- Alessandro Zampogna
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy; (A.Z.); (M.P.)
- IRCCS Neuromed Institute, 86077 Pozzilli, IS, Italy
| | - Luigi Borzì
- Data Analytics and Technologies for Health Lab (ANTHEA), Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy; (L.B.); (G.O.)
| | - Domiziana Rinaldi
- Department of Neuroscience, Mental Health and Sense Organs (NESMOS), Sapienza University of Rome, 00189 Rome, Italy; (D.R.); (F.P.)
| | - Carlo Alberto Artusi
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, 10126 Torino, Italy; (C.A.A.); (G.I.); (L.L.)
- Neurology 2 Unit, A.O.U, Città della Salute e della Scienza di Torino, 10126 Torino, Italy
| | - Gabriele Imbalzano
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, 10126 Torino, Italy; (C.A.A.); (G.I.); (L.L.)
| | - Martina Patera
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy; (A.Z.); (M.P.)
| | - Leonardo Lopiano
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, 10126 Torino, Italy; (C.A.A.); (G.I.); (L.L.)
- Neurology 2 Unit, A.O.U, Città della Salute e della Scienza di Torino, 10126 Torino, Italy
| | - Francesco Pontieri
- Department of Neuroscience, Mental Health and Sense Organs (NESMOS), Sapienza University of Rome, 00189 Rome, Italy; (D.R.); (F.P.)
| | - Gabriella Olmo
- Data Analytics and Technologies for Health Lab (ANTHEA), Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy; (L.B.); (G.O.)
| | - Antonio Suppa
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy; (A.Z.); (M.P.)
- IRCCS Neuromed Institute, 86077 Pozzilli, IS, Italy
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11
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Bremm RP, Pavelka L, Garcia MM, Mombaerts L, Krüger R, Hertel F. Sensor-Based Quantification of MDS-UPDRS III Subitems in Parkinson's Disease Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2024; 24:2195. [PMID: 38610406 PMCID: PMC11014392 DOI: 10.3390/s24072195] [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: 02/28/2024] [Revised: 03/19/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024]
Abstract
Wearable sensors could be beneficial for the continuous quantification of upper limb motor symptoms in people with Parkinson's disease (PD). This work evaluates the use of two inertial measurement units combined with supervised machine learning models to classify and predict a subset of MDS-UPDRS III subitems in PD. We attached the two compact wearable sensors on the dorsal part of each hand of 33 people with PD and 12 controls. Each participant performed six clinical movement tasks in parallel with an assessment of the MDS-UPDRS III. Random forest (RF) models were trained on the sensor data and motor scores. An overall accuracy of 94% was achieved in classifying the movement tasks. When employed for classifying the motor scores, the averaged area under the receiver operating characteristic values ranged from 68% to 92%. Motor scores were additionally predicted using an RF regression model. In a comparative analysis, trained support vector machine models outperformed the RF models for specific tasks. Furthermore, our results surpass the literature in certain cases. The methods developed in this work serve as a base for future studies, where home-based assessments of pharmacological effects on motor function could complement regular clinical assessments.
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Affiliation(s)
- Rene Peter Bremm
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, 1210 Luxembourg, Luxembourg (F.H.)
| | - Lukas Pavelka
- Parkinson’s Research Clinic, Centre Hospitalier de Luxembourg, 1210 Luxembourg, Luxembourg; (L.P.); (R.K.)
- Translational Neuroscience, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health, 1445 Strassen, Luxembourg
| | - Maria Moscardo Garcia
- Systems Control, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
| | - Laurent Mombaerts
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, 1210 Luxembourg, Luxembourg (F.H.)
| | - Rejko Krüger
- Parkinson’s Research Clinic, Centre Hospitalier de Luxembourg, 1210 Luxembourg, Luxembourg; (L.P.); (R.K.)
- Translational Neuroscience, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health, 1445 Strassen, Luxembourg
| | - Frank Hertel
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, 1210 Luxembourg, Luxembourg (F.H.)
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12
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Czech MD, Badley D, Yang L, Shen J, Crouthamel M, Kangarloo T, Dorsey ER, Adams JL, Cosman JD. Improved measurement of disease progression in people living with early Parkinson's disease using digital health technologies. COMMUNICATIONS MEDICINE 2024; 4:49. [PMID: 38491176 PMCID: PMC10942994 DOI: 10.1038/s43856-024-00481-3] [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: 08/10/2023] [Accepted: 03/08/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Digital health technologies show promise for improving the measurement of Parkinson's disease in clinical research and trials. However, it is not clear whether digital measures demonstrate enhanced sensitivity to disease progression compared to traditional measurement approaches. METHODS To this end, we develop a wearable sensor-based digital algorithm for deriving features of upper and lower-body bradykinesia and evaluate the sensitivity of digital measures to 1-year longitudinal progression using data from the WATCH-PD study, a multicenter, observational digital assessment study in participants with early, untreated Parkinson's disease. In total, 82 early, untreated Parkinson's disease participants and 50 age-matched controls were recruited and took part in a variety of motor tasks over the course of a 12-month period while wearing body-worn inertial sensors. We establish clinical validity of sensor-based digital measures by investigating convergent validity with appropriate clinical constructs, known groups validity by distinguishing patients from healthy volunteers, and test-retest reliability by comparing measurements between visits. RESULTS We demonstrate clinical validity of the digital measures, and importantly, superior sensitivity of digital measures for distinguishing 1-year longitudinal change in early-stage PD relative to corresponding clinical constructs. CONCLUSIONS Our results demonstrate the potential of digital health technologies to enhance sensitivity to disease progression relative to existing measurement standards and may constitute the basis for use as drug development tools in clinical research.
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Affiliation(s)
| | | | | | | | | | | | - E Ray Dorsey
- University of Rochester Medical Center, Rochester, NY, USA
| | - Jamie L Adams
- University of Rochester Medical Center, Rochester, NY, USA
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13
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Smid A, Dominguez-Vega ZT, van Laar T, Oterdoom DLM, Absalom AR, van Egmond ME, Drost G, van Dijk JMC. Objective clinical registration of tremor, bradykinesia, and rigidity during awake stereotactic neurosurgery: a scoping review. Neurosurg Rev 2024; 47:81. [PMID: 38355824 PMCID: PMC10866747 DOI: 10.1007/s10143-024-02312-4] [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: 12/06/2023] [Revised: 01/19/2024] [Accepted: 01/28/2024] [Indexed: 02/16/2024]
Abstract
Tremor, bradykinesia, and rigidity are incapacitating motor symptoms that can be suppressed with stereotactic neurosurgical treatment like deep brain stimulation (DBS) and ablative surgery (e.g., thalamotomy, pallidotomy). Traditionally, clinicians rely on clinical rating scales for intraoperative evaluation of these motor symptoms during awake stereotactic neurosurgery. However, these clinical scales have a relatively high inter-rater variability and rely on experienced raters. Therefore, objective registration (e.g., using movement sensors) is a reasonable extension for intraoperative assessment of tremor, bradykinesia, and rigidity. The main goal of this scoping review is to provide an overview of electronic motor measurements during awake stereotactic neurosurgery. The protocol was based on the PRISMA extension for scoping reviews. After a systematic database search (PubMed, Embase, and Web of Science), articles were screened for relevance. Hundred-and-three articles were subject to detailed screening. Key clinical and technical information was extracted. The inclusion criteria encompassed use of electronic motor measurements during stereotactic neurosurgery performed under local anesthesia. Twenty-three articles were included. These studies had various objectives, including correlating sensor-based outcome measures to clinical scores, identifying optimal DBS electrode positions, and translating clinical assessments to objective assessments. The studies were highly heterogeneous in device choice, sensor location, measurement protocol, design, outcome measures, and data analysis. This review shows that intraoperative quantification of motor symptoms is still limited by variable signal analysis techniques and lacking standardized measurement protocols. However, electronic motor measurements can complement visual evaluations and provide objective confirmation of correct placement of the DBS electrode and/or lesioning. On the long term, this might benefit patient outcomes and provide reliable outcome measures in scientific research.
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Affiliation(s)
- Annemarie Smid
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands.
| | - Zeus T Dominguez-Vega
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - Teus van Laar
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - D L Marinus Oterdoom
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - Anthony R Absalom
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - Martje E van Egmond
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - Gea Drost
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - J Marc C van Dijk
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
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14
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Kehnemouyi YM, Coleman TP, Tass PA. Emerging wearable technologies for multisystem monitoring and treatment of Parkinson's disease: a narrative review. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1354211. [PMID: 38414636 PMCID: PMC10896901 DOI: 10.3389/fnetp.2024.1354211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 01/12/2024] [Indexed: 02/29/2024]
Abstract
Parkinson's disease (PD) is a chronic movement disorder characterized by a variety of motor and nonmotor comorbidities, including cognitive impairment, gastrointestinal (GI) dysfunction, and autonomic/sleep disturbances. Symptoms typically fluctuate with different settings and environmental factors and thus need to be consistently monitored. Current methods, however, rely on infrequent rating scales performed in clinic. The advent of wearable technologies presents a new avenue to track objective measures of PD comorbidities longitudinally and more frequently. This narrative review discusses and proposes emerging wearable technologies that can monitor manifestations of motor, cognitive, GI, and autonomic/sleep comorbidities throughout the daily lives of PD individuals. This can provide more wholistic insight into real-time physiological versus pathological function with the potential to better assess treatments during clinical trials and allow physicians to optimize treatment regimens. Additionally, this narrative review briefly examines novel applications of wearables as therapy for PD patients.
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Affiliation(s)
- Yasmine M. Kehnemouyi
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
- Department of Bioengineering, Stanford University School of Engineering, Stanford, CA, United States
| | - Todd P. Coleman
- Department of Bioengineering, Stanford University School of Engineering, Stanford, CA, United States
| | - Peter A. Tass
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
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15
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Yang YY, Ho MY, Tai CH, Wu RM, Kuo MC, Tseng YJ. FastEval Parkinsonism: an instant deep learning-assisted video-based online system for Parkinsonian motor symptom evaluation. NPJ Digit Med 2024; 7:31. [PMID: 38332372 PMCID: PMC10853559 DOI: 10.1038/s41746-024-01022-x] [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: 09/27/2023] [Accepted: 01/18/2024] [Indexed: 02/10/2024] Open
Abstract
The Motor Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is designed to assess bradykinesia, the cardinal symptoms of Parkinson's disease (PD). However, it cannot capture the all-day variability of bradykinesia outside the clinical environment. Here, we introduce FastEval Parkinsonism ( https://fastevalp.cmdm.tw/ ), a deep learning-driven video-based system, providing users to capture keypoints, estimate the severity, and summarize in a report. Leveraging 840 finger-tapping videos from 186 individuals (103 patients with Parkinson's disease (PD), 24 participants with atypical parkinsonism (APD), 12 elderly with mild parkinsonism signs (MPS), and 47 healthy controls (HCs)), we employ a dilated convolution neural network with two data augmentation techniques. Our model achieves acceptable accuracies (AAC) of 88.0% and 81.5%. The frequency-intensity (FI) value of thumb-index finger distance was indicated as a pivotal hand parameter to quantify the performance. Our model also shows the usability for multi-angle videos, tested in an external database enrolling over 300 PD patients.
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Affiliation(s)
- Yu-Yuan Yang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei, 10617, Taiwan, ROC
| | - Ming-Yang Ho
- Department of Computer Science and Information Engineering, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei, 10617, Taiwan, ROC
| | - Chung-Hwei Tai
- Department of Neurology, National Taiwan University Hospital, No. 1 Changde St., Zhongzheng Dist., Taipei City, 100229, Taiwan, ROC
| | - Ruey-Meei Wu
- Department of Medicine, National Taiwan University Cancer Center, No. 57, Lane 155, Sec. 3, Keelung Rd., Da'an Dist., Taipei City, 106, Taiwan, ROC
| | - Ming-Che Kuo
- Department of Neurology, National Taiwan University Hospital, No. 1 Changde St., Zhongzheng Dist., Taipei City, 100229, Taiwan, ROC.
- Department of Medicine, National Taiwan University Cancer Center, No. 57, Lane 155, Sec. 3, Keelung Rd., Da'an Dist., Taipei City, 106, Taiwan, ROC.
| | - Yufeng Jane Tseng
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei, 10617, Taiwan, ROC.
- Department of Computer Science and Information Engineering, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei, 10617, Taiwan, ROC.
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16
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O'Keeffe AB, Merla A, Ashkan K. Deep brain stimulation of the subthalamic nucleus in Parkinson disease 2013-2023: where are we a further 10 years on? Br J Neurosurg 2024:1-9. [PMID: 38323603 DOI: 10.1080/02688697.2024.2311128] [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: 08/14/2023] [Accepted: 01/23/2024] [Indexed: 02/08/2024]
Abstract
Deep brain stimulation has been in clinical use for 30 years and during that time it has changed markedly from a small-scale treatment employed by only a few highly specialized centers into a widespread keystone approach to the management of disorders such as Parkinson's disease. In the intervening decades, many of the broad principles of deep brain stimulation have remained unchanged, that of electrode insertion into stereotactically targeted brain nuclei, however the underlying technology and understanding around the approach have progressed markedly. Some of the most significant advances have taken place over the last decade with the advent of artificial intelligence, directional electrodes, stimulation/recording implantable pulse generators and the potential for remote programming among many other innovations. New therapeutic targets are being assessed for their potential benefits and a surge in the number of deep brain stimulation implantations has given birth to a flourishing scientific literature surrounding the pathophysiology of brain disorders such as Parkinson's disease. Here we outline the developments of the last decade and look to the future of deep brain stimulation to attempt to discern some of the most promising lines of inquiry in this fast-paced and rapidly evolving field.
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Affiliation(s)
| | - Anca Merla
- King's College Hospital Department of Neurosurgery, Kings College Hospital, Denmark
| | - Keyoumars Ashkan
- King's College Hospital Department of Neurosurgery, Kings College Hospital, Denmark
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17
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Gonçalves HR, Branquinho A, Pinto J, Rodrigues AM, Santos CP. Digital biomarkers of mobility and quality of life in Parkinson's disease based on a wearable motion analysis LAB. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107967. [PMID: 38070392 DOI: 10.1016/j.cmpb.2023.107967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 11/13/2023] [Accepted: 12/01/2023] [Indexed: 01/26/2024]
Abstract
BACKGROUND AND OBJECTIVE Functional mobility, an indicator of the quality of life (QoL), requires fast and flexible changes during motion, which are limited in Parkinson's disease (PD). Recent body-worn sensors have emerged in the last decades as potential solutions to produce digital biomarkers able to quantify mobility outside routine consultations and during real-life scenarios for multiple days at a time. The proposed research aims to study the ability of a wearable motion analysis lab, developed by our team, to produce digital biomarkers of mobility and QoL levels in patients with PD. METHODS A cross-sectional study was followed, including 40 patients stratified into three subgroups according to a clinic motor examination and a QoL questionnaire. RESULTS The achieved outcomes demonstrate the ability of the proposed high-tech solution to measure prototypical gait impairments and discriminate motor condition (AUC=0,890) and patients' QoL levels (AUC=0,950). Also, from the measured multiple gait-associated parameters, we identified the variables with the most potential to be applied as digital biomarkers of mobility (67 % of the metrics) and QoL (72 % of the metrics) in PD. CONCLUSIONS Overall, we confirmed our hypothesis of using our body-worn sensor-based solution for passive or active monitoring of mobility and QoL in PD to produce objective, feasible, and continuous digital biomarkers.
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Affiliation(s)
- Helena R Gonçalves
- Center for MicroElectroMechanical Systems, University of Minho, Guimarães, Portugal; LABBELS - Associate Laboratory, Braga/Guimarães, Portugal.
| | - André Branquinho
- Center for MicroElectroMechanical Systems, University of Minho, Guimarães, Portugal; LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Joana Pinto
- Neurology Service, Hospital of Braga, Portugal
| | | | - Cristina P Santos
- Center for MicroElectroMechanical Systems, University of Minho, Guimarães, Portugal; LABBELS - Associate Laboratory, Braga/Guimarães, Portugal.
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18
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Sigcha L, Polvorinos-Fernández C, Costa N, Costa S, Arezes P, Gago M, Lee C, López JM, de Arcas G, Pavón I. Monipar: movement data collection tool to monitor motor symptoms in Parkinson's disease using smartwatches and smartphones. Front Neurol 2023; 14:1326640. [PMID: 38148984 PMCID: PMC10750794 DOI: 10.3389/fneur.2023.1326640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 11/21/2023] [Indexed: 12/28/2023] Open
Abstract
Introduction Parkinson's disease (PD) is a neurodegenerative disorder commonly characterized by motor impairments. The development of mobile health (m-health) technologies, such as wearable and smart devices, presents an opportunity for the implementation of clinical tools that can support tasks such as early diagnosis and objective quantification of symptoms. Objective This study evaluates a framework to monitor motor symptoms of PD patients based on the performance of standardized exercises such as those performed during clinic evaluation. To implement this framework, an m-health tool named Monipar was developed that uses off-the-shelf smart devices. Methods An experimental protocol was conducted with the participation of 21 early-stage PD patients and 7 healthy controls who used Monipar installed in off-the-shelf smartwatches and smartphones. Movement data collected using the built-in acceleration sensors were used to extract relevant digital indicators (features). These indicators were then compared with clinical evaluations performed using the MDS-UPDRS scale. Results The results showed moderate to strong (significant) correlations between the clinical evaluations (MDS-UPDRS scale) and features extracted from the movement data used to assess resting tremor (i.e., the standard deviation of the time series: r = 0.772, p < 0.001) and data from the pronation and supination movements (i.e., power in the band of 1-4 Hz: r = -0.662, p < 0.001). Conclusion These results suggest that the proposed framework could be used as a complementary tool for the evaluation of motor symptoms in early-stage PD patients, providing a feasible and cost-effective solution for remote and ambulatory monitoring of specific motor symptoms such as resting tremor or bradykinesia.
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Affiliation(s)
- Luis Sigcha
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Madrid, Spain
- ALGORITMI Research Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - Carlos Polvorinos-Fernández
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Madrid, Spain
| | - Nélson Costa
- ALGORITMI Research Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - Susana Costa
- ALGORITMI Research Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - Pedro Arezes
- ALGORITMI Research Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - Miguel Gago
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
| | - Chaiwoo Lee
- AgeLab, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Juan Manuel López
- Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación (ETSIT), Universidad Politécnica de Madrid, Madrid, Spain
| | - Guillermo de Arcas
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Madrid, Spain
| | - Ignacio Pavón
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Madrid, Spain
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19
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Borovečki F, Perković R, Kovacs N, LeWitt PA, Bar MR, Katzenschlager R. Are Parkinson's disease patients referred too late for device-aided therapies and how can better informed and earlier referrals be encouraged? J Neural Transm (Vienna) 2023; 130:1405-1409. [PMID: 37526767 DOI: 10.1007/s00702-023-02680-z] [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: 06/19/2023] [Accepted: 07/25/2023] [Indexed: 08/02/2023]
Abstract
In the advanced Parkinson's disease, motor and non-motor symptoms become more severe and more difficult to treat. Oral therapy may become insufficient in controlling a patient´s motor complications, which results in a substantial deterioration of the patient's quality of life, ability to work and self-reliance. This is when device-aided treatments should be considered and offered, if suitable for a given patient. They include subcutaneous and intestinal infusion therapies, deep brain stimulation and, more recently, MRI-guided focussed ultrasound. Device-aided treatments should be offered in accordance with guidelines and treatment standardization. Also there is a need to ensure availability of treatment and education of patients and physicians.
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Affiliation(s)
- Fran Borovečki
- Department of Neurology, University Hospital Center Zagreb, Zagreb, Croatia
| | - Romana Perković
- Department of Neurology, University Hospital Center Zagreb, Zagreb, Croatia.
| | - Norbert Kovacs
- Department of Neurology, University of Pecs, Pecs, Hungary
| | - Peter A LeWitt
- Department of Neurology, Wayne State University, Detroit, MI, USA
| | - Monika Rudzinska Bar
- Department of Neurology, School of Medicine in Katowice, Medical University of Silesia, Katowice, Poland
| | - Regina Katzenschlager
- Department of Neurology and Karl Landsteiner Institute for Neuroimmunological and Neurodegenerative Disorders, Danube Hospital, Vienna, Austria
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20
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Eguchi K, Takigawa I, Shirai S, Takahashi-Iwata I, Matsushima M, Kano T, Yaguchi H, Yabe I. Gait video-based prediction of unified Parkinson's disease rating scale score: a retrospective study. BMC Neurol 2023; 23:358. [PMID: 37798685 PMCID: PMC10552271 DOI: 10.1186/s12883-023-03385-2] [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: 03/15/2023] [Accepted: 09/11/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND The diagnosis of Parkinson's disease (PD) and evaluation of its symptoms require in-person clinical examination. Remote evaluation of PD symptoms is desirable, especially during a pandemic such as the coronavirus disease 2019 pandemic. One potential method to remotely evaluate PD motor impairments is video-based analysis. In this study, we aimed to assess the feasibility of predicting the Unified Parkinson's Disease Rating Scale (UPDRS) score from gait videos using a convolutional neural network (CNN) model. METHODS We retrospectively obtained 737 consecutive gait videos of 74 patients with PD and their corresponding neurologist-rated UPDRS scores. We utilized a CNN model for predicting the total UPDRS part III score and four subscores of axial symptoms (items 27, 28, 29, and 30), bradykinesia (items 23, 24, 25, 26, and 31), rigidity (item 22) and tremor (items 20 and 21). We trained the model on 80% of the gait videos and used 10% of the videos as a validation dataset. We evaluated the predictive performance of the trained model by comparing the model-predicted score with the neurologist-rated score for the remaining 10% of videos (test dataset). We calculated the coefficient of determination (R2) between those scores to evaluate the model's goodness of fit. RESULTS In the test dataset, the R2 values between the model-predicted and neurologist-rated values for the total UPDRS part III score and subscores of axial symptoms, bradykinesia, rigidity, and tremor were 0.59, 0.77, 0.56, 0.46, and 0.0, respectively. The performance was relatively low for videos from patients with severe symptoms. CONCLUSIONS Despite the low predictive performance of the model for the total UPDRS part III score, it demonstrated relatively high performance in predicting subscores of axial symptoms. The model approximately predicted the total UPDRS part III scores of patients with moderate symptoms, but the performance was low for patients with severe symptoms owing to limited data. A larger dataset is needed to improve the model's performance in clinical settings.
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Affiliation(s)
- Katsuki Eguchi
- Department of Neurology, Faculty of Medicine, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, 060-8638, Hokkaido, Japan.
| | - Ichigaku Takigawa
- RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103- 0027, Japan
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, Sapporo, 001-0021, Hokkaido, Japan
| | - Shinichi Shirai
- Department of Neurology, Faculty of Medicine, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, 060-8638, Hokkaido, Japan
| | - Ikuko Takahashi-Iwata
- Department of Neurology, Faculty of Medicine, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, 060-8638, Hokkaido, Japan
| | - Masaaki Matsushima
- Department of Neurology, Faculty of Medicine, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, 060-8638, Hokkaido, Japan
| | - Takahiro Kano
- Department of Neurology, Faculty of Medicine, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, 060-8638, Hokkaido, Japan
| | - Hiroaki Yaguchi
- Department of Neurology, Faculty of Medicine, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, 060-8638, Hokkaido, Japan
| | - Ichiro Yabe
- Department of Neurology, Faculty of Medicine, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, 060-8638, Hokkaido, Japan
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21
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Barbero JA, Unadkat P, Choi YY, Eidelberg D. Functional Brain Networks to Evaluate Treatment Responses in Parkinson's Disease. Neurotherapeutics 2023; 20:1653-1668. [PMID: 37684533 PMCID: PMC10684458 DOI: 10.1007/s13311-023-01433-w] [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] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
Network analysis of functional brain scans acquired with [18F]-fluorodeoxyglucose positron emission tomography (FDG PET, to map cerebral glucose metabolism), or resting-state functional magnetic resonance imaging (rs-fMRI, to map blood oxygen level-dependent brain activity) has increasingly been used to identify and validate reproducible circuit abnormalities associated with neurodegenerative disorders such as Parkinson's disease (PD). In addition to serving as imaging markers of the underlying disease process, these networks can be used singly or in combination as an adjunct to clinical diagnosis and as a screening tool for therapeutics trials. Disease networks can also be used to measure rates of progression in natural history studies and to assess treatment responses in individual subjects. Recent imaging studies in PD subjects scanned before and after treatment have revealed therapeutic effects beyond the modulation of established disease networks. Rather, other mechanisms of action may be at play, such as the induction of novel functional brain networks directly by treatment. To date, specific treatment-induced networks have been described in association with novel interventions for PD such as subthalamic adeno-associated virus glutamic acid decarboxylase (AAV2-GAD) gene therapy, as well as sham surgery or oral placebo under blinded conditions. Indeed, changes in the expression of these networks with treatment have been found to correlate consistently with clinical outcome. In aggregate, these attributes suggest a role for functional brain networks as biomarkers in future clinical trials.
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Affiliation(s)
- János A Barbero
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA
| | - Prashin Unadkat
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA
- Elmezzi Graduate School of Molecular Medicine, Manhasset, NY, 11030, USA
| | - Yoon Young Choi
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA.
- Molecular Medicine and Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA.
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22
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Shang J, Tang L, Guo K, Yang S, Cheng J, Dou J, Yang R, Zhang M, Jiang X. Electronic exoneuron based on liquid metal for the quantitative sensing of the augmented somatosensory system. MICROSYSTEMS & NANOENGINEERING 2023; 9:112. [PMID: 37719416 PMCID: PMC10504372 DOI: 10.1038/s41378-023-00535-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/06/2023] [Accepted: 04/03/2023] [Indexed: 09/19/2023]
Abstract
The increasing demands in augmented somatosensory have promoted quantitative sensing to be an emerging need for athletic training/performance evaluation and physical rehabilitation. Neurons for the somatosensory system in the human body can capture the information of movements in time but only qualitatively. This work presents an electronic Exo-neuron (EEN) that can spread throughout the limbs for realizing augmented somatosensory by recording both muscular activity and joint motion quantitatively without site constraints or drift instability, even in strenuous activities. Simply based on low-cost liquid metal and clinically used adhesive elastomer, the EEN could be easily fabricated in large areas for limbs. It is thin (~120 μm), soft, stretchable (>500%), and conformal and further shows wide applications in sports, rehabilitation, health care, and entertainment.
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Affiliation(s)
- Jin Shang
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, No. 1088, Xueyuan Rd., Nanshan District, Shenzhen, Guangdong 518055 P. R. China
- CAS Center for Excellence in Nanoscience, Center of Materials Science and Optoelectronics Engineering, National Center for Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100190 P. R. China
- Sino-Danish Center for Education and Research, Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 100190 P. R. China
| | - Lixue Tang
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, No. 1088, Xueyuan Rd., Nanshan District, Shenzhen, Guangdong 518055 P. R. China
- School of Biomedical Engineering, Capital Medical University, No.10 Xitoutiao, You An Men Wai, Beijing, 100069 China
| | - Kaiqi Guo
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, No. 1088, Xueyuan Rd., Nanshan District, Shenzhen, Guangdong 518055 P. R. China
| | - Shuaijian Yang
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, No. 1088, Xueyuan Rd., Nanshan District, Shenzhen, Guangdong 518055 P. R. China
- School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT UK
| | - Jinhao Cheng
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, No. 1088, Xueyuan Rd., Nanshan District, Shenzhen, Guangdong 518055 P. R. China
| | - Jiabin Dou
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, No. 1088, Xueyuan Rd., Nanshan District, Shenzhen, Guangdong 518055 P. R. China
| | - Rong Yang
- CAS Center for Excellence in Nanoscience, Center of Materials Science and Optoelectronics Engineering, National Center for Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100190 P. R. China
- Sino-Danish Center for Education and Research, Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 100190 P. R. China
| | - Mingming Zhang
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, No. 1088, Xueyuan Rd., Nanshan District, Shenzhen, Guangdong 518055 P. R. China
| | - Xingyu Jiang
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, No. 1088, Xueyuan Rd., Nanshan District, Shenzhen, Guangdong 518055 P. R. China
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23
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Vescio B, De Maria M, Crasà M, Nisticò R, Calomino C, Aracri F, Quattrone A, Quattrone A. Development of a New Wearable Device for the Characterization of Hand Tremor. Bioengineering (Basel) 2023; 10:1025. [PMID: 37760127 PMCID: PMC10525186 DOI: 10.3390/bioengineering10091025] [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: 07/19/2023] [Revised: 08/17/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023] Open
Abstract
Rest tremor (RT) is observed in subjects with Parkinson's disease (PD) and Essential Tremor (ET). Electromyography (EMG) studies have shown that PD subjects exhibit alternating contractions of antagonistic muscles involved in tremors, while the contraction pattern of antagonistic muscles is synchronous in ET subjects. Therefore, the RT pattern can be used as a potential biomarker for differentiating PD from ET subjects. In this study, we developed a new wearable device and method for differentiating alternating from a synchronous RT pattern using inertial data. The novelty of our approach relies on the fact that the evaluation of synchronous or alternating tremor patterns using inertial sensors has never been described so far, and current approaches to evaluate the tremor patterns are based on surface EMG, which may be difficult to carry out for non-specialized operators. This new device, named "RT-Ring", is based on a six-axis inertial measurement unit and a Bluetooth Low-Energy microprocessor, and can be worn on a finger of the tremulous hand. A mobile app guides the operator through the whole acquisition process of inertial data from the hand with RT, and the prediction of tremor patterns is performed on a remote server through machine learning (ML) models. We used two decision tree-based algorithms, XGBoost and Random Forest, which were trained on features extracted from inertial data and achieved a classification accuracy of 92% and 89%, respectively, in differentiating alternating from synchronous tremor segments in the validation set. Finally, the classification response (alternating or synchronous RT pattern) is shown to the operator on the mobile app within a few seconds. This study is the first to demonstrate that different electromyographic tremor patterns have their counterparts in terms of rhythmic movement features, thus making inertial data suitable for predicting the muscular contraction pattern of tremors.
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Affiliation(s)
- Basilio Vescio
- Biotecnomed S.C.aR.L., Viale Europa, 88100 Catanzaro, Italy;
| | - Marida De Maria
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University “Magna Graecia”, Viale Europa, 88100 Catanzaro, Italy; (M.D.M.); (M.C.); (R.N.); (C.C.); (F.A.); (A.Q.)
| | - Marianna Crasà
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University “Magna Graecia”, Viale Europa, 88100 Catanzaro, Italy; (M.D.M.); (M.C.); (R.N.); (C.C.); (F.A.); (A.Q.)
| | - Rita Nisticò
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University “Magna Graecia”, Viale Europa, 88100 Catanzaro, Italy; (M.D.M.); (M.C.); (R.N.); (C.C.); (F.A.); (A.Q.)
| | - Camilla Calomino
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University “Magna Graecia”, Viale Europa, 88100 Catanzaro, Italy; (M.D.M.); (M.C.); (R.N.); (C.C.); (F.A.); (A.Q.)
| | - Federica Aracri
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University “Magna Graecia”, Viale Europa, 88100 Catanzaro, Italy; (M.D.M.); (M.C.); (R.N.); (C.C.); (F.A.); (A.Q.)
| | - Aldo Quattrone
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University “Magna Graecia”, Viale Europa, 88100 Catanzaro, Italy; (M.D.M.); (M.C.); (R.N.); (C.C.); (F.A.); (A.Q.)
| | - Andrea Quattrone
- Institute of Neurology, Department of Medical and Surgical Sciences, University “Magna Graecia”, Viale Europa, 88100 Catanzaro, Italy
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24
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Tam W, Alajlani M, Abd-Alrazaq A. An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review. J Med Internet Res 2023; 25:e42950. [PMID: 37594791 PMCID: PMC10474516 DOI: 10.2196/42950] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 03/13/2023] [Accepted: 04/14/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom. OBJECTIVE In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom. METHODS A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors. RESULTS Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis. CONCLUSIONS This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.
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Affiliation(s)
- William Tam
- Insitute of Digital Healthcare, Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom
| | - Mohannad Alajlani
- Insitute of Digital Healthcare, Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom
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25
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Guerra A, D'Onofrio V, Ferreri F, Bologna M, Antonini A. Objective measurement versus clinician-based assessment for Parkinson's disease. Expert Rev Neurother 2023; 23:689-702. [PMID: 37366316 DOI: 10.1080/14737175.2023.2229954] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/18/2023] [Accepted: 06/22/2023] [Indexed: 06/28/2023]
Abstract
INTRODUCTION Although clinician-based assessment through standardized clinical rating scales is currently the gold standard for quantifying motor impairment in Parkinson's disease (PD), it is not without limitations, including intra- and inter-rater variability and a degree of approximation. There is increasing evidence supporting the use of objective motion analyses to complement clinician-based assessment. Objective measurement tools hold significant potential for improving the accuracy of clinical and research-based evaluations of patients. AREAS COVERED The authors provide several examples from the literature demonstrating how different motion measurement tools, including optoelectronics, contactless and wearable systems allow for both the objective quantification and monitoring of key motor symptoms (such as bradykinesia, rigidity, tremor, and gait disturbances), and the identification of motor fluctuations in PD patients. Furthermore, they discuss how, from a clinician's perspective, objective measurements can help in various stages of PD management. EXPERT OPINION In our opinion, sufficient evidence supports the assertion that objective monitoring systems enable accurate evaluation of motor symptoms and complications in PD. A range of devices can be utilized not only to support diagnosis but also to monitor motor symptom during the disease progression and can become relevant in the therapeutic decision-making process.
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Affiliation(s)
- Andrea Guerra
- Parkinson and Movement Disorder Unit, Study Center on Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | | | - Florinda Ferreri
- Unit of Neurology, Unit of Clinical Neurophysiology, Study Center of Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
- Department of Clinical Neurophysiology, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Matteo Bologna
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed, Pozzilli, Italy
| | - Angelo Antonini
- Parkinson and Movement Disorder Unit, Study Center on Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
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26
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Torres-Pardo A, Gomez-Garcia JA, Gomez-Suarez NE, Munoz-Gonzalez A, Gonzalez-Sanchez M, Grandas F, Moreno JC, Torricelli D. Is Lyapunov exponent a reliable metric to detect dynamic stability in Parkinson's disease? ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083092 DOI: 10.1109/embc40787.2023.10341028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Idiopathic Parkinson's disease (PD) is the second most common neurodegenerative disorder worldwide. It affects the nervous system, causing motor and non-motor symptomatology. However, its clinical diagnosis remains dependent on the expertise of clinicians, as perceptual clinical scales are often used. Gait stability is one of the most relevant motor signs in PD. Nonetheless, it is usually not reported or quantified, possibly due to its unclear meaning and the high variability of metrics used in the literature. This work aims to identify a reliable and objective indicator that clinicians can use to assess patients in realistic contexts. We focused on the Largest Lyapunov Exponent (LLE), being the most common metric used in previous research works to quantify gait stability. The short and long-term LLEs were calculated in a group of 34 healthy and 42 participants diagnosed with PD. The long-term LLE extracted from the chest, right arm and right foot sensors showed statistical differences between subjects with PD and healthy control (HC) subjects, showing that the HC subjects are more stable than PD patients, whereas the short-term LLE showed the opposite results. Further investigation is required to clarify the reliability of this metric to detect and rate gait stability in people affected with PD.Clinical Relevance- This study is the first step towards the identification of an objective methodology to assess gait stability in clinical settings. Achieving this goal will contribute to improve the understanding and support the diagnosis of gait disorders that cause gait stability problems.
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27
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Reichmann H, Klingelhoefer L, Bendig J. The use of wearables for the diagnosis and treatment of Parkinson's disease. J Neural Transm (Vienna) 2023; 130:783-791. [PMID: 36609737 PMCID: PMC10199831 DOI: 10.1007/s00702-022-02575-5] [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: 11/17/2022] [Accepted: 12/13/2022] [Indexed: 01/09/2023]
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disorder, with increasing numbers of affected patients. Many patients lack adequate care due to insufficient specialist neurologists/geriatricians, and older patients experience difficulties traveling far distances to reach their treating physicians. A new option for these obstacles would be telemedicine and wearables. During the last decade, the development of wearable sensors has allowed for the continuous monitoring of bradykinesia and dyskinesia. Meanwhile, other systems can also detect tremors, freezing of gait, and gait problems. The most recently developed systems cover both sides of the body and include smartphone apps where the patients have to register their medication intake and well-being. In turn, the physicians receive advice on changing the patient's medication and recommendations for additional supportive therapies such as physiotherapy. The use of smartphone apps may also be adapted to detect PD symptoms such as bradykinesia, tremor, voice abnormalities, or changes in facial expression. Such tools can be used for the general population to detect PD early or for known PD patients to detect deterioration. It is noteworthy that most PD patients can use these digital tools. In modern times, wearable sensors and telemedicine open a new window of opportunity for patients with PD that are easy to use and accessible to most of the population.
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Affiliation(s)
- Heinz Reichmann
- Department of Neurology, University Hospital Dresden, Fetscherstrasse 74, 01307 Dresden, Germany
| | - Lisa Klingelhoefer
- Department of Neurology, University Hospital Dresden, Fetscherstrasse 74, 01307 Dresden, Germany
| | - Jonas Bendig
- Department of Neurology, University Hospital Dresden, Fetscherstrasse 74, 01307 Dresden, Germany
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28
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Santiago JA, Potashkin JA. Physical activity and lifestyle modifications in the treatment of neurodegenerative diseases. Front Aging Neurosci 2023; 15:1185671. [PMID: 37304072 PMCID: PMC10250655 DOI: 10.3389/fnagi.2023.1185671] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 05/03/2023] [Indexed: 06/13/2023] Open
Abstract
Neurodegenerative diseases have reached alarming numbers in the past decade. Unfortunately, clinical trials testing potential therapeutics have proven futile. In the absence of disease-modifying therapies, physical activity has emerged as the single most accessible lifestyle modification with the potential to fight off cognitive decline and neurodegeneration. In this review, we discuss findings from epidemiological, clinical, and molecular studies investigating the potential of lifestyle modifications in promoting brain health. We propose an evidence-based multidomain approach that includes physical activity, diet, cognitive training, and sleep hygiene to treat and prevent neurodegenerative diseases.
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Affiliation(s)
| | - Judith A. Potashkin
- Center for Neurodegenerative Diseases and Therapeutics, Cellular and Molecular Pharmacology Department, The Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL, United States
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29
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Mammen JR, Speck RM, Stebbins GT, Müller MLTM, Yang PT, Campbell M, Cosman J, Crawford JE, Dam T, Hellsten J, Jensen-Roberts S, Kostrzebski M, Simuni T, Barowicz KW, Cedarbaum JM, Dorsey ER, Stephenson D, Adams JL. Relative Meaningfulness and Impacts of Symptoms in People with Early-Stage Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2023:JPD225068. [PMID: 37212071 DOI: 10.3233/jpd-225068] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
BACKGROUND Patient perspectives on meaningful symptoms and impacts in early Parkinson's disease (PD) are lacking and are urgently needed to clarify priority areas for monitoring, management, and new therapies. OBJECTIVE To examine experiences of people with early-stage PD, systematically describe meaningful symptoms and impacts, and determine which are most bothersome or important. METHODS Forty adults with early PD who participated in a study evaluating smartwatch and smartphone digital measures (WATCH-PD study) completed online interviews with symptom mapping to hierarchically delineate symptoms and impacts of disease from "Most bothersome" to "Not present," and to identify which of these were viewed as most important and why. Individual symptom maps were coded for types, frequencies, and bothersomeness of symptoms and their impacts, with thematic analysis of narratives to explore perceptions. RESULTS The three most bothersome and important symptoms were tremor, fine motor difficulties, and slow movements. Symptoms had the greatest impact on sleep, job functioning, exercise, communication, relationships, and self-concept- commonly expressed as a sense of being limited by PD. Thematically, most bothersome symptoms were those that were personally limiting with broadest negative impact on well-being and activities. However, symptoms could be important to patients even when not present or limiting (e.g., speech, cognition). CONCLUSION Meaningful symptoms of early PD can include symptoms that are present or anticipated future symptoms that are important to the individual. Systematic assessment of meaningful symptoms should aim to assess the extent to which symptoms are personally important, present, bothersome, and limiting.
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Affiliation(s)
| | | | - Glenn T Stebbins
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | | | - Phillip T Yang
- Center for Health + Technology, University of Rochester, Rochester, NY, USA
| | - Michelle Campbell
- Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), Silver Spring, MD, USA
| | | | | | | | | | | | - Melissa Kostrzebski
- Center for Health + Technology, University of Rochester, Rochester, NY, USA
- Department of Neurology, University of Rochester, Rochester, NY, USA
| | - Tanya Simuni
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Jesse M Cedarbaum
- Coeruleus Clinical Sciences LLC, Woodbridge, CT, USA
- Yale Medical School, New Haven, CT, USA
| | - E Ray Dorsey
- Center for Health + Technology, University of Rochester, Rochester, NY, USA
- Department of Neurology, University of Rochester, Rochester, NY, USA
| | | | - Jamie L Adams
- Center for Health + Technology, University of Rochester, Rochester, NY, USA
- Department of Neurology, University of Rochester, Rochester, NY, USA
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30
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Mammen JR, Speck RM, Stebbins GM, Müller MLTM, Yang PT, Campbell M, Cosman J, Crawford JE, Dam T, Hellsten J, Jensen-Roberts S, Kostrzebski M, Simuni T, Barowicz KW, Cedarbaum JM, Dorsey ER, Stephenson D, Adams JL. Mapping Relevance of Digital Measures to Meaningful Symptoms and Impacts in Early Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2023:JPD225122. [PMID: 37212073 DOI: 10.3233/jpd-225122] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
BACKGROUND Adoption of new digital measures for clinical trials and practice has been hindered by lack of actionable qualitative data demonstrating relevance of these metrics to people with Parkinson's disease. OBJECTIVE This study evaluated of relevance of WATCH-PD digital measures to meaningful symptoms and impacts of early Parkinson's disease from the patient perspective. METHODS Participants with early Parkinson's disease (N = 40) completed surveys and 1:1 online-interviews. Interviews combined: 1) symptom mapping to delineate meaningful symptoms/impacts of disease, 2) cognitive interviewing to assess content validity of digital measures, and 3) mapping of digital measures back to personal symptoms to assess relevance from the patient perspective. Content analysis and descriptive techniques were used to analyze data. RESULTS Participants perceived mapping as deeply engaging, with 39/40 reporting improved ability to communicate important symptoms and relevance of measures. Most measures (9/10) were rated relevant by both cognitive interviewing (70-92.5%) and mapping (80-100%). Two measures related to actively bothersome symptoms for more than 80% of participants (Tremor, Shape rotation). Tasks were generally deemed relevant if they met three participant context criteria: 1) understanding what the task measured, 2) believing it targeted an important symptom of PD (past, present, or future), and 3) believing the task was a good test of that important symptom. Participants did not require that a task relate to active symptoms or "real" life to be relevant. CONCLUSION Digital measures of tremor and hand dexterity were rated most relevant in early PD. Use of mapping enabled precise quantification of qualitative data for more rigorous evaluation of new measures.
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Affiliation(s)
| | | | - Glenn M Stebbins
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | | | - Phillip T Yang
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Michelle Campbell
- Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), Silver Spring, MD, USA
| | | | | | | | | | - Stella Jensen-Roberts
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Melissa Kostrzebski
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester, Medical Center, Rochester, NY, USA
| | - Tanya Simuni
- Northwestern University Feinberg School of Medicine, Chicago IL, USA
| | | | - Jesse M Cedarbaum
- Coeruleus Clinical Sciences LLC, Woodbridge, CT, USA
- Yale Medical School, New Haven, CT, USA
| | - E Ray Dorsey
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester, Medical Center, Rochester, NY, USA
| | | | - Jamie L Adams
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester, Medical Center, Rochester, NY, USA
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31
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Borzì L, Sigcha L, Olmo G. Context Recognition Algorithms for Energy-Efficient Freezing-of-Gait Detection in Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094426. [PMID: 37177629 PMCID: PMC10181532 DOI: 10.3390/s23094426] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 05/15/2023]
Abstract
Freezing of gait (FoG) is a disabling clinical phenomenon of Parkinson's disease (PD) characterized by the inability to move the feet forward despite the intention to walk. It is one of the most troublesome symptoms of PD, leading to an increased risk of falls and reduced quality of life. The combination of wearable inertial sensors and machine learning (ML) algorithms represents a feasible solution to monitor FoG in real-world scenarios. However, traditional FoG detection algorithms process all data indiscriminately without considering the context of the activity during which FoG occurs. This study aimed to develop a lightweight, context-aware algorithm that can activate FoG detection systems only under certain circumstances, thus reducing the computational burden. Several approaches were implemented, including ML and deep learning (DL) gait recognition methods, as well as a single-threshold method based on acceleration magnitude. To train and evaluate the context algorithms, data from a single inertial sensor were extracted using three different datasets encompassing a total of eighty-one PD patients. Sensitivity and specificity for gait recognition ranged from 0.95 to 0.96 and 0.80 to 0.93, respectively, with the one-dimensional convolutional neural network providing the best results. The threshold approach performed better than ML- and DL-based methods when evaluating the effect of context awareness on FoG detection performance. Overall, context algorithms allow for discarding more than 55% of non-FoG data and less than 4% of FoG episodes. The results indicate that a context classifier can reduce the computational burden of FoG detection algorithms without significantly affecting the FoG detection rate. Thus, implementation of context awareness can present an energy-efficient solution for long-term FoG monitoring in ambulatory and free-living settings.
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Affiliation(s)
- Luigi Borzì
- Data Analytics and Technologies for Health Lab (ANTHEA), Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
| | - Luis Sigcha
- Data-Driven Computer Engineering (D2iCE) Group, Department of Electronic and Computer Engineering, University of Limerick, V94 T9PX Limerick, Ireland
| | - Gabriella Olmo
- Data Analytics and Technologies for Health Lab (ANTHEA), Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
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Kremer NI, Smid A, Lange SF, Mateus Marçal I, Tamasi K, van Dijk JMC, van Laar T, Drost G. Supine MDS-UPDRS-III Assessment: An Explorative Study. J Clin Med 2023; 12:jcm12093108. [PMID: 37176549 PMCID: PMC10179103 DOI: 10.3390/jcm12093108] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 04/20/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
The Movement Disorder Society Unified Parkinson's Disease Rating Scale-part III (MDS-UPDRS-III) is designed to be applied in the sitting position. However, to evaluate the clinical effect during stereotactic neurosurgery or to assess bedridden patients with Parkinson's disease (PD), the MDS-UPDRS-III is often used in a supine position. This explorative study evaluates the agreement of the MDS-UPDRS-III in the sitting and the supine positions. In 23 PD patients, the MDS-UPDRS-III was applied in both positions while accelerometric measurements were performed. Video recordings of the assessments were evaluated by two certified raters. Agreement between the sitting and supine MDS-UPDRS-III was studied using Cohen's kappa coefficient. Relationships between the MDS-UPDRS-III tremor scores and accelerometric amplitudes were calculated for both positions with linear regression. A fair to substantial agreement was found for MDS-UPDRS-III scores of individual items in the sitting and supine positions, while combining all tests resulted in a substantial agreement. The inter-rater reliability was fair to moderate for both positions. A logarithmic relationship between tremor scores and accelerometric amplitude was revealed for both the sitting and supine positions. Nevertheless, these data are insufficient to fully support the supine application of the MDS-UPDRS-III. Several recommendations are made to address the sensitivity of the scale to inter-rater variability. In conclusion, although an overall substantial agreement between sitting and supine MDS-UPDRS-III is confirmed, its application in the supine position is not endorsed for the whole range of its individual items. Caution is warranted in interpreting the supine MDS-UPDRS-III, pending additional research.
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Affiliation(s)
- Naomi I Kremer
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Annemarie Smid
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Stèfan F Lange
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Iara Mateus Marçal
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Katalin Tamasi
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - J Marc C van Dijk
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
- Expertise Center Movement Disorders Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Teus van Laar
- Expertise Center Movement Disorders Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Gea Drost
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
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Giuliano C, Cerri S, Cesaroni V, Blandini F. Relevance of Biochemical Deep Phenotyping for a Personalised Approach to Parkinson's Disease. Neuroscience 2023; 511:100-109. [PMID: 36572171 DOI: 10.1016/j.neuroscience.2022.12.019] [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: 02/28/2022] [Revised: 10/05/2022] [Accepted: 12/19/2022] [Indexed: 12/25/2022]
Abstract
Parkinson's disease (PD) is a multifactorial neurodegenerative disorder characterised by the progressive loss of dopaminergic neurons in the nigrostriatal tract. The identification of disease-modifying therapies is the Holy Grail of PD research, but to date no drug has been approved as such a therapy. A possible reason is the remarkable phenotypic heterogeneity of PD patients, which can generate confusion in the interpretation of results or even mask the efficacy of a therapeutic intervention. This heterogeneity should be taken into account in clinical trials, stratifying patients by their expected response to drugs designed to engage selected molecular targets. In this setting, stratification methods (clinical and genetic) should be supported by biochemical phenotyping of PD patients, in line with the deep phenotyping concept. Collection, from single patients, of a range of biological samples would streamline the generation of these profiles. Several studies have proposed biochemical characterisations of patient cohorts based on analysis of blood, cerebrospinal fluid, urine, stool, saliva and skin biopsy samples, with extracellular vesicles attracting increasing interest as a source of biomarkers. In this review we report and critically discuss major studies that used a biochemical approach to stratify their PD cohorts. The analyte most studied is α-synuclein, while other studies have focused on neurofilament light chain, lysosomal proteins, inflammasome-related proteins, LRRK2 and the urinary proteome. At present, stratification of PD patients, while promising, is still a nascent approach. Deep phenotyping of patients will allow clinical researchers to identify homogeneous subgroups for the investigation of tailored disease-modifying therapies, enhancing the chances of therapeutic success.
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Affiliation(s)
- Claudio Giuliano
- Unit of Cellular and Molecular Neurobiology, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Silvia Cerri
- Unit of Cellular and Molecular Neurobiology, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Valentina Cesaroni
- Unit of Cellular and Molecular Neurobiology, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Fabio Blandini
- Unit of Cellular and Molecular Neurobiology, IRCCS Mondino Foundation, 27100 Pavia, Italy; Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy.
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Feasibility of a wearable inertial sensor to assess motor complications and treatment in Parkinson's disease. PLoS One 2023; 18:e0279910. [PMID: 36730238 PMCID: PMC9894418 DOI: 10.1371/journal.pone.0279910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 12/18/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Wearable sensors-based systems have emerged as a potential tool to continuously monitor Parkinson's Disease (PD) motor features in free-living environments. OBJECTIVES To analyse the responsivity of wearable inertial sensor (WIS) measures (On/Off-Time, dyskinesia, freezing of gait (FoG) and gait parameters) after treatment adjustments. We also aim to study the ability of the sensor in the detection of MF, dyskinesia, FoG and the percentage of Off-Time, under ambulatory conditions of use. METHODS We conducted an observational, open-label study. PD patients wore a validated WIS (STAT-ONTM) for one week (before treatment), and one week, three months after therapeutic changes. The patients were analyzed into two groups according to whether treatment changes had been indicated or not. RESULTS Thirty-nine PD patients were included in the study (PD duration 8 ± 3.5 years). Treatment changes were made in 29 patients (85%). When comparing the two groups (treatment intervention vs no intervention), the WIS detected significant changes in the mean percentage of Off-Time (p = 0.007), the mean percentage of On-Time (p = 0.002), the number of steps (p = 0.008) and the gait fluidity (p = 0.004). The mean percentage of Off-Time among the patients who decreased their Off-Time (79% of patients) was -7.54 ± 5.26. The mean percentage of On-Time among the patients that increased their On-Time (59% of patients) was 8.9 ± 6.46. The Spearman correlation between the mean fluidity of the stride and the UPDRS-III- Factor I was 0.6 (p = <0.001). The system detected motor fluctuations (MF) in thirty-seven patients (95%), whilst dyskinesia and FoG were detected in fifteen (41%), and nine PD patients (23%), respectively. However, the kappa agreement analysis between the UPDRS-IV/clinical interview and the sensor was 0.089 for MF, 0.318 for dyskinesia and 0.481 for FoG. CONCLUSIONS It's feasible to use this sensor for monitoring PD treatment under ambulatory conditions. This system could serve as a complementary tool to assess PD motor complications and treatment adjustments, although more studies are required.
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He T, Wen F, Yang Y, Le X, Liu W, Lee C. Emerging Wearable Chemical Sensors Enabling Advanced Integrated Systems toward Personalized and Preventive Medicine. Anal Chem 2023; 95:490-514. [PMID: 36625107 DOI: 10.1021/acs.analchem.2c04527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Tianyiyi He
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore.,Center for Intelligent Sensors and MEMS, National University of Singapore, Block E6 #05-11, 5 Engineering Drive 1, Singapore 117608, Singapore
| | - Feng Wen
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore.,Center for Intelligent Sensors and MEMS, National University of Singapore, Block E6 #05-11, 5 Engineering Drive 1, Singapore 117608, Singapore
| | - Yanqin Yang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore.,Center for Intelligent Sensors and MEMS, National University of Singapore, Block E6 #05-11, 5 Engineering Drive 1, Singapore 117608, Singapore
| | - Xianhao Le
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore.,Center for Intelligent Sensors and MEMS, National University of Singapore, Block E6 #05-11, 5 Engineering Drive 1, Singapore 117608, Singapore
| | - Weixin Liu
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore.,Center for Intelligent Sensors and MEMS, National University of Singapore, Block E6 #05-11, 5 Engineering Drive 1, Singapore 117608, Singapore
| | - Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore.,Center for Intelligent Sensors and MEMS, National University of Singapore, Block E6 #05-11, 5 Engineering Drive 1, Singapore 117608, Singapore
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Zhao H, Cao J, Xie J, Liao WH, Lei Y, Cao H, Qu Q, Bowen C. Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review. Digit Health 2023; 9:20552076231173569. [PMID: 37214662 PMCID: PMC10192816 DOI: 10.1177/20552076231173569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 04/17/2023] [Indexed: 05/24/2023] Open
Abstract
Objective Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases. Methods A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection. Results The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance. Conclusions This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.
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Affiliation(s)
- Huan Zhao
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi'an, P.R. China
| | - Junyi Cao
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi'an, P.R. China
| | - Junxiao Xie
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi'an, P.R. China
| | - Wei-Hsin Liao
- Department of Mechanical and Automation
Engineering, The Chinese University of Hong
Kong, Shatin, N.T., Hong Kong, China
| | - Yaguo Lei
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi'an, P.R. China
| | - Hongmei Cao
- Department of Neurology, The First
Affiliated Hospital of Xi’an Jiaotong University, Xi’an, P.R. China
| | - Qiumin Qu
- Department of Neurology, The First
Affiliated Hospital of Xi’an Jiaotong University, Xi’an, P.R. China
| | - Chris Bowen
- Department of Mechanical Engineering, University of Bath, Bath, UK
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Graeber J, Warmerdam E, Aufenberg S, Bull C, Davies K, Dixon J, Emmert K, Judd C, Maetzler C, Reilmann R, Ng WF, Macrae V, Maetzler W, Kaduszkiewicz H. Technology acceptance of digital devices for home use: Qualitative results of a mixed methods study. Digit Health 2023; 9:20552076231181239. [PMID: 37361435 PMCID: PMC10286539 DOI: 10.1177/20552076231181239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 05/04/2023] [Indexed: 06/28/2023] Open
Abstract
Objective Digital devices have demonstrated benefits to patients with chronic and neurodegenerative diseases. But when patients use medical devices in their homes, the technologies have to fit into their lives. We investigated the technology acceptance of seven digital devices for home use. Methods We conducted 60 semi-structured interviews with participants of a larger device study on their views on the acceptability of seven devices. Transcriptions were analysed using qualitative content analysis. Results Based on the unified theory of acceptance and use of technology, we evaluated effort, facilitating conditions, performance expectancy and social influence of each device.In the effort category, five themes emerged: (a) the hassle to use the device; (b) its usability; (c) comfort; (d) disturbance to daily life; and (e) problems during usage. Facilitating conditions consisted of five themes: (a) expectations regarding a device; (b) quality of the instructions; (c) insecurities with usage; (d) possibilities of optimization; and (e) possibilities to use the device longer. Regarding performance expectancy, we identified three themes: (a) insecurities with the performance of a device; (b) feedback; and (c) motivation for using a device. In the social influence category, three themes emerged: (a) reactions of peers; (b) concerns with the visibility of a device; and (c) concerns regarding data privacy. Conclusions We identify key factors that determine the acceptability of medical devices for home use from the participants' perspective. These include low effort of use, minor disruptions to their daily lives and good support from the study team.
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Affiliation(s)
- Johanna Graeber
- Institute of General Practice, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | - Elke Warmerdam
- Innovative Implant Development (Fracture Healing), Division of Surgery, Saarland University, Homburg, Germany
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Kiel and Kiel University, Kiel, Germany
| | | | - Christopher Bull
- Open Lab, School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Kristen Davies
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Jan Dixon
- NIHR Newcastle Clinical Research Facility, Newcastle upon Tyne NHS Hospitals Foundation Trust and Newcastle University, Newcastle upon Tyne, UK
| | - Kirsten Emmert
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Kiel and Kiel University, Kiel, Germany
| | - Claire Judd
- NIHR Newcastle Clinical Research Facility, Newcastle upon Tyne NHS Hospitals Foundation Trust and Newcastle University, Newcastle upon Tyne, UK
| | - Corina Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Kiel and Kiel University, Kiel, Germany
| | - Ralf Reilmann
- George-Huntington-Institute, Münster, Germany
- Department of Clinical Radiology University of Münster, Münster, Germany
- Department of Neurodegeneration, Hertie Institute for Clinical Brain Research, University of Tübingen, Germany
| | - Wan-Fai Ng
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- NIHR Newcastle Clinical Research Facility, Newcastle upon Tyne NHS Hospitals Foundation Trust and Newcastle University, Newcastle upon Tyne, UK
- Musculoskeletal and Inflammation Theme, NIHR Newcastle Biomedical Research Center, Newcastle upon Tyne NHS Hospitals Foundation Trust and Newcastle University Newcastle upon Tyne, UK
| | - Victoria Macrae
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Kiel and Kiel University, Kiel, Germany
| | - Hanna Kaduszkiewicz
- Institute of General Practice, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
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Knudson KC, Gupta AS. Assessing Cerebellar Disorders with Wearable Inertial Sensor Data Using Time-Frequency and Autoregressive Hidden Markov Model Approaches. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22239454. [PMID: 36502155 PMCID: PMC9737930 DOI: 10.3390/s22239454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/19/2022] [Accepted: 11/29/2022] [Indexed: 05/30/2023]
Abstract
Wearable sensor data is relatively easily collected and provides direct measurements of movement that can be used to develop useful behavioral biomarkers. Sensitive and specific behavioral biomarkers for neurodegenerative diseases are critical to supporting early detection, drug development efforts, and targeted treatments. In this paper, we use autoregressive hidden Markov models and a time-frequency approach to create meaningful quantitative descriptions of behavioral characteristics of cerebellar ataxias from wearable inertial sensor data gathered during movement. We create a flexible and descriptive set of features derived from accelerometer and gyroscope data collected from wearable sensors worn while participants perform clinical assessment tasks, and use these data to estimate disease status and severity. A short period of data collection (<5 min) yields enough information to effectively separate patients with ataxia from healthy controls with very high accuracy, to separate ataxia from other neurodegenerative diseases such as Parkinson’s disease, and to provide estimates of disease severity.
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Affiliation(s)
- Karin C. Knudson
- Data Intensive Studies Center, Tufts University, Medford, MA 02155, USA
| | - Anoopum S. Gupta
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
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Zahedi FM, Zhao H, Sanvanson P, Walia N, Jain H, Shaker R. My Real Avatar has a Doctor Appointment in the Wepital: A System for Persistent, Efficient, and Ubiquitous Medical Care. INFORMATION & MANAGEMENT 2022. [PMCID: PMC9487169 DOI: 10.1016/j.im.2022.103706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
COVID-19 created a great deal of personal, social, and economic anxiety in the USA and across the globe and exposed the inadequacy of traditional medical systems in handling large-scale emergencies. While telemedicine and virtual visits have become popular as a result, they end once a visit is over, hence lacking data persistence and continuity in caring for patients. Using the design science research approach with support from the theory of affordances, this paper proposes the design of a medical system (called wepital) in which patients receive care through their real avatars, enabling hospitals and other medical centers to provide immediate care that can continue for as long as a patient needs it. Real avatars are digital representations of patients that embody their real-time vital signs and health information. We have created a functional prototype to demonstrate how the proposed design can work. To assess the usability of the design, we have used the prototype in an experiment to provide medical advice to patient volunteers. Based on a theory-based conceptual model, we collected survey data after the experiment to identify factors contributing to the success of such a system, as measured by patient satisfaction. We report the factors that significantly contribute to the patients’ satisfaction. As part of the application and policy implications of our work, we propose a nationwide system that could supplement and expand the capacity of medical systems at the national or even global level.
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An integrated biometric voice and facial features for early detection of Parkinson's disease. NPJ Parkinsons Dis 2022; 8:145. [PMID: 36309501 PMCID: PMC9617232 DOI: 10.1038/s41531-022-00414-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 10/12/2022] [Indexed: 01/24/2023] Open
Abstract
Hypomimia and voice changes are soft signs preceding classical motor disability in patients with Parkinson's disease (PD). We aim to investigate whether an analysis of acoustic and facial expressions with machine-learning algorithms assist early identification of patients with PD. We recruited 371 participants, including a training cohort (112 PD patients during "on" phase, 111 controls) and a validation cohort (74 PD patients during "off" phase, 74 controls). All participants underwent a smartphone-based, simultaneous recording of voice and facial expressions, while reading an article. Nine different machine learning classifiers were applied. We observed that integrated facial and voice features could discriminate early-stage PD patients from controls with an area under the receiver operating characteristic (AUROC) diagnostic value of 0.85. In the validation cohort, the optimal diagnostic value (0.90) maintained. We concluded that integrated biometric features of voice and facial expressions could assist the identification of early-stage PD patients from aged controls.
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Amprimo G, Masi G, Priano L, Azzaro C, Galli F, Pettiti G, Mauro A, Ferraris C. Assessment Tasks and Virtual Exergames for Remote Monitoring of Parkinson's Disease: An Integrated Approach Based on Azure Kinect. SENSORS (BASEL, SWITZERLAND) 2022; 22:8173. [PMID: 36365870 PMCID: PMC9654712 DOI: 10.3390/s22218173] [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: 09/22/2022] [Revised: 10/17/2022] [Accepted: 10/22/2022] [Indexed: 06/16/2023]
Abstract
Motor impairments are among the most relevant, evident, and disabling symptoms of Parkinson’s disease that adversely affect quality of life, resulting in limited autonomy, independence, and safety. Recent studies have demonstrated the benefits of physiotherapy and rehabilitation programs specifically targeted to the needs of Parkinsonian patients in supporting drug treatments and improving motor control and coordination. However, due to the expected increase in patients in the coming years, traditional rehabilitation pathways in healthcare facilities could become unsustainable. Consequently, new strategies are needed, in which technologies play a key role in enabling more frequent, comprehensive, and out-of-hospital follow-up. The paper proposes a vision-based solution using the new Azure Kinect DK sensor to implement an integrated approach for remote assessment, monitoring, and rehabilitation of Parkinsonian patients, exploiting non-invasive 3D tracking of body movements to objectively and automatically characterize both standard evaluative motor tasks and virtual exergames. An experimental test involving 20 parkinsonian subjects and 15 healthy controls was organized. Preliminary results show the system’s ability to quantify specific and statistically significant (p < 0.05) features of motor performance, easily monitor changes as the disease progresses over time, and at the same time permit the use of exergames in virtual reality both for training and as a support for motor condition assessment (for example, detecting an average reduction in arm swing asymmetry of about 14% after arm training). The main innovation relies precisely on the integration of evaluative and rehabilitative aspects, which could be used as a closed loop to design new protocols for remote management of patients tailored to their actual conditions.
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Affiliation(s)
- Gianluca Amprimo
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
- Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Giulia Masi
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy
| | - Lorenzo Priano
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy
- Istituto Auxologico Italiano, IRCCS, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy
| | - Corrado Azzaro
- Istituto Auxologico Italiano, IRCCS, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy
| | - Federica Galli
- Istituto Auxologico Italiano, IRCCS, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy
| | - Giuseppe Pettiti
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Alessandro Mauro
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy
- Istituto Auxologico Italiano, IRCCS, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy
| | - Claudia Ferraris
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
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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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Torrado JC, Husebo BS, Allore HG, Erdal A, Fæø SE, Reithe H, Førsund E, Tzoulis C, Patrascu M. Digital phenotyping by wearable-driven artificial intelligence in older adults and people with Parkinson's disease: Protocol of the mixed method, cyclic ActiveAgeing study. PLoS One 2022; 17:e0275747. [PMID: 36240173 PMCID: PMC9565381 DOI: 10.1371/journal.pone.0275747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 09/22/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Active ageing is described as the process of optimizing health, empowerment, and security to enhance the quality of life in the rapidly growing population of older adults. Meanwhile, multimorbidity and neurological disorders, such as Parkinson's disease (PD), lead to global public health and resource limitations. We introduce a novel user-centered paradigm of ageing based on wearable-driven artificial intelligence (AI) that may harness the autonomy and independence that accompany functional limitation or disability, and possibly elevate life expectancy in older adults and people with PD. METHODS ActiveAgeing is a 4-year, multicentre, mixed method, cyclic study that combines digital phenotyping via commercial devices (Empatica E4, Fitbit Sense, and Oura Ring) with traditional evaluation (clinical assessment scales, in-depth interviews, and clinical consultations) and includes four types of participants: (1) people with PD and (2) their informal caregiver; (3) healthy older adults from the Helgetun living environment in Norway, and (4) people on the Helgetun waiting list. For the first study, each group will be represented by N = 15 participants to test the data acquisition and to determine the sample size for the second study. To suggest lifestyle changes, modules for human expert-based advice, machine-generated advice, and self-generated advice from accessible data visualization will be designed. Quantitative analysis of physiological data will rely on digital signal processing (DSP) and AI techniques. The clinical assessment scales are the Unified Parkinson's Disease Rating Scale (UPDRS), Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS), Geriatric Anxiety Inventory (GAI), Apathy Evaluation Scale (AES), and the REM Sleep Behaviour Disorder Screening Questionnaire (RBDSQ). A qualitative inquiry will be carried out with individual and focus group interviews and analysed using a hermeneutic approach including narrative and thematic analysis techniques. DISCUSSION We hypothesise that digital phenotyping is feasible to explore the ageing process from clinical and lifestyle perspectives including older adults and people with PD. Data is used for clinical decision-making by symptom tracking, predicting symptom evolution, and discovering new outcome measures for clinical trials.
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Affiliation(s)
- Juan C. Torrado
- Faculty of Medicine, Department of Global Public Health and Primary Care, Centre for Elderly and Nursing Home Medicine (SEFAS), University of Bergen, Bergen, Norway
| | - Bettina S. Husebo
- Faculty of Medicine, Department of Global Public Health and Primary Care, Centre for Elderly and Nursing Home Medicine (SEFAS), University of Bergen, Bergen, Norway
- Department of Nursing Home Medicine, Municipality of Bergen, Bergen, Norway
| | - Heather G. Allore
- Yale School of Medicine and Yale School of Public Health, New Haven, CT, United States of America
| | - Ane Erdal
- Faculty of Medicine, Department of Global Public Health and Primary Care, Centre for Elderly and Nursing Home Medicine (SEFAS), University of Bergen, Bergen, Norway
| | - Stein E. Fæø
- Faculty of Health Studies, Department of Nursing, VID Specialized University, Bergen, Norway
| | - Haakon Reithe
- Faculty of Medicine, Department of Global Public Health and Primary Care, Centre for Elderly and Nursing Home Medicine (SEFAS), University of Bergen, Bergen, Norway
| | - Elise Førsund
- Faculty of Medicine, Department of Global Public Health and Primary Care, Centre for Elderly and Nursing Home Medicine (SEFAS), University of Bergen, Bergen, Norway
| | - Charalampos Tzoulis
- Department of Neurology, Neuro-SysMed Center, Haukeland University Hospital, Bergen, Norway
- K.G Jebsen Center for Translational Research in Parkinson’s Disease, University of Bergen, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Monica Patrascu
- Faculty of Medicine, Department of Global Public Health and Primary Care, Centre for Elderly and Nursing Home Medicine (SEFAS), University of Bergen, Bergen, Norway
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A new scheme for the development of IMU-based activity recognition systems for telerehabilitation. Med Eng Phys 2022; 108:103876. [DOI: 10.1016/j.medengphy.2022.103876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 08/10/2022] [Accepted: 08/21/2022] [Indexed: 11/24/2022]
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Tam W, Alajlani M, Abd-alrazaq A. An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review (Preprint).. [DOI: 10.2196/preprints.42950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
BACKGROUND
The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom.
OBJECTIVE
In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom.
METHODS
A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors.
RESULTS
Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis.
CONCLUSIONS
This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.
CLINICALTRIAL
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Atri R, Urban K, Marebwa B, Simuni T, Tanner C, Siderowf A, Frasier M, Haas M, Lancashire L. Deep Learning for Daily Monitoring of Parkinson's Disease Outside the Clinic Using Wearable Sensors. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22186831. [PMID: 36146181 PMCID: PMC9502239 DOI: 10.3390/s22186831] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/25/2022] [Accepted: 09/02/2022] [Indexed: 06/01/2023]
Abstract
Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson's disease (PD). However, the unsupervised and "open world" nature of this type of data collection make such applications difficult to develop. In this proof-of-concept study, we used inertial sensor data from Verily Study Watches worn by individuals for up to 23 h per day over several months to distinguish between seven subjects with PD and four without. Since motor-related PD symptoms such as bradykinesia and gait abnormalities typically present when a PD subject is walking, we initially used human activity recognition (HAR) techniques to identify walk-like activity in the unconstrained, unlabeled data. We then used these "walk-like" events to train one-dimensional convolutional neural networks (1D-CNNs) to determine the presence of PD. We report classification accuracies near 90% on single 5-s walk-like events and 100% accuracy when taking the majority vote over single-event classifications that span a duration of one day. Though based on a small cohort, this study shows the feasibility of leveraging unconstrained wearable sensor data to accurately detect the presence or absence of PD.
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Affiliation(s)
- Roozbeh Atri
- Cohen Veterans Bioscience, New York, NY 10018, USA
| | - Kevin Urban
- Cohen Veterans Bioscience, New York, NY 10018, USA
| | - Barbara Marebwa
- The Michael J Fox Foundation for Parkinson’s Research, New York, NY 10163, USA
| | - Tanya Simuni
- Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Caroline Tanner
- Department of Neurology, Weill Institute for Neurosciences University of California, San Francisco, CA 94143, USA
- Parkinson’s Disease Research Education and Clinical Center, San Francisco Veteran’s Affairs Medical Center, San Francisco, CA 94121, USA
| | - Andrew Siderowf
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Mark Frasier
- The Michael J Fox Foundation for Parkinson’s Research, New York, NY 10163, USA
| | - Magali Haas
- Cohen Veterans Bioscience, New York, NY 10018, USA
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Tönges L, Buhmann C, Klebe S, Klucken J, Kwon EH, Müller T, Pedrosa DJ, Schröter N, Riederer P, Lingor P. Blood-based biomarker in Parkinson's disease: potential for future applications in clinical research and practice. J Neural Transm (Vienna) 2022; 129:1201-1217. [PMID: 35428925 PMCID: PMC9463345 DOI: 10.1007/s00702-022-02498-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 03/27/2022] [Indexed: 12/12/2022]
Abstract
The clinical presentation of Parkinson's disease (PD) is both complex and heterogeneous, and its precise classification often requires an intensive work-up. The differential diagnosis, assessment of disease progression, evaluation of therapeutic responses, or identification of PD subtypes frequently remains uncertain from a clinical point of view. Various tissue- and fluid-based biomarkers are currently being investigated to improve the description of PD. From a clinician's perspective, signatures from blood that are relatively easy to obtain would have great potential for use in clinical practice if they fulfill the necessary requirements as PD biomarker. In this review article, we summarize the knowledge on blood-based PD biomarkers and present both a researcher's and a clinician's perspective on recent developments and potential future applications.
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Affiliation(s)
- Lars Tönges
- Department of Neurology, Ruhr-University Bochum, St. Josef Hospital, Gudrunstr. 56, 44791, Bochum, Germany.
- Center for Protein Diagnostics (ProDi), Ruhr University Bochum, 44801, Bochum, Nordrhein-Westfalen, Germany.
| | - Carsten Buhmann
- Department of Neurology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Stephan Klebe
- Department of Neurology, University Hospital Essen, 45147, Essen, Germany
| | - Jochen Klucken
- Department of Digital Medicine, University Luxembourg, LCSB, L-4367, Belval, Luxembourg
- Digital Medicine Research Group, Luxembourg Institute of Health, L-1445, Strassen, Luxembourg
- Centre Hospitalier de Luxembourg, Digital Medicine Research Clinic, L-1210, Luxembourg, Luxembourg
| | - Eun Hae Kwon
- Department of Neurology, Ruhr-University Bochum, St. Josef Hospital, Gudrunstr. 56, 44791, Bochum, Germany
| | - Thomas Müller
- Department of Neurology, St. Joseph Hospital Berlin-Weissensee, 13088, Berlin, Germany
| | - David J Pedrosa
- Department of Neurology, Universitätsklinikum Gießen and Marburg, Marburg Site, 35043, Marburg, Germany
- Center of Mind, Brain and Behaviour (CMBB), Philipps-Universität Marburg, 35043, Marburg, Germany
| | - Nils Schröter
- Department of Neurology and Clinical Neuroscience, University of Freiburg, 79106, Freiburg, Germany
| | - Peter Riederer
- Psychosomatics and Psychotherapy, University Hospital Wuerzburg, Clinic and Policlinic for Psychiatry, 97080, Wuerzburg, Germany
- University of Southern Denmark Odense, 5000, Odense, Denmark
| | - Paul Lingor
- School of Medicine, Klinikum Rechts Der Isar, Department of Neurology, Technical University of Munich, 81675, München, Germany
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Albano L, Emedoli D, Basaia S, Balestrino R, Pompeo E, Barzaghi LR, Iannaccone S, Mortini P, Agosta F, Filippi M. Wearable motion sensors to track tremor changes after radiosurgical thalamotomy. J Neurol 2022; 269:6566-6571. [DOI: 10.1007/s00415-022-11322-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/02/2022] [Accepted: 08/02/2022] [Indexed: 10/15/2022]
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Mirelman A, Siderowf A, Chahine L. Outcome Assessment in Parkinson Disease Prevention Trials: Utility of Clinical and Digital Measures. Neurology 2022; 99:52-60. [PMID: 35970590 DOI: 10.1212/wnl.0000000000200236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 01/21/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The prodromal phase of Parkinson disease (PD) is accompanied by subtle clinical signs that are not sufficient for diagnosis but could potentially be measured in the context of clinical trials of therapies intended to delay or prevent more definitive clinical features. The objective of this study was to review the available literature on the presence and time course of subtle motor features in prodromal PD in the context of planning for possible clinical trials. METHODS We reviewed the available literature based on expert opinion. We considered a range of outcomes including measurement of clinical features, patient-reported outcomes, digital markers, and clinical diagnosis. RESULTS We considered these features and measures in the context of patient stratification, intermediate outcomes, and clinically relevant end points, including phenoconversion. DISCUSSION Substantial progress has been made in understanding how motor features evolve in the period immediately before a PD diagnosis. Digital measures hold substantial progress for measurement precision and may be additionally relevant because they can be used in naturalistic environments outside the clinic. Future studies should focus on advancing digital sensor technology and analysis and developing methods to implement available methods, particularly determination of a clinical diagnosis of PD, in a clinical trial context.
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Affiliation(s)
- Anat Mirelman
- From the Sackler School of Medicine and Sagol School of Neuroscience (A.M.), Tel Aviv University, Israel; Department of Neurology (A.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia; and Department of Neurology (L.C.), University of Pittsburgh, PA
| | - Andrew Siderowf
- From the Sackler School of Medicine and Sagol School of Neuroscience (A.M.), Tel Aviv University, Israel; Department of Neurology (A.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia; and Department of Neurology (L.C.), University of Pittsburgh, PA.
| | - Lana Chahine
- From the Sackler School of Medicine and Sagol School of Neuroscience (A.M.), Tel Aviv University, Israel; Department of Neurology (A.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia; and Department of Neurology (L.C.), University of Pittsburgh, PA
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50
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Deb R, An S, Bhat G, Shill H, Ogras UY. A Systematic Survey of Research Trends in Technology Usage for Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2022; 22:5491. [PMID: 35897995 PMCID: PMC9371095 DOI: 10.3390/s22155491] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/17/2022] [Accepted: 07/20/2022] [Indexed: 06/15/2023]
Abstract
Parkinson's disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The complexity of PD pathology is amplified due to its dependency on patient diaries and the neurologist's subjective assessment of clinical scales. A significant amount of recent research has explored new cost-effective and subjective assessment methods pertaining to PD symptoms to address this challenge. This article analyzes the application areas and use of mobile and wearable technology in PD research using the PRISMA methodology. Based on the published papers, we identify four significant fields of research: diagnosis, prognosis and monitoring, predicting response to treatment, and rehabilitation. Between January 2008 and December 2021, 31,718 articles were published in four databases: PubMed Central, Science Direct, IEEE Xplore, and MDPI. After removing unrelated articles, duplicate entries, non-English publications, and other articles that did not fulfill the selection criteria, we manually investigated 1559 articles in this review. Most of the articles (45%) were published during a recent four-year stretch (2018-2021), and 19% of the articles were published in 2021 alone. This trend reflects the research community's growing interest in assessing PD with wearable devices, particularly in the last four years of the period under study. We conclude that there is a substantial and steady growth in the use of mobile technology in the PD contexts. We share our automated script and the detailed results with the public, making the review reproducible for future publications.
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Affiliation(s)
| | - Sizhe An
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA;
| | - Ganapati Bhat
- School of Electrical Engineering & Computer Science, Washington State University, Pullman, WA 99164, USA;
| | - Holly Shill
- Lonnie and Muhammad Ali Movement Disorder Center, Phoenix, AZ 85013, USA;
| | - Umit Y. Ogras
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA;
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