Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/33282
Title: A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments
Authors: Kanzler, CM
Rinderknecht, MD
Schwarz, A
LAMERS, Ilse 
Gagnon, C
Held, JPO
FEYS, Peter 
Luft, AR
Gassert, R
Lambercy, O
Issue Date: 2020
Publisher: NATURE RESEARCH
Source: npj digital medicine, 3 (1) (Art N° 80)
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.
Document URI: http://hdl.handle.net/1942/33282
ISSN: 2398-6352
e-ISSN: 2398-6352
DOI: 10.1038/s41746-020-0286-7
ISI #: WOS:000536327200001
Rights: The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons. org/licenses/by/4.0/.
Category: A1
Type: Journal Contribution
Validations: ecoom 2021
Appears in Collections:Research publications

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