Giovanna Lopane1, Sabato Mellone2, Lorenzo Chiari2,3, Pietro Cortelli1,4, Giovanna Calandra-Buonaura1,4, Manuela Contin1,4. 1. IRCCS-Institute of Neurological Sciences of Bologna, Bologna, Italy. 2. Biomedical Engineering Unit, Department of Electrical, Electronic, and Information Engineering, University of Bologna, Bologna, Italy. 3. Health Sciences and Technologies-Interdepartmental Center for Industrial Research, University of Bologna, Bologna, Italy. 4. Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
Abstract
OBJECTIVE: In current clinical practice, assessment of levodopa-induced dyskinesias (LIDs) in Parkinson's disease (PD) is based on semiquantitative scales or patients' diaries. We aimed to assess the feasibility, clinical validity, and usability of a waist-worn inertial sensor for discriminating between LIDs and physiological sway in both supervised and unsupervised settings. METHODS: Forty-six PD patients on L-dopa therapy, 18 de novo PD patients, and 18 healthy controls were enrolled. Patients underwent clinical assessment of motor signs and dyskinesias and kinetic-dynamic L-dopa monitoring, tracked by serial measurements of plasma drug concentrations and motor and postural tests. RESULTS: A subset of features was selected, which showed excellent reliability. Sensitivity and specificity of the selected features for dyskinesia recognition were assessed in both supervised and unsupervised settings with an accuracy of 95% and 86%, respectively. CONCLUSIONS: Our preliminary findings suggest that it is feasible to design a reliable sensor-based application for dyskinesia monitoring at home.
OBJECTIVE: In current clinical practice, assessment of levodopa-induced dyskinesias (LIDs) in Parkinson's disease (PD) is based on semiquantitative scales or patients' diaries. We aimed to assess the feasibility, clinical validity, and usability of a waist-worn inertial sensor for discriminating between LIDs and physiological sway in both supervised and unsupervised settings. METHODS: Forty-six PDpatients on L-dopa therapy, 18 de novo PDpatients, and 18 healthy controls were enrolled. Patients underwent clinical assessment of motor signs and dyskinesias and kinetic-dynamic L-dopa monitoring, tracked by serial measurements of plasma drug concentrations and motor and postural tests. RESULTS: A subset of features was selected, which showed excellent reliability. Sensitivity and specificity of the selected features for dyskinesia recognition were assessed in both supervised and unsupervised settings with an accuracy of 95% and 86%, respectively. CONCLUSIONS: Our preliminary findings suggest that it is feasible to design a reliable sensor-based application for dyskinesia monitoring at home.
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