Previous research has demonstrated that the preferred transition speed during human locomotion is the speed at which critical levels of ankle angular velocity and acceleration (in the dorsiflexor direction) are reached, leading to the hypothesis that gait transition occurs to alleviate muscular stress on the dorsiflexors. Furthermore, it has been shown that the metabolic cost of running at the preferred transition speed is greater than that of walking at that speed. This increase in energetic cost at gait transition has been hypothesized to occur due to a greater demand being placed on the larger muscles of the lower extremity when gait changes from a walk to a run. This hypothesis was tested by monitoring electromyographic (EMG) activity of the tibialis anterior, medial gastrocnemius, vastus lateralis, biceps femoris, and gluteus maximus while participants (6 M, 3 F) walked at speeds of 70, 80, 90, and 100% of their preferred transition speed, and ran at their preferred transition speed. The EMG activity of the tibialis anterior increased as walking speed increased, then decreased when gait changed to a run at the preferred transition speed. Concurrently, the EMG activity of all other muscles that were monitored increased with increasing walking speed, and at a greater rate when gait changed to a run at the preferred transition speed. The results of this study supported the hypothesis presented.
Alan Hreljac, Alan Arata, Reed Ferber, John A. Mercer and Brandi S. Row
Patrick B. Wilson, Stacy J. Ingraham, Chris Lundstrom and Gregory Rhodes
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.
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.
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.
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.
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.
Jeremy A. Steeves, Brian M. Tyo, Christopher P. Connolly, Douglas A. Gregory, Nyle A. Stark and David R. Bassett
This study compared the validity of a new Omron HJ-303 piezoelectric pedometer and 2 other pedometers (Sportline Traq and Yamax SW200).
To examine the effect of speed, 60 subjects walked on a treadmill at 2, 3, and 4 mph. Twenty subjects also ran at 6, 7, and 8 mph. To test lifestyle activities, 60 subjects performed front-back-side-side stepping, elliptical machine and stair climbing/descending. Twenty others performed ballroom dancing. Sixty participants completed 5 100-step trials while wearing 5 different sets of the devices tested device reliability. Actual steps were determined using a hand tally counter.
Significant differences existed among pedometers (P < .05). For walking, the Omron pedometers were the most valid. The Sportline overestimated and the Yamax underestimated steps (P < .05). Worn on the waist or in the backpack, the Omron device and Sportline were valid for running. The Omron was valid for 3 activities (elliptical machine, ascending and descending stairs). The Sportline overestimated all of these activities, and Yamax was only valid for descending stairs. The Omron and Yamax were both valid and reliable in the 100-step trials.
The Omron HJ-303, worn on the waist, appeared to be the most valid of the 3 pedometers.
Pedro Rodrigues, Trampas TenBroek and Joseph Hamill
“Excessive” pronation is often implicated as a risk factor for anterior knee pain (AKP). The amount deemed excessive is typically calculated using the means and standard deviations reported in the literature. However, when using this method, few studies find an association between pronation and AKP. An alternative method of defining excessive pronation is to use the joints’ available range of motion (ROM). The purposes of this study were to (1) evaluate pronation in the context of the joints’ ROM and (2) compare this method to traditional pronation variables in healthy and injured runners. Thirty-six runners (19 healthy, 17 AKP) had their passive pronation ROM measured using a custom-built device and a motion capture system. Dynamic pronation angles during running were captured and compared with the available ROM. In addition, traditional pronation variables were evaluated. No significant differences in traditional pronation variables were noted between healthy and injured runners. In contrast, injured runners used significantly more of their available ROM, maintaining a 4.21° eversion buffer, whereas healthy runners maintained a 7.25° buffer (P = .03, ES = 0.77). Defining excessive pronation in the context of the joints’ available ROM may be a better method of defining excessive pronation and distinguishing those at risk for injury.
George Vagenas and Blaine Hoshizaki
The purpose of this study was to identify the kinematic characteristics of bilateral rearfoot asymmetry during heel–toe running under two experimental conditions: worn (broken-in) running shoes and new (standardized) running shoes. High-speed cinematography (150 fps) was used to film the lower limbs of four male runners in the frontal plane while running on a treadmill at their training pace. Six successive footfalls were analyzed for each subject and selected kinematical variables of the rearfoot function were calculated. Significant asymmetries were found in lower leg angle and Achilles tendon angle at touchdown and at maximum pronation. Total pronation and rearfoot angle were almost symmetric. The angular displacement graphs for the shank and foot revealed a distinct overall asymmetry between the lower limbs in both conditions. The mean values of the kinematical asymmetries were appreciably higher in the new shoe condition. It is proposed that the degree of these asymmetries is subject to changes due to injury, personal running style, and stability of the running shoe. Trends of bilateral dominance specific to rearfoot control in running were identified.
Alexandra S. Voloshina and Daniel P. Ferris
available exercise treadmill to allow for attachment of wooden blocks to the treadmill belt. The modified instrumented treadmill could then be used to collect biomechanical, metabolic, and other gait data during continuous walking or running on an uneven surface in a laboratory setting. Here, we describe
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.
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.
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.