, depending on which of the 4 receptor subtypes is activated, researchers have begun to consider the effects of caffeine on shorter and more intense exercise paradigms. Most of the research into the effects of caffeine on sprinting performance has been performed using 30-second cycle ergometer sprints usually
Mark Glaister, Colin Towey, Owen Jeffries, Daniel Muniz-Pumares, Paul Foley and Gillian McInnes
Kyle M.A. Thompson, Alanna K. Whinton, Shane Ferth, Lawrence L. Spriet and Jamie F. Burr
ability of muscle to work under similar conditions. Work supporting the use of IPC as an ergogenic technique has primarily used endurance 6 – 9 and repeated-sprint 10 – 12 exercise models. With some evidence suggesting that IPC alters metabolism in a way that may be beneficial in endurance sport
Paul F.J. Merkes, Paolo Menaspà and Chris R. Abbiss
The outcome of road-cycling races is often decided by a sprint. Indeed, over half of the mass-start stages during the 3 grand tours (ie, Giro d’Italia, Tour de France, and Vuelta a España), as well as several of the recent World Championships, were decided in either a head-to-head, small group, or
Edwin Chong, Kym J. Guelfi and Paul A. Fournier
This study investigated whether combined ingestion and mouth rinsing with a carbohydrate solution could improve maximal sprint cycling performance. Twelve competitive male cyclists ingested 100 ml of one of the following solutions 20 min before exercise in a randomized double-blinded counterbalanced order (a) 10% glucose solution, (b) 0.05% aspartame solution, (c) 9.0% maltodextrin solution, or (d) water as a control. Fifteen min after ingestion, repeated mouth rinsing was carried out with 11 × 15 ml bolus doses of the same solution at 30-s intervals. Each participant then performed a 45-s maximal sprint effort on a cycle ergometer. Peak power output was significantly higher in response to the glucose trial (1188 ± 166 W) compared with the water (1036 ± 177 W), aspartame (1088 ± 128 W) and maltodextrin (1024 ± 202W) trials by 14.7 ± 10.6, 9.2 ± 4.6 and 16.0 ± 6.0% respectively (p < .05). Mean power output during the sprint was significantly higher in the glucose trial compared with maltodextrin (p < .05) and also tended to be higher than the water trial (p = .075). Glucose and maltodextrin resulted in a similar increase in blood glucose, and the responses of blood lactate and pH to sprinting did not differ significantly between treatments (p > .05). These findings suggest that combining the ingestion of glucose with glucose mouth rinsing improves maximal sprint performance. This ergogenic effect is unlikely to be related to changes in blood glucose, sweetness, or energy sensing mechanisms in the gastrointestinal tract.
Lara Grobler, Suzanne Ferreira and Elmarie Terblanche
The Paralympic Games have undergone many changes since their inception in 1960, one being the advances made in running-specific prostheses (RSPs) for track athletes with lower-limb amputations.
To investigate the sprinting-performance changes in athletes with lower-limb amputations since 1992 to assess whether the influence of developments in RSP technology is evident.
The results of the Olympic and Paralympic Games ranging between 1992 and 2012 for the 100-m and 200-m were collected, and performance trends, percentage change in performance, and competition density (CD) were calculated.
The results indicate that the greatest performance increases were seen in athletes with lower-limb amputations (T42 = 26%, T44 = 14%). These performance improvements were greater than for Olympic athletes (<3%), as well as Paralympic athletes from other selected classes (<10%). The T42 and T44 classes also showed the lowest CD values.
These results suggest that although there is an overall trend for improved Paralympic sprint performances, RSP technology has played a noteworthy role in the progression of performances of athletes with amputations. It is also hypothesized that the difference in the performance improvements between the T42 and T44 classes is due to the level of disability and therefore the extent to which technology is required to enable locomotion.
It is evident that RSP technology has played a significant role in the progression of performances in athletes with lower-limb amputations.
Wing-Kai Lam, Winson Chiu-Chun Lee, Wei Min Lee, Christina Zong-Hao Ma and Pui Wah Kong
potential to enhance sport performance such as forward acceleration, jumping, and agility tasks. 1 – 3 Stefanyshyn and colleagues 1 found an improvement in maximum-effort sprint performance when participants ran in shoes inserted with very stiff carbon plates at the soles compared with those without. It
Simon A. Rogers, Chris S. Whatman, Simon N. Pearson and Andrew E. Kilding
Successful middle-distance (MD) running in distances from 800 m to 5000 m requires both rapid and economical movements. Athletes must sustain high running velocities at and above maximal aerobic speeds, 1 with sprint performance in the final lap of 1500-m races often determining medalists on the
Nobuaki Tottori, Tadashi Suga, Yuto Miyake, Ryo Tsuchikane, Mitsuo Otsuka, Akinori Nagano, Satoshi Fujita and Tadao Isaka
Superior sprint performance is achieved through the generation of large moments by the muscles crossing the hip, knee, and ankle joints ( 29 ). The magnitudes of these moments are primarily determined by agonist muscle size ( 2 , 11 , 12 , 20 , 32 ). In fact, trunk and lower limb muscles are larger
Stephen M. Suydam, Kurt Manal and Thomas S. Buchanan
peak EMG signal reliability. EMG signals from these dynamic movements (isokinetic, sprint and jump) will be compared to a constrained MVIC case. We hypothesize that the peak EMG during the ballistic tasks will be as repeatable as the MVIC, but with a greater magnitude. The single session, within
Reed D. Gurchiek, Hasthika S. Rupasinghe Arachchige Don, Lasanthi C. R. Pelawa Watagoda, Ryan S. McGinnis, Herman van Werkhoven, Alan R. Needle, Jeffrey M. McBride and Alan T. Arnholt
Recent developments in field-based sprint assessments 1 – 5 enable athlete-specific force–velocity profiling allowing targeted training. 6 , 7 These employ a simple model describing a sprinter’s velocity ( v ) over time ( t ) as per the following equation 8 : d v d t = a m − v τ . (1) The model