Evidence suggests that increasing protein distribution may be desirable to promote muscle protein synthesis (MPS) in combination with resistance exercise. However, there is a threshold above which additional protein consumption has limited benefit for MPS and may promote protein loss due to increased oxidation. This study aimed to measure daily protein intake and protein distribution in a cohort of rugby players. Twenty-five developing elite rugby union athletes (20.5 ± 2.3 years, 100.2 ± 13.3 kg, 184.4 ± 7.4 cm) were assessed at the start and end of a rugby preseason. Using a 7-day food diary the reported daily protein intake was 2.2 ± 0.7 g·kg·day-1 which exceeds daily recommendations. The reported carbohydrate intake was 3.6 ± 1.3 g·kg·day-1 which may reflect a suboptimal intake or dietary underreporting. In general, the rugby athletes were regularly consuming more than 20 g of protein; 3.8 ± 0.9 times per day (68 ± 18% of eating occasions). In addition to documenting current dietary intakes, an excess protein estimation score was calculated to determine how frequently the rugby athletes consumed protein above a known effective dose with a margin of error. 2.0 ± 0.9 eating occasions contained protein in excess of doses (20 g) known to promote MPS. Therefore, it is currently unclear whether the consumption of regular large doses of protein will benefit rugby athletes via increasing protein distribution, or whether high protein intakes may have unintended effects including a reduction in carbohydrate and/or energy intake.
Kristen MacKenzie, Gary Slater, Neil King and Nuala Byrne
Ava Kerr, Gary Slater, Nuala Byrne and Janet Chaseling
The three-compartment (3-C) model of physique assessment (fat mass, fat-free mass, water) incorporates total body water (TBW) whereas the two-compartment model (2-C) assumes a TBW of 73.72%. Deuterium dilution (D2O) is the reference method for measuring TBW but is expensive and time consuming. Multifrequency bioelectrical impedance spectroscopy (BIS SFB7) estimates TBW instantaneously and claims high precision. Our aim was to compare SFB7 with D2O for estimating TBW in resistance trained males (BMI >25kg/m2). We included TBWBIS estimates in a 3-C model and contrasted this and the 2-C model against the reference 3-C model using TBWD2O. TBW of 29 males (32.4 ± 8.5 years; 183.4 ± 7.2 cm; 92.5 ± 9.9 kg; 27.5 ± 2.6 kg/m2) was measured using SFB7 and D2O. Body density was measured by BODPOD, with body composition calculated using the Siri equation. TBWBIS values were consistent with TBWD2O (SEE = 2.65L; TE = 2.6L) as were %BF values from the 3-C model (BODPOD + TBWBIS) with the 3-C reference model (SEE = 2.20%; TE = 2.20%). For subjects with TBW more than 1% from the assumed 73.72% (n = 16), %BF from the 2-C model differed significantly from the reference 3-C model (Slope 0.6888; Intercept 5.093). The BIS SFB7 measured TBW accurately compared with D2O. The 2C model with an assumed TBW of 73.72% introduces error in the estimation of body composition. We recommend TBW should be measured, either via the traditional D2O method or when resources are limited, with BIS, so that body composition estimates are enhanced. The BIS can be accurately used in 3C equations to better predict TBW and BF% in resistance trained males compared with a 2C model.
Kristen L. MacKenzie-Shalders, Neil A. King, Nuala M. Byrne and Gary J. Slater
Increasing the frequency of protein consumption is recommended to stimulate muscle hypertrophy with resistance exercise. This study manipulated dietary protein distribution to assess the effect on gains in lean mass during a rugby preseason. Twenty-four developing elite rugby athletes (age 20.1 ± 1.4 years, mass 101.6 ± 12.0 kg; M ± SD) were instructed to consume high biological value (HBV) protein at their main meals and immediately after resistance exercise while limiting protein intake between meals. To manipulate protein intake frequency, the athletes consumed 3 HBV liquid protein supplements (22 g protein) either with main meals (bolus condition) or between meals (frequent condition) for 6 weeks in a 2 × 2 crossover design. Dietary intake and change in lean mass values were compared between conditions by analysis of covariance and correlational analysis. The dietary manipulation successfully altered the protein distribution score (average number of eating occasions containing > 20 g of protein) to 4.0 ± 0.8 and 5.9 ± 0.7 (p < .01) for the bolus and frequent conditions, respectively. There was no difference in gains in lean mass between the bolus (1.4 ± 1.5 kg) and frequent (1.5 ± 1.4 kg) conditions (p = .91). There was no clear effect of increasing protein distribution from approximately 4–6 eating occasions on changes in lean mass during a rugby preseason. However, other dietary factors may have augmented adaptation.
Shaea A. Alkahtani, Nuala M. Byrne, Andrew P. Hills and Neil A. King
Compensatory responses may attenuate the effectiveness of exercise training in weight management. The aim of this study was to compare the effect of moderate- and high-intensity interval training on eating behavior compensation.
Using a crossover design, 10 overweight and obese men participated in 4-week moderate (MIIT) and high (HIIT) intensity interval training. MIIT consisted of 5-min cycling stages at ±20% of mechanical work at 45%VO2peak, and HIIT consisted of alternate 30-s work at 90%VO2peak and 30-s rests, for 30 to 45 min. Assessments included a constant-load exercise test at 45%VO2peak for 45 min followed by 60-min recovery. Appetite sensations were measured during the exercise test using a Visual Analog Scale. Food preferences (liking and wanting) were assessed using a computer-based paradigm, and this paradigm uses 20 photographic food stimuli varying along two dimensions, fat (high or low) and taste (sweet or nonsweet). An ad libitum test meal was provided after the constant-load exercise test.
Exerciseinduced hunger and desire to eat decreased after HIIT, and the difference between MIIT and HIIT in desire to eat approached significance (p = .07). Exercise-induced liking for high-fat nonsweet food tended to increase after MIIT and decreased after HIIT (p = .09). Fat intake decreased by 16% after HIIT, and increased by 38% after MIIT, with the difference between MIIT and HIIT approaching significance (p = .07).
This study provides evidence that energy intake compensation differs between MIIT and HIIT.