Purpose: The aim of this study was to compare the power profile, internal and external workloads, and racing performance between U23 and professional cyclists and between varying rider types across 2 editions of a professional multistage race. Methods: Nine U23 cyclists from a Union Cycliste Internationale “Continental Team” (age 20.8 [0.9] y; body mass 71.2 [6.3] kg) and 8 professional cyclists (28.1 [3.2] y; 63.0 [4.6] kg) participated in this study. Rider types were defined as all-rounders, general classification (GC) riders, and domestiques. Data were collected during 2 editions of a 5-day professional multistage race and split into the following 4 categories: power profile, external and internal workloads, and race performance. Results: The professional group, including domestiques and GC riders, recorded higher relative power profile values after certain amounts of total work (1000–3000 kJ) than the U23 group or all-rounders (P ≤ .001–.049). No significant differences were found for external workload measures between U23 and professional cyclists, nor among rider types. Internal workloads were higher in U23 cyclists and all-rounders (P ≤ .001–.043) compared with professionals, domestiques, and GC riders, respectively. The power profile significantly predicted percentage general classification and Union Cycliste Internationale points (R2 = .90–.99), whereas external and internal workloads did not. Conclusion: These findings reveal that the power profile represents a practical tool to discriminate between professionals and U23 cyclists as well as rider types. The power profile after 1000 to 3000 kJ of total work could be used by practitioners to evaluate the readiness of U23 cyclists to move into the professional ranks, as well as differentiate between rider types.

Background: The purpose of this study was to investigate differences in the power profile derived from training and racing, the training characteristics across a competitive season and the relationships between training and power profile in U23 professional cyclists. Methods: Thirty male U23 professional cyclists (age, 20.0 ± 1.0 years; weight, 68.9 ± 6.9 kg; V˙O2max, 73.7 ± 2.5 mL·kg−1·min−1) participated in this study. The cycling season was split into pre-, early-, mid- and late-season periods. Power data 2, 5, 12 min mean maximum power (MMP), critical power (CP) and training characteristics (Hours, Total Work, eTRIMP, Work·h−1, eTRIMP·h−1, Time<VT1, TimeVT1-2 and Time>VT2) were recorded for each period. Power profiles derived exclusively from either training or racing data and training characteristics were compared between periods. The relationships between the changes in training characteristics and changes in the power profile were also investigated. Results: The absolute and relative power profiles were higher during racing than training at all periods (p ≤ 0.001–0.020). Training characteristics were significantly different between periods, with the lowest values in pre-season followed by late-season (p ≤ 0.001–0.040). Changes in the power profile between early- and mid-season significantly correlated with the changes in training characteristics (p < 0.05, r = −0.59 to 0.45). Conclusion: These findings reveal that a higher power profile was recorded during racing than training. In addition, training characteristics were lowest in pre-season followed by late-season. Changes in training characteristics correlated with changes in the power profile in early- and mid-season, but not in late-season. Practitioners should consider the influence of racing on the derived power profile and adequately balance training programs throughout a competitive season.

Purpose: To determine aerobic and anaerobic demands of mountain bike cross-country racing. Methods: Twelve elite cyclists (7 males;  = 73.8 [2.6] mL·min-1·kg−1, maximal aerobic power [MAP] = 370 [26] W, 5.7 [0.4] W·kg−1, and 5 females;  = 67.3 [2.9] mL·min−1·kg−1, MAP = 261 [17] W, 5.0 [0.1] W·kg−1) participated over 4 seasons at several (119) international and national races and performed laboratory tests regularly to assess their aerobic and anaerobic performance. Power output, heart rate, and cadence were recorded throughout the races. Results: The mean race time was 79 (12) minutes performed at a mean power output of 3.8 (0.4) W·kg−1; 70% (7%) MAP (3.9 [0.4] W·kg−1 and 3.6 [0.4] W·kg−1 for males and females, respectively) with a cadence of 64 (5) rev·min−1 (including nonpedaling periods). Time spent in intensity zones 1 to 4 (below MAP) were 28% (4%), 18% (8%), 12% (2%), and 13% (3%), respectively; 30% (9%) was spent in zone 5 (above MAP). The number of efforts above MAP was 334 (84), which had a mean duration of 4.3 (1.1) seconds, separated by 10.9 (3) seconds with a mean power output of 7.3 (0.6) W·kg−1 (135% [9%] MAP). Conclusions: These findings highlight the importance of the anaerobic energy system and the interaction between anaerobic and aerobic energy systems. Therefore, the ability to perform numerous efforts above MAP and a high aerobic capacity are essential to be competitive in mountain bike cross-country.

To assess the validity and reliability of the Garmin Vector against the SRM power meter, 6 cyclists completed 3 continuous trials at power outputs from 100-300 W at 50-90 rev·min and a 5-min time trial in laboratory and field conditions. In field conditions only, a 30-s sprint was performed. Data were compared with paired samples t-tests, with the 95% limits of agreement (LoA) and the typical error. Reliability was calculated as the coefficient of variation (CV). There was no significant difference between the devices in power output in laboratory (p=0.245) and field conditions (p=0.312). 1-s peak power was significantly different between the devices (p=0.043). The LoA were ~1.0±5.0 W and ~0.5±0.5 rev·min in both conditions. The LoA during the 30-s sprint was 6.3±38.9 W and for 1-s peak power it was 18.8±17.1 W. The typical error for power output was 2.9%, while during sprint cycling it was 7.4% for 30-s and 2.7% for 1-s peak power. For cadence, the typical error was below 1.0%. The mean CVs were ~1.0% and ~3.0% for the SRM and Garmin, respectively. These findings suggest, that the Garmin Vector is a valid alternative for training. However, during sprint cycling there is lower agreement with the SRM power meter. Both devices provide good reliability (CV<3.0%).

These findings support previous laboratory-based studies demonstrating reductions in GE with increasing cadence and gradient that might be attributed to changes in muscle-activity pattern.