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Remote Monitoring of Treatment Response in Parkinson's Disease: The Habit of Typing on a Computer
Author(s) -
Matarazzo Michele,
ArroyoGallego Teresa,
Montero Paloma,
PuertasMartín Verónica,
Butterworth Ian,
Mendoza Carlos S.,
LedesmaCarbayo María J.,
Catalán María José,
Molina José Antonio,
BermejoPareja Félix,
MartínezCastrillo Juan Carlos,
LópezManzanares Lydia,
AlonsoCánovas Araceli,
Rodríguez Jaime Herreros,
Obeso Ignacio,
MartínezMartín Pablo,
MartínezÁvila José Carlos,
Cámara Agustín Gómez,
Gray Martha,
Obeso José A.,
Giancardo Luca,
SánchezFerro Álvaro
Publication year - 2019
Publication title -
movement disorders
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.352
H-Index - 198
eISSN - 1531-8257
pISSN - 0885-3185
DOI - 10.1002/mds.27772
Subject(s) - dopaminergic , parkinson's disease , rating scale , receiver operating characteristic , physical medicine and rehabilitation , disease , typing , levodopa , psychology , medicine , physical therapy , computer science , developmental psychology , dopamine , speech recognition
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. 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. 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. 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