A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments

Abstract Digital health metrics promise to advance the understanding of impaired body functions, for example in neurological disorders. However, their clinical integration is challenged by an insufficient validation of the many existing and often abstract metrics. Here, we propose a data-driven fram...

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Autores principales: Christoph M. Kanzler, Mike D. Rinderknecht, Anne Schwarz, Ilse Lamers, Cynthia Gagnon, Jeremia P. O. Held, Peter Feys, Andreas R. Luft, Roger Gassert, Olivier Lambercy
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Publicado: Nature Portfolio 2020
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spelling oai:doaj.org-article:c562b14eba664541a738917133b5cc122021-12-02T16:53:20ZA data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments10.1038/s41746-020-0286-72398-6352https://doaj.org/article/c562b14eba664541a738917133b5cc122020-05-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0286-7https://doaj.org/toc/2398-6352Abstract Digital health metrics promise to advance the understanding of impaired body functions, for example in neurological disorders. However, their clinical integration is challenged by an insufficient validation of the many existing and often abstract metrics. Here, we propose a data-driven framework to select and validate a clinically relevant core set of digital health metrics extracted from a technology-aided assessment. As an exemplary use-case, the framework is applied to the Virtual Peg Insertion Test (VPIT), a technology-aided assessment of upper limb sensorimotor impairments. The framework builds on a use-case-specific pathophysiological motivation of metrics, models demographic confounds, and evaluates the most important clinimetric properties (discriminant validity, structural validity, reliability, measurement error, learning effects). Applied to 77 metrics of the VPIT collected from 120 neurologically intact and 89 affected individuals, the framework allowed selecting 10 clinically relevant core metrics. These assessed the severity of multiple sensorimotor impairments in a valid, reliable, and informative manner. These metrics provided added clinical value by detecting impairments in neurological subjects that did not show any deficits according to conventional scales, and by covering sensorimotor impairments of the arm and hand with a single assessment. The proposed framework provides a transparent, step-by-step selection procedure based on clinically relevant evidence. This creates an interesting alternative to established selection algorithms that optimize mathematical loss functions and are not always intuitive to retrace. This could help addressing the insufficient clinical integration of digital health metrics. For the VPIT, it allowed establishing validated core metrics, paving the way for their integration into neurorehabilitation trials.Christoph M. KanzlerMike D. RinderknechtAnne SchwarzIlse LamersCynthia GagnonJeremia P. O. HeldPeter FeysAndreas R. LuftRoger GassertOlivier LambercyNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-17 (2020)
institution DOAJ
collection DOAJ
language EN
topic Computer applications to medicine. Medical informatics
R858-859.7
spellingShingle Computer applications to medicine. Medical informatics
R858-859.7
Christoph M. Kanzler
Mike D. Rinderknecht
Anne Schwarz
Ilse Lamers
Cynthia Gagnon
Jeremia P. O. Held
Peter Feys
Andreas R. Luft
Roger Gassert
Olivier Lambercy
A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments
description Abstract Digital health metrics promise to advance the understanding of impaired body functions, for example in neurological disorders. However, their clinical integration is challenged by an insufficient validation of the many existing and often abstract metrics. Here, we propose a data-driven framework to select and validate a clinically relevant core set of digital health metrics extracted from a technology-aided assessment. As an exemplary use-case, the framework is applied to the Virtual Peg Insertion Test (VPIT), a technology-aided assessment of upper limb sensorimotor impairments. The framework builds on a use-case-specific pathophysiological motivation of metrics, models demographic confounds, and evaluates the most important clinimetric properties (discriminant validity, structural validity, reliability, measurement error, learning effects). Applied to 77 metrics of the VPIT collected from 120 neurologically intact and 89 affected individuals, the framework allowed selecting 10 clinically relevant core metrics. These assessed the severity of multiple sensorimotor impairments in a valid, reliable, and informative manner. These metrics provided added clinical value by detecting impairments in neurological subjects that did not show any deficits according to conventional scales, and by covering sensorimotor impairments of the arm and hand with a single assessment. The proposed framework provides a transparent, step-by-step selection procedure based on clinically relevant evidence. This creates an interesting alternative to established selection algorithms that optimize mathematical loss functions and are not always intuitive to retrace. This could help addressing the insufficient clinical integration of digital health metrics. For the VPIT, it allowed establishing validated core metrics, paving the way for their integration into neurorehabilitation trials.
format article
author Christoph M. Kanzler
Mike D. Rinderknecht
Anne Schwarz
Ilse Lamers
Cynthia Gagnon
Jeremia P. O. Held
Peter Feys
Andreas R. Luft
Roger Gassert
Olivier Lambercy
author_facet Christoph M. Kanzler
Mike D. Rinderknecht
Anne Schwarz
Ilse Lamers
Cynthia Gagnon
Jeremia P. O. Held
Peter Feys
Andreas R. Luft
Roger Gassert
Olivier Lambercy
author_sort Christoph M. Kanzler
title A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments
title_short A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments
title_full A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments
title_fullStr A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments
title_full_unstemmed A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments
title_sort data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments
publisher Nature Portfolio
publishDate 2020
url https://doaj.org/article/c562b14eba664541a738917133b5cc12
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