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Open access

Erratum: Sugihara Junior et al. 2018

Open access

Erratum: Anderson et al. (2019)

Open access

Training Load and Carbohydrate Periodization Practices of Elite Male Australian Football Players: Evidence of Fueling for the Work Required

Harry E. Routledge, Stuart Graham, Rocco Di Michele, Darren Burgess, Robert M. Erskine, Graeme L. Close, and James P. Morton

The authors aimed to quantify (a) the periodization of physical loading and daily carbohydrate (CHO) intake across an in-season weekly microcycle of Australian Football and (b) the quantity and source of CHO consumed during game play and training. Physical loading (via global positioning system technology) and daily CHO intake (via a combination of 24-hr recall, food diaries, and remote food photographic method) were assessed in 42 professional male players during two weekly microcycles comprising a home and away fixture. The players also reported the source and quantity of CHO consumed during all games (n = 22 games) and on the training session completed 4 days before each game (n = 22 sessions). The total distance was greater (p < .05) on game day (GD; 13 km) versus all training days. The total distance differed between training days, where GD-2 (8 km) was higher than GD-1, GD-3, and GD-4 (3.5, 0, and 7 km, respectively). The daily CHO intake was also different between training days, with reported intakes of 1.8, 1.4, 2.5, and 4.5 g/kg body mass on GD-4, GD-3, GD-2, and GD-1, respectively. The CHO intake was greater (p < .05) during games (59 ± 19 g) compared with training (1 ± 1 g), where in the former, 75% of the CHO consumed was from fluids as opposed to gels. Although the data suggest that Australian Football players practice elements of CHO periodization, the low absolute CHO intakes likely represent considerable underreporting in this population. Even when accounting for potential underreporting, the data also suggest Australian Football players underconsume CHO in relation to the physical demands of training and competition.

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The Effectiveness of Transdermal Iron Patches in Athletes With Suboptimal Iron Status (Part 1)

Rachel McCormick, Brian Dawson, Marc Sim, Leanne Lester, Carmel Goodman, and Peter Peeling

The authors compared the effectiveness of two modes of daily iron supplementation in athletes with suboptimal iron stores: oral iron (PILL) versus transdermal iron (PATCH). Endurance-trained runners (nine males and 20 females), with serum ferritin concentrations <50 μg/L, supplemented with oral iron or iron patches for 8 weeks, in a parallel group study design. Serum ferritin was measured at baseline and fortnightly intervals. Hemoglobin mass and maximal oxygen consumption ( V ˙ O 2 max ) were measured preintervention and postintervention in PATCH. A linear mixed effects model was used to assess the effectiveness of each mode of supplementation on sFer. A repeated-measures analysis of variance was used to assess hemoglobin mass and V ˙ O 2 max outcomes in PATCH. There was a significant time effect (p < .001), sex effect (p = .013), and Time × Group interaction (p = .009) for sFer. At Week 6, PILL had significantly greater sFer compared with PATCH (15.27 μg/L greater in PILL; p = .019). Serum ferritin was 15.53 μg/L greater overall in males compared with females (p = .013). There were no significant differences in hemoglobin mass (p = .727) or V ˙ O 2 max (p = .929) preintervention to postintervention in PATCH. Finally, there were six complaints of severe gastrointestinal side effects in PILL and none in PATCH. Therefore, this study concluded that PILL effectively increased sFer in athletes with suboptimal iron stores, whereas PATCH showed no beneficial effects.

Open access

Erratum: Lye, Mukherjee, and Burns (2019)

Open access

Reduced Glucose Tolerance and Skeletal Muscle GLUT4 and IRS1 Content in Cyclists Habituated to a Long-Term Low-Carbohydrate, High-Fat Diet

Christopher C. Webster, Kathryn M. van Boom, Nur Armino, Kate Larmuth, Timothy D. Noakes, James A. Smith, and Tertius A. Kohn

Very little is known about how long-term (>6 months) adaptation to a low-carbohydrate, high-fat (LCHF) diet affects insulin signaling in healthy, well-trained individuals. This study compared glucose tolerance; skeletal muscle glucose transporter 4 (GLUT4) and insulin receptor substrate 1 (IRS1) content; and muscle enzyme activities representative of the main energy pathways (3-hydroxyacetyl-CoA dehydrogenase, creatine kinase, citrate synthase, lactate dehydrogenase, phosphofructokinase, phosphorylase) in trained cyclists who followed either a long-term LCHF or a mixed-macronutrient (Mixed) diet. On separate days, a 2-hr oral glucose tolerance test was conducted, and muscle samples were obtained from the vastus lateralis of fasted participants. The LCHF group had reduced glucose tolerance compared with the Mixed group, as plasma glucose concentrations were significantly higher throughout the oral glucose tolerance test and serum insulin concentrations peaked later (LCHF, 60 min; Mixed, 30 min). Whole-body insulin sensitivity was not statistically significantly different between groups (Matsuda index: LCHF, 8.7 ± 3.4 vs. Mixed, 12.9 ± 4.6; p = .08). GLUT4 (LCHF: 1.13 ± 0.24; Mixed: 1.44 ± 0.16; p = .026) and IRS1 (LCHF: 0.25 ± 0.13; Mixed: 0.46 ± 0.09; p = .016) protein content was lower in LCHF muscle, but enzyme activities were not different. We conclude that well-trained cyclists habituated to an LCHF diet had reduced glucose tolerance compared with matched controls on a mixed diet. Lower skeletal muscle GLUT4 and IRS1 contents may partially explain this finding. This could possibly reflect an adaptation to reduced habitual glucose availability rather than the development of a pathological insulin resistance.

Open access

No Difference Between the Effects of Supplementing With Soy Protein Versus Animal Protein on Gains in Muscle Mass and Strength in Response to Resistance Exercise

Mark Messina, Heidi Lynch, Jared M. Dickinson, and Katharine E. Reed

Much attention has been given to determining the influence of total protein intake and protein source on gains in lean body mass (LBM) and strength in response to resistance exercise training (RET). Acute studies indicate that whey protein, likely related to its higher leucine content, stimulates muscle protein synthesis to a greater extent than proteins such as soy and casein. Less clear is the extent to which the type of protein supplemented impacts strength and LBM in long-term studies (≥6 weeks). Therefore, a meta-analysis was conducted to compare the effect of supplementation with soy protein to animal protein supplementation on strength and LBM in response to RET. Nine studies involving 266 participants suitable for inclusion in the meta-analysis were identified. Five studies compared whey with soy protein, and four studies compared soy protein with other proteins (beef, milk, or dairy protein). Meta-analysis showed that supplementing RET with whey or soy protein resulted in significant increases in strength but found no difference between groups (bench press: χ2 = 0.02, p = .90; squat: χ2 = 0.22, p = .64). There was no significant effect of whey or soy alone (n = 5) on LBM change and no differences between groups (χ2 = 0.00, p = .96). Strength and LBM both increased significantly in the “other protein” and the soy groups (n = 9), but there were no between-group differences (bench: χ2 = 0.02, p = .88; squat: χ2 = 0.78, p = .38; and LBM: χ2 = 0.06, p = .80). The results of this meta-analysis indicate that soy protein supplementation produces similar gains in strength and LBM in response to RET as whey protein.

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Organization of Dietary Control for Nutrition-Training Intervention Involving Periodized Carbohydrate Availability and Ketogenic Low-Carbohydrate High-Fat Diet

Joanne G. Mirtschin, Sara F. Forbes, Louise E. Cato, Ida A. Heikura, Nicki Strobel, Rebecca Hall, and Louise M. Burke

The authors describe the implementation of a 3-week dietary intervention in elite race walkers at the Australian Institute of Sport, with a focus on the resources and strategies needed to accomplish a complex study of this scale. Interventions involved: traditional guidelines of high carbohydrate (CHO) availability for all training sessions; a periodized CHO diet which integrated sessions with low and high CHO availability within the same total CHO intake; and a ketogenic low-CHO high-fat diet. Seven-day menus and recipes were constructed for a communal eating setting to meet nutritional goals as well as individualized food preferences and special needs. Menus also included nutrition support before, during, and after exercise. Daily monitoring, via observation and food checklists, showed that energy and macronutrient targets were achieved. Diets were matched for energy (∼14.8 MJ/d) and protein (∼2.1 g·kg−1·day−1) and achieved desired differences for fat and CHO, with high CHO availability and periodized CHO availability: CHO = 8.5 g·kg−1·day−1, 60% energy, fat = 20% of energy and low-CHO high-fat diet: 0.5 g·kg−1·day−1 CHO, fat = 78% energy.  There were no differences in micronutrient intake or density between the high CHO availability and periodized CHO availability diets; however, the micronutrient density of the low-CHO high-fat diet was significantly lower. Daily food costs per athlete were similar for each diet (∼AU$ 27 ± 10). Successful implementation and monitoring of dietary interventions in sports nutrition research of the scale of the present study require meticulous planning and the expertise of chefs and sports dietitians. Different approaches to sports nutrition support raise practical challenges around cost, micronutrient density, accommodation of special needs, and sustainability.

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Toward a Common Understanding of Diet–Exercise Strategies to Manipulate Fuel Availability for Training and Competition Preparation in Endurance Sport

Louise M. Burke, John A. Hawley, Asker Jeukendrup, James P. Morton, Trent Stellingwerff, and Ronald J. Maughan

From the breakthrough studies of dietary carbohydrate and exercise capacity in the 1960s through to the more recent studies of cellular signaling and the adaptive response to exercise in muscle, it has become apparent that manipulations of dietary fat and carbohydrate within training phases, or in the immediate preparation for competition, can profoundly alter the availability and utilization of these major fuels and, subsequently, the performance of endurance sport (events >30 min up to ∼24 hr). A variety of terms have emerged to describe new or nuanced versions of such exercise–diet strategies (e.g., train low, train high, low-carbohydrate high-fat diet, periodized carbohydrate diet). However, the nonuniform meanings of these terms have caused confusion and miscommunication, both in the popular press and among the scientific community. Sports scientists will continue to hold different views on optimal protocols of fuel support for training and competition in different endurance events. However, to promote collaboration and shared discussions, a commonly accepted and consistent terminology will help to strengthen hypotheses and experimental/experiential data around various strategies. We propose a series of definitions and explanations as a starting point for a more unified dialogue around acute and chronic manipulations of fat and carbohydrate in the athlete’s diet, noting philosophies of approaches rather than a single/definitive macronutrient prescription. We also summarize some of the key questions that need to be tackled to help produce greater insight into this exciting area of sports nutrition research and practice.

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Case Study: Body Composition Periodization in an Olympic-Level Female Middle-Distance Runner Over a 9-Year Career

Trent Stellingwerff

This case study features an Olympic-level female middle-distance runner implementing a science-based approach to body composition periodization. Data are emerging to suggest that it is not sustainable from a health and/or performance perspective to be at peak body composition year-round, so body composition needs to be strategically periodized. Anthropometric (n = 44), hematological, other health measures, and 1,500-m race performances (n = 83) were periodically assessed throughout a 9-year career. General preparation phase (September to April) featured the athlete at ∼2–4% over ideal competition phase body weight (BW) and body fat (%), with optimal energy availability being prioritized. The competition body composition optimization phase (May to August) included creating an individualized time frame and caloric deficit with various feedback metrics (BW, performance, and hunger) to guide the process. There were significant seasonal fluctuations in anthropometric outcomes between phases (47.3 ± 0.8 vs. 48.3 ± 0.9 kg BW; 53.6 ± 7.8 vs. 61.6 ± 9.7 mm International Society for the Advancement of Kinanthropometry sum of 8 [So8] skinfolds; p < .01), and a significant correlation of decreasing So8 during the peak competition period over her career (r = −.838; p = .018). The range of body composition during the competition period was 46.0–48.0 kg BW and a So8 range was 42.0–55.9 mm. There were also significant positive correlations between slower 1,500-m race times and increasing So8 (r = .437; p < .01), estimated fat mass (r = .445; p < .01), and BW (r = .511; p < .0001). The athlete only had two career injuries. This case study demonstrates a body composition periodization approach that allowed for targeted peak yearly performances, which improved throughout her career, while maximizing training adaptation and long-term athlete health through optimal energy availability.