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.

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, O_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

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; $\dot{\mathrm{V}}$O_2max, 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, Time_VT1-2 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 CP_test and W′_test. In addition, 2-, 5-, and 12-minute MMP during in-season were used to derive CP_field and W′_field. Results: There were no significant differences in absolute 2-, 5-, and 12-minute MMP, CP_field, 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 CP_field 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 CP_test and CP_field in early- and mid-season but not late-season. Bland–Altman plots and standard error of estimates showed good agreement between CP_test and in-season CP_field 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 CP_field in late-season. One pre-season and one in-season CP test are recommended to evaluate in-season CP_field and W′_field.

Background : The role of high-volume low-intensity training for enhancing endurance performance has gained growing interest in recent years. Specifically, so-called “zone 2 training” is currently receiving much attention, and many propose that this is the target intensity at which a large proportion of total endurance training should be performed. However, despite the popularity of this concept, there is no clear consensus among coaches, athletes, and scientists regarding the definition of zone 2 training. Purpose : This commentary summarizes the perspectives, experience, and knowledge of an expert panel of 14 applied sport scientists and professional coaches with the aim of providing insight and a basis for definitional consensus on zone 2 training. Moreover, potential training strategies at this intensity are proposed, and the expected physiological adaptations when exercising at this intensity and related research gaps are also discussed. Results : Experts reached consensus that zone 2 training should preferably be performed at intensities located immediately below the first lactate or ventilatory threshold through continuous, variable, or interval-type sessions. Furthermore, experts expected a broad range of central and peripheral adaptations from zone 2 training. These expected adaptations might not be unique to zone 2 and could also be induced with sessions performed at slightly higher and lower intensities. Conclusions : This commentary provides practical insight and unified criteria regarding the preferred intensity, duration, and session type for the optimization of zone 2 training based on the perspectives of acknowledged sport scientists and professional coaches.