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Gareth N. Sandford, Sian V. Allen, Andrew E. Kilding, Angus Ross and Paul B. Laursen

Preparation for 800-m running represents a unique challenge to the middle-distance coach. With close interplay required between aerobic and anaerobic/neuromuscular physiology, athletes with distinctly different profiles have an opportunity for success in the event. Recently, a “changing of the

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Nicola Giovanelli, Filippo Vaccari, Mirco Floreani, Enrico Rejc, Jasmine Copetti, Marco Garra, Lea Biasutti and Stefano Lazzer

to the best of our knowledge the effects of SMFR on running performance have not been investigated yet. The energy cost of running (Cr) plays a relevant role in determining performance among middle- and long-distance runners along with the maximal oxygen uptake and the fraction of it that is

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Ian Rollo, Clyde Williams, Nicholas Gant and Maria Nute

The purpose of this study was to examine the influences of a carbohydrate (CHO) mouth rinse on self-selected running speeds during a 30-min treadmill run. Ten endurance-trained men performed 2 trials, each involving a 10-min warm-up at 60% VO2max followed by a 30-min run. The run was performed on an automated treadmill that allowed the spontaneous selection of speeds without manual input. Participants were asked to run at speeds that equated to a rating of perceived exertion of 15, mouth rinsing with either a 6% CHO or taste-matched placebo (PLA) solution. In addition to recording self-selected speeds and total distance covered the authors assessed the runners’ subjective feelings. The total distance covered was greater during the CHO than during the PLA trial (p < .05). Faster speeds selected during the first 5 min of exercise corresponded with enhanced feelings of pleasure when mouth rinsing with the CHO solution. Mouth rinsing with a CHO solution increased total distance covered during a self-selected 30-min run in comparison with mouth rinsing with a color- and tastematched placebo.

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Trent Stellingwerff

The professionalization of any sport must include an appreciation for how and where nutrition can positively affect training adaptation and/or competition performance. Furthermore, there is an ever-increasing importance of nutrition in sports that feature very high training volumes and are of a long enough duration that both glycogen and fluid balance can limit performance. Indeed, modern marathon training programs and racing satisfy these criteria and are uniquely suited to benefit from nutritional interventions. Given that muscle glycogen is limiting during a 2-h marathon, optimizing carbohydrate (CHO) intake and delivery is of maximal importance. Furthermore, the last 60 y of marathon performance have seen lighter and smaller marathoners, which enhances running economy and heat dissipation and increases CHO delivery per kg body mass. Finally, periodically training under conditions of low CHO availability (eg, low muscle glycogen) or periods of mild fluid restriction may actually further enhance the adaptive responses to training. Accordingly, this commentary highlights these key nutrition and hydration interventions that have emerged over the last several years and explores how they may assist in world-class marathon performance.

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Patrick B. Wilson, Stacy J. Ingraham, Chris Lundstrom and Gregory Rhodes

Background:

The effects of dietary factors such as carbohydrate (CHO) on endurance-running performance have been extensively studied under laboratory-based and simulated field conditions. Evidence from “reallife” events, however, is poorly characterized. The purpose of this observational study was to examine the associations between prerace and in-race nutrition tendencies and performance in a sample of novice marathoners.

Methods:

Forty-six college students (36 women and 10 men) age 21.3 ± 3.3 yr recorded diet for 3 d before, the morning of, and during a 26.2-mile marathon. Anthropometric, physiological, and performance measurements were assessed before the marathon so the associations between diet and marathon time could be included as part of a stepwise-regression model.

Results:

Mean marathon time was 266 ± 42 min. A premarathon 2-mile time trial explained 73% of the variability in marathon time (adjusted R 2 = .73, p < .001). Day-before + morning-of CHO (DBMC) was the only other significant predictor of marathon time, explaining an additional 4% of the variability in marathon time (adjusted R 2 = .77, p = .006). Other factors such as age, body-mass index, gender, day-before + morning-of energy, and in-race CHO were not significant independent predictors of marathon time.

Conclusions:

In this sample of primarily novice marathoners, DBMC intake was associated with faster marathon time, independent of other known predictors. These results suggest that novice and recreational marathoners should consider consuming a moderate to high amount of CHO in the 24–36 hr before a marathon.

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Eric K. O’Neal, Brett A. Davis, Lauren K. Thigpen, Christina R. Caufield, Anthony D. Horton and Joyce R. McIntosh

The purpose of this study was to determine how accurately runners estimate their sweat losses. Male (n = 19) and female (n = 20) runners (41 ± 10 yr, VO2max 57 ± 9 ml · kg−1 · min−1) from the southeastern U.S. completed an ~1-hr run during late summer on a challenging outdoor road course (wet bulb globe temperature 24.1 ± 1.5 °C). Runs began at ~6:45 a.m. or p.m. Before and after running, participants filled race-aid-station paper cups with a volume of fluid they felt would be equivalent to their sweat losses. Total sweat losses and losses by percent body weight differed (p < .01) between men (1,797 ± 449 ml, 2.3% ± 0.6%) and women (1,155 ± 258 ml, 1.9% ± 0.4%). Postrun estimates (738 ± 470 ml) were lower (p < .001) than sweat losses (1,468 ± 484 ml), equaling underestimations of 50% ± 23%, with no differences in estimation accuracy by percentage between genders. Runners who reported measuring changes in pre- and postrun weight to assess sweat losses within the previous month (n = 9, –54% ± 18%) were no more accurate (p = .55) than runners who had not (n = 30, –48% ± 24%). These results suggest that inadequate fluid intake during runs or between runs may stem from underestimations of sweat losses and that runners who do assess sweat-loss changes may be making sweat-loss calculation errors or do not accurately translate changes in body weight to physical volumes of water.

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Samantha Kirsty Gill, Ana Maria Teixeira, Fatima Rosado, Martin Cox and Ricardo Jose Soares Costa

The study aimed to determine whether high-dose probiotic supplementation containing Lactobacillus casei (L. casei) for 7 consecutive days enhances salivary antimicrobial protein (S-AMP) responses to exertional–heat stress (EHS). Eight endurance-trained male volunteers (age 26 ± 6 years, nude body mass 70.2 ± 8.8 kg, height 1.75 ± 0.05 m, VO2max 59 ± 5 ml·kg-1·min-1 [M ± SD]) completed a blinded randomized and counterbalanced crossover design. Oral supplementation of the probiotic beverage (PRO; L. casei × 1011 colony-forming units·day-1) or placebo (PLA) was consumed for 7 consecutive days before 2 hr running exercise at 60% VO2max in hot ambient conditions (34.0 °C and 32% RH). Body mass and unstimulated saliva and venous blood samples were collected at baseline (7 days before EHS), pre-EHS, post-EHS (1 hr, 2 hr, and 4 hr), and at 24 hr. Saliva samples were analyzed for salivary (S) IgA, α-amylase, lysozyme, and cortisol. Plasma samples were analyzed for plasma osmolality. Body mass and plasma osmolality did not differ between trials. Saliva flow rate remained relatively constant throughout the experimental design in PRO (overall M ± SD = 601 ± 284 μ1/min) and PLA (557 ± 296 μl/min). PRO did not induce significant changes in resting S-AMP responses compared with PLA (p > .05). Increases in S-IgA, S-α-amylase, and S-cortisol responses, but not S-lysozyme responses, were observed after EHS (p < .05). No main effects of trial or Time × Trial interaction were observed for S-AMP and S-cortisol responses. Supplementation of a probiotic beverage containing L. casei for 7 days before EHS does not provide any further oral–respiratory mucosal immune protection, with respect to S-AMP, over PLA.

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Beate Pfeiffer, Alexandra Cotterill, Dominik Grathwohl, Trent Stellingwerff and Asker E. Jeukendrup

Two studies were conducted to investigate gastrointestinal (GI) tolerance of high carbohydrate (CHO) intakes during intense running. The first study investigated tolerance of a CHO gel delivering glucose plus fructose (GLU+FRC) at different rates. The second study investigated tolerance of high intakes of glucose (GLU) vs. GLU+FRC gel. Both studies used a randomized, 2-treatment, 2-period crossover design: Endurance-trained men and women (Study 1: 26 men, 8 women; 37 ± 11 yr; 73 ± 9 kg; 1.76 ± 0.07 m. Study 2: 34 men, 14 women; 35 ± 10 yr; 70 ± 9 kg; 1.75 ± 0.09 m) completed two 16-km outdoor-runs. In Study 1 gels were administered to provide 1.0 or 1.4 g CHO/min with ad libitum water intake every 3.2 km. In Study 2 GLU or GLU+FRC gels were given in a double-blind manner to provide 1.4 g CHO/min. In both studies a postexercise questionnaire assessed 17 symptoms on a 10-point scale (from 0 to 9). For all treatments, GI complaints were mainly scored at the low end of the scale. In Study 1 mean scores ranged from 0.00 ± 0.00 to 1.12 ± 1.90, and in Study 2, from 0.00 ± 0.0 to 1.27 ± 1.78. GI symptoms were grouped into upper abdominal, lower abdominal, and systemic problems. There were no significant treatment differences in these categories in either study. In conclusion, despite high CHOgel intake, and regardless of the blend (GLU vs. GLU+FRC), average scores for GI symptoms were at the low end of the scale, indicating predominantly good tolerance during a 16-km run. Nevertheless, some runners (~10–20%) experienced serious problems, and individualized feeding strategies might be required.

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Ben Desbrow, Katelyn Barnes, Caroline Young, Greg R. Cox and Chris Irwin

Immediate postexercise access to fruit/fluid via a recovery “station” is a common feature of mass participation sporting events. Yet little evidence exists examining their impact on subsequent dietary intake. The aim of this study was to determine if access to fruit/water/sports drinks within a recovery station significantly alters dietary and fluid intakes in the immediate postexercise period and influences hydration status the next morning. 127 (79 males) healthy participants (M ± SD, age = 22.5 ± 3.5y, body mass (BM) = 73 ± 13kg) completed two self-paced morning 10km runs separated by 1 week. Immediately following the first run, participants were randomly assigned to enter (or not) the recovery station for 30min. All participants completed the alternate recovery option the following week. Participants recorded BM before and after exercise and measured Urine Specific Gravity (USG) before running and again the following morning. For both trial days, participants also completed 24h food and fluid records via a food diary that included photographs. Paired-sample t tests were used to assess differences in hydration and dietary outcome variables (Recovery vs. No Recovery). No difference in preexercise USG or BM change from exercise were observed between treatments (p’s > .05). Attending the recovery zone resulted in a greater total daily fluid (Recovery = 3.37 ± 1.46L, No Recovery = 3.16 ± 1.32L, p = .009) and fruit intake (Recovery = 2.37 ± 1.76 servings, No Recovery = 1.55 ± 1.61 servings, p > .001), but had no influence on daily total energy (Recovery = 10.15 ± 4.2MJ, No Recovery = 10.15 ± 3.9MJ), or macronutrient intakes (p > .05). Next morning USG values were not different between treatments (Recovery = 1.018 ± 0.007, No Recovery = 1.019 ± 0.009, p > .05). Recovery stations provide an opportunity to modify dietary intake which promote positive lifestyle behaviors in recreational athletes.

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Martin D. Hoffman

Purpose:

To examine pacing among the most successful runners in the 161-km Western States Endurance Run (WSER) to determine if variations in segmental speed relate to performance, ambient temperature, and calendar year.

Methods:

Segmental speed and coefficient of variation (CV) in speed were analyzed for 10 race segments of 24 races from 1985 through 2013.

Results:

Segmental speeds did not differ between the eventual winners and lead runners and only differed between the 1st and 2nd finishers in the 2nd half of the race. Mean CV in speed was lower (P < .01) for the winners (12%) than for the other top-5 finishers (14–15%). CV in speed was related (r = .80, P = .006) to finish time for the fastest 10 finish times at the WSER. Multiple linearregression analysis revealed mean CV in speed for the top-5 runners to be related to maximum ambient temperature (coefficient =.14, P < .05) and calendar year (coefficient = –.086, P = .034).

Conclusions:

Mountain trail running is characterized by wide variations in speed, but the fastest times are achieved when speed fluctuations are limited. This is generally accomplished by the winners remaining relatively close behind the lead runners before taking the lead in the middle half of the race, and then avoiding slowing as much as the other top runners in the latter race stages. Variations in speed increase with high ambient temperatures, and the small decrease in segmental speed variability among top runners across the nearly 30 y of this study suggests that the best runners have improved at pacing this race.