Technology in Parkinson’s disease: challenges and opportunities
Open accessAbstract
The miniaturization, sophistication, proliferation, and accessibility of technologies are enabling the capture of more and previously inaccessible phenomena in Parkinson's disease (PD). However, more information has not translated into a greater understanding of disease complexity to satisfy diagnostic and therapeutic needs. Challenges include noncompatible technology platforms, the need for wide‐scale and long‐term deployment of sensor technology (among vulnerable elderly patients in particular), and the gap between the “big data” acquired with sensitive measurement technologies and their limited clinical application. Major opportunities could be realized if new technologies are developed as part of open‐source and/or open‐hardware platforms that enable multichannel data capture sensitive to the broad range of motor and nonmotor problems that characterize PD and are adaptable into self‐adjusting, individualized treatment delivery systems. The International Parkinson and Movement Disorders Society Task Force on Technology is entrusted to convene engineers, clinicians, researchers, and patients to promote the development of integrated measurement and closed‐loop therapeutic systems with high patient adherence that also serve to (1) encourage the adoption of clinico‐pathophysiologic phenotyping and early detection of critical disease milestones, (2) enhance the tailoring of symptomatic therapy, (3) improve subgroup targeting of patients for future testing of disease‐modifying treatments, and (4) identify objective biomarkers to improve the longitudinal tracking of impairments in clinical care and research. This article summarizes the work carried out by the task force toward identifying challenges and opportunities in the development of technologies with potential for improving the clinical management and the quality of life of individuals with PD. © 2016 International Parkinson and Movement Disorder Society
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Our ability to analyze large datasets from wearable and mobile technology to provide clinically relevant information to clinicians and patients remains limited at this time.
“our ability to analyze these datasets to provide clinically relevant information to clinicians and patients remains limited at this time (119).”
From Could Wearable and Mobile Technology Improve the Management of Essential Tremor? (2018) · cites this paper as [119]
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- Authors
- Espay, A. J., Bonato, P., Nahab, F. B., Maetzler, W., Dean, J. M., Klucken, J., Eskofier, B. M., Merola, A., Horak, F., Lang, A. E., Reilmann, R., Giuffrida, J., Nieuwboer, A., Horne, M., Little, M. A., Litvan, I., Simuni, T., Dorsey, E. R., Burack, M. A., Kubota, K., Kamondi, A., Godinho, C., Daneault, J., Mitsi, G., Krinke, L., Hausdorff, J. M., Bloem, B. R., Papapetropoulos, S., & on behalf of the Movement Disorders Society Task Force on Technology
- Affiliations
- University of Cincinnati (US)
- Harvard University (US)
- University of California San Diego (US)
- German Center for Neurodegenerative Diseases (DE)
- Davis Phinney Foundation (US)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Azienda Ospedaliera Citta' della Salute e della Scienza di Torino (IT)
- Oregon Health & Science University (US)
- Toronto Western Hospital (CA)
- University of Münster (DE)
- Great Lakes NeuroTechnologies (United States) (US)
- KU Leuven (BE)
- The University of Melbourne (AU)
- Aston University (GB)
- Northwestern University (US)
- University of Rochester Medicine (US)
- Michael J. Fox Foundation (US)
- Országos Idegsebészeti Tudományos Intézet (HU)
- Escola Superior de Saúde Egas Moniz (PT)
- Department of Physical Medicine and Rehabilitation Harvard Medical School Boston Massachusetts USA
- Apptomics LLC Wellesley Massachusetts USA
- Medtronic (United States) (US)
- Sackler School of Medicine, Tel Aviv University and Center for the Study of Movement Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center Tel Aviv Israel
- Radboud University Nijmegen (NL)
- Massachusetts General Hospital Boston Massachusetts USA
- Corresponding author
- Espay, Alberto J.
- ORCID
- Venue
- Movement Disorders
- Publisher
- Wiley
- Published
- 2016-04-29
- Volume
- 31
- Issue
- 9
- Pages
- 1272-1282
- ISSN
- 0885-3185 (ISSN-L)
- 1531-8257
- PMC
- PMC5014594
- OpenAlex
- W2343290709
- Cited by
- 654
- References
- 62
- Citations by year
- 2026: 50 · 2025: 66 · 2024: 71 · 2023: 69 · 2022: 70 · 2021: 72 · 2020: 72 · 2019: 70 · 2018: 57 · 2017: 44 · 2016: 13
- FWCI
- 8.50
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Movement Disorders (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Aston Publications Explorer (Aston University) (submittedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- eScholarship (California Digital Library) (submittedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- Lirias (KU Leuven) (submittedVersion)
- DZNE Pub (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Funders
- Parkinson's UK (J-1403, J-0901)
- National Institutes of Health (UL1TR000460)
- Canadian Institutes of Health Research
- National Institute on Aging (R01AG006457, P50AG005131)
- National Heart, Lung, and Blood Institute (T35HL007491)
- National Institute of Mental Health (K23MH092735)
- National Institute of Neurological Disorders and Stroke (U01NS086659, R01NS073683, U54NS092089, P20NS092529)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Health Sciences › Medicine › Neurology › Neurological disorders and treatments
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Keywords
- Parkinson's disease, wearable sensors, closed-loop therapy, open-source hardware, open hardware platform, objective biomarkers, phenotyping, disease-modifying therapy, personalized treatment, patient adherence
- MeSH terms
- Humans, Parkinson Disease, Biomedical Technology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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