Author(s): DELEO, J., ESERHAUT, D., PROVOST, J., ACKERMAN, K., FRY, A., Institution: UNIVERSITY OF KANSAS, Country: UNITED STATES, Abstract-ID: 2611

INTRODUCTION:
Countermovement jump (CMJ) metrics that are duration based have been shown to be more sensitive to neuromuscular fatigue compared to jump height or traditional force-time metrics (1). The purpose of this study was to build upon the 2024 data analysis and retrospectively analyze the 2025 season data with the identified CMJ metrics against weekly CMJs, training volume (TV), training load (TL), training strain (TS), and training monotony (TM) to determine the most sensitive metrics at an individual athlete level.
METHODS:
Thirty-one NCAA Division I female rowers (median +/- IQR) : age=21+/-1 years, Weight 75.75 +/- 17.24 kg, Height: 176.9+/- 5.27 cm (meanSD), performed three weekly CMJs on dual unidimensional (Hawkin Dynamics, Westbrook, ME, USA) force plates at the beginning of their strength and conditioning session. Data was collected throughout the spring competitive season (January 27, 2025 – May 6, 2025). Linear mixed-effects models with restricted maximum likelihood were used with athlete identity intercept as the random factor, weekly TV, TL, TS, and TM as the independent variable or fixed-factor in separate models, and the following CMJ variables as individual dependent variables: Braking Phase(s), Braking RFD (N/s), Flight Time (s), Positive Net Impulse (N⋅s), and Propulsive Phase (s). Athlete level analysis was performed by determining the smallest worthwhile change (0.2 x between-athlete SD at baseline), individual coefficient of variation (CV1) at baseline, double the CV (CV2) and signal to noise ratio (SNR = SWC/CV1) were calculated. The SNR was classified as good if > 1, OK if ≈ 1, and poor if < 1. Statistical significance was set a priori at p<0.05.
RESULTS:
Braking Phase (s) yielded the strongest model for TL (B = 0.002, p = 0.005, AIC = -2162.092, 95%CI = 0.001 to 0.004). Braking RFD (N/s) consistently displayed the largest Beta weight but only had significance for the TL predictor (B = -73.676, p = 0.049, AIC = 6670.688, 95%CI = -147.180 to -0.171). Athlete level analysis determined that Flight Time (s) had the strongest SNR yet had no significant models but displayed the lowest AIC values (B = 0.001, p = 0.063, AIC = -2308.188, 95%CI = -0.000 to 0.002.
CONCLUSION:
This study indicates that Flight Time (s) and Braking Phase (s) were the most reliable and sensitive to changes in TL, which aligns with other research around duration based CMJ metrics being most sensitive to neuromuscular fatigue (2). Braking Phase (s) also captures eccentric performance which may link to a potential lack of control in the rowing stroke and muscle soreness. Accurate athlete monitoring requires individual athlete level analysis to determine the most reliable and sensitive monitoring metrics. Practitioners should utilize the SWC, CV1, CV2 and SNR to determine the reliability and sensitivity of metrics.
References:
1. Bishop, et al. https://doi.org/10.1519/SSC.0000000000000772
2. Gathercole et al.https://doi.org/10.1519/jsc.0000000000000622