When weight loss (WL) is necessary, athletes are advised to accomplish it gradually, at a rate of 0.5–1 kg/wk. However, it is possible that losing 0.5 kg/wk is better than 1 kg/wk in terms of preserving lean body mass (LBM) and performance. The aim of this study was to compare changes in body composition, strength, and power during a weekly body-weight (BW) loss of 0.7% slow reduction (SR) vs. 1.4% fast reduction (FR). We hypothesized that the faster WL regimen would result in more detrimental effects on both LBM and strength-related performance. Twenty-four athletes were randomized to SR (n = 13, 24 ± 3 yr, 71.9 ± 12.7 kg) or FR (n = 11, 22 ± 5 yr, 74.8 ± 11.7 kg). They followed energy-restricted diets promoting the predetermined weekly WL. All athletes included 4 resistance-training sessions/wk in their usual training regimen. The mean times spent in intervention for SR and FR were 8.5 ± 2.2 and 5.3 ± 0.9 wk, respectively (p < .001). BW, body composition (DEXA), 1-repetition-maximum (1RM) tests, 40-m sprint, and countermovement jump were measured before and after intervention. Energy intake was reduced by 19% ± 2% and 30% ± 4% in SR and FR, respectively (p = .003). BW and fat mass decreased in both SR and FR by 5.6% ± 0.8% and 5.5% ± 0.7% (0.7% ± 0.8% vs. 1.0% ± 0.4%/wk) and 31% ± 3% and 21 ± 4%, respectively. LBM increased in SR by 2.1% ± 0.4% (p < .001), whereas it was unchanged in FR (–0.2% ± 0.7%), with significant differences between groups (p < .01). In conclusion, data from this study suggest that athletes who want to gain LBM and increase 1RM strength during a WL period combined with strength training should aim for a weekly BW loss of 0.7%.
Ina Garthe, Truls Raastad, Per Egil Refsnes, Anu Koivisto and Jorunn Sundgot-Borgen
Marina Fabre, Christophe Hausswirth, Eve Tiollier, Odeline Molle, Julien Louis, Alexandre Durguerian, Nathalie Neveux and Xavier Bigard
While effects of the two classes of proteins found in milk (i.e., soluble proteins, including whey, and casein) on muscle protein synthesis have been well investigated after a single bout of resistance exercise (RE), the combined effects of these two proteins on the muscle responses to resistance training (RT) have not yet been investigated. Therefore, the aim of this study was to examine the effects of protein supplementation varying by the ratio between milk soluble proteins (fast-digested protein) and casein (slow-digested protein) on the muscle to a 9-week RT program. In a double-blind protocol, 31 resistance-trained men, were assigned to 3 groups receiving a drink containing 20g of protein comprising either 100% of fast protein (FP(100), n = 10), 50% of fast and 50% of slow proteins (FP(50), n = 11) or 20% of fast protein and 80% of casein (FP(20), n = 10) at the end of training bouts. Body composition (DXA), and maximal strength in dynamic and isometric were analyzed before and after RT. Moreover, blood plasma aminoacidemia kinetic after RE was measured. The results showed a higher leucine bioavailability after ingestion of FP(100) and FP(50) drinks, when compared with FP(20) (p< .05). However, the RT-induced changes in lean body mass (p < .01), dynamic (p < .01), and isometric muscle strength (p < .05) increased similarly in all experimental groups. To conclude, compared with the FP(20) group, the higher rise in plasma amino acids following the ingestion of FP(100) and FP(50) did not lead to higher muscle long-term adaptations.
Ina Garthe, Truls Raastad and Jorunn Sundgot-Borgen
When weight loss (WL) is needed, it is recommended that athletes do it gradually by 0.5–1 kg/wk through moderate energy restriction. However, the effect of WL rate on long-term changes in body composition (BC) and performance has not been investigated in elite athletes.
To compare changes in body mass (BM), fat mass (FM), lean body mass (LBM), and performance 6 and 12 mo after 2 different WL interventions promoting loss of 0.7% vs. 1.4% of body weight per wk in elite athletes.
Twenty-three athletes completed 6- and 12-mo postintervention testing (slow rate [SR] n = 14, 23.5 ± 3.3 yr, 72.2 ± 12.2 kg; fast rate [FR] n = 9, 21.4 ± 4.0 yr, 71.6 ± 12.0 kg). The athletes had individualized diet plans promoting the predetermined weekly WL during intervention, and 4 strength-training sessions per wk were included. BM, BC, and strength (1-repetition maximum) were tested at baseline, postintervention, and 6 and 12 mo after the intervention.
BM decreased by ~6% in both groups during the intervention but was not different from baseline values after 12 mo. FM decreased in SR and FR during the intervention by 31% ± 3% vs. 23% ± 4%, respectively, but was not different from baseline after 12 mo. LBM and upper body strength increased more in SR than in FR (2.0% ± 1.3% vs. 0.8% ± 1.1% and 12% ± 2% vs. 6% ± 2%) during the intervention, but after 12 mo there were no significant differences between groups in BC or performance.
There were no significant differences between groups after 12 mo, suggesting that WL rate is not the most important factor in maintaining BC and performance after WL in elite athletes.
John Petrizzo, Frederick J. DiMenna, Kimberly Martins, John Wygand and Robert M. Otto
To achieve the criterion appearance before competing in a physique competition, athletes undergo preparatory regimens involving high-volume intense resistance and aerobic exercise with hypocaloric energy intake. As the popularity of “drug-free” competition increases, more athletes are facing this challenge without the recuperative advantage provided by performance-enhancing drugs. Consequently, the likelihood of loss of lean body and/or bone mass is increased. The purpose of this investigation was to monitor changes in body composition for a 29-year-old self-proclaimed drug-free female figure competitor during a 32-week preparatory regimen comprising high-volume resistance and aerobic exercise with hypocaloric energy intake. We used dual-energy x-ray absorptiometry (DXA) to evaluate regional fat and bone mineral density. During the initial 22 weeks, the subject reduced energy intake and engaged in resistance (4–5 sessions/week) and aerobic (3 sessions/week) training. During the final 10 weeks, the subject increased exercise frequency to 6 (resistance) and 4 (aerobic) sessions/week while ingesting 1130–1380 kcal/day. During this 10-week period, she consumed a high quantity of protein (~55% of energy intake) and nutritional supplements. During the 32 weeks, body mass and fat mass decreased by 12% and 55%, respectively. Conversely, lean body mass increased by 1.5%, an amount that exceeded the coefficient of variation associated with DXA-derived measurement. Total bone mineral density was unchanged throughout. In summary, in preparation for a figure competition, a self-proclaimed drug-free female achieved the low body-fat percentage required for success in competition without losing lean mass or bone density by following a 32-week preparatory exercise and nutritional regimen.
Gerald T. Mangine, Jay R. Hoffman, Jose Vazquez, Napoleon Pichardo, Maren S. Fragala and Jeffrey R. Stout
The ultimate zone-rating extrapolation (UZR/150) rates fielding performance by runs saved or cost within a zone of responsibility in comparison with the league average (150 games) for a position. Spring-training anthropometric and performance measures have been previously related to hitting performance; however, their relationships with fielding performance measures are unknown.
To examine the relationship between anthropometric and performance measurements on fielding performance in professional baseball players.
Body mass, lean body mass (LBM), grip strength, 10-yd sprint, proagility, and vertical-jump mean (VJMP) and peak power (VJPP) were collected during spring training over the course of 5 seasons (2007–2011) for professional corner infielders (CI; n = 17, fielding opportunities = 420.7 ± 307.1), middle infielders (MI; n = 14, fielding opportunities = 497.3 ± 259.1), and outfielders (OF; n = 16, fielding opportunities = 227.9 ± 70.9). The relationships between these data and regular-season (100-opportunity minimum) fielding statistics were examined using Pearson correlation coefficients, while stepwise regression identified the single best predictor of UZR/150.
Significant correlations (P < .05) were observed between UZR/150 and body mass (r = .364), LBM (r = .396), VJPP (r = .397), and VJMP (r = .405). Of these variables, stepwise regression indicated VJMP (R = .405, SEE = 14.441, P = .005) as the single best predictor for all players, although the addition of proagility performance strengthened (R = .496, SEE = 13.865, P = .002) predictive ability by 8.3%. The best predictor for UZR/150 was body mass for CI (R = .519, SEE = 15.364, P = .033) and MI (R = .672, SEE = 12.331, P = .009), while proagility time was the best predictor for OF (R = .514, SEE = 8.850, P = .042).
Spring-training measurements of VJMP and proagility time may predict the defensive run value of a player over the course of a professional baseball season.
Tai T. Tran, Lina Lundgren, Josh Secomb, Oliver R.L. Farley, G. Gregory Haff, Laurent B. Seitz, Robert U. Newton, Sophia Nimphius and Jeremy M. Sheppard
To determine whether a previously validated performance-testing protocol for competitive surfers is able to differentiate between Australian elite junior surfers selected (S) to the national team and those not selected (NS).
Thirty-two elite male competitive junior surfers were divided into 2 groups (S = 16, NS = 16). Their age, height, body mass, sum of 7 skinfolds, and lean-body-mass ratio (mean ± SD) were 16.17 ± 1.26 y, 173.40 ± 5.30 cm, 62.35 ± 7.40 kg, 41.74 ± 10.82 mm, 1.54 ± 0.35 for the S athletes and 16.13 ± 1.02 y, 170.56 ± 6.6 cm, 61.46 ± 10.10 kg, 49.25 ± 13.04 mm, 1.31 ± 0.30 for the NS athletes. Power (countermovement jump [CMJ]), strength (isometric midthigh pull), 15-m sprint paddling, and 400-m endurance paddling were measured.
There were significant (P ≤ .05) differences between the S and NS athletes for relative vertical-jump peak force (P = .01, d = 0.9); CMJ height (P = .01, d = 0.9); time to 5-, 10-, and 15-m sprint paddle; sprint paddle peak velocity (P = .03, d = 0.8; PV); time to 400 m (P = .04, d = 0.7); and endurance paddling velocity (P = .05, d = 0.7).
All performance variables, particularly CMJ height; time to 5-, 10-, and 15-m sprint paddle; sprint paddle PV; time to 400 m; and endurance paddling velocity, can effectively discriminate between S and NS competitive surfers, and this may be important for athlete profiling and training-program design.
Samuel Blais, Joel Blanchard and Frederic Dallaire
body mass in children was mostly influenced by height, body mass, and age, but that the influence of body mass was not linear. Indeed, the effect of body mass on lean body mass decreased as the adiposity (or body mass index) increased. In 2018, we published reference values and z score equations for
in tied swimming (peak force and average force), body mass, fat percentage, and lean body mass were also analyzed at the different stages of training to compare the changes of the IGF-I/IGFBP/ALS system with the physical performance and body composition of the athletes. Variations in the IGF
Nicole C.A. Strock, Kristen J. Koltun, Emily A. Southmayd, Nancy I. Williams and Mary Jane De Souza
Cunningham 1980 equation may yield a more accurate estimate of RMR among highly active individuals ( Thompson & Manore, 1996 ), particularly because of higher lean body mass and fat-free mass. Interestingly, two different Cunningham equations exist (1980 and 1991), one of which relies on lean body mass and
Rodrigo Rodrigues Gomes Costa, Rodrigo Luiz Carregaro and Frederico Ribeiro Neto
studies. 1 , 2 , 5 , 6 The TP, HP, and LP differ in several aspects, such as normalized strength by body mass, 7 peak torque, 7 , 8 and power. 8 However, other studies did not find significant differences in several outcomes for these groups, especially for HP and LP (eg, lean body mass), 9 fat, 9