Gutenberg Open Science: Identification of subject-specific responses to footwear during running

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-9729

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DC Field	Value	Language
dc.contributor.author	Horst, Fabian	-
dc.contributor.author	Hoitz, Fabian	-
dc.contributor.author	Slijepcevic, Djordje	-
dc.contributor.author	Schons, Nicolas	-
dc.contributor.author	Beckmann, Hendrik	-
dc.contributor.author	Nigg, Benno M.	-
dc.contributor.author	Schöllhorn, Wolfgang I.	-
dc.date.accessioned	2023-12-01T08:49:52Z	-
dc.date.available	2023-12-01T08:49:52Z	-
dc.date.issued	2023	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/9747	-
dc.description.abstract	Placing a stronger focus on subject-specific responses to footwear may lead to a better functional understanding of footwear’s effect on running and its influence on comfort perception, performance, and pathogenesis of injuries. We investigated subject-specific responses to different footwear conditions within ground reaction force (GRF) data during running using a machine learning-based approach. We conducted our investigation in three steps, guided by the following hypotheses: (I) For each subject x footwear combination, unique GRF patterns can be identified. (II) For each subject, unique GRF characteristics can be identified across footwear conditions. (III) For each footwear condition, unique GRF characteristics can be identified across subjects. Thirty male subjects ran ten times at their preferred (self-selected) speed on a level and approximately 15 m long runway in four footwear conditions (barefoot and three standardised running shoes). We recorded three-dimensional GRFs for one right-foot stance phase per running trial and classified the GRFs using support vector machines. The highest median prediction accuracy of 96.2% was found for the subject x footwear classification (hypothesis I). Across footwear conditions, subjects could be discriminated with a median prediction accuracy of 80.0%. Across subjects, footwear conditions could be discriminated with a median prediction accuracy of 87.8%. Our results suggest that, during running, responses to footwear are unique to each subject and footwear design. As a result, considering subject-specific responses can contribute to a more differentiated functional understanding of footwear effects. Incorporating holistic analyses of biomechanical data is auspicious for the evaluation of (subject-specific) footwear effects, as unique interactions between subjects and footwear manifest in versatile ways. The applied machine learning methods have demonstrated their great potential to fathom subject-specific responses when evaluating and recommending footwear.	en_GB
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	796 Sport	de_DE
dc.subject.ddc	796 Athletic and outdoor sports and games	en_GB
dc.title	Identification of subject-specific responses to footwear during running	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-9729	-
jgu.type.contenttype	Scientific article	de
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 02 Sozialwiss., Medien u. Sport	de
jgu.organisation.number	7910	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Scientific reports	de
jgu.journal.volume	13	de
jgu.pages.alternative	11284	de
jgu.publisher.year	2023	-
jgu.publisher.name	Springer Nature	de
jgu.publisher.place	London	de
jgu.publisher.issn	2045-2322	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	796	de
jgu.publisher.doi	10.1038/s41598-023-38090-0	de
jgu.organisation.ror	https://ror.org/023b0x485	-
jgu.subject.dfg	Geistes- und Sozialwissenschaften	de
Appears in collections:	DFG-491381577-G

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