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AB Purpose: Cold water immersion (CWI) may be beneficial for acute recovery from exercise, but it may impair long-term performance by attenuating the stimuli responsible for adaptation to training. We compared effects of CWI and passive rest on cycling performance during a simulated cycling grand tour. Methods: Thirty-four male endurance-trained competitive cyclists were randomized to CWI for four times per week for 15 min at 15[degrees]C or control (passive recovery) groups for 7 d of baseline training, 21 d of intensified training, and an 11-d taper. Criteria for completion of training and testing were satisfied by 10 cyclists in the CWI group (maximal aerobic power, 5.13 +/- 0.21 W[middle dot]kg-1; mean +/- SD) and 11 in the control group (5.01 +/- 0.41 W[middle dot]kg-1). Each week, cyclists completed a high-intensity interval cycling test and two 4-min bouts separated by 30 min. CWI was performed four times per week for 15 min at 15[degrees]C. Results: Between baseline and taper, cyclists in the CWI group had an unclear change in overall 4-min power relative to control (2.7% +/- 5.7%), although mean power in the second effort relative to the first was likely higher for the CWI group relative to control (3.0% +/- 3.8%). The change in 1-s maximum mean sprint power in the CWI group was likely beneficial compared with control (4.4% +/- 4.2%). Differences between groups for the 10-min time trial were unclear (-0.4% +/- 4.3%). Conclusion: Although some effects of CWI on performance were unclear, data from this study do not support recent speculation that CWI is detrimental to performance after increased training load in competitive cyclists

A nonlinear dynamic systems model has previously been proposed to explain pacing strategies employed during exercise.This study was conducted to examine the pacing strategies used under varying conditions during the cycle phase of an Ironman triathlon.The bicycles of six well-trained male triathletes were equipped with SRM power meters set to record power output, cadence, speed, and heart rate. The flat, three-lap, out-and-back cycle course, coupled with relatively consistent wind conditions (17-30 km x h(-1)), enabled comparisons to be made between three consecutive 60-km laps and relative wind direction (headwind vs tailwind).Participants finished the cycle phase (180 km) with consistently fast performance times (5 h, 11 +/- 2 min; top 10% of all finishers). Average power output (239 +/- 25 to 203 +/- 20 W), cadence (89 +/- 6 to 82 +/- 8 rpm), and speed (36.5 +/- 0.8 to 33.1 +/- 0.8 km x h(-1)) all significantly decreased with increasing number of laps (P0.05). These variables, however, were not significantly different between headwind and tailwind sections. The deviation (SD) in power output and cadence did not change with increasing number of laps; however, the deviations in torque (6.8 +/- 1.6 and 5.8 +/- 1.3 N x m) and speed (2.1 +/- 0.5 and 1.6 +/- 0.3 km x h(-1)) were significantly greater under headwind compared with tailwind conditions, respectively. The median power frequency tended to be lower in headwind (0.0480 +/- 0.0083) compared with tailwind (0.0531 +/- 0.0101) sections.These data show evidence that a nonlinear dynamic pacing strategy is used by well-trained triathletes throughout various segments and conditions of the Ironman cycle phase. Moreover, an increased variation in torque and speed was found in the headwind versus the tailwind condition.

MARTIN, J. C., A. S. GARDNER, M. BARRAS, and D. T. MARTIN. Modeling Sprint Cycling Using Field-Derived Parameters and Forward Integration. Med. Sci. Sports Exerc.,Vol. 38, No. 3, pp. 592–597, 2006. 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 T 0.006 m 2 ) did not differ (P = 0.53) from those measured in a wind tunnel (0.261 T 0.006 m 2 ). Forward integration modeling accurately predicted cycling speed (y = x, r 2 = 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.

Purpose: Although manufacturers of bicycle power monitoring devices SRM and Power Tap (PT) claim accuracy to within 2.5%, there are limited scientific data available in support. The purpose of this investigation was to assess the accuracy of SRM and PT under different conditions. Methods: First, 19 SRM were calibrated, raced for 11 months, and retested using a dynamic CALRIG (50-1000 W at 100 rpm). Second, using the same procedure, five PT were repeat tested on alternate days. Third, the most accurate SRM and PT were tested for the influence of cadence (60, 80, 100, 120 rpm), temperature (8 and 21degreesC) and time (1 h at similar to300 W) on accuracy. Finally, the same SRM and PT were downloaded and compared after random cadence and gear surges using the CALRIG and on a training ride. Results: The mean error scores for SRM and PT factory calibration over a range of 50-1000 W were 2.3 +/- 4.9% and -2.5 +/- 0.5%, respectively. A second set of trials provided stable results for 15 calibrated SRM after 11 months (-0.8 +/- 1.7%), and follow-up testing of all PT units confirmed these findings (-2.7 +/- 0.1%). Accuracy for SRM and PT was not largely influenced by time and cadence; however. power output readings were noticeably influenced by temperature (5.2% for SRM and 8.4% for PT). During field trials, SRM average and max power were 4.8% and 7.3% lower, respectively, compared with PT. Conclusions: When operated according to manufacturers instructions, both SRM and PT offer the coach, athlete, and sport scientist the ability to accurately monitor power output in the lab and the field. Calibration procedures matching performance tests (duration, power, cadence, and temperature) are, however, advised as the error associated with each unit may vary.

PURPOSE: We examined the effects of “living high, training low” on cycling maximal mean power for 4-min (MMP4min) and 30-min (MMP30min) ergometer tests in Australian National Team female road cyclists. METHODS: Five cyclists slept for 12 nights at a simulated altitude of 2650m in normobaric hypoxia (HIGH) (X ± SD age, mass, VO2 max = 25.5 ± 5.7 yrs; 59.1 ± 3.8 kg; 65.6 ± 5.6 ml·kg-1·min-1), while six team-mates pair-matched for performance slept in normobaric normoxia at 600m (CON) (25.9 ± 4.5 yrs; 60.4 ± 4.4 kg; 62.0 ± 2.2 ml·kg-1·min-1). HIGH and CON cyclists trained and raced as group throughout a 40-day camp (515–750 km·wk-1). The 4-min test was completed on D0, D17 and D31. The 30-min test was completed on D1, D18 and D32. After 16 d of controlled training, HIGH slept at simulated altitude for 12 consecutive nights from D18-D29. RESULTS: Prior to altitude treatment measurement of MMP4min (HIGH = 335 ± 22 W; CON = 324 ± 14 W) and MMP30min (HIGH = 264 ± 23 W; CON = 255 ± 15 W) were reliable (TEM MMP4min = 0.8%; MMP30min = 1.9%). Following altitude treatment MMP4min increased by 2.3% (95%CI= 0.8 to 3.7%) for HIGH and by 0.1% (95%CI= -1.4 to 1.3%) for CON. Interestingly, post altitude MMP30min had decreased by u1.1% (95%CI= -6.2 to 4.1%) for HIGH but increased by 2.4% (95%CI= -1.3 to 6.0%) for CON. Thus, the MMP30min:MMP4min ratio of 79% for both groups at baseline decreased for HIGH (76 ± 2%) after sleeping at altitude but improved for CON (82 ± 4%) after normal training. CONCLUSIONS: We observed that 1) both cycling MMP4min and MMP30min can be reliably quantified, 2) sleeping at moderate altitude for 12 nights can improve MMP4min but not MMP30min and 3) improvements in MMP4min can be observed within 48 hrs following the last night of altitude exposure.