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

Kanzler, Christoph M; Rinderknecht, Mike D; Schwarz, Anne; Lamers, Ilse; Gagnon, Cynthia; Held, Jeremia P O; Feys, Peter; Luft, Andreas R; Gassert, Roger; Lambercy, Olivier

doi:10.5167/uzh-192584

Citations

Citation copied

Kanzler, C. M., Rinderknecht, M. D., Schwarz, A., Lamers, I., Gagnon, C., Held, J. P. O., Feys, P., Luft, A. R., Gassert, R., & Lambercy, O. (2020). A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments. Npj Digital Medicine, 3, 80. https://doi.org/10.1038/s41746-020-0286-7

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 asses