Wearable full-body motion tracking of activities of daily living predicts disease trajectory in Duchenne muscular dystrophy

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00006987
URN to cite this document: urn:nbn:de:bvb:703-epub-6987-9

Title data

Ricotti, Valeria ; Kadirvelu, Balasundaram ; Selby, Victoria ; Festenstein, Richard ; Mercuri, Eugenio ; Voit, Thomas ; Faisal, A. Aldo:
Wearable full-body motion tracking of activities of daily living predicts disease trajectory in Duchenne muscular dystrophy.
In: Nature medicine. Vol. 29 (2023) Issue 1 . - pp. 95-103.
ISSN 1546-170X
DOI der Verlagsversion: https://doi.org/10.1038/s41591-022-02045-1

	Format: PDF Name: s41591-022-02045-1.pdf Version: Published Version Available under License Creative Commons BY 4.0: Attribution
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Abstract

Artificial intelligence has the potential to revolutionize healthcare, yet clinical trials in neurological diseases continue to rely on subjective, semiquantitative and motivation-dependent endpoints for drug development. To overcome this limitation, we collected a digital readout of whole-body movement behavior of patients with Duchenne muscular dystrophy (DMD) (n = 21) and age-matched controls (n = 17). Movement behavior was assessed while the participant engaged in everyday activities using a 17-sensor bodysuit during three clinical visits over the course of 12 months. We first defined new movement behavioral fingerprints capable of distinguishing DMD from controls. Then, we used machine learning algorithms that combined the behavioral fingerprints to make cross-sectional and longitudinal disease course predictions, which outperformed predictions derived from currently used clinical assessments. Finally, using Bayesian optimization, we constructed a behavioral biomarker, termed the KineDMD ethomic biomarker, which is derived from daily-life behavioral data and whose value progresses with age in an S-shaped sigmoid curve form. The biomarker developed in this study, derived from digital readouts of daily-life movement behavior, can predict disease progression in patients with muscular dystrophy and can potentially track the response to therapy.

Further data

Item Type:	Article in a journal
DDC Subjects:	500 Science > 500 Natural sciences
Institutions of the University:	Faculties > Faculty of Life Sciences: Food, Nutrition and Health > Lehrstuhl Digital Health mit Schwerpunkt Data Science in Lebenswissenschaften > Lehrstuhl Digital Health mit Schwerpunkt Data Science in Lebenswissenschaften - Univ.-Prof. Dr. Aldo Faisal Faculties Faculties > Faculty of Life Sciences: Food, Nutrition and Health Faculties > Faculty of Life Sciences: Food, Nutrition and Health > Lehrstuhl Digital Health mit Schwerpunkt Data Science in Lebenswissenschaften
Language:	English
Originates at UBT:	Yes
URN:	urn:nbn:de:bvb:703-epub-6987-9
Date Deposited:	10 May 2023 08:33
Last Modified:	10 May 2023 08:33
URI:	https://epub.uni-bayreuth.de/id/eprint/6987

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