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Shona L. Halson and David T. Martin

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Iñigo Mujika, Bent R. Rønnestad and David T. Martin

Despite early and ongoing debate among athletes, coaches, and sport scientists, it is likely that resistance training for endurance cyclists can be tolerated, promotes desired adaptations that support training, and can directly improve performance. Lower-body heavy strength training performed in addition to endurance-cycling training can improve both short- and long-term endurance performance. Strength-maintenance training is essential to retain strength gains during the competition season. Competitive female cyclists with greater lower-body lean mass (LBLM) tend to have ~4–9% higher maximum mean power per kg LBLM over 1 s to 10 min. Such relationships enable optimal body composition to be modeled. Resistance training off the bike may be particularly useful for modifying LBLM, whereas more cycling-specific training strategies like eccentric cycling and single-leg cycling with a counterweight have not been thoughtfully investigated in well-trained cyclists. Potential mechanisms for improved endurance include postponed activation of less efficient type II muscle fibers, conversion of type IIX fibers into more fatigue-resistant IIa fibers, and increased muscle mass and rate of force development.

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Paolo Menaspà, Chris R. Abbiss and David T. Martin

This investigation describes the sprint performances of the highest internationally ranked professional male road sprint cyclist during the 2008–2011 Grand Tours. Sprint stages were classified as won, lost, or dropped from the front bunch before the sprint. Thirty-one stages were video-analyzed for average speed of the last km, sprint duration, position in the bunch, and number of teammates at 60, 30, and 15 s remaining. Race distance, total elevation gain (TEG), and average speed of 45 stages were determined. Head-to-head performances against the 2nd–5th most successful professional sprint cyclists were also reviewed. In the 52 Grand Tour sprint stages the subject started, he won 30 (58%), lost 15 (29%), was dropped in 6 (12%), and had 1 crash. Position in the bunch was closer to the front and the number of team members was significantly higher in won than in lost at 60, 30, and 15 s remaining (P < .05). The sprint duration was not different between won and lost (11.3 ± 1.7 and 10.4 ± 3.2 s). TEG was significantly higher in dropped (1089 ± 465 m) than in won and lost (574 ± 394 and 601 ± 423 m, P < .05). The ability to finish the race with the front bunch was lower (77%) than that of other successful sprinters (89%). However, the subject was highly successful, winning over 60% of contested stages, while his competitors won less than 15%. This investigation explores methodology that can be used to describe important aspects of road sprint cycling and supports the concept that tactical aspects of sprinting can relate to performance outcomes.

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Chris R. Abbiss, Paolo Menaspà, Vincent Villerius and David T. Martin

A number of laboratory-based performance tests have been designed to mimic the dynamic and stochastic nature of road cycling. However, the distribution of power output and thus physical demands of high-intensity surges performed to establish a breakaway during actual competitive road cycling are unclear. Review of data from professional road-cycling events has indicated that numerous short-duration (5–15 s), high-intensity (~9.5–14 W/kg) surges are typically observed in the 5–10 min before athletes’ establishing a breakaway (ie, riding away from a group of cyclists). After this initial high-intensity effort, power output declined but remained high (~450–500 W) for a further 30 s to 5 min, depending on race dynamics (ie, the response of the chase group). Due to the significant influence competitors have on pacing strategies, it is difficult for laboratory-based performance tests to precisely replicate this aspect of mass-start competitive road cycling. Further research examining the distribution of power output during competitive road racing is needed to refine laboratory-based simulated stochastic performance trials and better understand the factors important to the success of a breakaway.

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David T. Martin, Mark B. Andersen and Ward Gates

This study examined whether the Profile of Mood States questionnaire (POMS) is a useful tool for monitoring training stress in cycling athletes. Participants (n = 11) completed the POMS weekly during six weeks of high-intensity interval cycling and a one-week taper. Cycling performance improved over the first three weeks of training, plateaued during Weeks 4 and 5, decreased slightly following Week 6, and then significantly increased during the one-week taper. Neither the high-intensity interval training nor the one-week taper significantly affected total mood or specific mood states. POMS data from two cyclists who did not show improved performance capabilities during the taper (overtraining) were not distinctly unique when compared to cyclists who did improve. Also, one cyclist, who on some days had the highest total mood disturbance, responded well to the taper and produced his best personal effort during this time period. These findings raise questions about the usefulness of POMS to distinguish, at an individual level, between periods of productive and counterproductive high-intensity training.

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Mary K. Martin, David T. Martin, Gregory R. Collier and Louise M. Burke

We estimated self-reported energy intake (EI) and cycling energy expenditure (CEE) during racing and training over 26 days (9 days recovery [REC], 9 days training [TRN], and 8 days racing [RACE], which included a 5-day stage race) for 8 members of the Australian National Training Squad [mean ± SD; 25.1 ± 4.0 years, 59.2 ± 4.4 kg, 3.74 ± 0.24 L · min−1 V̇O2peak, 13.6 ± 4.5 % Body fat (%Bfat)]. After 70 days of training and racing, average body mass increased by 1.1 kg (95%CI 0.5 to 1.7 kg; p < .01) and average %Bfat decreased by 0.9% (95%CI –1.7 to –0.1%; p < .05). These minor changes, however, were not considered clinically significant. CEE was different between RACE, TRN, and REC (2.15 ± 0.18 vs. 1.73 ± 0.25 vs. 0.72 ± 0.15 MJ · d−1, p < .05). Reported EI for RACE and TRN were higher than REC (14.87 ± 3.03, 13.70 ± 4.04 vs.11.98 ± 3.57 MJ · d−1, p < .05). Reported intake of carbohydrate for RACE and TRN were also higher than REC (588 ± 122, 536 ± 130 vs. 448 ± 138 g · d−1, p < .05). Reported intake of fat (59 ± 21–68 ± 21 g · d−1) was similar during RACE, TRN, and REC, whereas protein intake tended to be higher during TRN (158 ± 49 g · d−1) compared to RACE and REC (136 ± 33; 130 ± 33 g · d−1). There was a relationship between average CEE and average EI over the 26 days (r = 0.77, p < .05), but correlations between CEE and EI for each of the women varied (r =–0.02 to 0.67). There was a strong trend for an inverse relationship between average EI and %Bfat (r = –.68, p = .06, n = 8). In this study, increases in reported EI during heavy training and racing were the result of an increase in carbohydrate intake. Most but not all cyclists modulated EI based on CEE. Research is required to determine whether physiological or psychological factors are primarily responsible for the observed relationship between CEE and EI and also the inverse correlation between %Bfat and EI.

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Tammie R. Ebert, David T. Martin, Brian Stephens and Robert T. Withers

Purpose:

To quantify the power-output demands of men’s road-cycling stage racing using a direct measure of power output.

Methods:

Power-output data were collected from 207 races over 6 competition years on 31 Australian national male road cyclists. Subjects performed a maximal graded exercise test in the laboratory to determine maximum aerobic-power output, and bicycles were fitted with SRM power meters. Races were described as fl at, hilly, or criterium, and linear mixed modeling was used to compare the races.

Results:

Criterium was the shortest race and displayed the highest mean power output (criterium 262 ± 30 v hilly 203 ± 32 v fl at 188 ± 30 W), percentage total race time above 7.5 W/kg (crite-rium 15.5% ± 4.1% v hilly 3.8% ± 1.7% v fl at 3.5% ± 1.4%) and SD in power output (criterium 250 v hilly 165 v fl at 169 W). Approximately 67%, 80%, and 85% of total race time was spent below 5 W/kg for criterium, hilly and fl at races, respectively. About 70, 40, and 20 sprints above maximum aerobic-power output occurred during criterium, hilly, and fl at races, respectively, with most sprints being 6 to 10 s.

Conclusions:

These data extend previous research documenting the demands of men’s road cycling. Despite the relatively low mean power output, races were characterized by multiple high-intensity surges above maximum aerobic-power output. These data can be used to develop sport-specific interval-training programs that replicate the demands of competition.

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Tammie R. Ebert, David T. Martin, Brian Stephens, Warren McDonald and Robert T. Withers

Purpose:

To quantify the fluid and food consumed during a men’s and women’s professional road-cycling tour.

Methods:

Eight men (age 25 ± 5 y, body mass ± 7.4 kg, and height 177.4 ± 4.5 cm) and 6 women (age 26 ± 4 y, body mass ± 5.6 kg, and height 170.4 ± 5.2 cm) of the Australian Institute of Sport Road Cycling squads participated in the study. The men competed in the 6-d Tour Down Under (Adelaide, Australia), and the women, in the 10-d Tour De L’Aude (Aude, France). Body mass was recorded before and immediately after the race. Cyclists recalled the number of water bottles and amount of food they had consumed.

Results:

Men and women recorded body-mass losses of ~2 kg (2.8% body mass) and 1.5 kg (2.6% body mass), respectively, per stage during the long road races. Men had an average fluid intake of 1.0 L/h, whereas women only consumed on average 0.4 L/h. In addition, men consumed CHO at the rate suggested by dietitians (average CHO intake of 48 g/h), but again the women failed to reach recommendations, with an average intake of ~21 g/h during a road stage.

Conclusions:

Men appeared to drink and eat during racing in accordance with current nutritional recommendations, but women failed to reach these guidelines. Both men and women finished their races with a body-mass loss of ~2.6% to 2.8%. Further research is required to determine the impact of this loss on road-cycling performance and thermoregulation.

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Eric C. Haakonssen, David T. Martin, David G. Jenkins and Louise M. Burke

Purpose:

This study investigated the satisfaction of elite female cyclists with their body weight (BW) in the context of race performance, the magnitude of BW manipulation, and the association of these variables with menstrual function.

Methods:

Female competitors in the Australian National Road Cycling Championships (n = 32) and the Oceania Championships (n = 5) completed a questionnaire to identify current BW, BW fluctuations, perceived ideal BW for performance, frequency of weight consciousness, weight-loss techniques used, and menstrual regularity.

Results:

All but 1 cyclist reported that female cyclists are “a weight-conscious population,” and 54% reported having a desire to change BW at least once weekly; 62% reported that their current BW was not ideal for performance. Their perceived ideal BW was (mean ± SD) 1.6 ± 1.6 kg (2.5% ± 2.5%) less than their current weight (P < .01), and 73% reported that their career-lowest BW was either “beneficial” or “extremely beneficial” for performance. 65% reported successfully reducing BW in the previous 12 months with a mean loss of 2.4 ± 1.0 kg (4.1% ± 1.9%). The most common weight-loss technique was reduced energy intake (76%). Five cyclists (14%) had been previously diagnosed as having an eating disorder by a physician. Of the 18 athletes not using a hormonal contraceptive, 11 reported menstrual dysfunction (oligomenorrhea or amenorrhea).

Conclusion:

Elite Australian female cyclists are a weight-conscious population who may not be satisfied with their BW leading into a major competition and in some cases are frequently weight conscious.

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Eric C. Haakonssen, David T. Martin, Louise M. Burke and David G. Jenkins

Body composition in a female road cyclist was measured using dual-energy X-ray absorptiometry (5 occasions) and anthropometry (10 occasions) at the start of the season (Dec to Mar), during a period of chronic fatigue associated with poor weight management (Jun to Aug), and in the following months of recovery and retraining (Aug to Nov). Dietary manipulation involved a modest reduction in energy availability to 30–40 kcal · kg fat-free mass−1 · d−1 and an increased intake of high-quality protein, particularly after training (20 g). Through the retraining period, total body mass decreased (−2.82 kg), lean mass increased (+0.88 kg), and fat mass decreased (−3.47 kg). Hemoglobin mass increased by 58.7 g (8.4%). Maximal aerobic- and anaerobic-power outputs were returned to within 2% of preseason values. The presented case shows that through a subtle energy restriction associated with increased protein intake and sufficient energy intake during training, fat mass can be reduced with simultaneous increases in lean mass, performance gains, and improved health.