An improved methodology for estimating critical power from mean maximal power output data.

The aim of this study was to determine if Critical Power (CP) and W' can be estimated from mean maximal power output (MMP) data collected in cycling races. Data were collected from 13 under 23 professional cyclists (mean ± SD; age, 19.5 ± 1.1 y; body mass, 66.3 ± 5.0 kg; height, 180.0 ± 5.0 cm; CP, 5.7 ± 0.3 W · kg-1). Participants conducted a CP test in the field to determine CPTest and W'Test. MMP data were then collected in races for the subsequent 90 days. CP and W' were estimated from MMP values in two ways, using fixed MMP durations, 2, 5 and 12 min (CPFixed and W'Fixed), and via a novel filtering of second-by-second MMP data (CPFiltered and W'Filtered). CPFixed and CPFiltered were not significantly different from CPTest (Mean Difference (MD) 5 W and 7 W, respectively, p > 0.05). W'Fixed and W'Filtered were not significantly different from W'Test (MD 2.68 kJ and 0.89 kJ, respectively, p > 0.05). CPFixed and CPFiltered correlated significantly with CPTest (r = 0.872 and 0.922, respectively, p < 0.0001 for both). Neither W'Fixed nor W'Filtered correlated significantly with W'Test (p > 0.05). Both CPFixed and CPFiltered provide valid estimates of CPTest.; however, CPFiltered provides a better estimate.

The relationship between training characteristics and durability in professional cyclists across a competitive season.

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, V˙O2max: 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 (2MMPfresh, 5MMPfresh and 12MMPfresh) while the fatigued power profile consisted of power output values produced exclusively post 2000 kJ (2MMPfatigue 5MMPfatigue and 12MMPfatigue). Training characteristics were analysed to assess their influence on the power profiles. Absolute 5MMPfatigue, 12MMPfatigue and relative 12MMPfatigue were significantly lower in late-season compared with early- and mid-season (p < 0.05). The difference in absolute 12MMPfresh and 12MMPfatigue 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 2MMPfatigue (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 12MMPfatigue (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.HighlightsThe fatigued power profile varies throughout a competitive seasonThe difference between the fresh and fatigued power profiles is not fixed across a competitive seasonA tendency towards a polarized training intensity distribution is associated with an improvement in the fatigue power profile.

Predictors of cycling performance success: Traditional approaches and a novel method to assess performance capacity in U23 road cyclists.

OBJECTIVES: This study aimed to investigate predictors of cycling performance in U23 cyclists by comparing traditional approaches to a novel method - the compound score. Thirty male U23 cyclists (N = 30, age 20.1 ±â€¯1.1 yrs, body mass 69.0 ±â€¯6.9 kg, height 182.6 ±â€¯6.2 cm, V̇O2max 73.8 ±â€¯2.5 mL·kg-1·min-1) participated in this study. DESIGN: Power output information was derived from laboratory and field-testing during pre-season and mean maximal power outputs (MMP) from racing season. Absolute and relative 5-min MMP, 5-min MMP after 2000 kJ (MMP2000 kJ), allometric scaling and the compound score were compared to the race score and podium (top 3) performance during a competitive season. METHODS: Positive and negative predictive values were calculated for all significant performance variables for the likelihood of a podium performance. RESULTS: The absolute 5-min MMP of the field test revealed the highest negative predictive capacity (82.4%, p = 0.012) for a podium performance. The compound score of the 5-min MMP2000 kJ demonstrated the highest positive and average predictive capacity (83.3%, 78.0%, p = 0.007 - respectively). The multi-linear regression analysis revealed a significant predictive capacity between performance variables and the race score (R2 = 0.55, p = 0.015). CONCLUSIONS: Collectively the results of the present study reveal that the compound score, alongside absolute power, was able to predict the highest positive and average likelihood for a podium performance. These findings can help to better understand performance capacity from field data to predict future cycling success.

The Relationship between Physiological Characteristics and Durability in Male Professional Cyclists.

PURPOSE: This study aimed to determine if durability can be predicted from laboratory measures in a professional cycling population. METHODS: Data were collected from 10 professional cyclists (age = 19.2 ± 0.8 yr, body mass = 70.4 ± 5.5 kg, height = 182.9 ± 4.0 cm, body mass index = 21.0 ± 1.3 kg·m -2 , V̇O 2max = 74.4 ± 4.8 mL·kg -1 ·min -1 , critical power [CP] = 5.6 ± 0.6 W·kg -1 , W' = 23.7 ± 5.4 kJ). Participants completed a laboratory test and a CP test on two occasions. The second occasion was preceded by a novel fatiguing protocol, which consisted of five bouts of 8-min of exercise at 105%-110% of CP. CP in a fatigued state was expressed as a percentage of the fresh CP and coined delta CP (∆CP). The Pearson product correlation analysis was conducted to determine the relationship between laboratory-based measures and ∆CP. RESULTS: Significant positive relationships were found between ∆CP and relative peak power output ( r = 0.891, P < 0.001), relative maximum oxygen uptake ( r = 0.835, P = 0.003), relative power output at the second ventilatory threshold ( r = 0.738, P = 0.015), power output at the first ventilatory threshold ( r = 0.748, P = 0.013) and relative power output at the first ventilatory threshold ( r = 0.826, P = 0.003), gross efficiency at 300 W ( r = 0.869, P = 0.001), and at 200 W ( r = 0.792, P = 0.006). Significant negative relationships were found between ∆CP and carbohydrate oxidation at 200 W ( r = -0.702, P = 0.024). A multiple linear regression demonstrated that ∆CP can be predicted from laboratory measures ( R2 = 0.96-0.98, P < 0.001). CONCLUSIONS: These findings demonstrate the physiological determinants of durability in a professional cycling population.

Climbing Performance in U23 and Professional Cyclists during a Multi-stage Race.

The aim of this study was to analyze climbing performance across two editions of a professional multistage race, and assess the influence of climb category, prior workload, and intensity measures on climbing performance in U23 and professional cyclists. Nine U23 cyclists (age 20.8±0.9 years) and 8 professional cyclists (28.1±3.2 years) participated in this study. Data were divided into four types: overall race performance, climb category, climbing performance metrics (power output, ascent velocity, speed), and workload and intensity measures. Differences in performance metrics and workload and intensity measures between groups were investigated. Power output, ascent velocity, speed were higher in professionals than U23 cyclists for Cat 1 and Cat 2 (p≤0.001-0.016). Workload and intensity measures (Worktotal, Worktotal∙km-1, Elevationgain, eTRIMP and eTRIMP∙km-1) were higher in U23 compared to professionals (p=0.002-0.014). Climbing performance metrics were significantly predicted by prior workload and intensity measures for Cat 1 and 2 (R2=0.27-0.89, p≤0.001-0.030) but not Cat 3. These findings reveal that climbing performance in professional road cycling is influenced by climb categorization as well as prior workload and intensity measures. Combined, these findings suggest that Cat 1 and 2 climbing performance could be predicted from workload and intensity measures.

Power profiling and the power-duration relationship in cycling: a narrative review.

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.

Power Profiling, Workload Characteristics, and Race Performance of U23 and Professional Cyclists During the Multistage Race Tour of the Alps.

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.

Power Profiling in U23 Professional Cyclists During 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.

Training Characteristics and Power Profile of Professional U23 Cyclists throughout a Competitive Season.

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, TimeVT2) 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.