Sensitivity of Countermovement Jump Variables in Professional Rugby Union Players Within a Playing Season

Howarth, DJ, McLean, BD, Cohen, DD, and Coutts, AJ. Sensitivity of countermovement jump variables in professional rugby union players within a playing season. J Strength Cond Res 37(7): 1463-1469, 2023-The aim of this study was to explore the measurement sensitivity of a wide range of countermovement jump (CMJ) variables to a full European professional rugby union season. A secondary purpose was to compare 3 different data treatment methods for the calculation of CMJ variables. Twenty-nine professional rugby union players (mean ± SD; age 24 ± 4 years, height 183.7 ± 8.0 cm, body mass 101.6 ± 10.7 kg) completed a minimum of 12 CMJ testing sessions on Thursdays-a day preceded by a rest day and a minimum of 96 hours after a match-throughout a season. Measurement sensitivity, quantified by signal-to-noise ratio (SNR), was determined for 74 CMJ variables and was calculated by dividing the signal, (week-to-week variation expressed as a coefficient of variation [CV%]) by the noise (interday test/retest reliability expressed as CV%). We also identified variables which had no overlap between the 95% confidence intervals (CIs) for the signal and the noise. The 3 data treatment methods for comparison were (a) mean output across 3 jump trials (Mean3), (b) single output from the trial with the highest jump (BestJH), and (c) the trial with the highest flight time to contraction time ratio (BestFTCT). Most variables had an SNR >1.0 (Mean3 = 60/74; BestFTCT = 59/74; BestJH = 48/74). Fewer variables displayed a nonoverlap of 95% CIs (Mean3 = 23/60; BestFTCT = 22/59; BestJH = 16/48). Most CMJ variables during a professional rugby season demonstrated a signal that exceeded measured noise (SNR > 1.0) and that using the Mean3 or BestFTCT data treatment methods yields a greater number of variables considered sensitive within a season (i.e., SNR > 1.0) than when using BestJH. We also recommend the calculation of the 95% CIs for both signal and noise, with nonoverlap indicative of a greater probability that the responsiveness of the variable at team level (i.e., SNR) also applies at the individual level. As sensitivity analysis is cohort and environment specific, practitioners should conduct a sensitivity analysis using internal signal and noise data to inform their own monitoring protocols.