Dr Debbie Smith

Total energy expenditure (TEE) has been quantified in elite senior rugby league (RL) and rugby union (RU) players using multiple measures, with criterion measures lacking in RU and academy players. Robust measures of TEE are required as prediction equations used to estimate energy requirements are often unsuitable for athletes. This study quantified TEE of 27 elite male English academy (U16 and U20) and senior (U24) RL and RU players during a 14-day in-season period using doubly labelled water (DLW). Resting metabolic rate (RMR), using indirect calorimetry, and physical activity level (PAL) was also measured (TEE:RMR). Predicted TEE, determined by published equations, was compared to measured TEE by age group. Differences in TEE (RL, 4369 ± 979; RU, 4365 ± 1122; U16, 4010 ± 744; U20, 4414 ± 688; U24, 4761 ± 1523 Kcal.day-1) and PAL (overall mean 2.0 ± 0.4) were unclear. RMR was very likely greater for RL (2366 ± 296 Kcal.day-1) than RU players (2123 ± 269 Kcal.day-1). Relative RMR for U16, U20 and U24 (27 ± 4, 23 ± 3 and 26 ± 5 Kcal.Kg-1.day-1) was very likely greater for U20 than U24 players. Differences in TEE estimated by the Schofield, Cunningham and Harris-Benedict equations compared with DLW were unclear, likely and unclear for U16 (187 ± 614; -489 ± 564 and -90 ± 579 Kcal.day-1), likely, very likely and likely for U20 (-449 ± 698; -785 ± 650 and -452 ± 684 Kcal.day-1) and all unclear for U24 players (-428 ± 1292; -605 ± 1493 and -461 ± 1314 Kcal.day-1). Due to large variability between individuals, negligible differences in TEE were observed by code, and ~350-400 Kcal.day-1 differences between consecutive age groups were unclear. Differences in RMR may be due to training exposure and match play. The remaining components of TEE (i.e. thermic effect of feeding and activity thermogenesis) may reflect the differences in contact demands between codes, as RU players typically engage in more static exertions than RL players during match play. Prediction equations are currently insufficient to differentiate between individual variability in TEE. The importance of practitioners providing individual support for the elite rugby player is highlighted. Finally, the TEE measured in this study using the gold standard DLW method can be used as reference data for elite rugby players of different codes and ages, during an in-season training period.

Nutrition guidelines specific to short stature athletes are lacking. Achondroplasia, the most common cause for disproportionate short stature (dwarfism), potentially increases resting energy requirements. Therefore, for the first time, this case study aimed to establish the resting metabolic rate (RMR), physical activity energy expenditure (PAEE), and total energy expenditure (TEE) of a dwarf para-powerlifter across rest and training days. A second aim was to determine the suitability of RMR prediction equations recommended for use in athletic populations. RMR was measured using indirect calorimetry on four occasions (two days post-competition, one day post-training, one- and three-days post-training camps). PAEE was monitored using a wrist-worn accelerometer for two 7-day periods. The thermic effect of feeding was estimated; therefore, TEE was inferred. RMR was greater 1- and 3-days post-training camps (1896 and 1992 kcal∙day−1, respectively) than post-training (1613 kcal∙day−1) and post-competition (1409 kcal∙day−1). PAEE and TEE was similar between rest and training days (PAEE, 957 and 948; TEE, 2715 and 2705 kcal∙day−1, respectively). Accuracy of RMR prediction equations utilised ranged from − 580 to + 189 kcal∙day−1 when compared to measured RMR post competition and training. Underestimation was greater (−412 ± 107 kcal∙day−1) post training camps. Results highlight the importance of establishing the energy requirements of dwarf para-athletes, particularly because prediction equations were not accurate in this case study. Further investigation of energy demands during training camps, particularly the relationship of workload on energy expenditure, are now warranted in a larger population.

Sub-optimal calcium, vitamin D and iron intakes are typical in athletes. However, quantification by dietary intake may be erroneous, with biomarkers providing a more accurate assessment. This study aimed to determine the calcium, vitamin D and iron status of 8 junior (i.e., under-18 [U18]; age 15.5 ± 0.5 years; height 180.4 ± 6.7 cm; body mass 81.6 ± 14.3 kg) and 12 senior (i.e., over-18 [O18]; age 19.7 ± 1.8 years; height 184.9 ± 6.9 cm; body mass 97.4 ± 14.4 kg) male rugby union players, and assess their adequacy against reference values. Fasted serum calcium, 25(OH)D and ferritin concentrations were analysed using Enzyme-Linked Immunosorbent Assay during the in-season period (March-April). U18 had very likely greater calcium concentrations than O18 (2.40 ± 0.08 vs. 2.25 ± 0.19 mmol.l-1). Differences between U18 and O18 were unclear for 25(OH)D (20.21 ± 11.57 vs. 29.02 ± 33.69 nmol.l-1) and ferritin (59.33 ± 34.61 vs. 85.25 ± 73.53 µg.l-1). Compared to reference values, all U18 had adequate serum calcium concentrations, whereas 33% and 67% of O18 were deficient and adequate, respectively. All U18 and 83% of O18 had severely deficient, deficient or inadequate vitamin D concentrations. Adequate (8%) and optimal (8%) concentrations of vitamin D were observed in O18. All U18 and 75% of O18 had adequate ferritin concentrations. Potential toxicity (17%) and deficient (8%) ferritin concentrations were observed in O18. Vitamin D intake should be increased and multiple measures obtained throughout the season. More research is required on the variation of micronutrient status

Good nutrition is essential for the physical development of adolescent athletes, however data on dietary intakes of adolescent rugby players are lacking. This study quantified and evaluated dietary intake in 87 elite male English academy rugby league (RL) and rugby union (RU) players by age (under-16 (U16) and under-19 (U19) years old) and code (RL and RU). Relationships of intakes with body mass and composition (sum of 8 skinfolds) were also investigated. Using 4-day diet and physical activity diaries, dietary intake was compared to adolescent sports nutrition recommendations and the UK national food guide. Dietary intake did not differ by code, whereas U19s consumed greater energy (3366 ± 658 vs. 2995 ± 774 kcal.day-1), protein (207 ± 49 vs. 150 ± 53 g.day-1) and fluid (4221 ± 1323 vs. 3137 ± 1015 ml.day-1) than U16s. U19s consumed a better quality diet than U16s (greater intakes of fruit and vegetables; 4.4 ± 1.9 vs. 2.8 ± 1.5 servings.day-1; non-dairy proteins; 3.9 ± 1.1 vs. 2.9 ± 1.1 servings.day-1) and less fats and sugars (2.0 ± 1. vs. 93.6 ± 2.1 servings.day-1). Protein intake vs. body mass was moderate (r = 0.46, p < 0.001), and other relationships were weak. The findings of this study suggest adolescent rugby players consume adequate dietary intakes in relation to current guidelines for energy, macronutrient and fluid intake. Players should improve the quality of their diet by replacing intakes from the fats and sugars food group with healthier choices, while maintaining current energy, and macronutrient intakes.

Criterion data for total energy expenditure (TEE) in elite rugby are lacking, which prediction equations may not reflect accurately. This study quantified TEE of 27 elite male rugby league (RL) and rugby union (RU) players (U16, U20, U24 age groups) during a 14-day in-season period using doubly labelled water (DLW). Measured TEE was also compared to estimated, using prediction equations. Resting metabolic rate (RMR) was measured using indirect calorimetry, and physical activity level (PAL) estimated (TEE:RMR). Differences in measured TEE were unclear by code and age (RL, 4369 ± 979; RU, 4365 ± 1122; U16, 4010 ± 744; U20, 4414 ± 688; U24, 4761 ± 1523 Kcal.day-1). Differences in PAL (overall mean 2.0 ± 0.4) were unclear. Very likely differences were observed in RMR by code (RL, 2366 ± 296; RU, 2123 ± 269 Kcal.day-1). Differences in relative RMR between U20 and U24 were very likely (U16, 27 ± 4; U20, 23 ± 3; U24, 26 ± 5 Kcal.kg-1.day-1). Differences were observed between measured and estimated TEE, using Schofield, Cunningham and Harris-Benedict equations for U16 (187 ± 614, unclear; -489 ± 564, likely and -90 ± 579, unclear Kcal.day-1), U20 (-449 ± 698, likely; -785 ± 650, very likely and -452 ± 684, likely Kcal.day-1) and U24 players (-428 ± 1292; -605 ± 1493 and -461 ± 1314 Kcal.day-1, all unclear). Rugby players have high TEE, which should be acknowledged. Large inter-player variability in TEE was observed demonstrating heterogeneity within groups, thus published equations may not appropriately estimate TEE.

The Vendée Globe is a solo round-the-world sailing race without stopovers or assistance, a physically demanding challenge for which appropriate nutrition should maintain energy balance and ensure optimum performance. This is an account of prerace nutritional preparation with a professional and experienced female racer and assessment of daily nutritional intake (NI) during the race using a multimethod approach. A daily energy intake (EI) of 15.1 MJ/day was recommended for the race and negotiated down by the racer to 12.7 MJ/day, with carbohydrate and fluid intake goals of 480 g/day and 3,020 ml/day, respectively. Throughout the 99-day voyage, daily NI was recorded using electronic food diaries and inventories piloted during training races. NI was assessed and a postrace interview and questionnaire were used to evaluate the intervention. Fat mass (FM) and fat-free mass (FFM) were assessed pre- (37 days) and postrace (11 days) using dual-energy X-ray absorptiometry, and body mass was measured before the racer stepped on the yacht and immediately postrace. Mean EI was 9.2 MJ/day (2.4-14.3 MJ/day), representing a negative energy balance of 3.5 MJ/day under the negotiated EI goal, evidenced by a 7.9-kg loss of body mass (FM -7.5 kg, FFM -0.4 kg) during the voyage, with consequent underconsumption of carbohydrate by ~130 g/day. According to the postrace yacht food inventory, self-reported EI was underreported by 7%. This intervention demonstrates the practicality of the NI approach and assessment, but the racer's nutrition strategy can be further improved to facilitate meeting more optimal NI goals for performance and health. It also shows that evaluation of NI is possible in this environment over prolonged periods, which can provide important information for optimizing nutritional strategies for ocean racing.

Body composition analysis using dual energy X-ray absorptiometry (DXA) is becoming increasingly popular in both clinical and sports science settings. Obesity, characterised by high fat mass (FM), is associated with larger precision errors, however, precision error for athletic groups with high levels of lean mass (LM) are unclear. Total (TB) and regional (limbs and trunk) body composition were determined from two consecutive total body scans (GE Lunar iDXA) with re-positioning in 45 elite male rugby league players (age: 21.8 ±5.4 years BMI: 27.8 ±2.5 kg.m-1). The root mean squared standard deviation (percentage co-efficient of variation) were TB bone mineral content (BMC): 24g (1.7%), TB LM: 321g (1.6%), and TB FM: 280g (2.3%). Regional precision values were superior for measurements of BMC: 4.7-16.3g (1.7-2.1%) and LM: 137-402g (2.0-2.4%), than for FM: 63-299g (3.1-4.1%). Precision error of DXA body composition measurements in elite male rugby players is higher than those reported elsewhere for normal adult populations and similar to those reported in those who are obese. It is advised that caution is applied when interpreting longitudinal DXA-derived body composition measurements in male rugby players and population-specific least significant change should be adopted.