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

Abstract

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 (Work_total, Work_total∙km^-1, Elevation_gain, 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.

Key words

analysis - competition - endurance - uphill

Publication History

Received: 15 December 2020

Accepted: 25 May 2021

Article published online:
15 July 2021

• References

• 1 Pinot J, Grappe F. A six-year monitoring case study of a top-10 cycling Grand Tour finisher. J Sports Sci 2015; 33: 907-914.
• 2 Jeukendrup AE, Craig NP, Hawley JA. The bioenergetics of world class cycling. J Sci Med Sport 2000; 3: 414-433.
• 3 Giorgi A, Vicini M, Pollastri L. et al. Bioimpedance patterns and bioelectrical impedance vector analysis (BIVA) of road cyclists. J Sports Sci 2018; 36: 2608-2613
• 4 Nimmerichter A. Elite Youth Cycling. 1st Edit. London: Routledge; 2018
• 5 Impellizzeri FM, Ebert T, Sassi A. et al. Level ground and uphill cycling ability in elite female mountain bikers and road cyclists. Eur J Appl Physiol. 2008 102. 335-341
• 6 Bell PG, Furber MJW, Van Someren KA. et al. The physiological profile of a multiple tour de france winning cyclist. Med Sci Sport Exerc 2017; 49: 115-123.
• 7 Lamberts RP. Predicting cycling performance in trained to elite male and female cyclists. Int J Sports Physiol Perform 2014; 9: 610-614
• 8 Pinot J, Grappe F. The record power profile to assess performance in elite cyclists. Int J Sports Med 2011; 32: 839-844.
• 9 van Erp T, Foster C, de Koning JJ. Relationship between various training-load measures in elite cyclists during training, road races, and time trials. Int J Sports Physiol Perform 2019; 14: 2017-2022.
• 10 Van Erp T, Sanders D, De Koning JJ. Training characteristics of male and female professional road cyclists: A 4-year retrospective analysis. Int J Sport Physiol Perform 2019; Online ahead of print.
• 11 Zapico AG, Calderon FJ, Benito PJ. et al. Evolution of physiological and haematological parameters with training load in elite male road cyclists: a longitudinal study. J Sports Med Phys Fitness 2007; 47: 191-196
• 12 Pinot J, Grappe F. Determination of Maximal Aerobic Power from the Record Power Profile to improve cycling training. Journal of Science and Cycling 2014; 3: 26-31
• 13 Ebert TR, Martin DT, Stephens B. et al. Power output during a professional men’s road-cycling tour. Int J Sports Physiol Perform 2006; 1: 324-335.
• 14 Padilla S, Mujika I, Orbañanos J. et al. Exercise intensity and load during mass-start stage races in professional road cycling. Med Sci Sports Exerc 2001; 33: 796-802.
• 15 Padilla S, Mujika I, Santisteban J. et al. Exercise intensity and load during uphill cycling in professional 3-week races. Eur J Appl Physiol 2008; 102: 431-438.
• 16 Mujika I, Padilla S. Physiological and performance characteristics of male professional road cyclists. Sports Med 2001; 31: 479-487.
• 17 Vogt S, Schumacher YO, Roecker K. et al. Power output during the Tour de France. Int J Sports Med 2007; 28: 756-761.
• 18 Leo P, Spragg J, Simon D. et al. Power profiling, workload characteristics and race performance of U23 and professional cyclists during the multistage race Tour of the Alps. Int J Sports Physiol Perform. 2021
• 19 Lucia A, Joyos H, Chicharro JL. Physiological response to professional road cycling: Climbers vs. Time trialists. Int J Sports Med 2000; 21: 505-512.
• 20 Sanders D, Heijboer M. Physical demands and power profile of different stage types within a cycling grand tour. Eur J Sport Sci 2019; 19: 736-744.
• 21 Sanders D, Heijboer M, Hesselink MKC. et al. Analysing a cycling grand tour: Can we monitor fatigue with intensity or load ratios?. J Sports Sci 2018; 36: 1385-1391.
• 22 Padilla S, Mujika I, Cuesta G. et al. Level ground and uphill cycling ability in professional road cycling. Med Sci Sports Exerc 1999; 31: 878-885.
• 23 Rodríguez-Marroyo JA, López JG, Avila C. et al. Intensity of exercise according to topography in professional cyclists. Med Sci Sport Exerc 2003; 35: 1209-1215
• 24 Ferrari M. Measuring a riderʼs performance. 2003 https://www.53x12.com/measuring-a-rider ; Access 20.09.2021
• 25 Cintia P, Pappalardo L, Pedreschi D. “Engine Matters”: A First Large Scale Data Driven Study on Cyclistsʼ Performance. In: 2 IEEE 13th International Conference on Data Mining Workshops 2013. 147-153
• 26 Van Erp T, Hoozemans M, Foster C. et al. Case report: Load, intensity, and performance characteristics in multiple grand tours. Med Sci Sports Exerc 2020; 52: 868-875.
• 27 Nuttall FQ. Body mass index: obesity, BMI, and health: a critical review. Nutr today 2015; 50: 117-128
• 28 Harriss DJ, MacSween A, Atkinson G. Ethical standards in sport and exercise science research. 2020 update Int J Sport Med 2019; 40: 813-817
• 29 Maier T, Schmid L, Müller B. et al. Accuracy of cycling power meters against a mathematical model of treadmill cycling. Int J Sports Med 2017; 38: 454-461.
• 30 Edwards S. Heart Rate Monitor Book. New York: Polar Electro Oy; 1993
• 31 Cohen J. Statistical power Analysis For The Behavioral Sciences. Academic press; 2013
• 32 Norman G. Likert scales, levels of measurement and the “laws” of statistics. Adv Heal Sci Edu 2010; 15: 625-632
• 33 Ouvrard T, Groslambert A, Ravier G. et al. Mechanisms of performance improvements due to a leading teammate during uphill cycling. Int J Sports Med 2018; 13: 1215-1222
• 34 van Erp T, Sanders D, Lamberts RP. Maintaining power output with accumulating levels of work done is a key determinant for success in professional cycling. Med Sci Sport Exerc. 2021
• 35 Maunder E, Seiler S, Mildenhall MJ. et al. The importance of ‘Durabilityʼ in the physiological profiling of endurance athletes. Sports Med. 2021: 1-10.
• 36 van Druenen T, Blocken B. Aerodynamic analysis of uphill drafting in cycling. Sport Eng 2021; 24
• 37 Swain DP. A model for optimizing cycling performance by varying power on hills and in wind. Med Sci Sports Exerc 1997; 29: 1104-1108
• 38 Sallet P, Mathieu R, Fenech G. et al. Physiological differences of elite and professional road cyclists related to competition level and rider specialization. J Sports Med Phys Fitness 2006; 46: 361-365