Can We Just Play? Internal Validity of Assessing Physiological State With a Semistandardized Kicking Drill in Professional Australian Football.

ABSTRACT

PURPOSE: To examine associations between exercise heart rate (HRex) during a continuous-fixed submaximal fitness test (CF-SMFT) and an intermittent-variable protocol (semistandardized kicking drill [SSD]) in Australian Football athletes, controlling for external intensities, within-session scheduling, and environmental conditions. METHODS: Forty-four professional male Australian Football athletes (22.8 [8.0] y) were monitored over 10 sessions involving a 3-minute CF-SMFT (12 km·h-1) as the first activity and a SSD administered 35.7 (8.0) minutes after the CF-SMFT. Initial heart rate and HRex were collected, with external intensities measured as average velocity (in meters per minute) and average acceleration-deceleration (in meters per second squared). Environmental conditions were sampled. A penalized hierarchical linear mixed model was tuned for a Bayesian information criterion minima using a 10-fold cross-validation, with out-of-sample prediction accuracy assessed via root-mean-squared error. RESULTS: SSD average acceleration-deceleration, initial heart rate, temperature, and ground hardness were significant moderators in the tuned model. When model covariates were held constant, a 1%-point change in SSD HRex associated with a 0.4%-point change in CF-SMFT HRex (95% CI, 0.3-0.5). The tuned model predicted CF-SMFT HRex with an average root-mean-squared error of 2.64 (0.57) over the 10-fold cross-validation, with 74% and 86% of out-of-sample predictions falling within 2.7%-points and 3.7%-points, respectively, from observed values, representing the lower and upper limits for detecting meaningful changes in HRex according to the documented typical error. CONCLUSIONS: Our findings support the use of an SSD to monitor physiological state in Australian Football athletes, despite varied scheduling within session. Model predictions of CF-SMFT HRex from SSD HRex closely aligned with observed values, considering measurement imprecision.