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Internal validity refers to the degree of control exerted over potential confounding variables to reduce alternative explanations for the effects of various treatments. In exercise and sports-science research and routine testing, internal validity is commonly achieved by controlling variables such as exercise and warm-up protocols, prior training, nutritional intake before testing, ambient temperature, time of testing, hours of sleep, age, and gender. However, a number of other potential confounding variables often do not receive adequate attention in sports physiology and performance research. These confounding variables include instructions on how to perform the test, volume and frequency of verbal encouragement, knowledge of exercise endpoint, number and gender of observers in the room, influence of music played before and during testing, and the effects of mental fatigue on performance. In this review the authors discuss these variables in relation to common testing environments in exercise and sports science and present some recommendations with the goal of reducing possible threats to internal validity.

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.

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.

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.

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.

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 V̇O_2peak, 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.