https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:44972 Wed 26 Oct 2022 08:39:38 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:42535 Wed 24 Aug 2022 15:57:26 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:15584 14.4 km.h(-1)) distance] and physiological measures [heart rate, blood lactate concentration ([BLa-]), and rating of perceived exertion] were collected during 20 state-based AF matches. Results: The mean (+/- SD) TD covered by field umpires was 11,492 +/- 1,729 m, with boundary umpires covering 15,061 +/- 1,749 m. The average running speed in field umpires was 103 +/- 14 m.min(-1) and was 134 +/- 14 m.min(-1) in boundary umpires. Field and boundary umpires covered 3,095 +/- 752 m and 5,875 +/- 1,590 m, during HIA, respectively. In the first quarter, HIA distance (field: P = .004, eta(2) = 0.071, boundary: P < .001, eta(2) = 0.180) and average running speed (field: P = .002, eta(2) = 0.078, boundary: P < .001, eta(2) = 0.191) were significantly greater than in subsequent quarters. Conclusions: The results demonstrate that both AF field and boundary umpires complete similar running demands to elite AF players and are subject to physical fatigue. Further research is warranted to see if this physical fatigue impacts on the cognitive function of AF umpires during match play.]]> Wed 11 Apr 2018 12:16:04 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:19219 Wed 11 Apr 2018 12:05:08 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:14316 Wed 11 Apr 2018 09:34:43 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:43664 ‐1 load. J Strength Cond Res 35(2): 340–346, 2021—Although measuring movement velocity during resistance exercise is being increasingly used to monitor player readiness for competition in team sports, the validity and reliability of using set target velocities has not been examined. This study examined test-retest reliability of the load-velocity relationship during the back squat to predict loads corresponding to a mean velocity of 1 m·s^‐1 (V_1Load), test-retest reliability of mean concentric velocity at V_1Load, and criterion validity of mean concentric velocity at V_1Load. Twenty-seven resistance-trained male rugby league players completed 2 testing sessions on separate days to establish individualized back squat load-velocity relationship profiles (30, 40, 60, and 80% estimated 1 repetition maximum). Velocity during the back squat was assessed at each load and V1Load derived using individualized linear regression equations. A subset of subjects (n = 18) also performed the back squat at predicted V1Load to examine the test-retest reliability and compare the mean concentric velocity with the predicted target of 1 m·s^‐1. The mean concentric velocity was consistent across all loads during load-velocity relationship testing (p > 0.05, intraclass correlation coefficient [ICC] ≥0.75, coefficient of variation [CV] ≤5.7%, effect size [ES] ≤0.27), and for predicting V_1Load(p = 0.11, ICC = 0.95, CV = 3.9%, ES = 0.11). The mean concentric velocity at V_1Loadwas reliable (ICC = 0.77; CV = 2.6%; ES = 0.39) and not significantly different (p = 0.21) to the target velocity, supporting criterion validity. Individualized load-velocity profiles for the back squat can accurately predict V_1Load, and subsequent use of V_1Load to assess back squat velocity is valid and reliable. Using V1Load to assess changes in back squat velocity may have application in measuring changes in strength and power or readiness to train.]]> Tue 27 Sep 2022 15:01:09 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:29204 p > .05). Using linear regression, Vector data were fit to an SRM equivalent (slope = .99; intercept = −9.87) and TEE produced by this equation was 3.3% (3.0%–3.8%). While the data shows slight heteroscedasticity due to differing strain-gauge placement and resultant torque measurement variance, the Vector appears acceptable for measures of power output across various cycling efforts.]]> Tue 15 Aug 2017 11:46:15 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:27775 Thu 07 Feb 2019 14:52:24 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:10948 Sat 24 Mar 2018 08:14:16 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:17548 Sat 24 Mar 2018 08:03:52 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:27900 2max), anaerobic power and capacity, muscular strength, body composition, muscle fibre characteristics and metabolic efficiency. Despite these age-related changes, several studies have demonstrated that the continued training and competition undertaken by masters athletes slows the natural ageing-related reductions in both athletic performance and physiological function. It should also be highlighted that many other non-physiological age-related changes occur that contribute to the decline in masters athlete performances (e.g. social circumstances, family commitments, employment situations and musculoskeletal limitations). Regardless, it is well understood that athletic performance decreases with age across endurance, sprint and strength events after approximately 35-40 years. These highlighted losses in physiological function and performance accelerate rapidly after an individual reaches 70 years of age.]]> Sat 24 Mar 2018 07:38:01 AEDT ]]>