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Aaron T. Scanlan, Jordan L. Fox, Nattai R. Borges and Vincent J. Dalbo

Purpose:

Declines in high-intensity activity during game play (in-game approach) and performance tests measured pre- and postgame (across-game approach) have been used to assess player fatigue in basketball. However, a direct comparison of these approaches is not available. Consequently, this study examined the commonality between in- and across-game jump fatigue during simulated basketball game play.

Methods:

Australian, state-level, junior male basketball players (n = 10; 16.6 ± 1.1 y, 182.4 ± 4.3 cm, 68.3 ± 10.2 kg) completed 4 × 10-min standardized quarters of simulated basketball game play. In-game jump height during game play was measured using video analysis, while across-game jump height was determined pre-, mid-, and postgame play using an in-ground force platform. Jump height was determined using the flight-time method, with jump decrement calculated for each approach across the first half, second half, and entire game.

Results:

A greater jump decrement was apparent for the in-game approach than for the across-game approach in the first half (37.1% ± 11.6% vs 1.7% ± 6.2%; P = .005; d = 3.81, large), while nonsignificant, large differences were evident between approaches in the second half (d = 1.14) and entire game (d = 1.83). Nonsignificant associations were evident between in-game and across-game jump decrement, with shared variances of 3–26%.

Conclusions:

Large differences and a low commonality were observed between in- and across-game jump fatigue during basketball game play, suggesting that these approaches measure different constructs. Based on our findings, it is not recommended that basketball coaches use these approaches interchangeably to monitor player fatigue across the season.

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Thomas Kempton and Aaron J. Coutts

Purpose:

To describe the physical and technical demands of rugby league 9s (RL9s) match play for positional groups.

Methods:

Global positioning system data were collected during 4 games from 16 players from a team competing in the Auckland RL9s tournament. Players were classified into positional groups (pivots, outside backs, and forwards). Absolute and relative physical-performance data were classified as total high-speed running (HSR; >14.4 km/h), very-high-speed running (VHSR; >19.0 km/h), and sprint (>23.0 km/h) distances. Technical-performance data were obtained from a commercial statistics provider. Activity cycles were coded by an experienced video analyst.

Results:

Forwards (1088 m, 264 m) most likely completed less overall and high-speed distances than pivots (1529 m, 371 m) and outside backs (1328 m, 312 m). The number of sprint efforts likely varied between positions, although differences in accelerations were unclear. There were no clear differences in relative total (115.6−121.3 m/min) and HSR (27.8−29.8 m/min) intensities, but forwards likely performed less VHSR (7.7 m/min) and sprint distance (1.3 m/min) per minute than other positions (10.2−11.8 m/min, 3.7−4.8 m/min). The average activity and recovery cycle lengths were ~50 and ~27 s, respectively. The average longest activity cycle was ~133 s, while the average minimum recovery time was ~5 s. Technical involvements including tackles missed, runs, tackles received, total collisions, errors, off-loads, line breaks, and involvements differed between positions.

Conclusions:

Positional differences exist for both physical and technical measures, and preparation for RL9s play should incorporate these differences.

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Elizabeth L. Abbey and Janet Walberg Rankin

Purpose:

This study compared the effect of a honey-sweetened beverage with those of a commercial sports drink and a placebo on performance and inflammatory response to a 90-min soccer simulation.

Methods:

Ten experienced male soccer players randomly performed 3 trials (honey [H], sports drink [S], and placebo [P]), consuming the beverage before and during halftime for a total of 1.0 g/kg carbohydrate for H and S. Performance measures included 5 sets (T1–T5) of a high-intensity run and agility and ball-shooting tests followed by a final progressive shuttle-run (PSR) test to exhaustion. Blood samples were drawn pretest, posttest (B2), and 1 hr posttest (B3) for markers of inflammation, oxygen radical absorbance capacity (ORAC), and hormone response.

Results:

T2–T5 were significantly slower than T1 (p < .05), and a decrease in PSR time was observed from baseline (–22.9%) for all treatments. No significant effect of the interventions was observed for any performance measures. Plasma IL-1ra levels increased posttest for all treatments (65.5% S, 63.9% P, and 25.8% H), but H was significantly less than S at posttest and P at B3. Other cytokines and ORAC increased at B2 (548% IL-6, 514% IL-10, 15% ORAC) with no difference by treatment.

Conclusion:

Acute ingestion of honey and a carbohydrate sports drink before and during a soccer-simulation test did not improve performance, although honey attenuated a rise in IL-1ra. Ingestion of carbohydrate and/or antioxidant-containing beverages at frequencies typical of a regulation match may not be beneficial for trained soccer players.

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Dean J. McNamara, Tim J. Gabbett, Paul Chapman, Geraldine Naughton and Patrick Farhart

Purpose:

The use of wearable microtechnology to monitor the external load of fast bowling is challenged by the inherent variability of bowling techniques between bowlers. This study assessed the between-bowlers variability in PlayerLoad, bowling velocity, and performance execution across repeated bowling spells.

Methods:

Seven national-level fast bowlers completed two 6-over bowling spells at a batter during a competitive training session. Key dependent variables were PlayerLoad calculated with a MinimaxX microtechnology unit, ball velocity, and bowling execution based on a predetermined bowling strategy for each ball bowled. The between-bowlers coefficient of variation (CV), repeated-measures ANOVA, and smallest worthwhile change were calculated over the 2 repeated 6-over bowling spells and explored across 12-over, 6-over, and 3-over bowling segments.

Results:

From the sum of 6 consecutive balls, the between-bowlers CV for relative peak PlayerLoad was 1.2% over the 12-over bowling spell (P = .15). During this 12-over period, bowling-execution (P = .43) scores and ball-velocity (P = .31) CVs were calculated as 46.0% and 0.4%, respectively.

Conclusions:

PlayerLoad was found to be stable across the repeated bowling spells in the fast-bowling cohort. Measures of variability and change across the repeated bowling spells were consistent with the performance measure of ball velocity. The stability of PlayerLoad improved when assessed relative to the individual’s peak PlayerLoad. Only bowling-execution measures were found to have high variability across the repeated bowling spells. PlayerLoad provides a stable measure of external workload between fast bowlers.

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Deborah R. Smith, Ben Jones, Louise Sutton, Roderick F.G.J. King and Lauren C. Duckworth

Good nutrition is essential for the physical development of adolescent athletes, however data on dietary intakes of adolescent rugby players are lacking. This study quantified and evaluated dietary intake in 87 elite male English academy rugby league (RL) and rugby union (RU) players by age (under 16 (U16) and under 19 (U19) years old) and code (RL and RU). Relationships of intakes with body mass and composition (sum of 8 skinfolds) were also investigated. Using 4-day diet and physical activity diaries, dietary intake was compared with adolescent sports nutrition recommendations and the UK national food guide. Dietary intake did not differ by code, whereas U19s consumed greater energy (3366 ± 658 vs. 2995 ± 774 kcal·day-1), protein (207 ± 49 vs. 150 ± 53 g·day-1) and fluid (4221 ± 1323 vs. 3137 ± 1015 ml·day-1) than U16s. U19s consumed a better quality diet than U16s (greater intakes of fruit and vegetables; 4.4 ± 1.9 vs. 2.8 ± 1.5 servings·day-1; nondairy proteins; 3.9 ± 1.1 vs. 2.9 ± 1.1 servings·day-1) and less fats and sugars (2.0 ± 1. vs. 3.6 ± 2.1 servings·day-1). Protein intake vs. body mass was moderate (r = .46, p < .001), and other relationships were weak. The findings of this study suggest adolescent rugby players consume adequate dietary intakes in relation to current guidelines for energy, macronutrient and fluid intake. Players should improve the quality of their diet by replacing intakes from the fats and sugars food group with healthier choices, while maintaining current energy, and macronutrient intakes.

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Michael C. Andrews and Catherine Itsiopoulos

Athletes require sufficient nutrition knowledge and skills to enable appropriate selection and consumption of food and fluids to meet their health, body composition, and performance needs. This article reports the nutrition knowledge and dietary habits of male football (soccer) players in Australia. Players age 18 years and older were recruited from 1 A-League club (professional) and 4 National Premier League clubs (semiprofessional). No significant difference in general nutrition knowledge (GNK; 54.1% ± 13.4%; 56.8% ± 11.7%; M ± SD), t(71) = -0.91, p = .37, or sports nutrition knowledge (SNK; 56.9% ± 15.5%; 61.3% ± 15.9%), t(71) = -1.16, p = .25) were noted between professional (n = 29) and semiprofessional (n = 44) players. In general, players lacked knowledge in regard to food sources and types of fat. Although nutrition knowledge varied widely among players (24.6–82.8% correct responses), those who had recently studied nutrition answered significantly more items correctly than those who reported no recent formal nutrition education (62.6% ± 11.9%; 54.0% ± 11.4%), t(67) = 2.88, p = .005). Analysis of 3-day estimated food diaries revealed both professionals (n = 10) and semiprofessionals (n = 31) consumed on average less carbohydrate (3.5 ± 0.8 gC/kg; 3.9 ± 1.8 gC/kg) per day than football-specific recommendations (FIFA Medical and Assessment Research Centre [F-MARC]: 5–10 gC/kg). There was a moderate, positive correlation between SNK and carbohydrate intake (n = 41, ρ = 0.32, p = .04), indicating that players who exhibited greater SNK had higher carbohydrate intakes. On the basis of these findings, male football players in Australia would benefit from nutrition education targeting carbohydrate and fat in an attempt to improve nutrition knowledge and dietary practices.

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Benjamin G. Serpell, Barry G. Horgan, Carmen M.E. Colomer, Byron Field, Shona L. Halson and Christian J. Cook

Purpose: To examine changes in, and relationships between, sleep quality and quantity, salivary testosterone, salivary cortisol, testosterone-to-cortisol ratio (T:C), and self-reported muscle soreness during a residential-based training camp in elite rugby players. Methods: Nineteen male rugby players age 26.4 (3.9) years, height 186.0 (9.4) cm, and weight 104.1 (13.4) kg (mean [SD]) participated in this study. Wrist actigraphy devices were worn for 8 nights around a 4-d training camp (2 nights prior, during, and 2 nights after). Sleep-onset latency, sleep duration, sleep efficiency, and waking time were measured. Participants provided saliva samples during camp on waking and again 45 min later, which were then assayed for testosterone and cortisol levels. They also rated their general muscle soreness daily. Results: Little variation was observed for sleep quality and quantity or testosterone. However, significant differences were observed between and within days for cortisol, T:C, and muscle soreness (P < .001). Few relationships were observed for sleep and hormones; the strongest, an inverse relationship for sleep efficiency and T:C (r = −.372, P < .01). Conclusions: There may be no clear and useful relationship between sleep and hormone concentration in a short-term training camp context, and measures of sleep and testosterone and cortisol should be interpreted with caution because of individual variation. Alterations in hormone concentration, particularly cortisol, may be affected by other factors including anticipation of the day ahead. This study adds to our knowledge that changes in hormone concentration are individual and context specific.

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Carolina F. Wilke, Felipe Augusto P. Fernandes, Flávio Vinícius C. Martins, Anísio M. Lacerda, Fabio Y. Nakamura, Samuel P. Wanner and Rob Duffield

Purpose : To investigate the existence of faster vs slower recovery profiles in futsal and factors distinguishing them. Methods: 22 male futsal players were evaluated in countermovement jump, 10-m sprint, creatine kinase, total quality of recovery (TQR), and Brunel Mood Scale (fatigue and vigor) before and immediately and 3, 24, and 48 h posttraining. Hierarchical cluster analysis allocated players to different recovery profiles using the area under the curve (AUC) of the percentage differences from baseline. One-way ANOVA compared the time course of each variable and players’ characteristics between clusters. Results : Three clusters were identified and labeled faster recovery (FR), slower physiological recovery (SLphy), and slower perceptual recovery (SLperc). FR presented better AUC in 10-m sprint than SLphy (P = .001) and SLperc (P = .008), as well as better TQR SLphy (P = .018) and SLperc (P = .026). SLperc showed better AUC in countermovement jump than SLphy (P = .014) but presented worse fatigue AUC than SLphy (P = .014) and FR (P = .008). AUC of creatine kinase was worse in SLphy than in FR (P = .001) and SLperc (P < .001). The SLphy players were younger than SLperc players (P = .027), whereas FR were slower 10-m sprinters than SLphy players (P = .003) and SLperc (P = .013) and tended to have higher maximal oxygen consumption than SLphy (effect size =1.13). Conclusion : Different posttraining recovery profiles exist in futsal players, possibly influenced by their physical abilities and age/experience.

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Paul G. Montgomery, David B. Pyne and Clare L. Minahan

Purpose:

To characterize the physical and physiological responses during different basketball practice drills and games.

Methods:

Male basketball players (n = 11; 19.1 ± 2.1 y, 1.91 ± 0.09 m, 87.9 ± 15.1 kg; mean ± SD) completed offensive and defensive practice drills, half court 5on5 scrimmage play, and competitive games. Heart rate, VO2 and triaxial accelerometer data (physical demand) were normalized for individual participation time. Data were log-transformed and differences between drills and games standardized for interpretation of magnitudes and reported with the effect size (ES) statistic.

Results:

There was no substantial difference in the physical or physiological variables between offensive and defensive drills; physical load (9.5%; 90% confidence limits ±45); mean heart rate (-2.4%; ±4.2); peak heart rate (-0.9%; ±3.4); and VO2 (–5.7%; ±9.1). Physical load was moderately greater in game play compared with a 5on5 scrimmage (85.2%; ±40.5); with a higher mean heart rate (12.4%; ±5.4). The oxygen demand for live play was substantially larger than 5on5 (30.6%; ±15.6).

Conclusions:

Defensive and offensive drills during basketball practice have similar physiological responses and physical demand. Live play is substantially more demanding than a 5on5 scrimmage in both physical and physiological attributes. Accelerometers and predicted oxygen cost from heart rate monitoring systems are useful for differentiating the practice and competition demands of basketball.

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Stuart J. Cormack, Mitchell G. Mooney, Will Morgan and Michael R. McGuigan

Purpose:

To determine the impact of neuromuscular fatigue (NMF) assessed from variables obtained during a countermovement jump on exercise intensity measured with triaxial accelerometers (load per minute [LPM]) and the association between LPM and measures of running activity in elite Australian Football.

Methods:

Seventeen elite Australian Football players performed the Yo-Yo Intermittent Recovery Test level 2 (Yo-Yo IR2) and provided a baseline measure of NMF (flight time:contraction time [FT:CT]) from a countermovement jump before the season. Weekly samples of FT:CT, coaches’ rating of performance (votes), LPM, and percent contribution of the 3 vectors from the accelerometers in addition to high-speed-running meters per minute at >15 km/h and total distance relative to playing time (m/min) from matches were collected. Samples were divided into fatigued and nonfatigued groups based on reductions in FT:CT. Percent contributions of vectors to LPM were assessed to determine the likelihood of a meaningful difference between fatigued and nonfatigued groups. Pearson correlations were calculated to determine relationships between accelerometer vectors and running variables, votes, and Yo-Yo IR2 score.

Results:

Fatigue reduced the contribution of the vertical vector by (mean ± 90% CI) –5.8% ± 6.1% (86% likely) and the number of practically important correlations.

Conclusions:

NMF affects the contribution of individual vectors to total LPM, with a likely tendency toward more running at low speed and less acceleration. Fatigue appears to limit the influence of the aerobic and anaerobic qualities assessed via the Yo-Yo IR2 test on LPM and seems implicated in pacing.