Bayesian analysis of changes in standing horizontal and vertical jump after different modes of resistance training

Wilson, MT, Macgregor, LJ, Fyfe, J, Hunter, AM ORCID: https://orcid.org/0000-0001-7562-6145, Hamilton, DL and Gallagher, IJ, 2022. Bayesian analysis of changes in standing horizontal and vertical jump after different modes of resistance training. Journal of Sports Sciences, 40 (15), pp. 1700-1711. ISSN 0264-0414

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Abstract

Training interventions often have small effects and are tested in small samples. We used a Bayesian approach to examine the change in jump distance after different resistance training programmes. Thirty-three 18- to 45-year-old males completed one of three lower limb resistance training programmes: deadlift (DL), hip thrust (HT) or back squat (BS). Horizontal and vertical jump performance was assessed over the training intervention. Examination of Bayesian posterior distributions for jump distance estimated that the probability of a change above a horizontal jump smallest worthwhile change (SWC) of 4.7 cm for the DL group was ~12%. For the HT and BS groups, the probability of a change above the SWC was ~87%. The probability of a change above a vertical jump SWC of 1.3 cm for the DL group was ~31%. For the HT and BS groups, the probability of a change above the vertical jump SWC was ~62% and ~67%, respectively. Our study illustrates that a Bayesian approach provides a rich inferential interpretation for small sample training studies with small effects. The extra information from such a Bayesian approach is useful to practitioners in Sport and Exercise Science where small effects are expected and sample size is often constrained.

Item Type:	Journal article
Publication Title:	Journal of Sports Sciences
Creators:	Wilson, M.T., Macgregor, L.J., Fyfe, J., Hunter, A.M., Hamilton, D.L. and Gallagher, I.J.
Publisher:	Taylor and Francis
Date:	2022
Volume:	40
Number:	15
ISSN:	0264-0414
Identifiers:	Number Type 10.1080/02640414.2022.2100676 DOI 1898441 Other
Rights:	© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
Divisions:	Schools > School of Science and Technology
Record created by:	Melissa Cornwell
Date Added:	31 May 2024 09:09
Last Modified:	31 May 2024 09:09
URI:	https://irep.ntu.ac.uk/id/eprint/51497

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