The reliability of measuring gross efficiency during high intensity cycling exercise

Purpose: To evaluate the reliability of calculating gross efficiency (GE) conventionally and using a back extrapolation (BE) method during high-intensity exercise (HIE). Methods: A total of 12 trained participants completed 2 HIE bouts (P1 = 4 min at 80% maximal aerobic power [MAP]; P2 = 4 min at 100%MAP). GE was calculated conventionally in the last 3 minutes of submaximal (50%MAP) cycling bouts performed before and after HIE (Pre50%MAP and Post50%MAP). To calculate GE using BE (BGE), a linear regression of GE submaximal values post-HIE were back extrapolated to the end of the HIE bout. Results: BGE was significantly correlated with Post50%MAP GE in P1 (r = .63; P = .01) and in P2 (r = .85; P = .002). Reliability data for P1 and P2 BGE demonstrate a mean coefficient of variation of 7.8% and 9.8% with limits of agreement of 4.3% and 4.5% in relative GE units, respectively. P2 BGE was significantly lower than P2 Post50%MAP GE (18.1% [1.6%] vs 20.3% [1.7%]; P = .01). Using a declining GE from the BE method, there was a 44% greater anaerobic contribution compared with assuming a constant GE during 4-minute HIE at 100%MAP. Conclusion: HIE acutely reduced BGE at 100%MAP. A greater anaerobic contribution to exercise as well as excess postexercise oxygen consumption at 100%MAP may contribute to this decline in efficiency. The BE method may be a reliable and valid tool in both estimating GE during HIE and calculating aerobic and anaerobic contributions

The acute physiological and perceptual effects of recovery interval intensity during cycling‐based high‐intensity interval training

Purpose: The current study sought to investigate the role of recovery intensity on the physiological and perceptual responses during cycling-based aerobic high-intensity interval training. Methods: Fourteen well-trained cyclists (V˙O2peak: 62 ± 9 mL kg−1 min−1) completed seven laboratory visits. At visit 1, the participants’ peak oxygen consumption (V˙O2peak) and lactate thresholds were determined. At visits 2–7, participants completed either a 6 × 4 min or 3 × 8 min high-intensity interval training (HIIT) protocol with one of three recovery intensity prescriptions: passive (PA) recovery, active recovery at 80% of lactate threshold (80A) or active recovery at 110% of lactate threshold (110A).Results: The time spent at > 80%, > 90% and > 95% of maximal minute power during the work intervals was significantly increased with PA recovery, when compared to both 80A and 110A, during both HIIT protocols (all P ≤ 0.001). However, recovery intensity had no effect on the time spent at > 90% V˙O2peak (P = 0.11) or > 95% V˙O2peak (P = 0.50) during the work intervals of both HIIT protocols. Session RPE was significantly higher following the 110A recovery, when compared to the PA and 80A recovery during both HIIT protocols (P < 0.001).Conclusion: Passive recovery facilitates a higher work interval PO and similar internal stress for a lower sRPE when compared to active recovery and therefore may be the efficacious recovery intensity prescription

An Investigation of efficiency within Cycling

The effect of training on metabolic efficiency in cycling is an under researched area. Previous studies have not found significant differences in cycling efficiency between trained cyclists and untrained participants which has largely limited further research in this area. However upon closer examination of the literature problematic methods are apparent. The main aim of this thesis was to investigate the effects of training upon metabolic efficiency in cycling. Before this could be done, it was necessary to establish a testing protocol capable of producing reliable data for use in the calculation of metabolic efficiency. This enabled the calculation of an appropriate sample size to have a chance of detecting significant differences between groups of trained and recreational cyclists. Statistically significant differences were consequently found between these two populations. Training studies were therefore needed to establish whether cycling efficiency was affected by training and if so what type. The results of the subsequent training studies showed firstly, that alterations in training volume and intensity did result in changes in the metabolic efficiency of cycling. Secondly, using an intervention study the metabolic efficiency of cycling was specifically increased as a result of the addition of high intensity training. Training volume was shown to have little effect on metabolic efficiency. This thesis is the first to demonstrate that metabolic efficiency is directly influenced by training over a cycling season and is significantly increased as a result of high intensity training

The ergogenic effects of transcranial direct current stimulation on exercise performance

The physical limits of the human performance have been the object of study for a considerable time. Most of the research has focused on the locomotor muscles, lungs and heart. As a consequence, much of the contemporary literature has ignored the importance of the brain in the regulation of exercise performance. With the introduction and development of new non-invasive devices, the knowledge regarding the behaviour of the central nervous system during exercise has advanced. A first step has been provided from studies involving neuroimaging techniques where the role of specific brain areas have been identified during isolated muscle or whole-body exercise. Furthermore, a new interesting approach has been provided by studies involving non-invasive techniques to manipulate specific brain areas. These techniques most commonly involve the use of an electrical or magnetic field crossing the brain. In this regard, there has been emerging literature demonstrating the possibility to influence exercise outcomes in healthy people following stimulation of specific brain areas. Specifically, transcranial direct current stimulation (tDCS) has been recently used prior to exercise in order to improve exercise performance under a wide range of exercise types. In this review article, we discuss the evidence provided from experimental studies involving tDCS. The aim of this review is to provide a critical analysis of the experimental studies investigating the application of tDCS prior to exercise and how it influences brain function and performance. Finally, we provide a critical opinion of the usage of tDCS for exercise enhancement. This will consequently progress the current knowledge base regarding the effect of tDCS on exercise and provides both a methodological and theoretical foundation on which future research can be based

The Effect of Cycling Intensity on Cycling Economy During Seated and Standing Cycling

BACKGROUND: Previous research has shown that cycling in a standing position reduces cycling economy compared with seated cycling. It is unknown whether the cycling intensity moderates the reduction in cycling economy while standing. PURPOSE: The aim was to determine whether the negative effect of standing on cycling economy would be decreased at a higher intensity. METHODS: Ten cyclists cycled in 8 different conditions. Each condition was either at an intensity of 50% or 70% of maximal aerobic power, at a gradient of 4% or 8% and in the seated or standing cycling position. Cycling economy and muscle activation level of 8 leg muscles were recorded. RESULTS: There was an interaction between cycling intensity and position for cycling economy (P = 0.03), the overall activation of the leg muscles (P = 0.02) and the activation of the lower leg muscles (P = 0.05). The interaction showed decreased cycling economy when standing compared with seated cycling, but the difference was reduced at higher intensity. The overall activation of the leg muscles and the lower leg muscles respectively increased and decreased, but the differences between standing and seated cycling were reduced at higher intensity. CONCLUSIONS: Cycling economy was lower during standing cycling than seated cycling, but the difference in economy diminishes when cycling intensity increases. Activation of the lower leg muscles did not explain the lower cycling economy while standing. The increased overall activation therefore suggests that increased activation of the upper leg muscles explains part of the lower cycling economy while standing

Prescribing 6-weeks of running training using parameters from a self-paced maximal oxygen uptake protocol

The self-paced maximal oxygen uptake test (SPV) may offer effective training prescription metrics for athletes. This study aimed to examine whether SPV-derived data could be used for training prescription. Twenty-four recreationally active male and female runners were randomly assigned between two training groups: (1) Standardised (STND) and (2) Self-Paced (S-P). Participants completed 4 running sessions a week using a global positioning system-enabled (GPS) watch: 2 × interval sessions; 1 × recovery run; and 1 × tempo run. STND had training prescribed via graded exercise test (GXT) data, whereas S-P had training prescribed via SPV data. In STND, intervals were prescribed as 6 × 60% of the time that velocity at [Formula: see text] ([Formula: see text]) could be maintained (T ). In S-P, intervals were prescribed as 7 × 120 s at the mean velocity of rating of perceived exertion 20 ( RPE20). Both groups used 1:2 work:recovery ratio. Maximal oxygen uptake ([Formula: see text]), [Formula: see text], T RPE20, critical speed (CS), and lactate threshold (LT) were determined before and after the 6-week training. STND and S-P training significantly improved [Formula: see text] by 4 ± 8 and 6 ± 6%, CS by 7 ± 7 and 3 ± 3%; LT by 5 ± 4% and 7 ± 8%, respectively (all P < .05), with no differences observed between groups. Novel metrics obtained from the SPV can offer similar training prescription and improvement in [Formula: see text], CS and LT compared to training derived from a traditional GXT

Modeling Intermittent Running from a Single-visit Field Test

This study assessed whether the distance-time relationship could be modeled to predict time to exhaustion (TTE) during intermittent running. 13 male distance runners (age: 33 ± 14 years) completed a field test and 3 interval tests on an outdoor 400 m athletic track. Field-tests involved trials over 3600 m, 2400 m and 1200 m with a 30-min rest between each run. Interval tests consisted of: 1000 m at 107 % of CS with 200 m at 95 % CS; 600 m at 110 % of CS with 200 m at 90 % CS; 200 m at 150 % of CS with 200 m at 80 % CS. Interval sessions were separated by 24 h recovery. Field-test CS and D′ were applied to linear and non-linear models to estimate the point of interval session termination. Actual and predicted TTE using the linear model were not significantly different in the 1000 m and 600 m trials. Actual TTE was significantly lower (P = 0.01) than predicted TTE in the 200 m trial. Typical error was high across the trials (range 334–1709 s). The mean balance of D′ remaining at interval session termination was significantly lower when estimated from the non-linear model (− 21.2 vs. 13.4 m, P < 0.01), however no closer to zero than the linear model. Neither the linear or non-linear model could closely predict TTE during intermittent running

A Bayesian Approach for the Use of Athlete Performance Data Within Anti-doping

The World Anti-doping Agency currently collates the results of all doping tests for athletes involved in elite sporting competition with the aim of improving the fight against doping. Existing anti-doping strategies involve either the direct detection of use of banned substances, or abnormal variation in metabolites or biological markers related to their use. As the aim of any doping regime is to enhance athlete competitive performance, it is interesting to consider whether performance data could be used within the fight against doping. In this regard, the identification of unexpected increases in athlete performance could be used as a trigger for their closer scrutiny via a targeted anti-doping testing programme. This study proposes a Bayesian framework for the development of an “athlete performance passport” and documents some initial findings and limitations of such an approach. The Bayesian model was retrospectively applied to the competitive results of 1,115 shot put athletes from 1975 to 2016 in order establish the interindividual variability of intraindividual performance in order to create individualized career performance trajectories for a large number of presumed clean athletes. Data from athletes convicted for doping violations (3.69% of the sample) was used to assess the predictive performance of the Bayesian framework with a probit model. Results demonstrate the ability to detect performance differences (~1 m) between doped and presumed clean athletes, and achieves good predictive performance of doping status (i.e., doped vs. non-doped) with a high area under the curve (AUC = 0.97). However, the model prediction of doping status was driven by the correct classification of presume non-doped athletes, misclassifying doped athletes as non-doped. This lack of sensitivity is likely due to the need to accommodate additional longitudinal covariates (e.g., aging and seasonality effects) potentially affecting performance into the framework. Further research is needed in order to increase the framework structure and improve its accuracy and sensitivity

Gross efficiency and cycling performance: a review.

Efficiency, the ratio of work generated to the total metabolic energy cost, has been suggested to be a key determinant of endurance cycling performance. The purpose of this brief review is to evaluate the influence of gross efficiency on cycling power output and to consider whether or not gross efficiency can be modified. In a re-analysis of data from five separate studies, variation in gross efficiency explained ~30% of the variation in power output during cycling time-trials. Whilst other variables, notably VO2max and lactate threshold, have been shown to explain more of the variance in cycling power output, these results confirm the important influence of gross efficiency. Case study, cross-sectional, longitudinal, and intervention research designs have all been used to demonstrate that exercise training can enhance gross efficiency. Whilst improvements have been seen with a wide range of training types (endurance, strength, altitude), it would appear that high intensity training is the most potent stimulus for changes in gross efficiency. In addition to physiological adaptations, gross efficiency might also be improved through biomechanical adaptations. However, ‘intuitive’ technique and equipment adjustments may not always be effective. For example, whilst ‘pedalling in circles’ allows pedalling to become mechanically more effective, this technique does not result in short term improvements in gross efficiency