https://nova.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:43754 Wed 28 Sep 2022 13:38:44 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:36345 Wed 01 Apr 2020 14:00:32 AEDT ]]> 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:17321 Sat 24 Mar 2018 08:01:45 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:26125 met) were calculated for 10 different moving-average durations (1–10 min) for each position. A mixed-effects model was used to assess the effect of position for each duration, and individual comparisons were made using a magnitude-based-inference network. Results: There were almost certainly large differences in relative distance and P_met between the 10-min window and all moving averages <5 min in duration (ES = 1.21–1.88). Fullbacks, halves, and hookers covered greater relative distances than outside backs, edge forwards, and middle forwards for moving averages lasting 2–10 min. Acceleration/deceleration demands were greatest in hookers and halves compared with fullbacks, middle forwards, and outside backs. P_met was greatest in hookers, halves, and fullbacks compared with middle forwards and outside backs. Conclusions: Competition running intensities varied by both position and moving-average duration. Hookers exhibited the greatest P_met of all positions, due to high involvement in both attack and defense. Fullbacks also reached high P_met, possibly due to a greater absolute volume of running. This study provides coaches with match data that can be used for the prescription and monitoring of specific training drills.]]> Sat 24 Mar 2018 07:41:07 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:28052 Sat 24 Mar 2018 07:41:02 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:28051 Sat 24 Mar 2018 07:41:02 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: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 ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:23966 .9, P < .001). The correlation between change scores was stronger for the LMI (r = .69, SEE 1.3 kg) and the SkF method (r = .66, SEE = 1.4 kg) than for BIA (r = .50, SEE = 1.6 kg). Conclusions: The LMI is probably as accurate in predicting changes in FFM as SkF and very likely to be more appropriate than BIA. The LMI offers an adequate, practical alternative for assessing in FFM among rugby league athletes.]]> Sat 24 Mar 2018 07:10:09 AEDT ]]>