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.

Emerging trends in technological innovations, data analysis and practical applications have facilitated the measurement of cycling power output in the field, leading to improvements in training prescription, performance testing and race analysis. This review aimed to critically reflect on power profiling strategies in association with the power-duration relationship in cycling, to provide an updated view for applied researchers and practitioners. The authors elaborate on measuring power output followed by an outline of the methodological approaches to power profiling. Moreover, the deriving a power-duration relationship section presents existing concepts of power-duration models alongside exercise intensity domains. Combining laboratory and field testing discusses how traditional laboratory and field testing can be combined to inform and individualize the power profiling approach. Deriving the parameters of power-duration modelling suggests how these measures can be obtained from laboratory and field testing, including criteria for ensuring a high ecological validity (e.g. rider specialization, race demands). It is recommended that field testing should always be conducted in accordance with pre-established guidelines from the existing literature (e.g. set number of prediction trials, inter-trial recovery, road gradient and data analysis). It is also recommended to avoid single effort prediction trials, such as functional threshold power. Power-duration parameter estimates can be derived from the 2 parameter linear or non-linear critical power model: P(t) = W′/t + CP (W′—work capacity above CP; t—time). Structured field testing should be included to obtain an accurate fingerprint of a cyclist’s power profile.

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: The aim of this study was to investigate changes in the power profile of U23 professional cyclists during a competitive season based on maximal mean power output (MMP) and derived critical power (CP) and work capacity above CP (W′) obtained during training and racing. Methods: A total of 13 highly trained U23 professional cyclists (age = 21.1 [1.2] y, maximum oxygen consumption = 73.8 [1.9] mL·kg–1·min–1) participated in this study. The cycling season was split into pre-season and in-season. In-season was divided into early-, mid-, and late-season periods. During pre-season, a CP test was completed to derive CPtest and W′test. In addition, 2-, 5-, and 12-minute MMP during in-season were used to derive CPfield and W′field. Results: There were no significant differences in absolute 2-, 5-, and 12-minute MMP, CPfield, and W′field between in-season periods. Due to changes in body mass, relative 12-minute MMP was higher in late-season compared with early-season (P = .025), whereas relative CPfield was higher in mid- and late-season (P = .031 and P = .038, respectively) compared with early-season. There was a strong correlation (r = .77–.83) between CPtest and CPfield in early- and mid-season but not late-season. Bland–Altman plots and standard error of estimates showed good agreement between CPtest and in-season CPfield but not between W′test and W′field. Conclusion: These findings reveal that the power profile remains unchanged throughout the in-season, except for relative 12-minute MMP and CPfield in late-season. One pre-season and one in-season CP test are recommended to evaluate in-season CPfield and W′field.

This study investigated the influence of training characteristics on the fatigued power profile in professional cyclists. Data was collected from 30 under 23 professional cyclists (age: 20.1 ± 1.1 years, body mass: 69 ± 6.9 kg, height: 182.6 ± 6.2 cm, VO 2max : 73.8 ± 2.5 mL•kg −1 •min −1 , CP: 5.48 ± 0.38 W•kg −1 , W´: 17.83 ± 3.57 kJ) across a competitive season and collated in to 3 periods: early-, mid-and late-season. Two power profiles (fresh and fatigued) were created from absolute (W) and relative (W•kg −1 ) 2-, 5-, and 12-min maximal mean power outputs. The fresh power profile consisted exclusively of power output values produced prior to 2000 kJ work (2MMP fresh , 5MMP fresh and 12MMP fresh ) while the fatigued power profile consisted of power output values produced exclusively post 2000 kJ (2MMP fatigue 5MMP fatigue and 12MMP fatigue ). Training characteristics were analysed to assess their influence on the power profiles. Absolute 5MMP fatigue , 12MMP fatigue and relative 12MMP fatigue were significantly lower in late-season compared with early-and mid-season (p < 0.05). The difference in absolute 12MMP fresh and 12MMP fatigue was significantly greater in late than in early-and mid-season. A significant relationship was found between training time below the first ventilatory threshold (Time < VT1) and improvements in absolute and relative 2MMP fatigue (r = 0.43 p = 0.018 and r = 0.376 p = 0.04 respectively); and between a shift towards a polarized training intensity distribution and improvements in absolute and relative 12MMP fatigue (r = 0.414 p = 0.023 for both) between subsequent periods. In conclusion, there is greater variability in the fatigue power profile across a competitive season than the fresh power profile. Highlights. The fatigued power profile varies throughout a competitive season . The difference between the fresh and fatigued power profiles is not fixed across a competitive season . A tendency towards a polarized training intensity distribution is associated with an improvement in the fatigue power profile KEYWORDS