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Severe-intensity constant work rate (CWR) cycling tests simulate the high-intensity competition environment and are useful for monitoring training progression and adaptation, yet impose significant physiological and psychological strain, require substantial recovery, and may disrupt athlete training or competition preparation. A brief, minimally fatiguing test providing comparable information is desirable. Purpose To determine whether physiological variables measured during, and functional decline in maximal power output immediately after, a 2-min CWR test can act as a proxy for 4-min test outcomes. Methods Physiological stress (VO _2 kinetics, heart rate, blood lactate concentrations ([La-]b)) was monitored and performance fatigability was estimated (as pre-to-post-CWR changes in 10-s sprint power) during 2- and 4-min CWR tests in 16 high-level cyclists (VO _2peak ¼ 64:4 ± 6:0 ml·kg-1·min-1). The relationship between the 2- and 4-min CWR tests and the physiological variables that best relate to the performance fatigability were investigated. Results The 2-min CWR test evoked a smaller decline in sprint mechanical power (32% vs. 47%, p < 0.001). Both the physiological variables (r = 0.66–0.96) and sprint mechanical power (r = 0.67–0.92) were independently and strongly correlated between 2- and 4-min tests. Differences in VO _2peak and [La-]b in both CWR tests were strongly associated with the decline in sprint mechanical power. Conclusion Strong correlations between 2- and 4-min severe-intensity CWR test outcomes indicated that the shorter test can be used as a proxy for the longer test. A shorter test may be more practical within the elite performance environment due to lower physiological stress and performance fatigability and should have less impact on subsequent training and competition preparation.

Aim: The aim was to predict and understand variations in swimmer performance between individual and relay events, and develop a predictive model for the 4x200-m swimming freestyle relay event to help inform team selection and strategy. Data and methods: Race data for 716 relay finals (4 x 200-m freestyle) from 14 international competitions between 2010–2018 were analysed. Individual 200-m freestyle season best time for the same year was located for each swimmer. Linear regression and machine learning was applied to 4 x 200-m swimming freestyle relay events. Results: Compared to the individual event, the lowest ranked swimmer in the team (-0.62 s, CI = [−0.94, −0.30]) and American swimmers (−0.48 s [−0.89, −0.08]) typically swam faster 200-m times in relay events. Random forest models predicted gold, silver, bronze and non-medal with 100%, up to 41%, up to 63%, and 93% sensitivity, respectively. Discussion: Team finishing position was strongly associated with the differential time to the fastest team (mean decrease in Gini (MDG) when this variable was omitted = 31.3), world rankings of team members (average ranking MDG of 18.9), and the order of swimmers (MDG = 6.9). Differential times are based on the sum of individual swimmer’s season’s best times, and along with world rankings, reflect team strength. In contrast, the order of swimmers reflects strategy. This type of analysis could assist coaches and support staff in selecting swimmers and team orders for relay events to enhance the likelihood of success.

Body composition can substantially impact elite swimming performance. In practice, changes in fat and lean mass of elite swimmers are estimated using body mass, sum of seven skinfolds (∑7) and lean mass index (LMI). However, LMI may be insufficiently accurate to detect small changes in body composition which could meaningfully impact swimming performance. This study developed equations which estimate dual-energy x-ray absorptiometry (DXA)-derived lean and fat mass using body mass and ∑7 data. Elite Australian swimmers (n = 44; 18 male, 26 female) completed a DXA scan and standardised body mass and ∑7 measurements. Equations to estimate DXA-derived lean and fat mass based on body mass, ∑7 and sex were developed. The relationships between ∑7, body mass and DXA-derived lean and fat mass were non-linear. Fat mass (Adjusted R2 = 0.91; standard error = 1.0 kg) and lean mass (Adjusted R2 = 0.99; standard error = 1.0 kg) equations were considered sufficiently accurate. Lean mass estimates outperformed the LMI in identifying the correct direction of change in lean mass (82% correct; LMI 71%). Using the accurate estimations produced by these equations will enhance the prescription and evaluation of programmes to optimise the body composition and subsequent performance in swimmers.

Mitchell, L. J., Argus, C. K., Taylor, K. L., Sheppard, J. M., & Chapman, D. W. (2017). The effect of initial knee angle on concentric-only squat jump performance. Research Quarterly for Exercise and Sport, 88(2), 184-192. Available here.