A Paradigm Shift in the Management of Patients with Parkinson’s Disease

Background: Technological evolution leads to the constant enhancement of monitoring systems and recording symptoms of diverse disorders. Summary: For Parkinson’s disease, wearable devices empowered with machine learning analysis are the main modules for objective measurements. Software and hardware improvements have led to the development of reliable systems that can detect symptoms accurately and be implicated in the follow-up and treatment decisions. Key Messages: Among many different devices developed so far, the most promising ones are those that can record symptoms from all extremities and the trunk, in the home environment during the activities of daily living, assess gait impairment accurately, and be suitable for a long-term follow-up of the patients. Such wearable systems pave the way for a paradigm shift in the management of patients with Parkinson’s disease.

[1]  R. Vaz,et al.  Machine learning for adaptive deep brain stimulation in Parkinson’s disease: closing the loop , 2023, Journal of Neurology.

[2]  Lucia M. Li,et al.  Multicohort cross-sectional study of cognitive and behavioural digital biomarkers in neurodegeneration: the Living Lab Study protocol , 2023, BMJ open.

[3]  A. Di Capua,et al.  An Amperometric Biosensor Based on a Bilayer of Electrodeposited Graphene Oxide and Co-Crosslinked Tyrosinase for L-Dopa Detection in Untreated Human Plasma , 2023, Molecules.

[4]  F. Ferreri,et al.  Objective measurement versus clinician-based assessment for Parkinson’s disease , 2023, Expert review of neurotherapeutics.

[5]  H. Reichmann,et al.  Toward objective monitoring of Parkinson's disease motor symptoms using a wearable device: wearability and performance evaluation of PDMonitor® , 2023, Frontiers in Neurology.

[6]  D. Fotiadis,et al.  Clinical Evaluation in Parkinson’s Disease: Is the Golden Standard Shiny Enough? , 2023, Sensors.

[7]  R. Reilmann,et al.  Technology acceptance of digital devices for home use: Qualitative results of a mixed methods study , 2023, Digital health.

[8]  R. Pahwa,et al.  Comparative Effectiveness of Device-Aided Therapies on Quality of Life and Off-Time in Advanced Parkinson’s Disease: A Systematic Review and Bayesian Network Meta-analysis , 2022, CNS Drugs.

[9]  Houeto Jean-Luc [Parkinson's disease]. , 2022, La Revue du praticien.

[10]  Jeffrey A. Herron,et al.  Automated deep brain stimulation programming with safety constraints for tremor suppression in patients with Parkinson’s disease and essential tremor , 2022, Journal of neural engineering.

[11]  R. Gold,et al.  Parkinson’s disease multimodal complex treatment improves gait performance: an exploratory wearable digital device-supported study , 2022, Journal of Neurology.

[12]  Kang Ren,et al.  Recognition of freezing of gait in Parkinson’s disease based on combined wearable sensors , 2022, BMC Neurology.

[13]  Kuldeep Mahato,et al.  Closing the loop for patients with Parkinson disease: where are we? , 2022, Nature Reviews Neurology.

[14]  A. Rodríguez-Molinero,et al.  A New Paradigm in Parkinson's Disease Evaluation With Wearable Medical Devices: A Review of STAT-ONTM , 2022, Frontiers in Neurology.

[15]  G. Ebersbach,et al.  Validation of the PD home diary for assessment of motor fluctuations in advanced Parkinson’s disease , 2022, NPJ Parkinson's disease.

[16]  A. Pickard,et al.  The Cost Effectiveness of Levodopa-Carbidopa Intestinal Gel in the Treatment of Advanced Parkinson’s Disease in England , 2022, PharmacoEconomics.

[17]  I. J. Pomeraniec,et al.  Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson’s disease motor symptoms , 2022, npj Digital Medicine.

[18]  H. Reichmann,et al.  Feasibility of a Multimodal Telemedical Intervention for Patients with Parkinson’s Disease—A Pilot Study , 2022, Journal of clinical medicine.

[19]  B. Caulfield,et al.  Assessing the usability of wearable devices to measure gait and physical activity in chronic conditions: a systematic review , 2021, Journal of neuroengineering and rehabilitation.

[20]  J. Condell,et al.  The Views and Needs of People With Parkinson Disease Regarding Wearable Devices for Disease Monitoring: Mixed Methods Exploration , 2021, JMIR formative research.

[21]  M. McColl,et al.  Barriers to Accessing Healthcare Services for People with Parkinson’s Disease: A Scoping Review , 2021, Journal of Parkinson's disease.

[22]  T. Morishita,et al.  Personalized Medicine in Parkinson’s Disease: New Options for Advanced Treatments , 2021, Journal of personalized medicine.

[23]  J. Youn,et al.  Validation of Blood Pressure Measurement Using a Smartwatch in Patients With Parkinson's Disease , 2021, Frontiers in Neurology.

[24]  A. Cereatti,et al.  Detecting Sensitive Mobility Features for Parkinson's Disease Stages Via Machine Learning , 2021, Movement disorders : official journal of the Movement Disorder Society.

[25]  Ruth B. Schneider,et al.  Digital Technology in Movement Disorders: Updates, Applications, and Challenges , 2021, Current Neurology and Neuroscience Reports.

[26]  B. Guthrie,et al.  Walking Speed Reliably Measures Clinically Significant Changes in Gait by Directional Deep Brain Stimulation , 2021, Frontiers in Human Neuroscience.

[27]  A. Suppa,et al.  Prediction of Freezing of Gait in Parkinson’s Disease Using Wearables and Machine Learning , 2021, Sensors.

[28]  W. Poewe,et al.  Continuous Subcutaneous Levodopa Delivery for Parkinson’s Disease: A Randomized Study , 2020, Journal of Parkinson's disease.

[29]  Lina Chen,et al.  More Sensitive Identification for Bradykinesia Compared to Tremors in Parkinson’s Disease Based on Parkinson’s KinetiGraph (PKG) , 2020, Frontiers in Aging Neuroscience.

[30]  G. Deuschl,et al.  The economic benefit of timely, adequate, and adherence to Parkinson's disease treatment: the Value of Treatment Project 2 , 2020, European journal of neurology.

[31]  M. Versino,et al.  Kinematic but not clinical measures predict falls in Parkinson-related orthostatic hypotension , 2020, Journal of Neurology.

[32]  W. Poewe,et al.  Pharmacologic Treatment of Motor Symptoms Associated with Parkinson Disease. , 2020, Neurologic clinics.

[33]  Csaba Váradi Clinical Features of Parkinson’s Disease: The Evolution of Critical Symptoms , 2020, Biology.

[34]  B. Bloem,et al.  The Coronavirus Disease 2019 Crisis as Catalyst for Telemedicine for Chronic Neurological Disorders. , 2020, JAMA neurology.

[35]  Y. Sohn,et al.  Initial motor reserve and long-term prognosis in Parkinson's disease , 2020, Neurobiology of Aging.

[36]  M. Okun,et al.  Diagnosis and Treatment of Parkinson Disease: A Review. , 2020, JAMA.

[37]  Dimitrios I. Fotiadis,et al.  Feasibility and Utility of mHealth for the Remote Monitoring of Parkinson Disease: Ancillary Study of the PD_manager Randomized Controlled Trial , 2020, JMIR mHealth and uHealth.

[38]  C. Luca,et al.  Management of Motor Features in Advanced Parkinson Disease. , 2020, Clinics in geriatric medicine.

[39]  R. Barker,et al.  Motor complications in Parkinson's disease: 13‐year follow‐up of the CamPaIGN cohort , 2019, Movement disorders : official journal of the Movement Disorder Society.

[40]  S. Mantri,et al.  Comparing self-reported and objective monitoring of physical activity in Parkinson disease. , 2019, Parkinsonism & related disorders.

[41]  Bastiaan R Bloem,et al.  Measuring Parkinson's disease over time: The real‐world within‐subject reliability of the MDS‐UPDRS , 2019, Movement disorders : official journal of the Movement Disorder Society.

[42]  Babak Boroojerdi,et al.  Does the MDS-UPDRS provide the precision to assess progression in early Parkinson’s disease? Learnings from the Parkinson’s progression marker initiative cohort , 2019, Journal of Neurology.

[43]  M. Armstrong,et al.  Barriers and facilitators of communication about off periods in Parkinson’s disease: Qualitative analysis of patient, carepartner, and physician Interviews , 2019, PloS one.

[44]  Samuel J. Reinfelder,et al.  Wearable sensors objectively measure gait parameters in Parkinson’s disease , 2017, PloS one.

[45]  F. Horak,et al.  Disability Rating Scales in Parkinson's Disease: Critique and Recommendations , 2016, Movement disorders : official journal of the Movement Disorder Society.

[46]  E. Racine,et al.  Examining chronic care patient preferences for involvement in health‐care decision making: the case of Parkinson's disease patients in a patient‐centred clinic , 2016, Health expectations : an international journal of public participation in health care and health policy.

[47]  Y. Shimo,et al.  Referring Parkinson’s disease patients for deep brain stimulation: a RAND/UCLA appropriateness study , 2015, Journal of Neurology.

[48]  Jorik Nonnekes,et al.  Identifying freezing of gait in Parkinson's disease during freezing provoking tasks using waist-mounted accelerometry. , 2015, Parkinsonism & related disorders.

[49]  Bijan Najafi,et al.  Motor Performance Assessment in Parkinson’s Disease: Association between Objective In-Clinic, Objective In-Home, and Subjective/Semi-Objective Measures , 2015, PloS one.

[50]  P. Silburn,et al.  Wearable Sensor Use for Assessing Standing Balance and Walking Stability in People with Parkinson’s Disease: A Systematic Review , 2015, PloS one.

[51]  N. Jetté,et al.  The prevalence of Parkinson's disease: A systematic review and meta‐analysis , 2014, Movement disorders : official journal of the Movement Disorder Society.

[52]  Dimitrios I. Fotiadis,et al.  PERFORM: A System for Monitoring, Assessment and Management of Patients with Parkinson's Disease , 2014, Sensors.

[53]  Dimitrios I. Fotiadis,et al.  Automatic detection of freezing of gait events in patients with Parkinson's disease , 2013, Comput. Methods Programs Biomed..

[54]  Dimitrios I. Fotiadis,et al.  Assessment of Tremor Activity in the Parkinson’s Disease Using a Set of Wearable Sensors , 2012, IEEE Transactions on Information Technology in Biomedicine.

[55]  Dimitrios I. Fotiadis,et al.  Automated Levodopa-induced dyskinesia assessment , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[56]  S. Hamid Nawab,et al.  Dynamic neural network detection of tremor and dyskinesia from wearable sensor data , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[57]  J. Brotchie,et al.  Levodopa-induced dyskinesia in Parkinson’s disease , 2005, Journal of Neural Transmission.

[58]  Catherine Dehollain,et al.  Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring , 2004, IEEE Transactions on Biomedical Engineering.

[59]  S. Gielen,et al.  Online monitoring of dyskinesia in patients with Parkinson's disease , 2003, IEEE Engineering in Medicine and Biology Magazine.

[60]  Carl May,et al.  Remote Doctors and Absent Patients: Acting at a Distance in Telemedicine? , 2003 .

[61]  B. V. van Hilten,et al.  Ambulatory quantitative assessment of body position, bradykinesia, and hypokinesia in Parkinson's disease. , 1998, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[62]  J. D. Janssen,et al.  A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity , 1997, IEEE Transactions on Biomedical Engineering.

[63]  D. Fotiadis,et al.  Accurate Monitoring of Parkinson’s Disease Symptoms With a Wearable Device During COVID-19 Pandemic , 2021, In Vivo.

[64]  A. Rodríguez-Molinero,et al.  A "HOLTER" for Parkinson's disease: Validation of the ability to detect on-off states using the REMPARK system. , 2018, Gait & posture.

[65]  S. Iwarsson,et al.  Levodopa Effect and Motor Function in Late Stage Parkinson's Disease. , 2018, Journal of Parkinson's disease.

[66]  J. Stamford,et al.  Exploring Issues Around Wearing-off and Quality of Life: The OFF-PARK Survey of People with Parkinson's Disease and their Care Partners. , 2015, Journal of Parkinson's disease.

[67]  P. Kempster,et al.  Automated assessment of bradykinesia and dyskinesia in Parkinson's disease. , 2012, Journal of Parkinson's disease.

[68]  María Teresa Arredondo,et al.  Assessment of bradykinesia in Parkinson's disease patients through a multi-parametric system , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[69]  Lucila Ohno-Machado,et al.  Classification of Movement States in Parkinson's Disease Using a Wearable Ambulatory Monitor , 2003, AMIA.