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J Med Internet Res. 2018 Mar 26;20(3):e89. doi: 10.2196/jmir.9462.

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 information

1
Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States.
2
Biomedical Image Technologies, Universidad Politécnica de Madrid, Madrid, Spain.
3
Biomedical Research Networking Centre thematic area of Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain.
4
nQ Medical Inc, Cambridge, MA, United States.
5
Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, United States.
6
Centro Integral de Neurociencias A.C., Hospital Universitario HM Puerta del Sur, Móstoles, Spain.
7
Neurology Department, Instituto de Investigación del Hospital 12 de Octubre, Madrid, Spain.
8
Enfermedades Neurodegenerativas, Centro de Investigación Biomédica en Red, Madrid, Spain.
9
Pacific Parkinson's Research Centre, The University of British Columbia, Vancouver, BC, Canada.
10
Movement Disorders Unit, Hospital Clínico San Carlos, Madrid, Spain.
11
Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States.
12
Medical School, CEU-San Pablo University, Madrid, Spain.
#
Contributed equally

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.

KEYWORDS:

eHealth; machine learning; telemedicine

PMID:
29581092
PMCID:
PMC5891671
DOI:
10.2196/jmir.9462
[Indexed for MEDLINE]
Free PMC Article

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