What Does It Take to Complete the Cape Epic?

ABSTRACT: Reinpõld, K, Bossi, AH, and Hopker, JG. What does it take to complete the cape epic? J Strength Cond Res 36(12): 3513-3520, 2022-This study aimed to describe the racing and training demands of the Cape Epic. Six male mountain bike riders (age: 39 ± 7 years, height: 181 ± 3 cm, and body mass: 78.7 ± 8.1 kg) trained for 4.5 months and took part in the Cape Epic. Training and racing data (prologue, stage 1, and 2) were analyzed, and riders were tested in the laboratory on 3 distinct occasions for maximal oxygen uptake (V̇O 2 max), maximal work rate (Wmax), and power output associated with the respiratory compensation point (RCP PO ). Statistical significance was set at p ≤ 0.05. With race durations of 1.5 ± 0.2, 6.5 ± 1.2, and 6.4 ± 1.4 hours for, respectively, prologue, stage 1, and 2, normalized power was higher in prologue (3.73 ± 0.72 W·kg -1 ) compared with stages 1 (3.06 ± 0.59 W·kg -1 , p < 0.001) and 2 (2.94 ± 0.69 W·kg -1 , p < 0.001). Riders spent more time in power zones 1 and 2 (as %RCP PO ) and less time in zones 4 and 5, during stage 2 compared with prologue (all zones p ≤ 0.028). Despite no changes in V̇O 2 max or Wmax, RCP PO increased from midtraining (3.89 ± 0.61 W·kg -1 ) to prerace testing (4.08 ± 0.64 W·kg -1 , p = 0.048). No differences were found between base and build training phases for time in power zones. In conclusion, the Cape Epic requires an ability to sustain high submaximal power outputs for several hours as well as an ability to repeat high-intensity efforts throughout the race. A well-balanced program, incorporating a pyramidal intensity distribution, may be used as a starting point for the design of optimal training approaches.

Optimizing Interval Training Through Power-Output Variation Within the Work Intervals.

PURPOSE: Maximal oxygen uptake (V˙O2max) is a key determinant of endurance performance. Therefore, devising high-intensity interval training (HIIT) that maximizes stress of the oxygen-transport and -utilization systems may be important to stimulate further adaptation in athletes. The authors compared physiological and perceptual responses elicited by work intervals matched for duration and mean power output but differing in power-output distribution. METHODS: Fourteen cyclists (V˙O2max 69.2 [6.6] mL·kg-1·min-1) completed 3 laboratory visits for a performance assessment and 2 HIIT sessions using either varied-intensity or constant-intensity work intervals. RESULTS: Cyclists spent more time at >90%V˙O2max during HIIT with varied-intensity work intervals (410 [207] vs 286 [162] s, P = .02), but there were no differences between sessions in heart-rate- or perceptual-based training-load metrics (all P ≥ .1). When considering individual work intervals, minute ventilation (V˙E) was higher in the varied-intensity mode (F = 8.42, P = .01), but not respiratory frequency, tidal volume, blood lactate concentration [La], ratings of perceived exertion, or cadence (all F ≤ 3.50, ≥ .08). Absolute changes (Δ) between HIIT sessions were calculated per work interval, and Δ total oxygen uptake was moderately associated with ΔV˙E (r = .36, P = .002). CONCLUSIONS: In comparison with an HIIT session with constant-intensity work intervals, well-trained cyclists sustain higher fractions of V˙O2max when work intervals involved power-output variations. This effect is partially mediated by an increased oxygen cost of hyperpnea and not associated with a higher [La], perceived exertion, or training-load metrics.

Influence of a slow-start on overall performance and running kinematics during 6-h ultramarathon races.

The aim of this study was to describe the pacing during a 6-h ultramarathon (race 1) and to investigate whether a slow-start affects performance, running kinematic changes, ratings of perceived exertion (RPE) and fatigue (ROF) (race 2). After a critical speed test, participants completed two 6-h ultramarathons. Race 1 (n = 16) was self-paced, whereas in race 2 (n = 10), athletes performed the initial 36 min at speeds 18% below the mean speed of the initial 36 min of race 1. In race 1, participants adopted an inverse sigmoid pacing. Contact times increased after 1 h, and flight times decreased after 30 min (all P ≤ .009); stride length reduced after 1 h 30 min (all P = .022), and stride frequency did not change. Despite the lower speeds during the first 10% of race 2, and higher speeds at 50% and 90%, performance remained unchanged (57.5 ± 10.2 vs. 56.3 ± 8.5 km; P = .298). However, RPE and ROF were lowered for most of race 2 duration (all P < .001). For the comparison of kinematic variables between races, data were normalised by absolute running speed at each time point from 1 h onwards. No differences were found for any of the kinematic variables. In conclusion, decreasing initial speed minimises RPE and ROF, but does not necessarily affect performance. In addition, running kinematic changes do not seem to be affected by pacing manipulation.

Energy Expenditure Equation Choice: Effects on Cycling Efficiency and its Reliability.

PURPOSE: There are several published equations to calculate energy expenditure (EE) from gas exchanges. The authors assessed whether using different EE equations would affect gross efficiency (GE) estimates and their reliability. METHODS: Eleven male and 3 female cyclists (age 33 [10] y; height: 178 [11] cm; body mass: 76.0 [15.1] kg; maximal oxygen uptake: 51.4 [5.1] mL·kg-1·min-1; peak power output: 4.69 [0.45] W·kg-1) completed 5 visits to the laboratory on separate occasions. In the first visit, participants completed a maximal ramp test to characterize their physiological profile. In visits 2 to 5, participants performed 4 identical submaximal exercise trials to assess GE and its reliability. Each trial included three 7-minute bouts at 60%, 70%, and 80% of the gas exchange threshold. EE was calculated with 4 equations by Péronnet and Massicotte, Lusk, Brouwer, and Garby and Astrup. RESULTS: All 4 EE equations produced GE estimates that differed from each other (all P < .001). Reliability parameters were only affected when the typical error was expressed in absolute GE units, suggesting a negligible effect-related to the magnitude of GE produced by each EE equation. The mean coefficient of variation for GE across different exercise intensities and calculation methods was 4.2%. CONCLUSIONS: Although changing the EE equation does not affect GE reliability, exercise scientists and coaches should be aware that different EE equations produce different GE estimates. Researchers are advised to share their raw data to allow for GE recalculation, enabling comparison between previous and future studies.

Pacing Strategy and Tactical Positioning During Cyclo-Cross Races.

PURPOSE: To describe pacing strategy and competitive behavior in elite-level cyclo-cross races. METHODS: Data from 329 men and women competing in 5 editions (2012-2016) of Union Cycliste Internationale Cyclo-Cross World Championships were compiled. Individual mean racing speeds from each lap were normalized to the mean speeds of the whole race. Lap and overall rankings were also explored. Pacing strategy was compared between sexes and between top- and bottom-placed cyclists. RESULTS: A significant main effect of laps was found in 8 out of 10 races (4 positive, 3 variable, 2 even, and 1 negative pacing strategies), and an interaction effect of ranking-based groups was found in 2 (2016, male and female races). Kendall tau-b correlations revealed an increasingly positive relationship between intermediate and overall rankings throughout the races. The number of overtakes during races decreased from start to finish, as suggested by significant Friedman tests. In the first lap, normalized cycling speeds were different in 3 out of 5 editions-men were faster in 1 and slower in 2 editions. In the last lap, however, normalized cycling speeds of men were lower than those of women in 4 editions. CONCLUSIONS: Elite cyclo-cross competitors adopt slightly distinct pacing strategies in each race, but positive pacing strategies are highly probable in most events, with more changes in rankings during the first laps. Sporadically, top- and bottom-placed groups might adopt different pacing strategies during either men's or women's races. Men and women seem to distribute their efforts differently, but this effect is of small magnitude.

Pacing Strategy During 24-Hour Ultramarathon-Distance Running.

PURPOSE: To describe pacing strategy in a 24-h running race and its interaction with sex, age group, athletes' performance group, and race edition. METHODS: Data from 398 male and 103 female participants of 5 editions were obtained based on a minimum 19.2-h effective-running cutoff. Mean running speed from each hour was normalized to the 24-h mean speed for analyses. RESULTS: Mean overall performance was 135.6 ± 33.0 km with a mean effective-running time of 22.4 ± 1.3 h. Overall data showed a reverse J-shaped pacing strategy, with a significant reduction in speed from the second-to-last to the last hour. Two-way mixed ANOVAs showed significant interactions between racing time and both athlete performance group (F = 7.01, P < .001, ηp2 = .04) and race edition (F = 3.01, P < .001, ηp2 = .02) but not between racing time and either sex (F = 1.57, P = .058, ηp 2 < .01) or age group (F = 1.25, P = .053, ηp2 = .01). Pearson product-moment correlations showed an inverse moderate association between performance and normalized mean running speed in the first 2 h (r = -.58, P < .001) but not in the last 2 h (r = .03, P = .480). CONCLUSIONS: While the general behavior represents a rough reverse J-shaped pattern, the fastest runners start at lower relative intensities and display a more even pacing strategy than slower runners. The "herd behavior" seems to interfere with pacing strategy across editions, but not sex or age group of runners.