Background: A variety of intensity, load, and performance measures (eg, “power profile”) have been used to characterize the demands of professional cycling races with differing stage types. An increased understanding of the characteristics of these races could provide valuable insight for practitioners toward the design of training strategies to optimally prepare for these demands. However, current reviews within this area are outdated and do not include a recent influx of new articles describing the demands of professional cycling races. Purpose: To provide an updated overview of the intensity and load demands and power profile of professional cycling races. Typically adopted measures are introduced and their results summarized. Conclusion: There is a clear trend in the research that stage type significantly influences the intensity, load, and power profile of races with more elevation gain typically resulting in a higher intensity and load and longer-duration power outputs (ie, >10 min). Flat and semimountainous stages are characterized by higher maximal mean power outputs over shorter durations (ie, <2 min). Furthermore, single-day races tend to have a higher (daily) intensity and load compared with stages within multiday races. Nevertheless, while the presented mean (grouped) data provide some indications on the demands of these races and differences between varying competition elements, a limited amount of research is available describing the “race-winning efforts” in these races, and this is proposed as an important area for future research. Finally, practitioners should consider the limitations of each metric individually, and a multivariable approach to analyzing races is advocated.
Dajo Sanders and Teun van Erp
Teun van Erp, Marcel Kittel, and Robert P. Lamberts
Purpose: To describe the performance and tactical sprint characteristics of a world-class sprinter competing in the Tour de France. In addition, differences in the sprint tactics of 2 teams and won versus lost sprints are highlighted. Method: Power output (PO) and video footage of 21 sprints were analyzed. Position in the peloton and number of teammates supporting the sprinter at different times before the finish line together with PO for different time intervals were determined. Sprints were classified as team Shimano (2013–2014) and team Quick-step (2016–2017), as well as won or lost. Results: The sprinter was highly successful, winning 14 out of the 21 sprints. At time intervals 10 to 5, 3 to 2, and 1.5 to 1 minute, POs were significantly lower in team Quick-step compared with team Shimano, but the sprinter was positioned further away from the front at 10, 2, 1.5, 1, and 0.5 minutes at team Quick-step compared with team Shimano. The PO was higher at time interval 0.5 to 0.25 minutes before the finish line with team Quick-step when compared with team Shimano. The position of the sprinter in the peloton in lost sprints was further away from the front at 0.5 minutes before the finish compared with won sprints, while no differences were noted for PO and the number of teammates between won and lost sprints. Conclusions: Differences in sprint tactics (Shimano vs Quick-step) influence the PO and position in the peloton during the sprint preparation. In addition, the position at 0.5 minutes before the finish line influences the outcome (won or lost) of the sprint.
Teun van Erp, Marcel Kittel, and Robert P. Lamberts
Purpose: To describe the intensity, load, and performance characteristics of a world-class sprinter competing in the Tour de France (TdF). Method: Power output (PO) data were collected from 4 editions of the TdF (2013, 2014, 2016, and 2017) and analyzed. Load, intensity distribution in 5 PO zones, and the maximal mean PO for multiple durations were quantified. Stages were divided in accordance with the 4 different editions of the TdF, as well as the 4 different stage types, that is, flat (FLAT), semimountainous (SMT), mountain (MT), and (team) time trials. In addition, based on their location within the stage, mountain passes were further classified as BEGINNING, MIDDLE, or END of the stage. Results: No differences in load, intensity, and performance characteristics were found when the 4 editions of the TdF were compared. Time trials were associated with higher intensities but a lower load compared to the other stage types. MT showed higher load and intensity values compared to FLAT and SMT stages. FLAT stages were higher in short maximal mean PO (≤1 min), whereas MT stages showed higher longer endurance maximal mean PO values (≥20 min). In addition, mountain passes situated at the BEGINNING of the stage were completed with a higher PO, cadence, and speed compared with mountain passes situated at the END. Conclusions: A world-class sprinter sustains a higher load and spends more time in the high-intensity zones when competing in the TdF than previously reported values suggested. To finish the MT stages as efficiently as possible, sprinters adopt a reverse pacing strategy.
Teun van Erp, Carl Foster, and Jos J. de Koning
Purpose: The relationship between various training-load (TL) measures in professional cycling is not well explored. This study investigated the relationship between mechanical energy spent (in kilojoules), session rating of perceived exertion (sRPE), Lucia training impulse (LuTRIMP), and training stress score (TSS) in training, races, and time trials (TT). Methods: For 4 consecutive years, field data were collected from 21 professional cyclists and categorized as being collected in training, racing, or TTs. Kilojoules (kJ) spent, sRPE, LuTRIMP, and TSS were calculated, and the correlations between the various TLs were made. Results: 11,655 sessions were collected, from which 7596 sessions had heart-rate data and 5445 sessions had an RPE score available. The r between the various TLs during training was almost perfect. The r between the various TLs during racing was almost perfect or very large. The r between the various TLs during TTs was almost perfect or very large. For all relationships between TSS and 1 of the other measurements of TL (kJ spent, sRPE, and LuTRIMP), a significant different slope was found. Conclusion: kJ spent, sRPE, LuTRIMP, and TSS all have a large or almost perfect relationship with each other during training, racing, and TTs, but during racing, both sRPE and LuTRIMP have a weaker relationship with kJ spent and TSS. Furthermore, the significant different slope of TSS vs the other measurements of TL during training and racing has the effect that TSS collected in training and road races differs by 120%, whereas the other measurements of TL (kJ spent, sRPE, and LuTRIMP) differ by only 73%, 67%, and 68%, respectively.
Teun van Erp, Dajo Sanders, and Jos J. de Koning
Purpose: To describe the training intensity and load characteristics of professional cyclists using a 4-year retrospective analysis. Particularly, this study aimed to describe the differences in training characteristics between men and women professional cyclists. Method: For 4 consecutive years, training data were collected from 20 male and 10 female professional cyclists. From those training sessions, heart rate, rating of perceived exertion, and power output (PO) were analyzed. Training intensity distribution as time spent in different heart rate and PO zones was quantified. Training load was calculated using different metrics such as Training Stress Score, training impulse, and session rating of perceived exertion. Standardized effect size is reported as Cohen’s d. Results: Small to large higher values were observed for distance, duration, kilojoules spent, and (relative) mean PO in men’s training (d = 0.44–1.98). Furthermore, men spent more time in low-intensity zones (ie, zones 1 and 2) compared with women. Trivial differences in training load (ie, Training Stress Score and training impulse) were observed between men’s and women’s training (d = 0.07–0.12). However, load values expressed per kilometer were moderately (d = 0.67–0.76) higher in women compared with men’s training. Conclusions: Substantial differences in training characteristics exist between male and female professional cyclists. Particularly, it seems that female professional cyclists compensate their lower training volume, with a higher training intensity, in comparison with male professional cyclists.
Dajo Sanders, Teun van Erp, and Jos J. de Koning
Purpose: To provide a retrospective analysis of a large competition database describing the intensity and load demands of professional road-cycling races, highlighting the differences between men’s and women’s races. Methods: In total, 20 male and 10 female professional cyclists participated in this study. During 4 consecutive years, heart rate, rating of perceived exertion, and power-output data were collected during both men’s (n = 3024) and women’s (n = 667) professional races. Intensity distribution in 5 heart-rate zones was quantified. Competition load was calculated using different metrics, including Training Stress Score (TSS), training impulse (TRIMP), and session rating of perceived exertion. Standardized effect size is reported as Cohen d. Results: Large to very large higher values (d = 1.36–2.86) were observed for distance, duration, total work (in kilojoules), and mean power output in men’s races. Time spent in high-intensity heart-rate zones (ie, zones 4 and 5) was largely higher in women’s races (d = 1.38–1.55) than in men’s races. Small higher loads were observed in men’s races quantified using TSS (d = 0.53) and TRIMP (d = 0.23). However, load metrics expressed per kilometer were large to very largely higher in women’s races for TSS·km–1 (d = 1.50) and TRIMP·km–1 (d = 2.31). Conclusions: Volume and absolute load are higher in men’s races, whereas intensity and time spent in high-intensity zones is higher in women’s races. Coaches and practitioners should consider these differences in demands in the preparation of professional road cyclists.
Jos J de Koning, Teun van Erp, Rob Lamberts, Stephen Cheung, and Dionne Noordhof
Teun van Erp, Marco Hoozemans, Carl Foster, and Jos J. de Koning
Purpose: A valid measure for training load (TL) is an important tool for cyclists, trainers, and sport scientists involved in professional cycling. The aim of this study was to explore the influence of exercise intensity on the association between kilojoules (kJ) spent and different measures of TL to arrive at valid measures of TL. Methods: Four years of field data were collected from 21 cyclists of a professional cycling team, including 11,716 training and race sessions. kJ spent was obtained from power output measurements, and others TLs were calculated based on the session rating of perceived exertion (sRPE), heart rate (Lucia training impulse [luTRIMP]), and power output (training stress score [TSS]). Exercise intensity was expressed by the intensity factor (IF). To study the effect of exercise intensity on the association between kJ spent and various other TLs (sRPE, luTRIMP, and TSS), data from low- and high-intensity sessions were subjected to regression analyses using generalized estimating equations. Results: This study shows that the IF is significantly different for training and race sessions (0.59 [0.03] vs 0.73 [0.03]). Significant regression coefficients show that kJ spent is a good predictor of sRPE, and luTRIMP, as well as TSS. However, IF does not influence the associations between kJ spent and sRPE and luTRIMP, while the association with TSS is different when sessions are done with low or high IF. Conclusion: It seems that the TSS reacts differently to exercise intensity than sRPE and luTRIMP. A possible explanation could be the quadratic relation between IF and TSS.
Blaine E. Arney, Reese Glover, Andrea Fusco, Cristina Cortis, Jos J. de Koning, Teun van Erp, Salvador Jaime, Richard P. Mikat, John P. Porcari, and Carl Foster
Purpose: The session rating of perceived exertion (sRPE) is a well-accepted method of monitoring training load in athletes in many different sports. It is based on the category-ratio (0–10) RPE scale (BORG-CR10) developed by Borg. There is no evidence how substitution of the Borg 6–20 RPE scale (BORG-RPE) might influence the sRPE in athletes. Methods: Systematically training, recreational-level athletes from a number of sport disciplines performed 6 randomly ordered, 30-min interval-training sessions, at intensities based on peak power output (PPO) and designed to be easy (50% PPO), moderate (75% PPO), or hard (85% PPO). Ratings of sRPE were obtained 30 min postexercise using either the BORG-CR10 or BORG-RPE and compared for matched exercise conditions. Results: The average percentage of heart-rate reserve was well correlated with sRPE from both BORG-CR10 (r = .76) and BORG-RPE (r = .69). The sRPE ratings from BORG-CR10 and BORG-RPE were very strongly correlated (r = .90) at matched times. Conclusions: Although producing different absolute numbers, sRPE derived from either the BORG-CR10 or BORG-RPE provides essentially interchangeable estimates of perceived exercise training intensity.
Carl Foster, Daniel Boullosa, Michael McGuigan, Andrea Fusco, Cristina Cortis, Blaine E. Arney, Bo Orton, Christopher Dodge, Salvador Jaime, Kim Radtke, Teun van Erp, Jos J. de Koning, Daniel Bok, Jose A. Rodriguez-Marroyo, and John P. Porcari
The session rating of perceived exertion (sRPE) method was developed 25 years ago as a modification of the Borg concept of rating of perceived exertion (RPE), designed to estimate the intensity of an entire training session. It appears to be well accepted as a marker of the internal training load. Early studies demonstrated that sRPE correlated well with objective measures of internal training load, such as the percentage of heart rate reserve and blood lactate concentration. It has been shown to be useful in a wide variety of exercise activities ranging from aerobic to resistance to games. It has also been shown to be useful in populations ranging from patients to elite athletes. The sRPE is a reasonable measure of the average RPE acquired across an exercise session. Originally designed to be acquired ∼30 minutes after a training bout to prevent the terminal elements of an exercise session from unduly influencing the rating, sRPE has been shown to be temporally robust across periods ranging from 1 minute to 14 days following an exercise session. Within the training impulse concept, sRPE, or other indices derived from sRPE, has been shown to be able to account for both positive and negative training outcomes and has contributed to our understanding of how training is periodized to optimize training outcomes and to understand maladaptations such as overtraining syndrome. The sRPE as a method of monitoring training has the advantage of extreme simplicity. While it is not ideal for the precise recording of the details of the external training load, it has large advantages relative to evaluating the internal training load.