https://nova.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://nova.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://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:52077 Thu 28 Sep 2023 09:06:32 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:37102 Thu 20 Aug 2020 12:10:20 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:34259 Mon 25 Feb 2019 11:39:34 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:36024 max to total cycling mass (body mass including competition clothing and bicycle mass), maximum power output sustained over 60 s relative to total cycling mass, peak left hand grip strength and two-line decision-making score. Previous models for Olympic distance MTB performance demonstrated merit (R² = 0.93; P > 0.05) although subtle changes improved the fit, significance and normal distribution of residuals within the model (R² = 0.99; P < 0.01), highlighting differences between the disciplines. The high level of predictive accuracy of the new XC4H model further supports the use of a multidimensional approach in predicting MTB performance. The difference between the new, XC4H and previous Olympic MTB predictive models demonstrates subtle differences in physiological requirements and performance predictors between the two MTB disciplines.]]> Fri 24 Jan 2020 16:36:12 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:36015 −1 · min⁻¹) completed an incremental cycling test, maximal hand grip strength test, cycling power profile (maximal efforts lasting 6–600 s), decision-making test and an individual XCO-MTB time-trial (34.25 km). A hierarchical approach using multiple linear regression analyses was used to develop predictive models of performance across 10 circuit subsections and the total time-trial. The strongest model to predict overall time-trial performance achieved prediction accuracy of 127.1 s across 6246.8 ± 452.0 s (adjusted R² = 0.92; P < 0.01). This model included VO2max relative to total cycling mass, maximal mean power across 5 and 30 s, peak left hand grip strength, and response time for correct decisions in the decision-making task. A range of factors contributed to the models for each individual subsection of the circuit with varying predictive strength (adjusted R2: 0.62–0.97; P < 0.05). The high prediction accuracy for the total time-trial supports that a multidimensional approach should be taken to develop XCO-MTB performance. Additionally, individual models for circuit subsections may help guide training practices relative to the specific trail characteristics of various XCO-MTB circuits.]]> Fri 24 Jan 2020 16:29:09 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:38022 −1 (5-second effort) and 4.1 ± 0.6 W·kg⁻¹ (600-second effort). No differences in absolute peak power or cadence were identified between groups across any effort length (p > 0.05). However, the XCO-MTB cyclists produced greater mean power outputs relative to body mass than the NC-MTB during the 60-second (6.9 ± 0.8 vs 6.4 ± 0.6 W·kg−1; p = 0.002), 240-second (4.7 ± 0.7 vs. 3.8 ± 0.4 W·kg−1; p < 0.001), and 600-second (4.1 ± 0.6 vs. 3.4 ± 0.3 W·kg⁻¹; p < 0.001) efforts. The PPA is a useful discriminative assessment tool for XCO-MTB and highlights the importance of aerobic power for XCO-MTB performance.]]> Fri 23 Jul 2021 16:22:56 AEST ]]>