https://nova.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:33313 2max (P = 0.002) and push-ups_{1 min} (P = 0.004) were superior in selected under-16 players, and sprint times (P ≤ 0.045), push-ups_{1 min} (P < 0.001) and chin-ups_{1 min} (P = 0.013) were superior in selected under-18 players. Further, 10-m sprint (β = −7.706, standard error [SE] = 2.412), VO_2max (β = 0.168, SE = 0.052) and body mass (β = 0.071, SE = 0.023) significantly predicted selection (R² = 0.339) in under-16 players, while push-ups_{1 min} (β = 0.564, SE = 0.250), 10-m sprint (β = −68.477, SE = 28.107), body mass (β = 0.360, SE = 0.155) and chronological age (β = −3.577, SE = 1.720) significantly predicted selection (R² = 0.894) in under-18 players. These findings emphasise the importance of performance attributes in junior rugby league and indicate talent identification test batteries should be age-specific in older adolescent players.]]> Wed 10 Oct 2018 13:20:23 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:41668 n = 52; mean age 16.0 ± 1.0 years) from one club participated in this study. Functional Movement Screen scores, height, and mass were collected at the beginning of the preseason. Training, match exposure, and injury incidence data (non-contact match and training injuries with three levels of severity) were recorded for each individual athlete throughout the season. Linear and logistic regression analyses were conducted to investigate the association between Functional Movement Screen score (continuous score, ≤ 14 or > 14, and three subscores) and injury risk, whilst controlling for exposure time. The mean Functional Movement Screen score for the sample was 13.4 (95% CI: 11.0–14.0). A total of 72 non-contact injuries were recorded (incidence rate: 18.7 per 1000 exposure hours; 95% CI: 11.6–24.8). There were no statistically significant associations between non-contact injury and Functional Movement Screen score for any of the analyses conducted. Our results suggest that the Functional Movement Screen does not reflect non-contact injury risk in elite adolescent rugby league players. Further research should investigate whether a more sport-specific movement screen in the preseason can more effectively predict injury risk in this population.]]> Wed 10 Aug 2022 13:16:26 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:32862 IFT) within rugby league. Sixty-three Australian elite and junior-elite rugby league players (22.5 ± 4.5 years, 96.1 ± 9.5 kg, Σ7 skinfolds: 71.0 ± 18.7 mm) from a professional club participated in this study. Players were assessed for anthropometry (body mass, Σ7 skinfolds, lean mass index), prolonged high-intensity intermittent running (PHIR; measured by 30-15_IFT), predicted aerobic capacity (MSFT) and power (AAS), speed (40 m sprint), repeated sprint, and change of direction (COD—505 agility test) ability before and after an 11-week preseason training period. Validity of the 30-15_IFT was established using Pearson’s coefficient correlations. Forward stepwise regression model identified the fewest variables that could predict individual final velocity (V_IFT) and change within 30-15_IFT performance. Significant correlations between V_IFT and Σ7 skinfolds, repeated sprint decrement, VO₂max_MSFT, and average aerobic speed were observed. A total of 71.8% of the adjusted variance in 30-15_IFT performance was explained using a 4-step best fit model (VO₂max_MSFT, 61.4%; average aerobic speed, 4.7%; maximal velocity, 4.1%; lean mass index, 1.6%). Across the training period, 25% of the variance was accounted by ΔVO₂max_MSFT (R² = 0.25). These relationships suggest that the 30-15_IFT is a valid test of PHIR within rugby league. Poor correlations were observed with measures of acceleration, speed, and COD. These findings demonstrate that although the 30-15_IFT is a valid measure of PHIR, it also simultaneously examines various physiological capacities that differ between sporting cohorts.]]> Tue 31 Jul 2018 11:55:18 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:50319 Tue 18 Jul 2023 14:29:54 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:43235 Thu 15 Sep 2022 09:28:48 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:30145 p < 0.01; r(s) = 0.71, p < 0.01, respectively). No significant correlations were evident for defensive skill involvements during SSG and match-play. Overall, it appears that the selected SSG provided players with ample opportunity to practice match-specific skills. In addition, the transfer of these opportunities seems confined to offensive rather then defensive skills.]]> Sat 24 Mar 2018 07:34:34 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:29722 2282 AU, weekly-TL >2786 AU and monotony >0.78 AU) to best predict when athletes are at increased risk of self-reported illness. In addition, a reduction in overall wellbeing (<7.25 AU) in the presence of increased internal-TL as previously stated, was highlighted as a contributor to self-reported illness occurrence.These results indicate that self-report data can be successfully utilized to provide a novel understanding of the interactions between competition-associated stressors experienced by professional team-sport athletes and their susceptibility to illness. This may assist coaching staff to more effectively monitor players during the season and to potentially implement preventative measures to reduce the likelihood of illnesses occurring.]]> Sat 24 Mar 2018 07:33:25 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:29670 -1) of offensive, defensive and overall involvements was coded for each group using a notation system and a practical coach skill analysis tool. Multivariate analysis of variance (MANOVA) revealed a significant effect of playing position on skill involvements (F = 9.06; P < 0.001; ES = 0.41). Hit-up forwards performed a significantly greater frequency of offensive (0.31 ± 0.10), defensive (0.42 ± 0.15) and overall involvements (0.74 ± 0.19) when compared to adjustables (0.20 ± 0.08, 0.28 ± 0.08 and 0.52 ± 0.15, respectively) and outside backs (0.20 ± 0.12, 0.11 ± 0.07 and ± 0.31 ± 0.17, respectively). Further, adjustables performed a significantly greater number of defensive (0.28 ± 0.08) and overall involvements (0.52 ± 0.15) when compared to outside backs (0.11 ± 0.07 and 0.31 ± 0.17, respectively). The findings of this study suggest that it is important to consider a junior player's positional group when analysing their skill involvements. Information gained from this study could assist in the design of specific training methodologies for junior rugby league players in high-level talent development programmes.]]> Sat 24 Mar 2018 07:32:21 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:28336 0.7), whereas the ICCs for Under 16s and Under 18s were almost perfect (r > 0.9). Coefficients of variation were 1.9% (95% confidence interval, 1.6–2.4) for the combined test-retest of the 30-15IFT and 0.6% (0.5–1.0) for HRpeak. As the typical error of measurement (TE) (0.36 km·h−1) was greater than the smallest worthwhile change (SWC) (0.21 km·h−1) value, the usefulness of the VIFT was rated as “marginal.” The TE for HRpeak was similar to the SWC, rating the usefulness of this variable as “OK.” Despite the usefulness of the 30-15IFT being deemed Marginal, a change as small as 0.5 km·h−1 (1 stage) in VIFT could be considered substantial or “real.” As a consequence, the 30-15IFT presents as both a reliable and useful field test in the assessment of intermittent fitness for rugby league players.]]> Sat 24 Mar 2018 07:25:14 AEDT ]]>