Factors that influence running intensity in interchange players in professional rugby league

Background: Rugby league coaches adopt replacement strategies for their interchange players to maximize running intensity; however, it is important to understand the factors that may influence match performance. Purpose: To assess the independent factors affecting running intensity sustained by interchange players during professional rugby league. Methods: Global positioning system (GPS) data were collected from all interchanged players (starters and nonstarters) in a professional rugby league squad across 24 matches of a National Rugby League season. A multilevel mixed-model approach was employed to establish the effect of various technical (attacking and defensive involvements), temporal (bout duration, time in possession, etc), and situational (season phase, recovery cycle, etc) factors on the relative distance covered and average metabolic power (Pmet) during competition. Significant effects were standardized using correlation coefficients, and the likelihood of the effect was described using magnitude-based inferences. Results: Superior intermittent running ability resulted in very likely large increases in both relative distance and Pmet. As the length of a bout increased, both measures of running intensity exhibited a small decrease. There were at least likely small increases in running intensity for matches played after short recovery cycles and against strong opposition. During a bout, the number of collision-based involvements increased running intensity, whereas time in possession and ball time out of play decreased demands. Conclusions: These data demonstrate a complex interaction of individual- and match-based factors that require consideration when developing interchange strategies, and the manipulation of training loads during shorter recovery periods and against stronger opponents may be beneficial

Acceleration-Based Running Intensities of Professional Rugby League Match-Play

Purpose: To quantify the energetic cost of running and acceleration efforts during rugby league competition to aid in prescription and monitoring of training. Methods: Global positioning system (GPS) data were collected from 37 professional rugby league players across 2 seasons. Peak values for relative distance, average acceleration/deceleration, and metabolic power (P_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

Predicting feed intake using modelling based on feeding behaviour in finishing beef steers.

Current techniques for measuring feed intake in housed cattle are both expensive and time-consuming making them unsuitable for use on commercial farms. Estimates of individual animal intake are required for assessing production efficiency. The aim of this study was to predict individual animal intake using parameters that can be easily obtained on commercial farms including feeding behaviour, liveweight and age. In total, 80 steers were used, and each steer was allocated to one of two diets (40 per diet) which consisted of (g/kg; DM) forage to concentrate ratios of either 494:506 (MIXED) or 80:920 (CONC). Individual daily fresh weight intakes (FWI; kg/day) were recorded for each animal using 32 electronic feeders over a 56-day period, and individual DM intakes (DMI; kg/day) subsequently calculated. Individual feeding behaviour variables were calculated for each day of the measurement period from the electronic feeders and included: total number of visits to the feeder, total time spent at the feeder (TOTFEEDTIME), total time where feed was consumed (TIMEWITHFEED) and average length of time during each visit to the feeder. These feeding behaviour variables were chosen due to ease of obtaining from accelerometers. Four modelling techniques to predict individual animal intake were examined, based on (i) individual animal TOTFEEDTIME relative expressed as a proportion of the dietary group (GRP) and total GRP intake, (ii) multiple linear regression (REG) (iii) random forests (RF) and (iv) support vector regressor (SVR). Each model was used to predict CONC and MIXED diets separately, giving eight prediction models, (i) GRP_CONC, (ii) GRP_MIXED, (iii) REG_CONC, (iv) REG_MIXED, (v) RF_CONC, (vi) RF_MIXED, (vii) SVR_CONC and (viii) SVR_MIXED. Each model was tested on FWI and DMI. Model performance was assessed using repeated measures correlations (R2_RM) to capture the repeated nature of daily intakes compared with standard R2, RMSE and mean absolute error (MAE). REG, RF and SVR models predicted FWI with R2_RM = 0.1–0.36, RMSE = 1.51–2.96 kg and MAE = 1.19–2.49 kg, and DMI with R2_RM = 0.13–0.19, RMSE = 1.15–1.61 kg and MAE = 0.9–1.28 kg. The GRP models predicted FWI with R2_RM = 0.42–0.49, RMSE = 2.76–3.88 kg and MAE = 2.46–3.47 kg, and DMI with R2_RM = 0.32–0.44, RMSE = 0.32–0.44 kg, MAE = 1.55–2.22 kg. Whilst more simplistic GRP models showed higher R2_RM than regression and machine learning techniques, these models had larger errors, likely due to individual feeding patterns not being captured. Although regression and machine learning techniques produced lower errors associated with individual intakes, overall precision of prediction was too low for practical use

Sensitivity of markers of DNA stability and DNA repair activity to folate supplementation in healthy volunteers

We have previously reported that supplementation with folic acid (1.2 mg day−1 for 12 week) elicited a significant improvement in the folate status of 61 healthy volunteers. We have examined effects of this supplement on markers of genomic stability. Little is known about the effect of folate supplementation on DNA stability in a cohort, which is not folate deficient. Preintervention, there was a significant inverse association between uracil misincorporation in lymphocyte DNA and red cell folate (P<0.05). In contrast, there were no associations between folate status and DNA strand breakage, global DNA methylation or DNA base excision repair (measured as the capacity of the lymphocyte extract to repair 8-oxoGua ex vivo). Folate supplementation elicited a significant reduction in uracil misincorporation (P<0.05), while DNA strand breakage and global DNA methylation remained unchanged. Increasing folate status significantly decreased the base excision repair capacity in those volunteers with the lowest preintervention folate status (P<0.05). Uracil misincorporation was more sensitive to changes in folate status than other measures of DNA stability and therefore could be considered a specific and functional marker of folate status, which may also be relevant to cancer risk in healthy people