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Cox E, Wade R, Hodgson R, Fulbright H, Phung TH, Meader N, Walker S, Rothery C, Simmonds M. Devices for remote continuous monitoring of people with Parkinson's disease: a systematic review and cost-effectiveness analysis. Health Technol Assess 2024; 28:1-187. [PMID: 39021200 PMCID: PMC11331379 DOI: 10.3310/ydsl3294] [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] [Indexed: 07/20/2024] Open
Abstract
Background Parkinson's disease is a brain condition causing a progressive loss of co ordination and movement problems. Around 145,500 people have Parkinson's disease in the United Kingdom. Levodopa is the most prescribed treatment for managing motor symptoms in the early stages. Patients should be monitored by a specialist every 6-12 months for disease progression and treatment of adverse effects. Wearable devices may provide a novel approach to management by directly monitoring patients for bradykinesia, dyskinesia, tremor and other symptoms. They are intended to be used alongside clinical judgement. Objectives To determine the clinical and cost-effectiveness of five devices for monitoring Parkinson's disease: Personal KinetiGraph, Kinesia 360, KinesiaU, PDMonitor and STAT-ON. Methods We performed systematic reviews of all evidence on the five devices, outcomes included: diagnostic accuracy, impact on decision-making, clinical outcomes, patient and clinician opinions and economic outcomes. We searched MEDLINE and 12 other databases/trial registries to February 2022. Risk of bias was assessed. Narrative synthesis was used to summarise all identified evidence, as the evidence was insufficient for meta-analysis. One included trial provided individual-level data, which was re-analysed. A de novo decision-analytic model was developed to estimate the cost-effectiveness of Personal KinetiGraph and Kinesia 360 compared to standard of care in the UK NHS over a 5-year time horizon. The base-case analysis considered two alternative monitoring strategies: one-time use and routine use of the device. Results Fifty-seven studies of Personal KinetiGraph, 15 of STAT-ON, 3 of Kinesia 360, 1 of KinesiaU and 1 of PDMonitor were included. There was some evidence to suggest that Personal KinetiGraph can accurately measure bradykinesia and dyskinesia, leading to treatment modification in some patients, and a possible improvement in clinical outcomes when measured using the Unified Parkinson's Disease Rating Scale. The evidence for STAT-ON suggested it may be of value for diagnosing symptoms, but there is currently no evidence on its clinical impact. The evidence for Kinesia 360, KinesiaU and PDMonitor is insufficient to draw any conclusions on their value in clinical practice. The base-case results for Personal KinetiGraph compared to standard of care for one-time and routine use resulted in incremental cost-effectiveness ratios of £67,856 and £57,877 per quality-adjusted life-year gained, respectively, with a beneficial impact of the Personal KinetiGraph on Unified Parkinson's Disease Rating Scale domains III and IV. The incremental cost-effectiveness ratio results for Kinesia 360 compared to standard of care for one-time and routine use were £38,828 and £67,203 per quality-adjusted life-year gained, respectively. Limitations The evidence was limited in extent and often low quality. For all devices, except Personal KinetiGraph, there was little to no evidence on the clinical impact of the technology. Conclusions Personal KinetiGraph could reasonably be used in practice to monitor patient symptoms and modify treatment where required. There is too little evidence on STAT-ON, Kinesia 360, KinesiaU or PDMonitor to be confident that they are clinically useful. The cost-effectiveness of remote monitoring appears to be largely unfavourable with incremental cost-effectiveness ratios in excess of £30,000 per quality-adjusted life-year across a range of alternative assumptions. The main driver of cost-effectiveness was the durability of improvements in patient symptoms. Study registration This study is registered as PROSPERO CRD42022308597. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135437) and is published in full in Health Technology Assessment; Vol. 28, No. 30. See the NIHR Funding and Awards website for further award information.
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Affiliation(s)
- Edward Cox
- CHE Technology Assessment Group, University of York, York, UK
| | - Ros Wade
- CRD Technology Assessment Group, University of York, York, UK
| | - Robert Hodgson
- CRD Technology Assessment Group, University of York, York, UK
| | - Helen Fulbright
- CRD Technology Assessment Group, University of York, York, UK
| | - Thai Han Phung
- CHE Technology Assessment Group, University of York, York, UK
| | - Nicholas Meader
- CRD Technology Assessment Group, University of York, York, UK
| | - Simon Walker
- CHE Technology Assessment Group, University of York, York, UK
| | - Claire Rothery
- CHE Technology Assessment Group, University of York, York, UK
| | - Mark Simmonds
- CRD Technology Assessment Group, University of York, York, UK
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Lee S, Moon J, Lee YS, Shin SC, Lee K. Wearable-Based Integrated System for In-Home Monitoring and Analysis of Nocturnal Enuresis. SENSORS (BASEL, SWITZERLAND) 2024; 24:3330. [PMID: 38894140 PMCID: PMC11174938 DOI: 10.3390/s24113330] [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: 03/24/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 06/21/2024]
Abstract
Nocturnal enuresis (NE) is involuntary bedwetting during sleep, typically appearing in young children. Despite the potential benefits of the long-term home monitoring of NE patients for research and treatment enhancement, this area remains underexplored. To address this, we propose NEcare, an in-home monitoring system that utilizes wearable devices and machine learning techniques. NEcare collects sensor data from an electrocardiogram, body impedance (BI), a three-axis accelerometer, and a three-axis gyroscope to examine bladder volume (BV), heart rate (HR), and periodic limb movements in sleep (PLMS). Additionally, it analyzes the collected NE patient data and supports NE moment estimation using heuristic rules and deep learning techniques. To demonstrate the feasibility of in-home monitoring for NE patients using our wearable system, we used our datasets from 30 in-hospital patients and 4 in-home patients. The results show that NEcare captures expected trends associated with NE occurrences, including BV increase, HR increase, and PLMS appearance. In addition, we studied the machine learning-based NE moment estimation, which could help relieve the burdens of NE patients and their families. Finally, we address the limitations and outline future research directions for the development of wearable systems for NE patients.
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Affiliation(s)
- Sangyeop Lee
- Department of Computer Science, College of Computing, Yonsei University, Seoul 03722, Republic of Korea; (S.L.); (K.L.)
| | - Junhyung Moon
- Department of Computer Science, College of Computing, Yonsei University, Seoul 03722, Republic of Korea; (S.L.); (K.L.)
| | - Yong Seung Lee
- Department of Urology, Urological Science Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea;
| | - Seung-chul Shin
- System LSI Business, Samsung Electronics Co., Hwaseong-si 18448, Republic of Korea;
| | - Kyoungwoo Lee
- Department of Computer Science, College of Computing, Yonsei University, Seoul 03722, Republic of Korea; (S.L.); (K.L.)
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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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Horder J, Mrotek LA, Casadio M, Bassindale KD, McGuire J, Scheidt RA. Utility and usability of a wearable system and progressive-challenge cued exercise program for encouraging use of the more involved arm at-home after stroke-a feasibility study with case reports. J Neuroeng Rehabil 2024; 21:66. [PMID: 38685012 PMCID: PMC11059679 DOI: 10.1186/s12984-024-01359-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 04/16/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Understanding the role of adherence to home exercise programs for survivors of stroke is critical to ensure patients perform prescribed exercises and maximize effectiveness of recovery. METHODS Survivors of hemiparetic stroke with impaired motor function were recruited into a 7-day study designed to test the utility and usability of a low-cost wearable system and progressive-challenge cued exercise program for encouraging graded-challenge exercise at-home. The wearable system comprised two wrist-worn MetaMotionR+ activity monitors and a custom smartphone app. The progressive-challenge cued exercise program included high-intensity activities (one repetition every 30 s) dosed at 1.5 h per day, embedded within 8 h of passive activity monitoring per day. Utility was assessed using measures of system uptime and cue response rate. Usability and user experience were assessed using well-validated quantitative surveys of system usability and user experience. Self-efficacy was assessed at the end of each day on a visual analog scale that ranged from 0 to 100. RESULTS The system and exercise program had objective utility: system uptime was 92 ± 6.9% of intended hours and the rate of successful cue delivery was 99 ± 2.7%. The system and program also were effective in motivating cued exercise: activity was detected within 5-s of the cue 98 ± 3.1% of the time. As shown via two case studies, accelerometry data can accurately reflect graded-challenge exercise instructions and reveal differentiable activity levels across exercise stages. User experience surveys indicated positive overall usability in the home settings, strong levels of personal motivation to use the system, and high degrees of satisfaction with the devices and provided training. Self-efficacy assessments indicated a strong perception of proficiency across participants (95 ± 5.0). CONCLUSIONS This study demonstrates that a low-cost wearable system providing frequent haptic cues to encourage graded-challenge exercise after stroke can have utility and can provide an overall positive user experience in home settings. The study also demonstrates how combining a graded exercise program with all-day activity monitoring can provide insight into the potential for wearable systems to assess adherence to-and effectiveness of-home-based exercise programs on an individualized basis.
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Affiliation(s)
- Jake Horder
- Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA
| | - Leigh A Mrotek
- Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA
| | - Maura Casadio
- Biomedical Engineering, University of Genoa, Genoa, Italy
| | - Kimberly D Bassindale
- Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA
| | - John McGuire
- Physical Medicine and Rehabilitation, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Robert A Scheidt
- Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA.
- Biomedical Engineering, Marquette University and the Medical College of Wisconsin, Engineering Hall, Rm 342, P.O. Box 1881, Milwaukee, WI, 53201-1881, USA.
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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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Ferini-Strambi L, Liguori C, Lucey BP, Mander BA, Spira AP, Videnovic A, Baumann C, Franco O, Fernandes M, Gnarra O, Krack P, Manconi M, Noain D, Saxena S, Kallweit U, Randerath W, Trenkwalder C, Rosenzweig I, Iranzo A, Bradicich M, Bassetti C. Role of sleep in neurodegeneration: the consensus report of the 5th Think Tank World Sleep Forum. Neurol Sci 2024; 45:749-767. [PMID: 38087143 DOI: 10.1007/s10072-023-07232-7] [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: 10/10/2023] [Accepted: 11/26/2023] [Indexed: 01/18/2024]
Abstract
Sleep abnormalities may represent an independent risk factor for neurodegeneration. An international expert group convened in 2021 to discuss the state-of-the-science in this domain. The present article summarizes the presentations and discussions concerning the importance of a strategy for studying sleep- and circadian-related interventions for early detection and prevention of neurodegenerative diseases. An international expert group considered the current state of knowledge based on the most relevant publications in the previous 5 years; discussed the current challenges in the field of relationships among sleep, sleep disorders, and neurodegeneration; and identified future priorities. Sleep efficiency and slow wave activity during non-rapid eye movement (NREM) sleep are decreased in cognitively normal middle-aged and older adults with Alzheimer's disease (AD) pathology. Sleep deprivation increases amyloid-β (Aβ) concentrations in the interstitial fluid of experimental animal models and in cerebrospinal fluid in humans, while increased sleep decreases Aβ. Obstructive sleep apnea (OSA) is a risk factor for dementia. Studies indicate that positive airway pressure (PAP) treatment should be started in patients with mild cognitive impairment or AD and comorbid OSA. Identification of other measures of nocturnal hypoxia and sleep fragmentation could better clarify the role of OSA as a risk factor for neurodegeneration. Concerning REM sleep behavior disorder (RBD), it will be crucial to identify the subset of RBD patients who will convert to a specific neurodegenerative disorder. Circadian sleep-wake rhythm disorders (CSWRD) are strong predictors of caregiver stress and institutionalization, but the absence of recommendations or consensus statements must be considered. Future priorities include to develop and validate existing and novel comprehensive assessments of CSWRD in patients with/at risk for dementia. Strategies for studying sleep-circadian-related interventions for early detection/prevention of neurodegenerative diseases are required. CSWRD evaluation may help to identify additional biomarkers for phenotyping and personalizing treatment of neurodegeneration.
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Affiliation(s)
- Luigi Ferini-Strambi
- Sleep Disorders Center, Division of Neuroscience, Università Vita-Salute San Raffaele, Milan, Italy.
| | - Claudio Liguori
- Sleep Medicine Center, University of Rome Tor Vergata, Rome, Italy
| | - Brendan P Lucey
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Bryce A Mander
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | - Adam P Spira
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Aleksandar Videnovic
- Department of Neurology, Division of Sleep Medicine, Massachussets General Hospital, Harvard Medical School, Boston, MA, USA
| | - Christian Baumann
- Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - Oscar Franco
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | | | - Oriella Gnarra
- Department of Neurology, University of Bern, Bern, Switzerland
| | - Paul Krack
- Department of Neurology, University of Bern, Bern, Switzerland
| | - Mauro Manconi
- Sleep Medicine Unit, Faculty of Biomedical Sciences, Neurocenter of the Southern Switzerland, Regional Hospital of Lugano, Università Della Svizzera Italiana, Lugano, Switzerland
| | - Daniela Noain
- Department of Neurology, University of Bern, Bern, Switzerland
| | - Smita Saxena
- Department of Neurology, University of Bern, Bern, Switzerland
| | - Ulf Kallweit
- Clinical Sleep and Neuroimmunology, University Witten/Herdecke, Witten, Germany
| | | | - C Trenkwalder
- Department of Neurosurgery, Paracelsus-Elena Klinik, University Medical Center, KasselGoettingen, Germany
| | - Ivana Rosenzweig
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, King's College London, London, UK
| | - Alex Iranzo
- Sleep Center, Neurology Service, Hospital Clinic de Barcelona, Barcelona, IDIBAPS, CIBERNED, Barcelona, Spain
| | - Matteo Bradicich
- Department of Pulmonology and Sleep Disorders Centre, University Hospital Zurich, Zurich, Switzerland
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Fay-Karmon T, Galor N, Heimler B, Zilka A, Bartsch RP, Plotnik M, Hassin-Baer S. Home-based monitoring of persons with advanced Parkinson's disease using smartwatch-smartphone technology. Sci Rep 2024; 14:9. [PMID: 38167434 PMCID: PMC10761812 DOI: 10.1038/s41598-023-48209-y] [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: 05/14/2023] [Accepted: 11/23/2023] [Indexed: 01/05/2024] Open
Abstract
Movement deterioration is the hallmark of Parkinson's disease (PD), characterized by levodopa-induced motor-fluctuations (i.e., symptoms' variability related to the medication cycle) in advanced stages. However, motor symptoms are typically too sporadically and/or subjectively assessed, ultimately preventing the effective monitoring of their progression, and thus leading to suboptimal treatment/therapeutic choices. Smartwatches (SW) enable a quantitative-oriented approach to motor-symptoms evaluation, namely home-based monitoring (HBM) using an embedded inertial measurement unit. Studies validated such approach against in-clinic evaluations. In this work, we aimed at delineating personalized motor-fluctuations' profiles, thus capturing individual differences. 21 advanced PD patients with motor fluctuations were monitored for 2 weeks using a SW and a smartphone-dedicated app (Intel Pharma Analytics Platform). The SW continuously collected passive data (tremor, dyskinesia, level of activity using dedicated algorithms) and active data, i.e., time-up-and-go, finger tapping, hand tremor and hand rotation carried out daily, once in OFF and once in ON levodopa periods. We observed overall high compliance with the protocol. Furthermore, we observed striking differences among the individual patterns of symptoms' levodopa-related variations across the HBM, allowing to divide our participants among four data-driven, motor-fluctuations' profiles. This highlights the potential of HBM using SW technology for revolutionizing clinical practices.
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Affiliation(s)
- Tsviya Fay-Karmon
- Movement Disorders Institute, Department of Neurology, Sheba Medical Center, Ramat Gan, Israel
| | - Noam Galor
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel
| | - Benedetta Heimler
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel
| | - Asaf Zilka
- Movement Disorders Institute, Department of Neurology, Sheba Medical Center, Ramat Gan, Israel
| | - Ronny P Bartsch
- Department of Physics, Bar-Ilan University, Ramat Gan, Israel
| | - Meir Plotnik
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Sharon Hassin-Baer
- Movement Disorders Institute, Department of Neurology, Sheba Medical Center, Ramat Gan, Israel.
- Department of Neurology and Neurosurgery, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
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Kroneberg D, Al-Fatly B, Morkos C, Steiner LA, Schneider GH, Kühn A. Kinematic Effects of Combined Subthalamic and Dorsolateral Nigral Deep Brain Stimulation in Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2024; 14:269-282. [PMID: 38363617 PMCID: PMC10977420 DOI: 10.3233/jpd-230181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/20/2023] [Indexed: 02/17/2024]
Abstract
Background Additional stimulation of the substantia nigra (SNr) has been proposed to target axial symptoms and gait impairment in patients with Parkinson's disease (PD). Objective This study aimed to characterize effects of combined deep brain stimulation (DBS) of the subthalamic nucleus (STN) and SNr on gait performance in PD and to map stimulation sites within the SNr. Methods In a double-blinded crossover design, 10 patients with PD and gait impairment underwent clinical examination and kinematic assessment with STN DBS, combined STN+SNr DBS and OFF DBS 30 minutes after reprogramming. To confirm stimulation within the SNr, electrodes, active contacts, and stimulation volumes were modeled in a common space and overlap with atlases of SNr was computed. Results Overlap of stimulation volumes with dorsolateral SNr was confirmed for all patients. UPDRS III, scoring of freezing during turning and transitioning, stride length, stride velocity, and range of motion of shank, knee, arm, and trunk as well as peak velocities during turning and transitions and turn duration were improved with STN DBS compared to OFF. On cohort level, no further improvement was observed with combined STN+SNr DBS but additive improvement of spatiotemporal gait parameters was observed in individual subjects. Conclusions Combined high frequency DBS of the STN and dorsolateral SNr did not consistently result in additional short-term kinematic or clinical benefit compared to STN DBS. Stimulation intervals, frequency, and patient selection for target symptoms as well as target region within the SNr need further refinement in future trials.
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Affiliation(s)
- Daniel Kroneberg
- Department of Neurology with Experimental Neurology, Movement Disorders and Neuromodulation Unit, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Bassam Al-Fatly
- Department of Neurology with Experimental Neurology, Movement Disorders and Neuromodulation Unit, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Cornelia Morkos
- Department of Neurology with Experimental Neurology, Movement Disorders and Neuromodulation Unit, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Leon Amadeus Steiner
- Department of Neurology with Experimental Neurology, Movement Disorders and Neuromodulation Unit, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Gerd-Helge Schneider
- Department of Neurosurgery, Charité – Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - A. Kühn
- Department of Neurology with Experimental Neurology, Movement Disorders and Neuromodulation Unit, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Charite - Universitatsmedizin Berlin, Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Charité – Universitätsmedizin Berlin, Berlin, Germany
- NeuroCure, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité – Universitätsmedizin Berlin, Berlin, Germany
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Gnarra O, Calvello C, Schirinzi T, Beozzo F, De Masi C, Spanetta M, Fernandes M, Grillo P, Cerroni R, Pierantozzi M, Bassetti CLA, Mercuri NB, Stefani A, Liguori C. Exploring the Association Linking Head Position and Sleep Architecture to Motor Impairment in Parkinson's Disease: An Exploratory Study. J Pers Med 2023; 13:1591. [PMID: 38003906 PMCID: PMC10671918 DOI: 10.3390/jpm13111591] [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: 08/19/2023] [Revised: 11/05/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
Patients with Parkinson's disease (PD) tend to sleep more frequently in the supine position and less often change head and body position during sleep. Besides sleep quality and continuity, head and body positions are crucial for glymphatic system (GS) activity. This pilot study evaluated sleep architecture and head position during each sleep stage in idiopathic PD patients without cognitive impairment, correlating sleep data to patients' motor and non-motor symptoms (NMS). All patients underwent the multi-night recordings, which were acquired using the Sleep Profiler headband. Sleep parameters, sleep time in each head position, and percentage of slow wave activity (SWA) in sleep, stage 3 of non-REM sleep (N3), and REM sleep in the supine position were extracted. Lastly, correlations with motor impairment and NMS were performed. Twenty PD patients (65.7 ± 8.6 y.o, ten women) were included. Sleep architecture did not change across the different nights of recording and showed the prevalence of sleep performed in the supine position. In addition, SWA and N3 were more frequently in the supine head position, and N3 in the supine decubitus correlated with REM sleep performed in the same position; this latter correlated with the disease duration (correlation coefficient = 0.48, p-value = 0.03) and motor impairment (correlation coefficient = 0.53, p-value = 0.02). These preliminary results demonstrated the importance of monitoring sleep in PD patients, supporting the need for preventive strategies in clinical practice for maintaining the lateral head position during the crucial sleep stages (SWA, N3, REM), essential for permitting the GS function and activity and ensuring brain health.
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Affiliation(s)
- Oriella Gnarra
- Sleep-Wake-Epilepsy Center, Department of Neurology, University Hospital of Bern, 3010 Bern, Switzerland; (O.G.); (C.L.A.B.)
- Sensory-Motor Systems Lab, Institute of Robotics and Intelligent Systems, Department of Health Sciences and Technology, ETH Zurich, 8092 Zurich, Switzerland
| | - Carmen Calvello
- Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy; (C.C.); (T.S.); (F.B.); (M.P.); (N.B.M.); (A.S.)
| | - Tommaso Schirinzi
- Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy; (C.C.); (T.S.); (F.B.); (M.P.); (N.B.M.); (A.S.)
- Parkinson’s Disease Unit, University Hospital of Rome “Tor Vergata”, 00133 Rome, Italy; (C.D.M.); (P.G.); (R.C.)
| | - Francesca Beozzo
- Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy; (C.C.); (T.S.); (F.B.); (M.P.); (N.B.M.); (A.S.)
| | - Claudia De Masi
- Parkinson’s Disease Unit, University Hospital of Rome “Tor Vergata”, 00133 Rome, Italy; (C.D.M.); (P.G.); (R.C.)
| | | | - Mariana Fernandes
- Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy; (C.C.); (T.S.); (F.B.); (M.P.); (N.B.M.); (A.S.)
| | - Piergiorgio Grillo
- Parkinson’s Disease Unit, University Hospital of Rome “Tor Vergata”, 00133 Rome, Italy; (C.D.M.); (P.G.); (R.C.)
| | - Rocco Cerroni
- Parkinson’s Disease Unit, University Hospital of Rome “Tor Vergata”, 00133 Rome, Italy; (C.D.M.); (P.G.); (R.C.)
| | - Mariangela Pierantozzi
- Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy; (C.C.); (T.S.); (F.B.); (M.P.); (N.B.M.); (A.S.)
- Parkinson’s Disease Unit, University Hospital of Rome “Tor Vergata”, 00133 Rome, Italy; (C.D.M.); (P.G.); (R.C.)
| | - Claudio L. A. Bassetti
- Sleep-Wake-Epilepsy Center, Department of Neurology, University Hospital of Bern, 3010 Bern, Switzerland; (O.G.); (C.L.A.B.)
| | - Nicola Biagio Mercuri
- Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy; (C.C.); (T.S.); (F.B.); (M.P.); (N.B.M.); (A.S.)
- Neurology Unit, University Hospital of Rome “Tor Vergata”, 00133 Rome, Italy
| | - Alessandro Stefani
- Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy; (C.C.); (T.S.); (F.B.); (M.P.); (N.B.M.); (A.S.)
- Parkinson’s Disease Unit, University Hospital of Rome “Tor Vergata”, 00133 Rome, Italy; (C.D.M.); (P.G.); (R.C.)
| | - Claudio Liguori
- Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy; (C.C.); (T.S.); (F.B.); (M.P.); (N.B.M.); (A.S.)
- Neurology Unit, University Hospital of Rome “Tor Vergata”, 00133 Rome, Italy
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10
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Chen M, Sun Z, Xin T, Chen Y, Su F. An Interpretable Deep Learning Optimized Wearable Daily Detection System for Parkinson's Disease. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3937-3946. [PMID: 37695969 DOI: 10.1109/tnsre.2023.3314100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
Walking detection in the daily life of patients with Parkinson's disease (PD) is of great significance for tracking the progress of the disease. This study aims to implement an accurate, objective, and passive detection algorithm optimized based on an interpretable deep learning architecture for the daily walking of patients with PD and to explore the most representative spatiotemporal motor features. Five inertial measurement units attached to the wrist, ankle, and waist are used to collect motion data from 100 subjects during a 10-meter walking test. The raw data of each sensor are subjected to the continuous wavelet transform to train the classification model of the constructed 6-channel convolutional neural network (CNN). The results show that the sensor located at the waist has the best classification performance with an accuracy of 98.01%±0.85% and the area under the receiver operating characteristic curve (AUC) of 0.9981±0.0017 under ten-fold cross-validation. The gradient-weighted class activation mapping shows that the feature points with greater contribution to PD were concentrated in the lower frequency band (0.5~3Hz) compared with healthy controls. The visual maps of the 3D CNN show that only three out of the six time series have a greater contribution, which is used as a basis to further optimize the model input, greatly reducing the raw data processing costs (50%) while ensuring its performance (AUC=0.9929±0.0019). To the best of our knowledge, this is the first study to consider the visual interpretation-based optimization of an intelligent classification model in the intelligent diagnosis of PD.
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11
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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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12
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Manupibul U, Tanthuwapathom R, Jarumethitanont W, Kaimuk P, Limroongreungrat W, Charoensuk W. Integration of force and IMU sensors for developing low-cost portable gait measurement system in lower extremities. Sci Rep 2023; 13:10653. [PMID: 37391570 PMCID: PMC10313649 DOI: 10.1038/s41598-023-37761-2] [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: 12/02/2022] [Accepted: 06/27/2023] [Indexed: 07/02/2023] Open
Abstract
Gait analysis is the method to accumulate walking data. It is useful in diagnosing diseases, follow-up of symptoms, and rehabilitation post-treatment. Several techniques have been developed to assess human gait. In the laboratory, gait parameters are analyzed by using a camera capture and a force plate. However, there are several limitations, such as high operating costs, the need for a laboratory and a specialist to operate the system, and long preparation time. This paper presents the development of a low-cost portable gait measurement system by using the integration of flexible force sensors and IMU sensors in outdoor applications for early detection of abnormal gait in daily living. The developed device is designed to measure ground reaction force, acceleration, angular velocity, and joint angles of the lower extremities. The commercialized device, including the motion capture system (Motive-OptiTrack) and force platform (MatScan), is used as the reference system to validate the performance of the developed system. The results of the system show that it has high accuracy in measuring gait parameters such as ground reaction force and joint angles in lower limbs. The developed device has a strong correlation coefficient compared with the commercialized system. The percent error of the motion sensor is below 8%, and the force sensor is lower than 3%. The low-cost portable device with a user interface was successfully developed to measure gait parameters for non-laboratory applications to support healthcare applications.
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Affiliation(s)
- Udomporn Manupibul
- Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Phuttamonthon, Nakhon Pathom, Thailand
| | - Ratikanlaya Tanthuwapathom
- Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Phuttamonthon, Nakhon Pathom, Thailand
| | - Wimonrat Jarumethitanont
- Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Phuttamonthon, Nakhon Pathom, Thailand
- Faculty of Physical Therapy, Mahidol University, Phuttamonthon, Nakhon Pathom, Thailand
| | - Panya Kaimuk
- Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Phuttamonthon, Nakhon Pathom, Thailand
| | - Weerawat Limroongreungrat
- College of Sports Science and Technology, Mahidol University, Phuttamonthon, Nakhon Pathom, Thailand
| | - Warakorn Charoensuk
- Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Phuttamonthon, Nakhon Pathom, Thailand.
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13
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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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14
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Wolff A, Schumacher NU, Pürner D, Machetanz G, Demleitner AF, Feneberg E, Hagemeier M, Lingor P. Parkinson's disease therapy: what lies ahead? J Neural Transm (Vienna) 2023; 130:793-820. [PMID: 37147404 PMCID: PMC10199869 DOI: 10.1007/s00702-023-02641-6] [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: 02/15/2023] [Accepted: 04/25/2023] [Indexed: 05/07/2023]
Abstract
The worldwide prevalence of Parkinson's disease (PD) has been constantly increasing in the last decades. With rising life expectancy, a longer disease duration in PD patients is observed, further increasing the need and socioeconomic importance of adequate PD treatment. Today, PD is exclusively treated symptomatically, mainly by dopaminergic stimulation, while efforts to modify disease progression could not yet be translated to the clinics. New formulations of approved drugs and treatment options of motor fluctuations in advanced stages accompanied by telehealth monitoring have improved PD patients care. In addition, continuous improvement in the understanding of PD disease mechanisms resulted in the identification of new pharmacological targets. Applying novel trial designs, targeting of pre-symptomatic disease stages, and the acknowledgment of PD heterogeneity raise hopes to overcome past failures in the development of drugs for disease modification. In this review, we address these recent developments and venture a glimpse into the future of PD therapy in the years to come.
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Affiliation(s)
- Andreas Wolff
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Nicolas U Schumacher
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Dominik Pürner
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Gerrit Machetanz
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Antonia F Demleitner
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Emily Feneberg
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Maike Hagemeier
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Paul Lingor
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
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15
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Liuzzi P, Carpinella I, Anastasi D, Gervasoni E, Lencioni T, Bertoni R, Carrozza MC, Cattaneo D, Ferrarin M, Mannini A. Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors. Sci Rep 2023; 13:8640. [PMID: 37244933 PMCID: PMC10224964 DOI: 10.1038/s41598-023-35744-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 05/23/2023] [Indexed: 05/29/2023] Open
Abstract
Poor dynamic balance and impaired gait adaptation to different contexts are hallmarks of people with neurological disorders (PwND), leading to difficulties in daily life and increased fall risk. Frequent assessment of dynamic balance and gait adaptability is therefore essential for monitoring the evolution of these impairments and/or the long-term effects of rehabilitation. The modified dynamic gait index (mDGI) is a validated clinical test specifically devoted to evaluating gait facets in clinical settings under a physiotherapist's supervision. The need of a clinical environment, consequently, limits the number of assessments. Wearable sensors are increasingly used to measure balance and locomotion in real-world contexts and may permit an increase in monitoring frequency. This study aims to provide a preliminary test of this opportunity by using nested cross-validated machine learning regressors to predict the mDGI scores of 95 PwND via inertial signals collected from short steady-state walking bouts derived from the 6-minute walk test. Four different models were compared, one for each pathology (multiple sclerosis, Parkinson's disease, and stroke) and one for the pooled multipathological cohort. Model explanations were computed on the best-performing solution; the model trained on the multipathological cohort yielded a median (interquartile range) absolute test error of 3.58 (5.38) points. In total, 76% of the predictions were within the mDGI's minimal detectable change of 5 points. These results confirm that steady-state walking measurements provide information about dynamic balance and gait adaptability and can help clinicians identify important features to improve upon during rehabilitation. Future developments will include training of the method using short steady-state walking bouts in real-world settings, analysing the feasibility of this solution to intensify performance monitoring, providing prompt detection of worsening/improvements, and complementing clinical assessments.
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Affiliation(s)
- Piergiuseppe Liuzzi
- AIRLab, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 50143, Florence, Italy
- Scuola Superiore Sant'Anna, Istituto di BioRobotica, 56025, Pontedera, Italy
| | - Ilaria Carpinella
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy.
| | - Denise Anastasi
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - Elisa Gervasoni
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - Tiziana Lencioni
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - Rita Bertoni
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | | | - Davide Cattaneo
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
- Dipartimento di Fisiopatologia Medico-Chirurgica e dei Trapianti, Università di Milano, 20122, Milan, Italy
| | - Maurizio Ferrarin
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - Andrea Mannini
- AIRLab, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 50143, Florence, Italy
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16
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Antonini A, Reichmann H, Gentile G, Garon M, Tedesco C, Frank A, Falkenburger B, Konitsiotis S, Tsamis K, Rigas G, Kostikis N, Ntanis A, Pattichis C. Toward objective monitoring of Parkinson's disease motor symptoms using a wearable device: wearability and performance evaluation of PDMonitor ®. Front Neurol 2023; 14:1080752. [PMID: 37260606 PMCID: PMC10228366 DOI: 10.3389/fneur.2023.1080752] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 02/27/2023] [Indexed: 06/02/2023] Open
Abstract
Parkinson's disease (PD) is characterized by a variety of motor and non-motor symptoms. As disease progresses, fluctuations in the response to levodopa treatment may develop, along with emergence of freezing of gait (FoG) and levodopa induced dyskinesia (LiD). The optimal management of the motor symptoms and their complications, depends, principally, on the consistent detection of their course, leading to improved treatment decisions. During the last few years, wearable devices have started to be used in the clinical practice for monitoring patients' PD-related motor symptoms, during their daily activities. This work describes the results of 2 multi-site clinical studies (PDNST001 and PDNST002) designed to validate the performance and the wearability of a new wearable monitoring device, the PDMonitor®, in the detection of PD-related motor symptoms. For the studies, 65 patients with Parkinson's disease and 28 healthy individuals (controls) were recruited. Specifically, during the Phase I of the first study, participants used the monitoring device for 2-6 h in a clinic while neurologists assessed the exhibited parkinsonian symptoms every half hour using the Unified Parkinson's Disease Rating Scale (UPDRS) Part III, as well as the Abnormal Involuntary Movement Scale (AIMS) for dyskinesia severity assessment. The goal of Phase I was data gathering. On the other hand, during the Phase II of the first study, as well as during the second study (PDNST002), day-to-day variability was evaluated, with patients in the former and with control subjects in the latter. In both cases, the device was used for a number of days, with the subjects being unsupervised and free to perform any kind of daily activities. The monitoring device produced estimations of the severity of the majority of PD-related motor symptoms and their fluctuations. Statistical analysis demonstrated that the accuracy in the detection of symptoms and the correlation between their severity and the expert evaluations were high. As a result, the studies confirmed the effectiveness of the system as a continuous telemonitoring solution, easy to be used to facilitate decision-making for the treatment of patients with Parkinson's disease.
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Affiliation(s)
- Angelo Antonini
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | - Heinz Reichmann
- Department of Neurology, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universitat Dresden, Dresden, Germany
| | - Giovanni Gentile
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | - Michela Garon
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | - Chiara Tedesco
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | - Anika Frank
- Department of Neurology, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universitat Dresden, Dresden, Germany
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Bjoern Falkenburger
- Department of Neurology, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universitat Dresden, Dresden, Germany
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Spyridon Konitsiotis
- Department of Neurology, University Hospital of Ioannina and Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
| | - Konstantinos Tsamis
- Department of Neurology, University Hospital of Ioannina and Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
| | | | | | | | - Constantinos Pattichis
- Department of Computer Science and Biomedical Engineering Research Centre, University of Cyprus, Nicosia, Cyprus
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17
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Oyama G, Burq M, Hatano T, Marks WJ, Kapur R, Fernandez J, Fujikawa K, Furusawa Y, Nakatome K, Rainaldi E, Chen C, Ho KC, Ogawa T, Kamo H, Oji Y, Takeshige-Amano H, Taniguchi D, Nakamura R, Sasaki F, Ueno S, Shiina K, Hattori A, Nishikawa N, Ishiguro M, Saiki S, Hayashi A, Motohashi M, Hattori N. Analytical and clinical validity of wearable, multi-sensor technology for assessment of motor function in patients with Parkinson's disease in Japan. Sci Rep 2023; 13:3600. [PMID: 36918552 PMCID: PMC10015076 DOI: 10.1038/s41598-023-29382-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 02/03/2023] [Indexed: 03/16/2023] Open
Abstract
Continuous, objective monitoring of motor signs and symptoms may help improve tracking of disease progression and treatment response in Parkinson's disease (PD). This study assessed the analytical and clinical validity of multi-sensor smartwatch measurements in hospitalized and home-based settings (96 patients with PD; mean wear time 19 h/day) using a twice-daily virtual motor examination (VME) at times representing medication OFF/ON states. Digital measurement performance was better during inpatient clinical assessments for composite V-scores than single-sensor-derived features for bradykinesia (Spearman |r|= 0.63, reliability = 0.72), tremor (|r|= 0.41, reliability = 0.65), and overall motor features (|r|= 0.70, reliability = 0.67). Composite levodopa effect sizes during hospitalization were 0.51-1.44 for clinical assessments and 0.56-1.37 for VMEs. Reliability of digital measurements during home-based VMEs was 0.62-0.80 for scores derived from weekly averages and 0.24-0.66 for daily measurements. These results show that unsupervised digital measurements of motor features with wrist-worn sensors are sensitive to medication state and are reliable in naturalistic settings.Trial Registration: Japan Pharmaceutical Information Center Clinical Trials Information (JAPIC-CTI): JapicCTI-194825; Registered June 25, 2019.
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Affiliation(s)
- Genko Oyama
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan.
| | - Maximilien Burq
- Verily Life Sciences, 269 East Grand Avenue, South San Francisco, CA, USA
| | - Taku Hatano
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - William J Marks
- Verily Life Sciences, 269 East Grand Avenue, South San Francisco, CA, USA
| | - Ritu Kapur
- Verily Life Sciences, 269 East Grand Avenue, South San Francisco, CA, USA
| | - Jovelle Fernandez
- Takeda Pharmaceutical Company Limited, 2 Chome-1-1 Nihonbashihoncho, Chuo-Ku, Tokyo, 103-0023, Japan
| | - Keita Fujikawa
- Takeda Pharmaceutical Company Limited, 2 Chome-1-1 Nihonbashihoncho, Chuo-Ku, Tokyo, 103-0023, Japan
| | - Yoshihiko Furusawa
- Takeda Pharmaceutical Company Limited, 2 Chome-1-1 Nihonbashihoncho, Chuo-Ku, Tokyo, 103-0023, Japan
| | - Keisuke Nakatome
- Takeda Pharmaceutical Company Limited, 2 Chome-1-1 Nihonbashihoncho, Chuo-Ku, Tokyo, 103-0023, Japan
| | - Erin Rainaldi
- Verily Life Sciences, 269 East Grand Avenue, South San Francisco, CA, USA
| | - Chen Chen
- Verily Life Sciences, 269 East Grand Avenue, South San Francisco, CA, USA
| | - King Chung Ho
- Verily Life Sciences, 269 East Grand Avenue, South San Francisco, CA, USA
| | - Takashi Ogawa
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Hikaru Kamo
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Yutaka Oji
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Haruka Takeshige-Amano
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Daisuke Taniguchi
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Ryota Nakamura
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Fuyuko Sasaki
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Shinichi Ueno
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Kenta Shiina
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Anri Hattori
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Noriko Nishikawa
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Mayu Ishiguro
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Shinji Saiki
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Ayako Hayashi
- Takeda Pharmaceutical Company Limited, 2 Chome-1-1 Nihonbashihoncho, Chuo-Ku, Tokyo, 103-0023, Japan
| | - Masatoshi Motohashi
- Takeda Pharmaceutical Company Limited, 2 Chome-1-1 Nihonbashihoncho, Chuo-Ku, Tokyo, 103-0023, Japan
| | - Nobutaka Hattori
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
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Bargiotas I, Wang D, Mantilla J, Quijoux F, Moreau A, Vidal C, Barrois R, Nicolai A, Audiffren J, Labourdette C, Bertin-Hugaul F, Oudre L, Buffat S, Yelnik A, Ricard D, Vayatis N, Vidal PP. Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall. J Neurol 2023; 270:618-631. [PMID: 35817988 PMCID: PMC9886639 DOI: 10.1007/s00415-022-11251-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 06/03/2022] [Accepted: 06/20/2022] [Indexed: 02/03/2023]
Abstract
Nowadays, it becomes of paramount societal importance to support many frail-prone groups in our society (elderly, patients with neurodegenerative diseases, etc.) to remain socially and physically active, maintain their quality of life, and avoid their loss of autonomy. Once older people enter the prefrail stage, they are already likely to experience falls whose consequences may accelerate the deterioration of their quality of life (injuries, fear of falling, reduction of physical activity). In that context, detecting frailty and high risk of fall at an early stage is the first line of defense against the detrimental consequences of fall. The second line of defense would be to develop original protocols to detect future fallers before any fall occur. This paper briefly summarizes the current advancements and perspectives that may arise from the combination of affordable and easy-to-use non-wearable systems (force platforms, 3D tracking motion systems), wearable systems (accelerometers, gyroscopes, inertial measurement units-IMUs) with appropriate machine learning analytics, as well as the efforts to address these challenges.
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Affiliation(s)
- Ioannis Bargiotas
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France. .,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.
| | - Danping Wang
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Juan Mantilla
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Flavien Quijoux
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,ORPEA Group, Puteaux, France
| | - Albane Moreau
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Catherine Vidal
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Service of Otorhinolaryngology (ENT), AP-HP, Hôpital Universitaire Pitié Salpêtrière, Paris, 75013, France
| | - Remi Barrois
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Alice Nicolai
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Julien Audiffren
- Department of Neuroscience, University of Fribourg, Fribourg, Switzerland
| | - Christophe Labourdette
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | | | - Laurent Oudre
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Stephane Buffat
- Laboratoire d'accidentologie de biomécanique et du comportement des conducteurs, GIE Psa Renault Groupes, Nanterre, France
| | - Alain Yelnik
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Service of Physical and Rehabilitation Medicine (PRM), AP- HP, GH St Louis, Lariboisière, F. Widal, Paris, 75010, France
| | - Damien Ricard
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Service of Neurology, AP-HP, Hôpital d'Instruction des Armées de Percy, Service de Santé des Armées, Clamart, 92140, France.,École d'application du Val-de-Grâce, Service de Santé des Armée, Paris, France
| | - Nicolas Vayatis
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Pierre-Paul Vidal
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, China
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Smart Consumer Wearables as Digital Diagnostic Tools: A Review. Diagnostics (Basel) 2022; 12:diagnostics12092110. [PMID: 36140511 PMCID: PMC9498278 DOI: 10.3390/diagnostics12092110] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 11/17/2022] Open
Abstract
The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.
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Chen M, Sun Z, Su F, Chen Y, Bu D, Lyu Y. An Auxiliary Diagnostic System for Parkinson's Disease Based on Wearable Sensors and Genetic Algorithm Optimized Random Forest. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2254-2263. [PMID: 35947560 DOI: 10.1109/tnsre.2022.3197807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder characterized mainly by motor-related impairment, an accurate, quantitative, and objective diagnosis is an effective way to slow the disease deterioration process. In this paper, a user-friendly auxiliary diagnostic system for PD is constructed based on the upper limb movement conditions of 100 subjects consisting of 50 PD patients and 50 healthy subjects. This system includes wearable sensors that collect upper limb movement data, host computer for data processing and classification, and graphic user interface (GUI). The genetic algorithm optimized random forest classifier is introduced to classify PD and normal states based on the selected optimal features, and the 50 trials leave-one-out cross-validation is used to evaluate the performance of the classifier, with the highest accuracy of 94.4%. The classification accuracy among different upper limb movement tasks and with the different number of sensors are compared, results show that the task with only alternation hand movement also has satisfactory classification accuracy, and sensors on both wrists performance better than one sensor on a single wrist. The utility of the proposed system is illustrated by neurologists with a deployed GUI during the clinical inquiry, opening the possibility for a wide range of applications in the auxiliary diagnosis of PD.
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21
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Home-Based Measurements of Dystonia in Cerebral Palsy Using Smartphone-Coupled Inertial Sensor Technology and Machine Learning: A Proof-of-Concept Study. SENSORS 2022; 22:s22124386. [PMID: 35746168 PMCID: PMC9231145 DOI: 10.3390/s22124386] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/30/2022] [Accepted: 06/07/2022] [Indexed: 02/06/2023]
Abstract
Accurate and reliable measurement of the severity of dystonia is essential for the indication, evaluation, monitoring and fine-tuning of treatments. Assessment of dystonia in children and adolescents with dyskinetic cerebral palsy (CP) is now commonly performed by visual evaluation either directly in the doctor’s office or from video recordings using standardized scales. Both methods lack objectivity and require much time and effort of clinical experts. Only a snapshot of the severity of dyskinetic movements (i.e., choreoathetosis and dystonia) is captured, and they are known to fluctuate over time and can increase with fatigue, pain, stress or emotions, which likely happens in a clinical environment. The goal of this study was to investigate whether it is feasible to use home-based measurements to assess and evaluate the severity of dystonia using smartphone-coupled inertial sensors and machine learning. Video and sensor data during both active and rest situations from 12 patients were collected outside a clinical setting. Three clinicians analyzed the videos and clinically scored the dystonia of the extremities on a 0–4 scale, following the definition of amplitude of the Dyskinesia Impairment Scale. The clinical scores and the sensor data were coupled to train different machine learning models using cross-validation. The average F1 scores (0.67 ± 0.19 for lower extremities and 0.68 ± 0.14 for upper extremities) in independent test datasets indicate that it is possible to detected dystonia automatically using individually trained models. The predictions could complement standard dyskinetic CP measures by providing frequent, objective, real-world assessments that could enhance clinical care. A generalized model, trained with data from other subjects, shows lower F1 scores (0.45 for lower extremities and 0.34 for upper extremities), likely due to a lack of training data and dissimilarities between subjects. However, the generalized model is reasonably able to distinguish between high and lower scores. Future research should focus on gathering more high-quality data and study how the models perform over the whole day.
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22
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Events Detection of Anticipatory Postural Adjustments through a Wearable Accelerometer Sensor Is Comparable to That Measured by the Force Platform in Subjects with Parkinson's Disease. SENSORS 2022; 22:s22072668. [PMID: 35408282 PMCID: PMC9003325 DOI: 10.3390/s22072668] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 03/26/2022] [Accepted: 03/28/2022] [Indexed: 02/06/2023]
Abstract
Out-of-the-lab instrumented gait testing focuses on steady-state gait and usually does not include gait initiation (GI) measures. GI involves Anticipatory Postural Adjustments (APAs), which propel the center of mass (COM) forward and laterally before the first step. These movements are impaired in persons with Parkinson’s disease (PD), contributing to their pathological gait. The use of a simple GI testing system, outside the lab, would allow improving gait rehabilitation of PD patients. Here, we evaluated the metrological quality of using a single inertial measurement unit for APA detection as compared with the use of a gold-standard system, i.e., the force platforms. Twenty-five PD and eight elderly subjects (ELD) were asked to initiate gait in response to auditory stimuli while wearing an IMU on the trunk. Temporal parameters (APA-Onset, Time-to-Toe-Off, Time-to-Heel-Strike, APA-Duration, Swing-Duration) extracted from the accelerometric data and force platforms were significantly correlated (mean(SD), r: 0.99(0.01), slope: 0.97(0.02)) showing a good level of agreement (LOA [s]: 0.04(0.01), CV [%]: 2.9(1.7)). PD showed longer APA-Duration compared to ELD ([s] 0.81(0.17) vs. 0.59(0.09) p < 0.01). APA parameters showed moderate correlation with the MDS-UPDRS Rigidity, Characterizing-FOG questionnaire and FAB-2 planning. The single IMU-based reconstruction algorithm was effective in measuring APAs timings in PD. The current work sets the stage for future developments of tele-rehabilitation and home-based exercises.
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23
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Wearable gait analysis systems: ready to be used by medical practitioners in geriatric wards? Eur Geriatr Med 2022; 13:817-824. [PMID: 35243600 PMCID: PMC9378320 DOI: 10.1007/s41999-022-00629-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 02/16/2022] [Indexed: 11/06/2022]
Abstract
Aim To investigate the feasibility of wearable gait analysis in geriatric wards by testing the effectiveness and acceptance of the system. Findings Wearable gait analysis can be implemented into geriatric wards, showing its readiness for a transformation from a pure research tool to a practically usable gait analysis system. Message Despite good transferability into clinical practice, future research should aim to increase functionality and applicability of wearable gait analysis systems in clinical contexts. Purpose We assess feasibility of wearable gait analysis in geriatric wards by testing the effectiveness and acceptance of the system. Methods Gait parameters of 83 patients (83.34 ± 5.88 years, 58/25 female/male) were recorded at admission and/or discharge to/from two geriatric inpatient wards. Gait parameters were tested for statistically significant differences between admission and discharge. Walking distance measured by a wearable gait analysis system was correlated with distance assessed by physiotherapists. Examiners rated usability using the system usability scale. Patients reported acceptability on a five-point Likert-scale. Results The total distance measures highly correlate (r = 0.89). System Usability Scale is above the median threshold of 68, indicating good usability. Majority of patients does not have objections regarding the use of the system. Among other gait parameters, mean heel strike angle changes significantly between admission and discharge. Conclusion Wearable gait analysis system is objectively and subjectively usable in a clinical setting and accepted by patients. It offers a reasonably valid assessment of gait parameters and is a feasible way for instrumented gait analysis.
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24
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Salchow-Hömmen C, Skrobot M, Jochner MCE, Schauer T, Kühn AA, Wenger N. Review-Emerging Portable Technologies for Gait Analysis in Neurological Disorders. Front Hum Neurosci 2022; 16:768575. [PMID: 35185496 PMCID: PMC8850274 DOI: 10.3389/fnhum.2022.768575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 01/07/2022] [Indexed: 01/29/2023] Open
Abstract
The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments.
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Affiliation(s)
- Christina Salchow-Hömmen
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Matej Skrobot
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Magdalena C E Jochner
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas Schauer
- Control Systems Group, Technische Universität Berlin, Berlin, Germany
| | - Andrea A Kühn
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Charité-Universitätsmedizin Berlin, Berlin, Germany
- NeuroCure Clinical Research Centre, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Neurodegenerative Diseases, DZNE, Berlin, Germany
| | - Nikolaus Wenger
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
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Morgan C, Tonkin EL, Craddock I, Whone AL. Acceptability of an In-Home Multimodal Sensor Platform in Parkinson’s Disease: A Qualitative Study (Preprint). JMIR Hum Factors 2022; 9:e36370. [PMID: 35797101 PMCID: PMC9305404 DOI: 10.2196/36370] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/07/2022] [Accepted: 05/23/2022] [Indexed: 12/28/2022] Open
Abstract
Background Parkinson disease (PD) symptoms are complex, gradually progressive, and fluctuate hour by hour. Home-based technological sensors are being investigated to measure symptoms and track disease progression. A smart home sensor platform, with cameras and wearable devices, could be a useful tool to use to get a fuller picture of what someone’s symptoms are like. High-resolution video can capture the ground truth of symptoms and activities. There is a paucity of information about the acceptability of such sensors in PD. Objective The primary objective of our study was to explore the acceptability of living with a multimodal sensor platform in a naturalistic setting in PD. Two subobjectives are to identify any suggested limitations and to explore the sensors’ impact on participant behaviors. Methods A qualitative study was conducted with an inductive approach using semistructured interviews with a cohort of PD and control participants who lived freely for several days in a home-like environment while continuously being sensed. Results This study of 24 participants (12 with PD) found that it is broadly acceptable to use multimodal sensors including wrist-worn wearables, cameras, and other ambient sensors passively in free-living in PD. The sensor that was found to be the least acceptable was the wearable device. Suggested limitations on the platform for home deployment included camera-free time and space. Behavior changes were noted by the study participants, which may have related to being passively sensed. Recording high-resolution video in the home setting for limited periods of time was felt to be acceptable to all participants. Conclusions The results broaden the knowledge of what types of sensors are acceptable for use in research in PD and what potential limitations on these sensors should be considered in future work. The participants’ reported behavior change in this study should inform future similar research design to take this factor into account. Collaborative research study design, involving people living with PD at every stage, is important to ensure that the technology is acceptable and that the data outcomes produced are ecologically valid and accurate. International Registered Report Identifier (IRRID) RR2-10.1136/bmjopen-2020-041303
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Affiliation(s)
- Catherine Morgan
- Translational Health Sciences, University of Bristol Medical School, Bristol, United Kingdom
- Movement Disorders Group, Bristol Brain Centre, North Bristol NHS Trust, Bristol, United Kingdom
| | - Emma L Tonkin
- School of Computer Science, Electrical and Electronic Engineering, University of Bristol, Bristol, United Kingdom
| | - Ian Craddock
- School of Computer Science, Electrical and Electronic Engineering, University of Bristol, Bristol, United Kingdom
| | - Alan L Whone
- Translational Health Sciences, University of Bristol Medical School, Bristol, United Kingdom
- Movement Disorders Group, Bristol Brain Centre, North Bristol NHS Trust, Bristol, United Kingdom
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Vignoud G, Desjardins C, Salardaine Q, Mongin M, Garcin B, Venance L, Degos B. Video-Based Automated Assessment of Movement Parameters Consistent with MDS-UPDRS III in Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2022; 12:2211-2222. [PMID: 35964204 PMCID: PMC9661322 DOI: 10.3233/jpd-223445] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/24/2022] [Indexed: 06/01/2023]
Abstract
BACKGROUND Among motor symptoms of Parkinson's disease (PD), including rigidity and resting tremor, bradykinesia is a mandatory feature to define the parkinsonian syndrome. MDS-UPDRS III is the worldwide reference scale to evaluate the parkinsonian motor impairment, especially bradykinesia. However, MDS-UPDRS III is an agent-based score making reproducible measurements and follow-up challenging. OBJECTIVE Using a deep learning approach, we developed a tool to compute an objective score of bradykinesia based on the guidelines of the gold-standard MDS-UPDRS III. METHODS We adapted and applied two deep learning algorithms to detect a two-dimensional (2D) skeleton of the hand composed of 21 predefined points, and transposed it into a three-dimensional (3D) skeleton for a large database of videos of parkinsonian patients performing MDS-UPDRS III protocols acquired in the Movement Disorder unit of Avicenne University Hospital. RESULTS We developed a 2D and 3D automated analysis tool to study the evolution of several key parameters during the protocol repetitions of the MDS-UPDRS III. Scores from 2D automated analysis showed a significant correlation with gold-standard ratings of MDS-UPDRS III, measured with coefficients of determination for the tapping (0.609) and hand movements (0.701) protocols using decision tree algorithms. The individual correlations of the different parameters measured with MDS-UPDRS III scores carry meaningful information and are consistent with MDS-UPDRS III guidelines. CONCLUSION We developed a deep learning-based tool to precisely analyze movement parameters allowing to reliably score bradykinesia for parkinsonian patients in a MDS-UPDRS manner.
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Affiliation(s)
- Gaëtan Vignoud
- Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université PSL, Paris, France
- INRIA Paris, MAMBA (Modelling and Analysis in Medical and Biological Applications), Paris, France
| | - Clément Desjardins
- APHP, Hôpital Avicenne, Hôpitaux Universitaires de Paris-Seine Saint Denis (HUPSSD), Department of Neurology, Sorbonne Paris Nord, NS-PARK/FCRIN network, Bobigny, France
| | - Quentin Salardaine
- APHP, Hôpital Avicenne, Hôpitaux Universitaires de Paris-Seine Saint Denis (HUPSSD), Department of Neurology, Sorbonne Paris Nord, NS-PARK/FCRIN network, Bobigny, France
| | - Marie Mongin
- APHP, Hôpital Avicenne, Hôpitaux Universitaires de Paris-Seine Saint Denis (HUPSSD), Department of Neurology, Sorbonne Paris Nord, NS-PARK/FCRIN network, Bobigny, France
| | - Béatrice Garcin
- APHP, Hôpital Avicenne, Hôpitaux Universitaires de Paris-Seine Saint Denis (HUPSSD), Department of Neurology, Sorbonne Paris Nord, NS-PARK/FCRIN network, Bobigny, France
| | - Laurent Venance
- Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université PSL, Paris, France
| | - Bertrand Degos
- Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université PSL, Paris, France
- APHP, Hôpital Avicenne, Hôpitaux Universitaires de Paris-Seine Saint Denis (HUPSSD), Department of Neurology, Sorbonne Paris Nord, NS-PARK/FCRIN network, Bobigny, France
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Godkin FE, Turner E, Demnati Y, Vert A, Roberts A, Swartz RH, McLaughlin PM, Weber KS, Thai V, Beyer KB, Cornish B, Abrahao A, Black SE, Masellis M, Zinman L, Beaton D, Binns MA, Chau V, Kwan D, Lim A, Munoz DP, Strother SC, Sunderland KM, Tan B, McIlroy WE, Van Ooteghem K. Feasibility of a continuous, multi-sensor remote health monitoring approach in persons living with neurodegenerative disease. J Neurol 2021; 269:2673-2686. [PMID: 34705114 PMCID: PMC8548705 DOI: 10.1007/s00415-021-10831-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 01/02/2023]
Abstract
BACKGROUND Remote health monitoring with wearable sensor technology may positively impact patient self-management and clinical care. In individuals with complex health conditions, multi-sensor wear may yield meaningful information about health-related behaviors. Despite available technology, feasibility of device-wearing in daily life has received little attention in persons with physical or cognitive limitations. This mixed methods study assessed the feasibility of continuous, multi-sensor wear in persons with cerebrovascular (CVD) or neurodegenerative disease (NDD). METHODS Thirty-nine participants with CVD, Alzheimer's disease/amnestic mild cognitive impairment, frontotemporal dementia, Parkinson's disease, or amyotrophic lateral sclerosis (median age 68 (45-83) years, 36% female) wore five devices (bilateral ankles and wrists, chest) continuously for a 7-day period. Adherence to device wearing was quantified by examining volume and pattern of device removal (non-wear). A thematic analysis of semi-structured de-brief interviews with participants and study partners was used to examine user acceptance. RESULTS Adherence to multi-sensor wear, defined as a minimum of three devices worn concurrently, was high (median 98.2% of the study period). Non-wear rates were low across all sensor locations (median 17-22 min/day), with significant differences between some locations (p = 0.006). Multi-sensor non-wear was higher for daytime versus nighttime wear (p < 0.001) and there was a small but significant increase in non-wear over the collection period (p = 0.04). Feedback from de-brief interviews suggested that multi-sensor wear was generally well accepted by both participants and study partners. CONCLUSION A continuous, multi-sensor remote health monitoring approach is feasible in a cohort of persons with CVD or NDD.
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Affiliation(s)
- F Elizabeth Godkin
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Erin Turner
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Youness Demnati
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Adam Vert
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Angela Roberts
- School of Communication Sciences and Disorders, Elborn College, Western University, London, ON, Canada.,Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA
| | - Richard H Swartz
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | | | - Kyle S Weber
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Vanessa Thai
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Kit B Beyer
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Benjamin Cornish
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Agessandro Abrahao
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Sandra E Black
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Mario Masellis
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Lorne Zinman
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Derek Beaton
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Malcolm A Binns
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Vivian Chau
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Donna Kwan
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Andrew Lim
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Douglas P Munoz
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Kelly M Sunderland
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Brian Tan
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - William E McIlroy
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Karen Van Ooteghem
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
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Abstract
PURPOSE OF REVIEW The COVID-pandemic has facilitated the implementation of telemedicine in both clinical practice and research. We highlight recent developments in three promising areas of telemedicine: teleconsultation, telemonitoring, and teletreatment. We illustrate this using Parkinson's disease as a model for other chronic neurological disorders. RECENT FINDINGS Teleconsultations can reliably administer parts of the neurological examination remotely, but are typically not useful for establishing a reliable diagnosis. For follow-ups, teleconsultations can provide enhanced comfort and convenience to patients, and provide opportunities for blended and proactive care models. Barriers include technological challenges, limited clinician confidence, and a suboptimal clinician-patient relationship. Telemonitoring using wearable sensors and smartphone-based apps can support clinical decision-making, but we lack large-scale randomized controlled trials to prove effectiveness on clinical outcomes. Increasingly many trials are now incorporating telemonitoring as an exploratory outcome, but more work remains needed to demonstrate its clinical meaningfulness. Finding a balance between benefits and burdens for individual patients remains vital. Recent work emphasised the promise of various teletreatment solutions, such as remotely adjustable deep brain stimulation parameters, virtual reality enhanced exercise programs, and telephone-based cognitive behavioural therapy. Personal contact remains essential to ascertain adherence to teletreatment. SUMMARY The availability of different telemedicine tools for remote consultation, monitoring, and treatment is increasing. Future research should establish whether telemedicine improves outcomes in routine clinical care, and further underpin its merits both as intervention and outcome in research settings.
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Affiliation(s)
- Robin van den Bergh
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders
| | - Bastiaan R. Bloem
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders
| | - Marjan J. Meinders
- Radboud University Medical Center, Radboud Institute for Health Sciences, Scientific Center for Quality of Healthcare, Nijmegen, The Netherlands
| | - Luc J.W. Evers
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders
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Tsamis KI, Rigas G, Nikolaos K, Fotiadis DI, Konitsiotis S. Accurate Monitoring of Parkinson's Disease Symptoms With a Wearable Device During COVID-19 Pandemic. In Vivo 2021; 35:2327-2330. [PMID: 34182513 DOI: 10.21873/invivo.12507] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 05/03/2021] [Accepted: 05/04/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Accurate assessment of symptoms in Parkinson's disease (PD) is essential for optimal treatment decisions. During the past few years, different monitoring modalities have started to be used in the everyday clinical practice, mainly for the evaluation of motor symptoms. However, monitoring technologies for PD have not yet gained wide acceptance among physicians, patients, and caregivers. The COVID-19 pandemic disrupted the patients' access to healthcare, bringing to the forefront the need for wearable sensors, which provide effective remote symptoms' evaluation and follow-up. CASE REPORT We report two cases with PD, whose symptoms were monitored with a new wearable CE-marked system (PDMonitor®), enabling appropriate treatment modifications. CONCLUSION Objective assessment of the patient's motor symptoms in his daily home environment is essential for an accurate monitoring in PD and enhances treatment decisions.
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Affiliation(s)
| | - George Rigas
- Unit of Medical Technology and Intelligent Information System, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece.,PD Neurotechnology Ltd, Ioannina, Greece
| | | | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information System, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
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