To evaluate the criterion and longitudinal validity of field- and laboratory-derived heart rate (HR) indices of resting and submaximal fitness tests (SMFTs) as measures of cardiorespiratory fitness. Observational, repeated measures. Twenty-nine semi-professional footballers participated. Laboratory assessments took place at the start and end of a preseason training period, whereby resting, SMFT HR-derived indices, and criterion measures of cardiorespiratory fitness (running economy [RE], maximal oxygen uptake [V̇O₂ max] and aerobic speed [MAS]) were collected. Throughout this training period, two field-based SMFT protocols, prescribed at different intensities, were administered weekly. Individual slopes were calculated from the analysis of within-athlete change scores. Associations between laboratory and field measures were assessed via Pearson's correlation coefficient (r) and linear regression models. Relationships between SMFT HR-derived indices from laboratory and field were very-large for exercise HR (r = 0.74 to 0.87) and moderate to very-large for HR recovery (0.43 to 0.76). Moderate to very-large inverse relationships were observed between exercise HR and HR recovery with V̇O₂ max and MAS (-0.41 to -0.78), whereas resting HR showed no substantial relationships. Changes in exercise HR showed large and very-large inverse correlations with preseason changes in V̇O₂ max (-0.54 to -0.60) and MAS (-0.64 to -0.83). Relationships between changes in HR recovery and maximal cardiorespiratory criterion measures were moderate to large (-0.32 to -0.63). SMFT exercise HR is a valid proxy measure of cardiorespiratory fitness irrespective of test setting, whereas the validity of HRR remains elusive and appears to vary between exercise intensities.

Sprint performance is commonly assessed via discrete sprint tests and analyzed through kinematic estimates modeled using a mono-exponential equation, including estimated maximal sprinting speed ($\mathrm{M}\mathrm{S}\mathrm{S}$), relative acceleration ($\mathrm{T}\mathrm{A}\mathrm{U}$), maximum acceleration ($\mathrm{M}\mathrm{A}\mathrm{C}$), and relative propulsive maximal power ($\mathrm{P}\mathrm{M}\mathrm{A}\mathrm{X}$). The acceleration–velocity profile (AVP) provides a simple summary of short sprint performance using two parameters: MSS and MAC, which are useful for simplifying descriptions of sprint performance, comparison between athletes and groups of athletes, and estimating changes in performance over time or due to training intervention. However, discrete testing poses logistical challenges and defines an athlete’s AVP exclusively from the performance achieved in an isolated testing environment. Recently, an in situ AVP (velocity–acceleration method) was proposed to estimate kinematic parameters from velocity and acceleration data obtained via global or local positioning systems (GPS/LPS) over multiple training sessions, plausibly improving the time efficiency of sprint monitoring and increasing the sample size that defines the athlete’s AVP. However, the validity and sensitivity of estimates derived from the velocity–acceleration method in relation to changes in criterion scores remain elusive. To assess the concurrent validity and sensitivity of kinematic measures from the velocity–acceleration method, 31 elite youth basketball athletes (23 males and 8 females) completed two maximal effort 30 m sprint trials. Performance was simultaneously measured by a laser gun and an LPS (Kinexon), with kinematic parameters estimated using the time–velocity and velocity–acceleration methods. Agreement ($\mathrm{\%}\mathrm{B}\mathrm{i}\mathrm{a}\mathrm{s}$) between laser gun and LPS-derived estimates was within the practically significant magnitude ($\pm$5%), while confidence intervals for the percentage mean absolute difference ($\mathrm{\%}\mathrm{M}\mathrm{A}\mathrm{D}$) overlapped practical significance for $\mathrm{T}\mathrm{A}\mathrm{U}$, $\mathrm{M}\mathrm{A}\mathrm{C}$, and $\mathrm{P}\mathrm{M}\mathrm{A}\mathrm{X}$ using the velocity–acceleration method. Only the $\mathrm{M}\mathrm{S}\mathrm{S}$ parameter showed a sensitivity ($\mathrm{\%}\mathrm{M}\mathrm{D}{\mathrm{C}}_{95}$) within practical significance (10%) for both the time–velocity and velocity–acceleration methods. Thus, sports practitioners may be confident in the concurrent validity and sensitivity of $\mathrm{M}\mathrm{S}\mathrm{S}$ estimates derived in situ using the velocity–acceleration method, while caution should be applied when using this method to infer an athlete’s maximal acceleration capabilities.

Purpose : To examine associations between exercise heart rate (HR _ex ) 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 (24.6 [4.9] 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 HR _ex 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 HR _ex  associated with a 0.4%-point change in CF-SMFT HR _ex  (95% CI, 0.3–0.5). The tuned model predicted CF-SMFT HR _ex  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 HR _ex  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 HR _ex  from SSD HR _ex  closely aligned with observed values, considering measurement imprecision.

Purpose: To examine associations between exercise heart rate (HRex) during a continuous-fixed submaximal fitness test (CF-SMFT) and an intermittent-variable protocol (semi-standardised kicking drill, SSD) in Australian Football (AF) athletes, controlling for external intensities, within-session scheduling, and environmental conditions. Methods: Forty-four professional AF athletes (22.8±8.0 years) were monitored over 10 sessions involving a 3-minute CF-SMFT (12 kmh-1) as the first activity, and a SSD administered 35.7±8.0 minutes after the CF-SMFT. Initial HR (HRi) and HRex were collected, with external intensities measured as average velocity (mmin-1) and average acceleration-deceleration (ms-2). Environmental conditions were sampled. A penalised hierarchical linear mixed model was tuned for a Bayesian Information Criterion (BIC) minima using a 10-fold cross-validation, with out-of-sample of prediction accuracy assessed via Root-Mean-Squared Error (RMSE). Results: SSD average acceleration-deceleration, HRi, 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 to 0.5). The tuned model predicted CF-SMFT HRex with an average RMSE of 2.64±0.57 over the 10-fold cross-validation, with 74% and 86% of out-of-sample predictions falling within 2.7 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 a SSD to monitor physiological state in AF athletes, despite varied scheduling within-session. Model predictions of CF-SMFT HRex from SSD HRex closely aligned with observed values, considering measurement imprecision.

Objectives: To evaluate the criterion and longitudinal validity of field- and laboratory-derived heart rate (HR) indices of resting and submaximal fitness tests (SMFTs) as measures of cardiorespiratory fitness. Design: Observational, repeated measures. Methods: Twenty-nine semi-professional footballers participated. Laboratory assessments took place at the start and end of a preseason training period, whereby resting, SMFT HR-derived indices, and criterion measures of cardiorespiratory fitness (running economy [RE], maximal oxygen uptake [V ̇O2max] and aerobic speed [MAS]) were collected. Throughout this training period, two field-based SMFT protocols, prescribed at different intensities, were administered weekly. Individual slopes were calculated from the analysis of within-athlete change scores. Associations between laboratory and field measures were assessed via Pearson’s correlation coefficient (r) and linear regression models.Results: Relationships between SMFT HR-derived indices from laboratory and field were very-large for exercise HR (r=0.74 to 0.87) and moderate to very-large for HR recovery (0.43 to 0.76). Moderate to very-large inverse relationships were observed between exercise HR and HR recovery with V ̇O2max and MAS (–0.41 to –0.78), whereas resting HR showed no substantial relationships. Changes in exercise HR showed large and very-large inverse correlations with preseason changes in V ̇O2max (–0.54 to –0.60) and MAS (–0.64 to –0.83). Relationships between changes in HR recovery and maximal cardiorespiratory criterion measures were moderate to large (–0.32 to –0.63). Conclusion: SMFT exercise HR is a valid proxy measure of cardiorespiratory fitness irrespective of test setting, whereas the validity of HRR remains elusive and appears to vary between exercise intensities.

Generic neuromuscular assessments are widely used to monitor training responses in team sports. Emerging running-based measures have been proposed to offer greater sensitivity to residual neuromuscular training effects by aligning more closely with the exercise dose, yet their practical utility remains ambiguous. Concurrent evaluation is thus necessary to identify athlete monitoring assessments that best capture meaningful responses. We examined within-athlete relationships among common training response measures and their associations with prior weekly external training load in professional Australian Football athletes. Repeated-measures, observational. Forty-three male athletes were regularly assessed over two pre-seasons following 48-hours of no training. Outcome measures were derived from maximal performances in adductor strength and countermovement jump (CMJ) assessments, the final minute of a continuous-fixed submaximal fitness test (CF-SMFT: 3-minutes, 12 km⋅h^-1), and the plateau-phase of a high-intensity intermittent-fixed run (HI-IR_plateau: 50 m, ~ 25 km⋅h^-1). External loads were derived from Global Navigation Satellite Systems. Repeated-measures correlations assessed relationships between the response measures, while linear mixed-effects models evaluated measurement variability. Generalised Additive Models examined associations between prior weekly external training loads and the response measures. Adductor strength and CMJ measures showed low within-athlete variability (coefficient of variation, CV = 4.3-6.6%) and limited associations with weekly external loads. Conversely, 2 standard deviation (SD) increases in weekly total distance were associated with reductions in CF-SMFT HR_ex of - 1.3%-points (95% Confidence Intervals, CI: - 0.9, - 1.8), and as large as - 3.2%-points. Similarly, 2 SD increases in very-high speed running (> 25 km.h^-1) were associated with declines in HI-IR_plateau velocity of - 0.4 km·h^-1 (95% CI: - 0.1, - 0.8), and as much as - 1.2 km·h^-1. Both effects exceeded thresholds of practical significance, as indicated by the typical error of measurement. Accelerometer-vector measures presented inconsistent associations. Weekly external load changes were reflected in CF-SMFT HR_ex and HI-IR_plateau velocity, supporting their practical utility for athlete monitoring; whereas measures derived from generic neuromuscular tests (adductor strength, and CMJ) showed limited responsiveness. Associations between external loads and accelerometer-derived measures presented significant uncertainty, which currently limits confident recommendations on the utility of these emerging techniques.