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Assessment of energy availability and associated risk factors in professional female soccer playersThis study aimed to assess energy availability (EA), alongside possible risk factors of reduced or low EA of professional female soccer players during a competitive season. Thirteen players (age: 23.7 ± 3.4 y, stature: 1.69 ± 0.08 m, body mass: 63.7 ± 7.0 kg) engaged in a 5-day (two rest days, one light training, heavy training and match day) monitoring period. Energy intake (EI) and expenditure during exercise (EEE) were measured. EA was calculated and categorised as optimal, reduced or low (≥45, 31-44, ≤30 kcal·kg FFM-1·day-1, respectively). Relationships between EA and bone mineral density, resting metabolic rate (RMR), plasma micronutrient status, biochemical markers and survey data were assessed. EA was optimal for 15%, reduced for 62% and low for 23% of players. Higher EA was observed on rest days compared to others (P<0.05). EA was higher for the light compared to the heavy training day (P<0.001). EEE differed significantly between days (P<0.05). EI (2124 ± 444 kcal), carbohydrate (3.31 ± 0.64 g·kg·day-1) and protein (1.83 ± 0.41 g·kg·day-1) intake remained similar (P>0.05). Survey data revealed 23% scored ≥ 8 on the Low Energy Availability in Females Questionnaire and met criteria for low RMR (ratio <0.90). Relationships between EA and risk factors were inconclusive. Most players displayed reduced EA and did not alter EI or carbohydrate intake to training or match demands. Although cases of low EA were identified, further work is needed to investigate possible long-term effects and risk factors of low and reduced EA separately to inform player recommendations.
Development of anthropometric characteristics in professional Rugby League players: Is there too much emphasis on the pre-season period?Rugby League is a team sport requiring players to experience large impact collisions, thus requiring high amounts of muscle mass. Many players (academy and senior) strive to increase muscle mass during the pre-season, however, quantification of changes during this period have not been thoroughly investigated. We therefore assessed changes in body-composition using Dual X-Ray Absorptiometry (DXA) in eleven academy players over three successive pre-seasons and ninety-three senior players from four different European Super League clubs prior to, and at the end of, a pre-season training period. There was no meaningful change in lean mass of the academy players during any of the pre-season periods (year 1 = 72.3 ± 7.1–73.2 ± 7.2kg; ES 0.05, year 2 = 74.4 ± 6.9–75.5 ± 6.9kg; ES 0.07, year 3 = 75.9 ± 6.7–76.8 ± 6.6kg; ES 0.06) with small changes only occurring over the three-year study period (72.3–75.9kg; ES = 0.22). Senior players showed trivial changes in all characteristics during the pre-season period (total mass = 95.1–95.0kg; ES −0.01, lean mass = 74.6–75.1kg; ES 0.07, fat mass = 13.6–12.9kg; ES −0.17, body fat percentage = 14.8–14.1%; ES −0.19). These data suggest that academy players need time to develop towards profiles congruent with senior players. Moreover, once players reach senior level, body-composition changes are trivial during the pre-season and therefore teams may need to individualise training for players striving to gain muscle mass by reducing other training loads.
Influence of contextual factors, technical performance and movement demands on the subjective task load associated with professional rugby league match-playPurpose: The aim of the study was to identify the association between several contextual match factors, technical performance and external movement demands on the subjective task load of elite rugby league players. Methods: Individual subjective task load, quantified using the National Aeronautics and Space Administration Task Load Index (NASA-TLX), was collected from 29 professional rugby league players from one club competing in the European Super League throughout the 2017 season. The sample consisted of 26 matches, culminating in 441 individual data points. Linear mixed-modelling was adopted to analyze the data for relationships and revealed that various combinations of contextual factors, technical performance and movement demands were associated with subjective task load. Results: Greater number of tackles (effect size correlation ± 90% CI; η2= 0.18 ±0.11), errors (η2= 0.15 ±0.08) decelerations (η2= 0.12 ±0.08), increased sprint distance (η2= 0.13 ±0.08), losing matches (η2= 0.36 ±0.08) and increased perception of effort (η2= 0.27 ±0.08) led to most likely – very likely increases in subjective total task load. The independent variables included in the final model for subjective mental demand (match outcome, time played and number of accelerations) were unclear, excluding a likely small correlation with the number of technical errors (η2= 0.10 ±0.08). Conclusions: These data provide a greater understanding of the subjective task load and their association with several contextual factors, technical performance and external movement demands during rugby league competition. Practitioners could use this detailed quantification of internal loads to inform the prescription of recovery sessions and current training practices.