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Remote Monitoring of Treatment Response in Parkinson's Disease: The Habit of Typing on a Computer.

Authors :
Matarazzo M
Arroyo-Gallego T
Montero P
Puertas-Martín V
Butterworth I
Mendoza CS
Ledesma-Carbayo MJ
Catalán MJ
Molina JA
Bermejo-Pareja F
Martínez-Castrillo JC
López-Manzanares L
Alonso-Cánovas A
Rodríguez JH
Obeso I
Martínez-Martín P
Martínez-Ávila JC
de la Cámara AG
Gray M
Obeso JA
Giancardo L
Sánchez-Ferro Á
Source :
Movement disorders : official journal of the Movement Disorder Society [Mov Disord] 2019 Oct; Vol. 34 (10), pp. 1488-1495. Date of Electronic Publication: 2019 Jun 18.
Publication Year :
2019

Abstract

Objective: The recent advances in technology are opening a new opportunity to remotely evaluate motor features in people with Parkinson's disease (PD). We hypothesized that typing on an electronic device, a habitual behavior facilitated by the nigrostriatal dopaminergic pathway, could allow for objectively and nonobtrusively monitoring parkinsonian features and response to medication in an at-home setting.<br />Methods: We enrolled 31 participants recently diagnosed with PD who were due to start dopaminergic treatment and 30 age-matched controls. We remotely monitored their typing pattern during a 6-month (24 weeks) follow-up period before and while dopaminergic medications were being titrated. The typing data were used to develop a novel algorithm based on recursive neural networks and detect participants' responses to medication. The latter were defined by the Unified Parkinson's Disease Rating Scale-III (UPDRS-III) minimal clinically important difference. Furthermore, we tested the accuracy of the algorithm to predict the final response to medication as early as 21 weeks prior to the final 6-month clinical outcome.<br />Results: The score on the novel algorithm based on recursive neural networks had an overall moderate kappa agreement and fair area under the receiver operating characteristic (ROC) curve with the time-coincident UPDRS-III minimal clinically important difference. The participants classified as responders at the final visit (based on the UPDRS-III minimal clinically important difference) had higher scores on the novel algorithm based on recursive neural networks when compared with the participants with stable UPDRS-III, from the third week of the study onward.<br />Conclusions: This preliminary study suggests that remotely gathered unsupervised typing data allows for the accurate detection and prediction of drug response in PD. © 2019 International Parkinson and Movement Disorder Society.<br /> (© 2019 International Parkinson and Movement Disorder Society.)

Details

Language :
English
ISSN :
1531-8257
Volume :
34
Issue :
10
Database :
MEDLINE
Journal :
Movement disorders : official journal of the Movement Disorder Society
Publication Type :
Academic Journal
Accession number :
31211469
Full Text :
https://doi.org/10.1002/mds.27772