Stevens, CJ, Bennett, KJM, Novak, AR, Kittel, AB & Dascombe, BJ 2019, 'The Cycling Power Profile Characteristics Of National Level Junior Triathletes.', Journal of Strength and Conditioning Research, vol. 33, no. 1, pp. 197-202.View/Download from: UTS OPUS or Publisher's site
With the draft-legal rule recently introduced to junior triathlon competition, it has become difficult to assess cycling performance through race results. Therefore, this study assessed the cycling power profile characteristics of national level junior triathletes to assist with physical assessment and program design. Thirteen male (17.0 ± 1.0 yr) and eleven female (17.2 ± 1.3 yr) national level junior triathletes completed a cycling power profile that consisted of maximal intervals that lasted 6, 15, 30, 60, 240 and 600 seconds in duration. Each power profile was completed on a LeMond ergometer using the subject's own bicycle, with power output and cadence recorded for all intervals. Mean power output values for males (783 ± 134, 768 ± 118, 609 ± 101, 470 ± 65, 323 ± 38, 287 ± 34 W) were significantly (P<0.05) higher than females (554 ± 92, 510 ± 89, 437 ± 75, 349 ± 56, 248 ± 39, 214 ± 37 W) across all intervals, respectively. Peak power output values for males across the 6 and 15 second intervals (1011 ± 178 and 962 ± 170 W) were also significantly higher than for females (674 ± 116 and 624 ± 114 W), respectively (P<0.05). Developing junior triathletes should aim to increase their capacity across the power profile above the mean values listed. Athletes should further aim to have power outputs equal to that of the best performers and beyond to ensure that they can meet the demands of any competition situation.
Novak, AR, Bennett, KJM, Pluss, MA, Fransen, J, Watsford, ML & Dascombe, BJ 2019, 'Power profiles of competitive and non-competitive mountain bikers.', Journal of Strength and Conditioning Research.View/Download from: UTS OPUS or Publisher's site
The performance of Olympic distance cross-country mountain bikers (XCO-MTB) is affected by constraints such as erosion of track surfaces and mass start congestion which can affect race results. Standardised laboratory assessments quantify inter-seasonal and intra-seasonal cycling potential through the assessment of multiple physiological capacities. Therefore, this study examined whether the power profile assessment could discriminate between competitive XCO-MTB and non-competitive mountain bikers (NC-MTB). Secondly, it aimed to report normative power profile data for competitive XCO-MTB cyclists. Twenty-nine male participants were recruited across groups of XCO-MTB (n=14) and NC-MTB (n=15) mountain bikers. Each cyclist completed a power profile assessment that consisted of increasing duration maximal efforts (6, 15, 30, 60, 240 and 600 s) that were interspersed by longer rest periods (174, 225, 330, 480 and 600 s) between efforts. Normative power outputs were established for XCO-MTB cyclists ranging between 13.8 ± 1.5 W·kg (5 s effort) to 4.1 ± 0.6 W·kg (600 s 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 s (6.9 ± 0.8 vs 6.4 ± 0.6 W·kg; p=0.002), 240 s (4.7 ± 0.7 vs 3.8 ± 0.4 W·kg; p<0.001) and 600 s (4.1 ± 0.6 vs 3.4 ± 0.3 W·kg; p<0.001) efforts. The power profile assessment is a useful discriminative assessment tool for XCO-MTB and highlights the importance of aerobic power for XCO-MTB performance.
Bennett, KJM, Novak, AR, Pluss, MA, Coutts, AJ & Fransen, J 2019, 'Assessing the validity of a video-based decision-making assessment for talent identification in youth soccer.', Journal of Science and Medicine in Sport.View/Download from: Publisher's site
OBJECTIVES:To investigate the construct and discriminant validity of a video-based decision-making assessment for talent identification in youth soccer. DESIGN:Observational study. METHOD:A total of 328 academy youth soccer players (tier one, tier two, and tier three) from three developmental stages (late childhood, early adolescence, and mid-adolescence) participated in this study. The control group consisted of 59 youth athletes with no soccer experience in the last five years. Players completed a video-based decision-making assessment on an iPad, with response accuracy and response time recorded for various attacking situations (2 vs. 1, 3 vs. 1, 3 vs. 2, 4 vs. 3, and 5 vs. 3). RESULTS:The video-based decision-making assessment showed some construct validity. Response times were significantly faster in early and mid-adolescent players when compared to those in the late childhood group. Furthermore, an overall decline in decision-making performance (i.e. decrease in response accuracy and increase in response time) was observed from the 2 vs. 1 to the 4 vs. 3 situations. The video-based decision-making assessment lacked discriminant validity as minimal differences between academies were evident for response accuracy and response time. Only response accuracy was able to discriminate youth academy soccer players from the control group to some extent. CONCLUSIONS:Coaches and sporting professionals should apply caution when interpreting data from practical, video-based decision-making assessments. There is currently limited conclusive evidence supporting the effectiveness of these assessments for talent identification.
Pluss, MA, Bennett, KJM, Novak, AR, Panchuk, D, Coutts, AJ & Fransen, J 2019, 'Esports: The Chess of the 21st Century', FRONTIERS IN PSYCHOLOGY, vol. 10.View/Download from: UTS OPUS or Publisher's site
Novak, AR, Bennett, KJM, Fransen, J & Dascombe, BJ 2018, 'A multidimensional approach to performance prediction in Olympic distance cross-country mountain bikers.', Journal of Sports Sciences, vol. 36, no. 1, pp. 71-78.View/Download from: UTS OPUS or Publisher's site
This study adopted a multidimensional approach to performance prediction within Olympic distance cross-country mountain biking (XCO-MTB). Twelve competitive XCO-MTB cyclists (VO2max 60.8 ± 6.7 ml · kg(-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 R(2) = 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 R(2): 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.
Novak, AR, Bennett, KJM, Fransen, J & Dascombe, BJ 2018, 'Predictors of performance in a 4-h mountain-bike race.', Journal of Sports Sciences, vol. 36, no. 4, pp. 462-468.View/Download from: UTS OPUS or Publisher's site
This study aimed to cross validate previously developed predictive models of mountain biking performance in a new cohort of mountain bikers during a 4-h event (XC4H). Eight amateur XC4H cyclists completed a multidimensional assessment battery including a power profile assessment that consisted of maximal efforts between 6 and 600 s, maximal hand grip strength assessments, a video-based decision-making test as well as a XC4H race. A multiple linear regression model was found to predict XC4H performance with good accuracy (R(2) = 0.99; P < 0.01). This model consisted of [Formula: see text] relative 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 (R(2) = 0.93; P > 0.05) although subtle changes improved the fit, significance and normal distribution of residuals within the model (R(2) = 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.
Fransen, J, Bush, S, Woodcock, S, Novak, A, Deprez, D, Baxter-Jones, ADG, Vaeyens, R & Lenoir, M 2018, 'Improving the Prediction of Maturity From Anthropometric Variables Using a Maturity Ratio', Pediatric Exercise Science, vol. 30, no. 2, pp. 296-307.View/Download from: UTS OPUS or Publisher's site
Purpose: This study aimed to improve the prediction accuracy of age at peak height velocity (APHV) from anthropometric assessment using nonlinear models and a maturity ratio rather than a maturity offset. Methods: The dataset used to develop the original prediction equations was used to test a new prediction model, utilizing the maturity ratio and a polynomial prediction equation. This model was then applied to a sample of male youth academy soccer players (n = 1330) to validate the new model in youth athletes. Results: A new equation was developed to estimate APHV more accurately than the original model (new model: Akaike information criterion: −6062.1, R2 = 90.82%; original model: Akaike information criterion = 3048.7, R2 = 88.88%) within a general population of boys, particularly with relatively high/low APHVs. This study has also highlighted the successful application of the new model to estimate APHV using anthropometric variables in youth athletes, thereby supporting the use of this model in sports talent identification and development. Conclusion: This study argues that this newly developed equation should become standard practice for the estimation of maturity from anthropometric variables in boys from both a general and an athletic population.
Novak, AR, Bennett, KJM, Beavan, A, Pion, J, Spiteri, T, Fransen, J & Lenoir, M 2017, 'The Applicability of a Short Form of the Körperkoordinationstest für Kinder for Measuring Motor Competence in Children Aged 6 to 11 Years', Journal of Motor Learning and Development, vol. 5, no. 2, pp. 227-239.View/Download from: UTS OPUS or Publisher's site
This study aimed to determine if the Körperkoordinationstest für Kinder (KTK) remained a valid assessment of motor competence following the removal of the hopping for height subtest (KTK3). Children (n = 2479) aged 6–11 years completed all KTK subtests (KTK4) and motor quotient sum scores (MQS) were determined for the KTK3 and KTK4. Classifications were established as MQS below percentile 5 (P5), MQS between percentile 5–15 (P15), MQS between percentile 15–50 (P15–50), MQS between percentile 50–85 (P50–85), MQS between percentile 85–95 (P85), and MQS higher than percentile 95 (P95). Pearson's correlation (r = .97) and cross-tabs (Chi2 = 6822.53, p < .001; Kappa = 0.72) identified substantial agreement overall between the KTK3 and KTK4. However, when classified into separate age and gender categories, poor agreement (< 60%) was found in girls: P15 at 8–11 years and P85 at 6–7 years; and in boys: P5 and P15 at 6 years, P85 at 8 years, and P15 at 10 years. Researchers should consider these findings when selecting which KTK protocol to use.
Novak, AR & Dascombe, BJ 2016, 'Agreement of Power Measures between Garmin Vector and SRM Cycle Power Meters', Measurement in Physical Education and Exercise Science, vol. 20, no. 3, pp. 167-172.View/Download from: Publisher's site
© 2016 Taylor & Francis. This study aimed to determine if the Garmin Vector (Schaffhausen, Switzerland) power meter produced acceptable measures when compared with the Schoberer Rad Messetechnik (SRM; Julich, Germany) power meter across a range of high-intensity efforts. Twenty-one well-trained cyclists completed power profiles (seven maximal mean efforts between 5 and 600 s) using Vector and SRM power meters. Data were compared using assessments of heteroscedasticity, t tests, linear regression, and typical error of estimate (TEE). The data were heteroscedastic, whereby the Vector pedals increasingly overestimated values at higher power outputs; however, t tests did not identify any significant differences between power meters (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.