Detecting motor impairment in early Parkinson's disease via natural typing interaction with keyboards: Validation of the neuroQWERTY approach in an uncontrolled at-home setting

Arroyo Gallego, Teresa and Ledesma Carbayo, Maria Jesus and Butterworth, Ian and Matarazzo, Michele and Montero Escribano, Paloma and Puertas Martín, Verónica and Gray, Martha L. and Giancardo, Luca and Sánchez Ferro, Álvaro (2018). Detecting motor impairment in early Parkinson's disease via natural typing interaction with keyboards: Validation of the neuroQWERTY approach in an uncontrolled at-home setting. "Journal of Medical Internet Research", v. 20 (n. 3); pp. 1-14. ISSN 1438-8871. https://doi.org/10.2196/jmir.9462.

Description

Title: Detecting motor impairment in early Parkinson's disease via natural typing interaction with keyboards: Validation of the neuroQWERTY approach in an uncontrolled at-home setting
Author/s:
  • Arroyo Gallego, Teresa
  • Ledesma Carbayo, Maria Jesus
  • Butterworth, Ian
  • Matarazzo, Michele
  • Montero Escribano, Paloma
  • Puertas Martín, Verónica
  • Gray, Martha L.
  • Giancardo, Luca
  • Sánchez Ferro, Álvaro
Item Type: Article
Título de Revista/Publicación: Journal of Medical Internet Research
Date: March 2018
ISSN: 1438-8871
Volume: 20
Subjects:
Freetext Keywords: eHealth, machine learning, telemedicine
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

Funding Projects

TypeCodeAcronymLeaderTitle
FP7291820MVISIONUnspecifiedMVISION

More information

Item ID: 54989
DC Identifier: http://oa.upm.es/54989/
OAI Identifier: oai:oa.upm.es:54989
DOI: 10.2196/jmir.9462
Official URL: https://www.jmir.org/2018/3/e89/
Deposited by: Memoria Investigacion
Deposited on: 21 May 2019 16:20
Last Modified: 21 May 2019 16:20
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