Validation of a Portable Device for Mapping Motor and Gait Disturbances in Parkinson’s Disease
Open accessAbstract
BACKGROUND: Patients with severe idiopathic Parkinson's disease experience motor fluctuations, which are often difficult to control. Accurate mapping of such motor fluctuations could help improve patients' treatment. OBJECTIVE: The objective of the study was to focus on developing and validating an automatic detector of motor fluctuations. The device is small, wearable, and detects the motor phase while the patients walk in their daily activities. METHODS: Algorithms for detection of motor fluctuations were developed on the basis of experimental data from 20 patients who were asked to wear the detector while performing different daily life activities, both in controlled (laboratory) and noncontrolled environments. Patients with motor fluctuations completed the experimental protocol twice: (1) once in the ON, and (2) once in the OFF phase. The validity of the algorithms was tested on 15 different patients who were asked to wear the detector for several hours while performing daily activities in their habitual environments. In order to assess the validity of detector measurements, the results of the algorithms were compared with data collected by trained observers who were accompanying the patients all the time. RESULTS: The motor fluctuation detector showed a mean sensitivity of 0.96 (median 1; interquartile range, IQR, 0.93-1) and specificity of 0.94 (median 0.96; IQR, 0.90-1). CONCLUSIONS: ON/OFF motor fluctuations in Parkinson's patients can be detected with a single sensor, which can be worn in everyday life.
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In Parkinson's disease, wearables were used to detect and quantify medication-evoked adverse symptoms such as dyskinesia and motor fluctuations.
“In PD, wearables were used to detect and quantify cardinal motor symptoms including bradykinesia [19], tremor [20, 21], and postural sway [22, 23] as well as medication-evoked adverse symptoms such as dyskinesia [24–27] and motor fluctuations [28, 29].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [29]
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- Authors
- Rodríguez-Molinero, A., Samà, A., Pérez-Martínez, D. A., Pérez López, C., Romagosa, J., Bayés, À., Sanz, P., Calopa, M., Gálvez-Barrón, C., de Mingo, E., Rodríguez Martín, D., Gonzalo, N., Formiga, F., Cabestany, J., & Catalá, A.
- Affiliations
- Universitat Politècnica de Catalunya (ES)
- Hospital Universitario Infanta Cristina (ES)
- Hospital Quirón Teknon (ES)
- Bellvitge University Hospital (ES)
- Consorci Sanitari Garraf (ES)
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2015-02-02
- Volume
- 3
- Issue
- 1
- Pages
- e9
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC4342689
- OpenAlex
- W2139806192
- Cited by
- 64
- References
- 18
- Citations by year
- 2025: 7 · 2024: 6 · 2023: 4 · 2022: 6 · 2021: 6 · 2020: 5 · 2019: 7 · 2018: 8 · 2017: 7 · 2016: 3 · 2015: 5
- FWCI
- 6.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ARAN (University of Galway Research Repository) (Ollscoil na Gaillimhe – University of Galway) (acceptedVersion, cc-by-nc-nd)
- Arrow@dit (Dublin Institute of Technology) (submittedVersion)
- ARAN (University of Galway Research Repository) (Ollscoil na Gaillimhe – University of Galway) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, cc-by-nc-nd)
- ARAN (University of Galway Research Repository) (Ollscoil na Gaillimhe – University of Galway) (submittedVersion, cc-by-nc)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, cc-by-nc-nd)
- Research Repository UCD (University College Dublin) (cc-by-nc)
- Funders
- Ministerio de Economía y Competitividad (PI12/)
- Instituto de Salud Carlos III (PI12/03028)
- European Regional Development Fund
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Keywords
- Parkinson's disease, motor fluctuations, wearable sensors, gait analysis, automated detection algorithm, sensor validation, activities of daily living, motor disturbances
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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