Sprinting after having sprinted: Prior high-intensity stochastic cycling impairs the winning strike for gold

Bunch riding in closed circuit cycling courses and some track cycling events are often typified by highly variable power output and a maximal sprint to the finish. How criterium style race demands affect final sprint performance however, is unclear. We studied the effects of 1 h variable power cycling on a subsequent maximal 30 s sprint in the laboratory. Nine well-trained male cyclists/triathletes (O2peak 4.9 ± 0.4 Lmin -1 ; mean ± SD) performed two 1 h cycling trials in a randomized order with either a constant (CON) or variable (VAR) power output matched for mean power output. The VAR protocol comprised intervals of varying intensities (40-135% of maximal aerobic power) and durations (10 to 90 s). A 30 s maximal sprint was performed before and immediately after each 1 h cycling trial. When compared with CON, there was a greater reduction in peak (-5.1 ± 6.1%; mean ± 90% confidence limits) and mean (-5.9 ± 5.2%) power output during the 30 s sprint after the 1 h VAR cycle. Variable power cycling, commonly encountered during criterium and triathlon races can impair an optimal final sprint, potentially compromising race performance. Athletes, coaches, and staff should evaluate training (to improve repeat sprint-ability) and race-day strategies (minimize power variability) to optimize the final sprint

Position statement part two: maintaining immune health

The physical training undertaken by athletes is one of a set of lifestyle or behavioural factors that can influence immune function, health and ultimately exercise performance. Others factors including potential exposure to pathogens, health status, lifestyle behaviours, sleep and recovery, nutrition and psychosocial issues, need to be considered alongside the physical demands of an athlete’s training programme. The general consensus on managing training to maintain immune health is to start with a programme of low to moderate volume and intensity; employ a gradual and periodised increase in training volumes and loads; add variety to limit training monotony and stress; avoid excessively heavy training loads that could lead to exhaustion, illness or injury; include non-specific cross-training to offset staleness; ensure sufficient rest and recovery; and instigate a testing programme for identifying signs of performance deterioration and manifestations of physical stress. Inter-individual variability in immunocompetence, recovery, exercise capacity, non-training stress factors, and stress tolerance likely explains the different vulnerability of athletes to illness. Most athletes should be able to train with high loads provided their programme includes strategies devised to control the overall strain and stress. Athletes, coaches and medical personnel should be alert to periods of increased risk of illness (e.g. intensive training weeks, the taper period prior to competition, and during competition) and pay particular attention to recovery and nutritional strategies. [...continues]

Consensus statement immunonutrition and exercise

In this consensus statement on immunonutrition and exercise, a panel of knowledgeable contributors from across the globe provides a consensus of updated science, including the background, the aspects for which a consensus actually exists, the controversies and, when possible, suggested directions for future research

Manipulating graded exercise test variables affects the validity of the lactate threshold and - Fig 7

<p>(A-B) Bland-Altman plots displaying agreement between measures of the power associated with the (A) OBLA 3.0 mmol^.L^-1, (B) OBLA 3.5 mmol^.L^-1 calculated from <b>GXT</b>_<b>10</b> and the MLSS. The differences between measures (y-axis) are plotted as a function of the mean of the two measures (x-axis) in power (Watts). The horizontal solid line represents the mean difference between the two measures (i.e., bias). The two horizontal dashed lines represent the limits of agreement (1.96 x standard deviation of the mean difference between the lactate threshold and the maximal lactate steady state). The dotted diagonal lines represent the boundaries of the 95% CI for MLSS reliability (CV = 3.0%; 95%; CI = 3.8%) calculated from Hauser et al., 2014) (OBLA = onset of blood lactate accumulation.).</p

Mean and standard deviation of —highest measured during any graded exercise test (GXT); GXT -highest measured during each GXT; VEB highest measured during each verification exhaustive bout (VEB); , highest measured during either the GXT or corresponding VEB.

<p>Mean and standard deviation of GXT duration, max power (Watts) from each GXT, percentage of maximum power from the prolonged GXT expressed as a percentage of W maximum power from GXT₁ and power of each VEB (Watts) from the GXTs. Relative power of the verification exhaustive bout expressed as a percentage (%) of the maximal power measured during the GXT. The subscript (i.e., 1, 3, 4, 7 or 10) refers to the stage duration (minutes) for each test.</p

Representative blood lactate curve with 14 LTs calculated from GXT₄ (participant #9).

<p>The power of the MLSS was 302 W and the blood lactate concentration was 2.85 mmol^.L^-1. Log-log = power at the intersection of two linear lines with the lowest residual sum of squares; log = using the log-log method as the point of the initial data point when calculating the D_max or Modified D_max; poly = Modified D_max method calculated using a third order polynomial regression equation; exp = Modified D_max method calculated using a constant plus exponential regression equation; OBLA = onset of blood lactate accumulation; B + absolute value = the intensity where blood lactate increases above baseline.</p

Bland-Altman plots displaying agreement between measures of the power associated with the RCP regression equation (RCP_MLSS) calculated from GXT₁ and the MLSS.

<p>The differences between measures (y-axis) are plotted as a function of the mean of the two measures (x-axis) in power (Watts). The horizontal solid line represents the mean difference between the two measures (i.e., bias). The two horizontal dashed lines represent the limits of agreement (1.96 x standard deviation of the mean difference between the estimated lactate threshold via the RCP_MLSS and the maximal lactate steady state). The dotted diagonal lines represent the boundaries of the 95% CI for MLSS reliability (CV = 3.0%; 95%; CI = 3.8%) calculated from Hauser et al., 2014) (RCP = respiratory compensation point).</p

Mean difference (MD) and standard deviation, effect size (ES), coefficient of the variation (CV) and p-value (p) for the measured values from GXT₁ compared with the values from GXT₃, GXT₄, GXT₇, and GXT₁₀ and for the values from GXT₁ compared with the values from the VEB following GXT₃, GXT₄, GXT₇, and GXT₁₀.

<p>The subscript (i.e., 1, 3, 4, 7 or 10) refers to the stage duration (minutes) for each test.</p

The mean ± standard deviation (SD) of the 14 lactate thresholds calculated from the 4 prolonged graded exercise tests (i.e., GXT₃, GXT₄, GXT₇ and GXT₁₀), and the respiratory compensation point (RCP) and the maximal lactate steady state (MLSS) estimated from the RCP (RCP_MLSS) calculated from GXT₁.

<p>The mean ± standard deviation (SD) of the 14 lactate thresholds calculated from the 4 prolonged graded exercise tests (i.e., GXT₃, GXT₄, GXT₇ and GXT₁₀), and the respiratory compensation point (RCP) and the maximal lactate steady state (MLSS) estimated from the RCP (RCP_MLSS) calculated from GXT₁.</p

Bland-Altman plots displaying agreement between measures of the power associated with the baseline plus 1.5 mmol^.L^-1 calculated from GXT₃ and the MLSS.

<p>The differences between measures (y-axis) are plotted as a function of the mean of the two measures (x-axis) in power (Watts). The horizontal solid line represents the mean difference between the two measures (i.e., bias). The two horizontal dashed lines represent the limits of agreement (1.96 x standard deviation of the mean difference between the lactate threshold and the maximal lactate steady state). The dotted diagonal lines represent the boundaries of the 95% CI for MLSS reliability (CV = 3.0%; 95%; CI = 3.8%) calculated from Hauser et al., 2014).</p