Scopus: 20 cites, Google Scholar: cites,
A Kinematic Sensor and Algorithm to Detect Motor Fluctuations in Parkinson Disease : Validation Study Under Real Conditions of Use
Rodríguez-Molinero, Alejandro (Consorci Sanitari del Garraf. Research Department)
Pérez-López, Carlos (Sense4Care)
Samà, Albert (Sense4Care)
de Mingo, Eva (Consorci Sanitari del Garraf. Geriatrics Department)
Rodríguez-Martín, Daniel (Universitat Politècnica de Catalunya. Centre Específic de Recerca Centre d'Estudis Tecnològics per l'Atenció a la Dependència i la Vida Autònoma-CETpD)
Hernández-Vara, Jorge (Hospital Universitari Vall d'Hebron)
Bayés, Àngels (Hospital Quirónsalud Barcelona)
Moral, Alfons (Consorci Sanitari del Garraf. Department of Neurology)
Álvarez, Ramiro (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)
Pérez-Martínez, David A (Hospital Universitario 12 de Octubre (Madrid))
Català, Andreu (Sense4Care)
Universitat Autònoma de Barcelona

Data: 2018
Resum: A new algorithm has been developed, which combines information on gait bradykinesia and dyskinesia provided by a single kinematic sensor located on the waist of Parkinson disease (PD) patients to detect motor fluctuations (On- and Off-periods). The goal of this study was to analyze the accuracy of this algorithm under real conditions of use. This validation study of a motor-fluctuation detection algorithm was conducted on a sample of 23 patients with advanced PD. Patients were asked to wear the kinematic sensor for 1 to 3 days at home, while simultaneously keeping a diary of their On- and Off-periods. During this testing, researchers were not present, and patients continued to carry on their usual daily activities in their natural environment. The algorithm's outputs were compared with the patients' records, which were used as the gold standard. The algorithm produced 37% more results than the patients' records (671 vs 489). The positive predictive value of the algorithm to detect Off-periods, as compared with the patients' records, was 92% (95% CI 87. 33%-97. 3%) and the negative predictive value was 94% (95% CI 90. 71%-97. 1%); the overall classification accuracy was 92. 20%. The kinematic sensor and the algorithm for detection of motor-fluctuations validated in this study are an accurate and useful tool for monitoring PD patients with difficult-to-control motor fluctuations in the outpatient setting.
Ajuts: Instituto de Salud Carlos III DTS15-00209
Instituto de Salud Carlos III PI12-03028
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Llengua: Anglès
Document: Article ; recerca ; Versió publicada
Matèria: Parkinson disease ; Movement disorders ; Movement ; Gait
Publicat a: JMIR Rehabilitation and Assistive Technologies, Vol. 5 (april 2018) , ISSN 2369-2529

DOI: 10.2196/rehab.8335
PMID: 29695377


10 p, 1.6 MB

El registre apareix a les col·leccions:
Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències de la salut i biociències > Institut d'Investigació en Ciencies de la Salut Germans Trias i Pujol (IGTP)
Articles > Articles de recerca
Articles > Articles publicats

 Registre creat el 2018-06-18, darrera modificació el 2021-08-08



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