Body composition of female road and track endurance cyclists: Normative values and typical changes.

The aims of this study were to describe normative values and seasonal variation of body composition in female cyclists comparing female road and track endurance cyclists, and to validate the use of anthropometry to monitor lean mass changes. Anthropometric profiles (seven site skinfolds) were measured over 16 years from 126 female cyclists. Lean mass index (LMI) was calculated as body weight × skinfolds(-x). The exponent (x) was calculated as the slope of the natural logarithm of body weight and skinfolds. Percentage changes in LMI were compared to lean mass changes measured using dual-energy X-ray absorptiometry (DXA) in a subset of 25 road cyclists. Compared to sub-elite and elite cyclists, world class cyclists were (mean [95% CI]) 1.18 kg [0.46, 1.90] and 0.60 kg [0.05, 1.15] lighter and had skinfolds that were 7.4 mm [3.8, 11.0] and 4.6 mm [1.8, 7.4] lower, respectively. Body weight (0.41 kg [0.04, 0.77]) and skinfolds (4.0 mm [2.1, 6.0]) were higher in the off-season compared to the early-season. World class female road cyclists had lower body weight (6.04 kg [2.73, 9.35]) and skinfolds (11.5 mm [1.1, 21.9]) than track endurance cyclists. LMI (mean exponent 0.15 [0.13, 0.18]) explained 87% of the variance in DXA lean mass. In conclusion, higher performing female cyclists were lighter and leaner than their less successful peers, road cyclists were lighter and leaner than track endurance cyclists, and weight and skinfolds were lowest early in the season. LMI appears to be a reasonably valid tool for monitoring lean mass changes.

The contribution of haemoglobin mass to increases in cycling performance induced by simulated LHTL.

We sought to determine whether improved cycling performance following 'Live High-Train Low' (LHTL) occurs if increases in haemoglobin mass (Hb(mass)) are prevented via periodic phlebotomy during hypoxic exposure. Eleven, highly trained, female cyclists completed 26 nights of simulated LHTL (16 h day(-1), 3000 m). Hb(mass) was determined in quadruplicate before LHTL and in duplicate weekly thereafter. After 14 nights, cyclists were pair-matched, based on their Hb(mass) response (ΔHb(mass)) from baseline, to form a response group (Response, n = 5) in which Hb(mass) was free to adapt, and a Clamp group (Clamp, n = 6) in which ΔHb(mass) was negated via weekly phlebotomy. All cyclists were blinded to the blood volume removed. Cycling performance was assessed in duplicate before and after LHTL using a maximal 4-min effort (MMP(4min)) followed by a ride time to exhaustion test at peak power output (T (lim)). VO(2peak) was established during the MMP(4min). Following LHTL, Hb(mass) increased in Response (mean ± SD, 5.5 ± 2.9%). Due to repeated phlebotomy, there was no ΔHb(mass) in Clamp (-0.4 ± 0.6%). VO(2peak) increased in Response (3.5 ± 2.3%) but not in Clamp (0.3 ± 2.6%). MMP(4min) improved in both the groups (Response 4.5 ± 1.1%, Clamp 3.6 ± 1.4%) and was not different between groups (p = 0.58). T (lim) increased only in Response, with Clamp substantially worse than Response (-37.6%; 90% CL -58.9 to -5.0, p = 0.07). Our novel findings, showing an ~4% increase in MMP(4min) despite blocking an ~5% increase in Hb(mass), suggest that accelerated erythropoiesis is not the sole mechanism by which LHTL improves performance. However, increases in Hb(mass) appear to influence the aerobic contribution to high-intensity exercise which may be important for subsequent high-intensity efforts.

Velocity-specific fatigue: quantifying fatigue during variable velocity cycling.

UNLABELLED: Previous investigators have quantified fatigue during short maximal cycling trials ( approximately 30 s) by calculating a fatigue index. Other investigators have reported a curvilinear power-pedaling rate relationship during short fatigue-free maximal cycling trials (<6 s). During maximal trials, pedaling rates may change with fatigue. Quantification of fatigue using fatigue index is therefore complicated by the power-pedaling rate relationship. PURPOSE: The purpose of this study was to quantify fatigue while accounting for the effects of pedaling rate on power. METHODS: Power and pedaling rate were recorded during Union Cycliste Internationale sanctioned 200-m time trials by eight male (height = 181.5 +/- 4.3 cm, mass = 87.0 +/- 8.0 kg) world-class sprint cyclists with SRM power meters and fixed-gear track bicycles. Data from the initial portion of maximal acceleration were used to establish maximal power-pedaling rate relationships. Fatigue was quantified three ways: 1) traditional fatigue index, 2) fatigue index modified to account for the power-pedaling rate relationship (net fatigue index), and 3) work deficit, the difference between actual work done and work that might have been accomplished without fatigue. RESULTS: Fatigue index (55.4% +/- 6.4%) was significantly greater than net fatigue index (41.0% +/- 7.9%, P < 0.001), indicating that the power-pedaling rate relationship accounted for 14.3% +/- 7% of the traditional fatigue index value. Work deficit (23.3% +/- 6%) was significantly less than either measure of fatigue (P < 0.001). CONCLUSION: Net fatigue index and work deficit account for the power-pedaling rate relation and therefore more precisely quantify fatigue during variable velocity cycling. These measures can be used to compare fatigue during different fatigue protocols, including world-class sprint cycling competition. Precise quantification of fatigue during elite cycling competition may improve evaluation of training status, gear ratio selection, and fatigue resistance.

Maximal torque- and power-pedaling rate relationships for elite sprint cyclists in laboratory and field tests.

Performance models provide an opportunity to examine cycling in a broad parameter space. Variables used to drive such models have traditionally been measured in the laboratory. The assumption, however, that maximal laboratory power is similar to field power has received limited attention. The purpose of the study was to compare the maximal torque- and power-pedaling rate relationships during "all-out" sprints performed on laboratory ergometers and on moving bicycles with elite cyclists. Over a 3-day period, seven male (mean +/- SD; 180.0 +/- 3.0 cm; 86.2 +/- 6.1 kg) elite track cyclists completed two maximal 6 s cycle ergometer trials and two 65 m sprints on a moving bicycle; calibrated SRM powermeters were used and data were analyzed per revolution to establish torque- and power-pedaling rate relationships, maximum power, maximum torque and maximum pedaling rate. The inertial load of our laboratory test was (37.16 +/- 0.37 kg m(2)), approximately half as large as the field trials (69.7 +/- 3.8 kg m(2)). There were no statistically significant differences between laboratory and field maximum power (1791 +/- 169; 1792 +/- 156 W; P = 0.863), optimal pedaling rate (128 +/- 7; 129 +/- 9 rpm; P = 0.863), torque-pedaling rate linear regression slope (-1.040 +/- 0.09; -1.035 +/- 0.10; P = 0.891) and maximum torque (266 +/- 20; 266 +/- 13 Nm; P = 0.840), respectively. Similar torque- and power-pedaling rate relationships were demonstrated in laboratory and field settings. The findings suggest that maximal laboratory data may provide an accurate means of modeling cycling performance.

Modeling sprint cycling using field-derived parameters and forward integration.

UNLABELLED: We previously reported that a mathematical model could accurately predict steady-state road-cycling power when all the model parameters were known. Application of that model to competitive cycling has been limited by the need to obtain accurate parameter values, the non-steady-state nature of many cycling events, and because the validity of the model at maximal power has not been established. PURPOSE: We determined whether modeling parameters could be accurately determined during field trials and whether the model could accurately predict cycling speed during maximal acceleration using forward integration. METHODS: First, we quantified aerodynamic drag area of six cyclists using both wind tunnel and field trials allowing for these two techniques to be compared. Next, we determined the aerodynamic drag area of three world-class sprint cyclists using the field-test protocol. Track cyclists also performed maximal standing-start time trials, during which we recorded power and speed. Finally, we used forward integration to predict cycling speed from power-time data recorded during the maximal trials allowing us to compare predicted speed with measured speed. RESULTS: Field-based values of aerodynamic drag area (0.258 +/- 0.006 m) did not differ (P = 0.53) from those measured in a wind tunnel (0.261 +/- 0.006 m2). Forward integration modeling accurately predicted cycling speed (y = x, r2 = 0.989) over the duration of the standing-start sprints. CONCLUSIONS: Field-derived values for aerodynamic drag area can be equivalent to values derived from wind tunnel testing, and these values can be used to accurately predict speed even during maximal-power acceleration by world-class sprint cyclists. This model could be useful for assessing aerodynamic issues and for predicting how subtle changes in riding position, mass, or power output will influence cycling speed.