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Arasteh E, Mirian MS, Verchere WD, Surathi P, Nene D, Allahdadian S, Doo M, Park KW, Ray S, McKeown MJ. An Individualized Multi-Modal Approach for Detection of Medication "Off" Episodes in Parkinson's Disease via Wearable Sensors. J Pers Med 2023; 13:jpm13020265. [PMID: 36836501 PMCID: PMC9962500 DOI: 10.3390/jpm13020265] [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: 12/15/2022] [Revised: 01/20/2023] [Accepted: 01/27/2023] [Indexed: 02/04/2023] Open
Abstract
The primary treatment for Parkinson's disease (PD) is supplementation of levodopa (L-dopa). With disease progression, people may experience motor and non-motor fluctuations, whereby the PD symptoms return before the next dose of medication. Paradoxically, in order to prevent wearing-off, one must take the next dose while still feeling well, as the upcoming off episodes can be unpredictable. Waiting until feeling wearing-off and then taking the next dose of medication is a sub-optimal strategy, as the medication can take up to an hour to be absorbed. Ultimately, early detection of wearing-off before people are consciously aware would be ideal. Towards this goal, we examined whether or not a wearable sensor recording autonomic nervous system (ANS) activity could be used to predict wearing-off in people on L-dopa. We had PD subjects on L-dopa record a diary of their on/off status over 24 hours while wearing a wearable sensor (E4 wristband®) that recorded ANS dynamics, including electrodermal activity (EDA), heart rate (HR), blood volume pulse (BVP), and skin temperature (TEMP). A joint empirical mode decomposition (EMD) / regression analysis was used to predict wearing-off (WO) time. When we used individually specific models assessed with cross-validation, we obtained > 90% correlation between the original OFF state logged by the patients and the reconstructed signal. However, a pooled model using the same combination of ASR measures across subjects was not statistically significant. This proof-of-principle study suggests that ANS dynamics can be used to assess the on/off phenomenon in people with PD taking L-dopa, but must be individually calibrated. More work is required to determine if individual wearing-off detection can take place before people become consciously aware of it.
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Affiliation(s)
- Emad Arasteh
- Department of Neonatology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, 3585 EA Utrecht, The Netherlands
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, B-3001 Leuven, Belgium
| | - Maryam S. Mirian
- Pacific Parkinson’s Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 2B5, Canada
| | - Wyatt D. Verchere
- Pacific Parkinson’s Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 2B5, Canada
| | - Pratibha Surathi
- Clinical Fellow-Neurophysiology, Columbia New York Presbyterian, New York, NY 1032, USA
| | - Devavrat Nene
- Department of Medicine, Division of Neurology, The University of Ottawa, Ottawa, ON K1Y 4E9, Canada
| | - Sepideh Allahdadian
- Pacific Parkinson’s Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 2B5, Canada
- Department of Neurology, Penn State Milton S. Hershey Medical Center, Hershey, PA 17033, USA
| | - Michelle Doo
- Pacific Parkinson’s Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 2B5, Canada
| | - Kye Won Park
- Pacific Parkinson’s Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 2B5, Canada
| | - Somdattaa Ray
- Pacific Parkinson’s Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 2B5, Canada
| | - Martin J. McKeown
- Pacific Parkinson’s Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 2B5, Canada
- Faculty of Medicine (Neurology), University of British Columbia, Vancouver, BC V6T 2B5, Canada
- Correspondence:
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Saranza G, Lang AE. Levodopa challenge test: indications, protocol, and guide. J Neurol 2020; 268:3135-3143. [PMID: 32333167 DOI: 10.1007/s00415-020-09810-7] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 03/20/2020] [Accepted: 03/24/2020] [Indexed: 12/21/2022]
Abstract
A drug challenge test in Parkinson's disease, such as the levodopa challenge test (LCT), is an easy and generally safe procedure, which has been used by clinicians for various indications. The results of the test have significant implications in the management of patients, from preoperative evaluation for deep brain stimulation to providing the basis for medication adjustments to address motor or non-motor fluctuations and dyskinesias. This paper reviews the different indications and protocols commonly used in an acute LCT. Potential complications of the procedure and an overview of levodopa responsiveness and unresponsiveness are also discussed.
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Affiliation(s)
- Gerard Saranza
- Edmond J. Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, Toronto, ON, Canada
| | - Anthony E Lang
- Edmond J. Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, Toronto, ON, Canada. .,Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada. .,Movement Disorders Clinic, Toronto Western Hospital, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada.
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Rovini E, Maremmani C, Cavallo F. How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review. Front Neurosci 2017; 11:555. [PMID: 29056899 PMCID: PMC5635326 DOI: 10.3389/fnins.2017.00555] [Citation(s) in RCA: 192] [Impact Index Per Article: 27.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Accepted: 09/21/2017] [Indexed: 01/15/2023] Open
Abstract
Background: Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology. OBJECTIVES This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring). DATA SOURCES The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016. STUDY ELIGIBILITY CRITERIA Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers. RESULTS Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.
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Affiliation(s)
- Erika Rovini
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Carlo Maremmani
- U.O. Neurologia, Ospedale delle Apuane (AUSL Toscana Nord Ovest), Massa, Italy
| | - Filippo Cavallo
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
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