Purpose: To assess pacing strategies using prescribed and self-selected interset rest periods and their influence on performance in strength-trained athletes. Methods: A total of 16 strength-trained male athletes completed 3 randomized heavy strength-training sessions (5 sets and 5 repetitions) with different interset rest periods. The interset rest periods were 3 min (3MIN), 5 min (5MIN), and self-selected (SS). Mechanical (power, velocity, work, and displacement), surface electromyography (sEMG), and subjective (rating of perceived exertion) and readiness-to-lift data were recorded for each set. Results: SS-condition interset rest periods increased from sets 1 to 4 (from 207.52 to 277.71 s; P = .01). No differences in mechanical performance were shown between the different interset rest-period conditions. Power output (210 W; 8.03%) and velocity (0.03 m·s−1; 6.73%) decreased as sets progressed for all conditions (P < .001) from set 1 to set 5. No differences in sEMG activity between conditions were shown; however, vastus medialis sEMG decreased as the sets progressed for each condition (1.75%; P = .005). All conditions showed increases in rating of perceived exertion as sets progressed (set 1 = 6.1, set 5 = 7.9; P < .001). Participants reported greater readiness to lift in the 5MIN condition (7.81) than in the 3MIN (7.09) and SS (7.20) conditions (P < .001). Conclusions: Self-selecting interset rest periods does not significantly change performance compared with 3MIN and 5MIN conditions. Given the opportunity, athletes will vary their interset rest periods to complete multiple sets of heavy strength training. Self-selection of interset rest periods may be a feasible alternative to prescribed interset rest periods.
Peter Ibbott, Nick Ball, Marijke Welvaert, and Kevin G. Thompson
Luke J. Boyd, Kevin Ball, and Robert J. Aughey
To assess the reliability of triaxial accelerometers as a measure of physical activity in team sports.
Eight accelerometers (MinimaxX 2.0, Catapult, Australia) were attached to a hydraulic universal testing machine (Instron 8501) and oscillated over two protocols (0.5 g and 3.0 g) to assess within- and between-device reliability. A static assessment was also conducted. Secondly, 10 players were instrumented with two accelerometers during Australian football matches. The vector magnitude was calculated, expressed as Player load and assessed for reliability using typical error (TE) ± 90% confidence intervals (CI), and expressed as a coefficient of variation (CV%). The smallest worthwhile difference (SWD) in Player load was calculated to determine if the device was capable of detecting differences in physical activity.
Laboratory: Within- (Dynamic: CV 0.91 to 1.05%; Static: CV 1.01%) and between-device (Dynamic: CV 1.02 to 1.04%; Static: CV 1.10%) reliability was acceptable across each test. Field: The between-device reliability of accelerometers during Australian football matches was also acceptable (CV 1.9%). The SWD was 5.88%.
The reliability of the MinimaxX accelerometer is acceptable both within and between devices under controlled laboratory conditions, and between devices during field testing. MinimaxX accelerometers can be confidently utilized as a reliable tool to measure physical activity in team sports across multiple players and repeated bouts of activity. The noise (CV%) of Player load was lower than the signal (SWD), suggesting that accelerometers can detect changes or differences in physical activity during Australian football.
Luke J. Boyd, Kevin Ball, and Robert J. Aughey
To describe the external load of Australian football matches and training using accelerometers.
Nineteen elite and 21 subelite Australian footballers wore accelerometers during matches and training. Accelerometer data were expressed in 2 ways: from all 3 axes (player load; PL) and from all axes when velocity was below 2 m/s (PLSLOW). Differences were determined between 4 playing positions (midfielders, nomadics, deeps, and ruckmen), 2 playing levels (elite and subelite), and matches and training using percentage change and effect size with 90% confidence intervals.
In the elite group, midfielders recorded higher PL than nomadics and deeps did (8.8%, 0.59 ± 0.24; 34.2%, 1.83 ± 0.39 respectively), and ruckmen were higher than deeps (37.2%, 1.27 ± 0.51). Elite midfielders, nomadics, and ruckmen recorded higher PLSLOW than deeps (13.5%, 0.65 ± 0.37; 11.7%, 0.55 ± 0.36; and 19.5%, 0.83 ± 0.50, respectively). Subelite midfielders were higher than nomadics, deeps, and ruckmen (14.0%, 1.08 ± 0.30; 31.7%, 2.61 ± 0.42; and 19.9%, 0.81 ± 0.55, respectively), and nomadics and ruckmen were higher than deeps for PL (20.6%, 1.45 ± 0.38; and 17.4%, 0.57 ± 0.55, respectively). Elite midfielders, nomadics, and ruckmen recorded higher PL (7.8%, 0.59 ± 0.29; 12.9%, 0.89 ± 0.25; and 18.0%, 0.67 ± 0.59, respectively) and PLSLOW (9.4%, 0.52 ± 0.30; 11.3%, 0.68 ± 0.25; and 14.1%, 0.84 ± 0.61, respectively) than subelite players. Small-sided games recorded the highest PL and PLSLOW and were the only training drill to equal or exceed the load from matches across positions and playing levels.
PL differed between positions, with midfielders the highest, and between playing levels, with elite higher. Differences between matches and training were also evident, with PL from small-sided games equivalent to or higher than matches.
Jocelyn K. Mara, Kevin G. Thompson, Kate L. Pumpa, and Nick B. Ball
To investigate the variation in training demands, physical performance, and player well-being across a women’s soccer season.
Seventeen elite female players wore GPS tracking devices during every training session (N = 90) throughout 1 national-league season. Intermittent high-speed-running capacity and 5-, 15-, and 25-m-sprint testing were conducted at the beginning of preseason, end of preseason, midseason, and end of season. In addition, subjective well-being measures were selfreported daily by players over the course of the season.
Time over 5 m was lowest at the end of preseason (mean 1.148 s, SE 0.017 s) but then progressively deteriorated to the end of the season (P < .001). Sprint performance over 15 m improved by 2.8% (P = .013) after preseason training, while 25-m-sprint performance peaked at midseason, with a 3.1% (P = .05) improvement from the start of preseason, before declining at the end of season (P = .023). Training demands varied between phases, with total distance and high-speed distance greatest during preseason before decreasing (P < .001) during the early- and late-season phases. Endurance capacity and well-being measures did not change across training phases.
Monitoring training demands and subsequent physical performance in elite female soccer players allow coaches to ensure that training periodization goals are being met and related positive training adaptations are being elicited.
Elaine Tor, David L. Pease, Kevin A. Ball, and Will G. Hopkins
Time trials are commonly used in the lead-up to competition. A method that evaluates the relationship between time trial and competition performance in swimming would be useful for developing performance-enhancement strategies.
To use linear mixed modeling to identify key parameters that can be used to relate time-trial and competition performance.
Ten swimmers participated in the study. Each swimmer was analyzed during 3 time trials and 1 competition. Race video footage was analyzed to determine several key parameters. Pooling of strokes and distances was achieved by modeling changes in parameters between time trials and competition within each subject as linear predictors of percent change in performance using mixed modeling of log-transformed race times.
When parameters were evaluated as the effect of 2 SD on performance time, there were very large effects of start time (2.6%, 90% confidence interval 1.8–3.3%) and average velocity (–2.3%, –2.8% to –1.8%). There was also a small effect for stroke rate (–0.6%, –1.3% to 0.2%). Further analysis revealed an improvement in performance time of 2.4% between time trials and competition, of which 1.8% (large; 1.4–2.1%) was due to a change in average velocity and 0.9% (moderate; 0.6–1.1%) was due to a change in start time; changes in remaining parameters had trivial effects on performance.
This study illustrates effective analytical strategies for identifying key parameters that can be the focus of training to improve performance in small squads of elite swimmers and other athletes.