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 (Cornellà de Llobregat, Catalunya))
Samà, Albert (Sense4Care (Cornellà de Llobregat, Catalunya))
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)
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 Andrés (Hospital Universitario 12 de Octubre (Madrid))
Català, Andreu (Sense4Care (Cornellà de Llobregat, Catalunya))
Universitat Autònoma de Barcelona

Fecha:	2018
Resumen:	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.
Ayudas:	Instituto de Salud Carlos III DTS15-00209 Instituto de Salud Carlos III PI12-03028
Derechos:	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.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Parkinson disease ; Movement disorders ; Movement ; Gait
Publicado en:	JMIR Rehabilitation and Assistive Technologies, Vol. 5 (april 2018) , ISSN 2369-2529

DOI: 10.2196/rehab.8335
PMID: 29695377

10 p, 1.6 MB

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