https://ogma.newcastle.edu.au/vital/access/ /manager/Index en-au 5 A contemporary variable-power cycling protocol to discriminate race-specific performance ability https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:38076 2max test and two 1-h VCT protocols on 3 separate occasions. The VCT consisted of 10 × 6-min segments containing prescribed (3.5 W·kg−1) and open-ended phases. The open-ended phases consisted of 4 x 30–40 s of "recovery," 3 x 10 s at "hard" intensity, and 3 x 6-s "sprint" with a final 10-s "all-out" effort. Results: Power output for the 6- and 10-s phases was moderately higher for the national- compared with club-level cyclists (mean [SD] 10.4 [2.0] vs 8.6 [1.6] W·kg−1, effect size; ±90% confidence limits = −0.87; ±0.65 and mean [SD] 7.5 [0.7] vs 6.2 [1.0] W·kg−1, effect size; ±90% confidence limits = −1.24; ±0.66, respectively). Power output for the final 10-s "all-out" sprint was 15.4 (1.5) for the national- versus 13.2 (1.9) W·kg−1 for club-level cyclists. Conclusion: The 1-h VCT can successfully differentiate repeat high-intensity effort performance between higher-caliber cyclists and their lower-performing counterparts.]]> Tue 03 Aug 2021 14:02:28 AEST ]]> Predictors of performance in a 4-h mountain-bike race https://ogma.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 ]]> A multidimensional approach to performance prediction in Olympic distance cross-country mountain bikers https://ogma.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 ]]>