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Predicting Cycling Performance in Trained to Elite Male and Female Cyclists

Robert P. Lamberts

In high-performance cycling, it is important to maintain a healthy balance between training load and recovery. Recently a new submaximal cycle test, known as the Lamberts and Lambert Submaximal Cycle Test (LSCT), has been shown to be able to accurately predict cycling performance in 15 well-trained cyclists. The aim of this study was to determine the predictive value of the LSCT in 102 trained to elite cyclists (82 men and 20 women). All cyclists performed an LSCT test followed by a peak-power-output (PPO) test, which included respiratory-gas analysis for the determination of maximal oxygen consumption (VO2max). They then performed the LSCT test followed by a 40-km time trial (TT) 72 h later. Average power output during the 3 stages of the LSCT increased from 31%, 60%, and 79% of PPO, while the ratings of perceived exertion increased from 8 to 13 to 16. Very good relationships were found between actual and LSCT-predicted PPO (r = .98, 95%CI: .97–.98, P < .0001), VO2max (r = .96, 95%CI: .97–.99, P < .0001) and 40-km-TT time (r = .98, 95%CI: .94–.97, P < .0001). No gender differences were found when predicting cycling performance from the LSCT (P = .95). The findings of this study show that the LSCT is able to accurately predict cycling performance in trained to elite male and female cyclists and potentially can be used to prescribe and fine-tune training prescription in cycling.

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Monitoring Progress in Professional Cycling: From Submaximal Testing to the Use of Field Data

Robert P. Lamberts and Teun van Erp

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Performance Characteristics of TOP5 Versus NOT-TOP5 Races in Female Professional Cycling

Teun van Erp and Robert P. Lamberts

Introduction: Maximal mean power output (MMP) is commonly used to describe the demands and performances of races in professional male cycling. In the female professional cyclist domain, however, there is limited knowledge regarding MMPs in races. Therefore, this study aimed to describe MMPs in female professional cycling races while investigating differences between TOP5 and NOT-TOP5 races. Methods: Race data (N = 1324) were collected from 14 professional female cyclists between 2013 and 2019. Races were categorized as TOP5 or NOT-TOP5. The MMPs were consequently determined over a range of different time frames (5 s to 60 min). To provide these MMPs with additional context, 2 factors were determined: when these MMPs were attained in a race (based on duration and kilojoules spent [kJspent·kg−1]) and these MMPs relative to the cyclist’s season’s best MMP (MMP%best). Results: Short-duration power outputs (≤1 min) were higher in TOP5 races compared with NOT-TOP5 races. In addition, the timing (both duration and kJspent·kg−1) of all MMPs was later and after more workload in the race in TOP5 compared with NOT-TOP5 races. In contrast, no difference in MMP%best was noted between TOP5 and NOT-TOP5 races. Conclusions: TOP5 races in female cycling are presented with higher short-duration MMPs (≤1 min) when compared with NOT-TOP5 races, and cyclists were able to reach a higher percentage of their seasonal best MMP when they were able to finish TOP5. In addition, these MMPs are performed later and after more kJspent·kg−1 in TOP5 versus NOT-TOP5 races, which confirms the importance of “fatigue resistance” in professional (female) cycling.

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Standardized Versus Customized High-Intensity Training: Effects on Cycling Performance

Benoit Capostagno, Michael I. Lambert, and Robert P. Lamberts


To determine whether a submaximal cycling test could be used to monitor and prescribe high-intensity interval training (HIT).


Two groups of male cyclists completed 4 HIT sessions over a 2-wk period. The structured-training group (SG; n = 8, VO2max = 58.4 ± 4.2 mL · min−1 · kg−1) followed a predetermined training program while the flexible-training group (FG; n = 7, VO2max = 53.9 ± 5.0 mL · min−1 · kg−1) had the timing of their HIT sessions prescribed based on the data of the Lamberts and Lambert Submaximal Cycle Test (LSCT).


Effect-size calculations showed large differences in the improvements in 40-km time-trial performance after the HIT training between SG (8 ± 45 s) and FG (48 ± 42 s). Heart-rate recovery, monitored during the study, tended to increase in FG and remain unchanged in SG.


The results of the current study suggest that the LSCT may be a useful tool for coaches to monitor and prescribe HIT.

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A Systematic Review of Submaximal Cycle Tests to Predict, Monitor, and Optimize Cycling Performance

Benoit Capostagno, Michael I. Lambert, and Robert P. Lamberts

Finding the optimal balance between high training loads and recovery is a constant challenge for cyclists and their coaches. Monitoring improvements in performance and levels of fatigue is recommended to correctly adjust training to ensure optimal adaptation. However, many performance tests require a maximal or exhaustive effort, which reduces their real-world application. The purpose of this review was to investigate the development and use of submaximal cycling tests that can be used to predict and monitor cycling performance and training status. Twelve studies met the inclusion criteria, and 3 separate submaximal cycling tests were identified from within those 12. Submaximal variables including gross mechanical efficiency, oxygen uptake (VO2), heart rate, lactate, predicted time to exhaustion (pTE), rating of perceived exertion (RPE), power output, and heart-rate recovery (HRR) were the components of the 3 tests. pTE, submaximal power output, RPE, and HRR appear to have the most value for monitoring improvements in performance and indicate a state of fatigue. This literature review shows that several submaximal cycle tests have been developed over the last decade with the aim to predict, monitor, and optimize cycling performance. To be able to conduct a submaximal test on a regular basis, the test needs to be short in duration and as noninvasive as possible. In addition, a test should capture multiple variables and use multivariate analyses to interpret the submaximal outcomes correctly and alter training prescription if needed.

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Demands of the Tour de France: A Case Study of a World-Class Sprinter (Part I)

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.

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Sprint Tactics in the Tour de France: A Case Study of a World-Class Sprinter (Part II)

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.

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Power Profile of Top 5 Results in World Tour Cycling Races

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.

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Can the Lamberts and Lambert Submaximal Cycle Test Reflect Overreaching in Professional Cyclists?

Lieselot Decroix, Robert P. Lamberts, and Romain Meeusen

Context: The Lamberts and Lambert Submaximal Cycle Test (LSCT) consists of 3 stages during which cyclists cycle for 6 min at 60%, 6 min at 80%, and 3 min at 90% of their maximal heart rate, followed by 1-min recovery. Purpose: To determine if the LSCT is able to reflect a state of functional overreaching in professional female cyclists during an 8-d training camp and the following recovery days. Methods: Six professional female cyclists performed an LSCT on days 1, 5, and 8 of the training camp and 3 d after the training camp. During each stage of the LSCT, power output and rating of perceived exertion (RPE) were determined. Training diaries and Profile of Mood States (POMS) were also completed. Results: At the middle and the end of the training camp, increased power output during the 2nd and 3rd stages of the LSCT was accompanied with increased RPE during these stages and/or the inability to reach 90% of maximal heart rate. All athletes reported increased feelings of fatigue and muscle soreness, while changes in energy balance, calculated from the POMS, were less indicative of a state of overreaching. After 3 d of recovery, all parameters of the LSCT returned to baseline, indicating a state of functional overreaching during the training camp. Conclusion: The LSCT is able to reflect a state of overreaching in elite professional female cyclists during an 8-d training camp and the following recovery days.

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Physiological Indicators of Trail Running Performance: A Systematic Review

Simon J. de Waal, Josu Gomez-Ezeiza, Rachel E. Venter, and Robert P. Lamberts

Purpose: To provide a systematic overview of physiological parameters used to determine the training status of a trail runner and how well these parameters correlate with real-world trail running performance. Method: An electronic literature search of the PubMed and Scopus digital databases was performed. Combinations of the terms “trail run” or “trail runner” or “trail running” and “performance” were used as search terms. Seven studies met the inclusion criteria. Results: Trail running performance most commonly correlated (mean [SD]) with maximal aerobic capacity (71%; r = −.50 [.32]), lactate threshold (57%; r = −.48 [.28]), velocity at maximal aerobic capacity (43%; r = −.68 [.08]), running economy (43%; r = −.31 [.22]), body fat percentage (43%; r = .55 [.21]), and age (43%; r = .52 [.14]). Regression analyses in 2 studies were based on a single variable predicting 48% to 60% of performance variation, whereas 5 studies included multiple variable regression analyses predicting 48% to 99% of performance variation. Conclusions: Trail running performance is multifaceted. The classic endurance model shows a weaker association with performance in trail running than in road running. Certain variables associated with trail running research (such as testing procedures, race profiles, and study participants) hinder the execution of comparative studies. Future research should employ trail-specific testing protocols and clear, objective descriptions of both the race profile and participants’ training status.