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 However, current reviews within this area are outdated and do not include a recent influx of new articles describing the demands of professional cycling races.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.
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Dajo Sanders and Teun van Erp
Dajo Sanders, David J. Spindler, and Jamie Stanley
Purpose: This case study aims to describe the multidisciplinary preparation of a multiple medal-winning Paralympic cyclist active in the C5 class. Specifically, it describes the 12-month preparation period toward the Tokyo 2020 Paralympic Games. Method: The participant (height 173 cm; weight approximately 63 kg) is active in the C5 para-cycling class (right arm impairment) and was preparing for the individual pursuit, road time trial, and mass-start race in the Tokyo Paralympic Games. The participant was supported by a multidisciplinary practitioner team focusing on multiple facets of athletic preparation. Morning resting heart rate (HR) and HR variability, as well as daily training data, were collected during the 12 months prior to Tokyo. Weekly and monthly trends in training, performance, and morning measures were analyzed. Training intensity zones were divided into zone 1 (<lactate threshold), zone 2(>lactate threshold, <critical power), and zone 3 (>critical power). Results: The participant won a silver (individual pursuit) and a bronze (time trial) medal at the Paralympic Games. Annual sums of volume and total work (in kilojoules) were, respectively, 1039 hours and 620,715 kJ. Analyzing all road sessions, 85% was spent in zone 1, 9% in zone 2, and 6% in zone 3. Physiological (eg, high training loads, hypoxic stimuli) and psychological stressors (ie, significant life events) were clearly reflected in morning HR and HR-variability responses. Conclusions: This case study demonstrates how a multidisciplinary team of specialist practitioners successfully prepared an elite Paralympic cyclist utilizing a holistic approach to training and health using data to manage allostatic load.
Dajo Sanders, Tony Myers, and Ibrahim Akubat
To evaluate training-intensity distribution using different intensity measures based on rating of perceived exertion (RPE), heart rate (HR), and power output (PO) in well-trained cyclists.
Fifteen road cyclists participated in the study. Training data were collected during a 10-wk training period. Training-intensity distribution was quantified using RPE, HR, and PO categorized in a 3-zone training-intensity model. Three zones for HR and PO were based around a 1st and 2nd lactate threshold. The 3 RPE zones were defined using a 10-point scale: zone 1, RPE scores 1–4; zone 2, RPE scores 5–6; zone 3, RPE scores 7–10.
Training-intensity distributions as percentages of time spent in zones 1, 2, and 3 were moderate to very largely different for RPE (44.9%, 29.9%, 25.2%) compared with HR (86.8%, 8.8%, 4.4%) and PO (79.5%, 9.0%, 11.5%). Time in zone 1 quantified using RPE was largely to very largely lower for RPE than PO (P < .001) and HR (P < .001). Time in zones 2 and 3 was moderately to very largely higher when quantified using RPE compared with intensity quantified using HR (P < .001) and PO (P < .001).
Training-intensity distribution quantified using RPE demonstrates moderate to very large differences compared with intensity distributions quantified based on HR and PO. The choice of intensity measure affects intensity distribution and has implications for training-load quantification, training prescription, and the evaluation of training characteristics.
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.
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.
Teun van Erp, Robert P. Lamberts, and Dajo Sanders
Purpose: This study evaluated the power profile of a top 5 result achieved in World Tour cycling races of varying types, namely: flat sprint finish, semi-mountain race with a sprint finish, semi-mountain race with uphill finish, and mountain races (MT). Methods: Power output data from 33 professional cyclists were collected between 2012 and 2019. This large data set was filtered so that it only included top 5 finishes in World Tour races (18 participants and 177 races). Each of these top 5 finishes were subsequently classified as flat sprint finish, semi-mountain race with uphill finish, semi-mountain race with a sprint finish, and MT based on set criteria. Maximal mean power output (MMP) for a wide range of durations (5 s to 60 min), expressed in both absolute (in Watts) and relative terms (in Watts per kilogram), were assessed for each race type. Result: Short-duration power outputs (<60 s), both in relative and in absolute terms, are of higher importance to be successful in flat sprint finish and semi-mountain race with a sprint finish. Longer-duration power outputs (≥3 min) are of higher importance to be successful in semi-mountain race with uphill finish and MT. In addition, relative power outputs of >10 minutes seem to be a key determining factor for success in MT. These race-type specific MMPs of importance (ie, short-duration MMPs for sprint finishes, longer-duration MMPs for races with more elevation gain) are performed at a wide range (80%–97%) of the cyclist’s personal best MMP. Conclusions: This study shows that the relative importance of certain points on the power–duration spectrum varies with different race types and provides insight into benchmarks for achieving a result in a World Tour cycling race.
Richard J. Taylor, Dajo Sanders, Tony Myers, Grant Abt, Celia A. Taylor, and Ibrahim Akubat
Purpose: To identify the dose-response relationship between measures of training load (TL) and changes in aerobic fitness in academy rugby union players. Method: Training data from 10 academy rugby union players were collected during a 6-wk in-season period. Participants completed a lactate-threshold test that was used to assess VO2max, velocity at VO2max, velocity at 2 mmol/L (lactate threshold), and velocity at 4 mmol/L (onset of lactate accumulation; vOBLA) as measures of aerobic fitness. Internal-TL measures calculated were Banister training impulse (bTRIMP), Edwards TRIMP, Lucia TRIMP, individualized TRIMP (iTRIMP), and session RPE (sRPE). External-TL measures calculated were total distance, PlayerLoad™, high-speed distance >15 km/h, very-high-speed distance >18 km/h, and individualized high-speed distance based on each player’s vOBLA. Results: A second-order-regression (quadratic) analysis found that bTRIMP (R 2 = .78, P = .005) explained 78% of the variance and iTRIMP (R 2 = .55, P = .063) explained 55% of the variance in changes in VO2max. All other HR-based internal-TL measures and sRPE explained less than 40% of variance with fitness changes. External TL explained less than 42% of variance with fitness changes. Conclusions: In rugby players, bTRIMP and iTRIMP display a curvilinear dose-response relationship with changes in maximal aerobic fitness.
Robert P. Lamberts, Teun van Erp, Dajo Sanders, Karen E. Welman, and Øyvind Sandbakk
Dajo Sanders, Grant Abt, Matthijs K.C. Hesselink, Tony Myers, and Ibrahim Akubat
To assess the dose-response relationships between different training-load methods and aerobic fitness and performance in competitive road cyclists.
Training data from 15 well-trained competitive cyclists were collected during a 10-wk (December–March) preseason training period. Before and after the training period, participants underwent a laboratory incremental exercise test with gas-exchange and lactate measures and a performance assessment using an 8-min time trial (8MT). Internal training load was calculated using Banister TRIMP, Edwards TRIMP, individualized TRIMP (iTRIMP), Lucia TRIMP (luTRIMP), and session rating of perceived exertion (sRPE). External load was measured using Training Stress Score (TSS).
Large to very large relationships (r = .54–.81) between training load and changes in submaximal fitness variables (power at 2 and 4 mmol/L) were observed for all training-load calculation methods. The strongest relationships with changes in aerobic fitness variables were observed for iTRIMP (r = .81 [95% CI .51–.93, r = .77 [95% CI .43–.92]) and TSS (r = .75 [95% CI .31–.93], r = .79 [95% CI .40–.94]). The strongest dose-response relationships with changes in the 8MT test were observed for iTRIMP (r = .63 [95% CI .17–.86]) and luTRIMP (r = .70 [95% CI .29–.89).
Training-load quantification methods that integrate individual physiological characteristics have the strongest dose-response relationships, suggesting this to be an essential factor in the quantification of training load in cycling.
Dajo Sanders, Mathieu Heijboer, Ibrahim Akubat, Kenneth Meijer, and Matthijs K. Hesselink
To assess if short-duration (5 to ~300 s) high-power performance can accurately be predicted using the anaerobic power reserve (APR) model in professional cyclists.
Data from 4 professional cyclists from a World Tour cycling team were used. Using the maximal aerobic power, sprint peak power output, and an exponential constant describing the decrement in power over time, a power-duration relationship was established for each participant. To test the predictive accuracy of the model, several all-out field trials of different durations were performed by each cyclist. The power output achieved during the all-out trials was compared with the predicted power output by the APR model.
The power output predicted by the model showed very large to nearly perfect correlations to the actual power output obtained during the all-out trials for each cyclist (r = .88 ± .21, .92 ± .17, .95 ± .13, and .97 ± .09). Power output during the all-out trials remained within an average of 6.6% (53 W) of the predicted power output by the model.
This preliminary pilot study presents 4 case studies on the applicability of the APR model in professional cyclists using a field-based approach. The decrement in all-out performance during high-intensity exercise seems to conform to a general relationship with a single exponential-decay model describing the decrement in power vs increasing duration. These results are in line with previous studies using the APR model to predict performance during brief all-out trials. Future research should evaluate the APR model with a larger sample size of elite cyclists.