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

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.