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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., (© 2019 International Parkinson and Movement Disorder Society.)

Background: Parkinson's disease (PD) is the second most prevalent neurodegenerative disease and one of the most common forms of movement disorder. Although there is no known cure for PD, existing therapies can provide effective symptomatic relief. However, optimal titration is crucial to avoid adverse effects. Today, decision making for PD management is challenging because it relies on subjective clinical evaluations that require a visit to the clinic. This challenge has motivated recent research initiatives to develop tools that can be used by nonspecialists to assess psychomotor impairment. Among these emerging solutions, we recently reported the neuroQWERTY index, a new digital marker able to detect motor impairment in an early PD cohort through the analysis of the key press and release timing data collected during a controlled in-clinic typing task., Objective: The aim of this study was to extend the in-clinic implementation to an at-home implementation by validating the applicability of the neuroQWERTY approach in an uncontrolled at-home setting, using the typing data from subjects' natural interaction with their laptop to enable remote and unobtrusive assessment of PD signs., Methods: We implemented the data-collection platform and software to enable access and storage of the typing data generated by users while using their computer at home. We recruited a total of 60 participants; of these participants 52 (25 people with Parkinson's and 27 healthy controls) provided enough data to complete the analysis. Finally, to evaluate whether our in-clinic-built algorithm could be used in an uncontrolled at-home setting, we compared its performance on the data collected during the controlled typing task in the clinic and the results of our method using the data passively collected at home., Results: Despite the randomness and sparsity introduced by the uncontrolled setting, our algorithm performed nearly as well in the at-home data (area under the receiver operating characteristic curve [AUC] of 0.76 and sensitivity/specificity of 0.73/0.69) as it did when used to evaluate the in-clinic data (AUC 0.83 and sensitivity/specificity of 0.77/0.72). Moreover, the keystroke metrics presented a strong correlation between the 2 typing settings, which suggests a minimal influence of the in-clinic typing task in users' normal typing., Conclusions: The finding that an algorithm trained on data from an in-clinic setting has comparable performance with that tested on data collected through naturalistic at-home computer use reinforces the hypothesis that subtle differences in motor function can be detected from typing behavior. This work represents another step toward an objective, user-convenient, and quasi-continuous monitoring tool for PD., (©Teresa Arroyo-Gallego, María J Ledesma-Carbayo, Ian Butterworth, Michele Matarazzo, Paloma Montero-Escribano, Verónica Puertas-Martín, Martha L Gray, Luca Giancardo, Álvaro Sánchez-Ferro. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 26.03.2018.)

Objectives: Patients with Parkinson's disease (PD) and essential tremor (ET) have a higher risk of cognitive impairment than age-matched controls. Only a few small studies (11-18 subjects per group) have directly compared the cognitive profile of these conditions. Our aim was to compare the cognitive profile of patients with these two conditions to each other and to healthy individuals in a population-based study of non-demented participants., Materials and Methods: This investigation was part of the NEDICES study, a survey of the elderly in which 2438 dementia-free participants underwent a short neuropsychological battery. We used nonparametric techniques to evaluate whether there are differences and/or a gradient of impairment across the groups (PD, ET, and controls). Also, we performed a head-to-head comparison of ET and PD, adjusting for age and education., Results: Patients with PD (N=46) and ET (N=180) had poorer cognition than controls (N=2212). An impaired gradient of performance was evident. PD scored lower than ET, and then each of these lower than controls, in memory (P<.05) and verbal fluency (P<.001) tasks. When we compared PD and ET, the former had lower scores in verbal fluency (P<.05), whereas the later had a poorer cognitive processing speed (P<.05)., Conclusions: This large population-based study demonstrates that both conditions influence cognitive performance, that a continuum exists from normal controls to ET to PD (most severe), and that although deficits are in many of the same cognitive domains, the affected cognitive domains do not overlap completely., (© 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)