Time-course Changes of Field- and Laboratory-based Performance Indicators in Junior Cyclists Through a Season

Abstract

This study aimed to assess the seasonal evolution of field-based and laboratory-based performance indicators in cyclists. Thirteen Junior male road cyclists (age 17.4±0.5 years) were followed up during a season, which was divided in three phases: early season (involving mainly training sessions), mid-season (including the first competitions), and late season (including the major competitions of the season). During each phase, field-based power output data were registered for the assessment of maximum mean power values, and laboratory-based endurance (ramp test and simulated 8-minute time trial), muscle strength/power (squat, lunge, hip thrust) and body composition indicators (dual-energy X-ray absorptiometry) were also assessed. A progressive (p<0.01) increase in maximum mean power values (e.g., 3.8±0.3 and 4.5±0.4 watts/kg in early and late season, respectively, for 60-minute efforts) and on 8-minute time trial performance (i.e., 5.3±0.3 and 5.6±0.4 watts/kg, respectively) was observed through the season. Yet, more “traditional” endurance indicators (i.e., ventilatory threshold, respiratory compensation point, or maximum oxygen uptake) seemed to show a ceiling effect beyond the mid-season. In addition, neither peak power output, body composition, nor muscle strength indicators followed a similar pattern to the aforementioned field-based indicators. In summary, in Junior cyclists field-based indicators seem more sensitive to monitor endurance cyclists’ changes in actual fitness and performance capacity than more “traditional” laboratory-based markers in Junior cyclists.

Key words

endurance - assessment - testing - cycling - monitoring

Publication History

Received: 17 October 2023

Accepted: 19 December 2023

Accepted Manuscript online:
19 December 2023

Article published online:
12 February 2024

• References

• 1 Hawley JA, Noakes TD. Peak power output predicts maximal oxygen uptake and performance time in trained cyclists. Eur J Appl Physiol Occup Physiol 1992; 65: 79-83 DOI: 10.1007/BF01466278.
• 2 Bishop D, Jenkins DG, Mackinnon LT. The relationship between plasma lactate parameters, Wpeak and 1-h cycling performance in women. Med Sci Sports Exerc 1998; 30: 1270-1275 DOI: 10.1097/00005768-199808000-00014.
• 3 Balmer J, Davison RCR, Bird SR. Peak power predicts performance power during an outdoor 16.1-km cycling time trial. Med Sci Sports Exerc 2000; 32: 1485-1490
• 4 Bentley DJ, Mcnaughton LR, Thompson D. et al. Peak power output, the lactate threshold, and time trial performance in cyclists. Med Sci Sports Exerc 2001; 33: 2077-2081
• 5 Amann M, Subudhi AW, Foster C. Predictive validity of ventilatory and lactate thresholds for cycling time trial performance. Scand J Med Sci Sports 2006; 16: 27-34
• 6 Valenzuela PL, Muriel X, Van Erp T. et al. The record power profile of male professional cyclists: normative values obtained from a large database. Int J Sports Physiol Perform 2022; 17: 701-710
• 7 Muriel X, Valenzuela P, Mateo-March M. et al. Physical demands and performance indicators in male professional cyclists during a grand tour: WorldTour versus ProTeam category. Int J Sports Physiol Perform 2021; 17: 22-30
• 8 Leo P, Spragg J, Podlogar T. et al. Power profiling and the power-duration relationship in cycling: a narrative review. Eur J Appl Physiol 2022; 122: 301-316 DOI: 10.1007/s00421-021-04833-y.
• 9 Miura A, Endo M, Sato H. et al. Relationship between the curvature constant parameter of the power-duration curve and muscle cross-sectional area of the thigh for cycle ergometry in humans. Eur J Appl Physiol 2002; 87: 238-244 DOI: 10.1007/s00421-002-0623-3.
• 10 Kordi M, Menzies C, Parker Simpson L. Relationship between power–duration parameters and mechanical and anthropometric properties of the thigh in elite cyclists. Eur J Appl Physiol 2018; 118: 637-645 DOI: 10.1007/s00421-018-3807-1.
• 11 Kordi M, Folland J, Goodall S. et al. Mechanical and morphological determinants of peak power output in elite cyclists. Scand J Med Sci Sports 2020; 30: 227-237 DOI: 10.1111/sms.13570.
• 12 Douglas J, Ross A, Martin JC. Maximal muscular power: lessons from sprint cycling. Sports Med Open 2021; 7: 48 DOI: 10.1186/s40798-021-00341-7.
• 13 Alejo LB, Montalvo-pérez A, Valenzuela PL. et al. Comparative analysis of endurance, strength and body composition indicators in professional, under-23 and junior cyclists. Front Physiol 2022; 13: 945552 DOI: 10.3389/fphys.2022.945552.
• 14 van der Zwaard S, de Ruiter CJ, Jaspers RT. et al. Anthropometric clusters of competitive cyclists and their sprint and endurance performance. Front Physiol 2019; 10: 1-10 DOI: 10.3389/fphys.2019.01276.
• 15 Kordi M, Simpson LP, Thomas K. et al. The relationship between neuromuscular function and the W′ in elite cyclists. Int J Sports Physiol Perform 2021; 16: 1656-1662 DOI: 10.1123/ijspp.2020-0861.
• 16 Lucía A, Hoyos J, Pérez M. et al. Heart rate and performance parameters in elite cyclists: a longitudinal study. Med Sci Sports Exerc 2000; 32: 1777-1782 DOI: 10.1097/00005768-200010000-00018.
• 17 Sassi A, Impellizzeri FM, Morelli A. et al. Seasonal changes in aerobic fitness indices in elite cyclists. Appl Physiol Nutr Metab 2008; 33: 735-742 DOI: 10.1139/H08-046.
• 18 Hopker J, Coleman D, Passfield L. Changes in cycling efficiency during a competitive season. Med Sci Sports Exerc 2009; 41: 912-919 DOI: 10.1249/MSS.0b013e31818f2ab2.
• 19 Paton CD, Hopkins WC. Seasonal changes in power of competitive cyclists: Implications for monitoring performance. J Sci Med Sport 2005; 8: 375-381 DOI: 10.1016/S1440-2440(05)80052-0.
• 20 Artetxe-Gezuraga X, Maldonado-Martín S, Freemye BG. et al. Gross efficiency and the relationship with maximum oxygen uptake in young elite cyclists during the competitive season. J Hum Kinet 2019; 67: 123-131 DOI: 10.2478/hukin-2018-0089.
• 21 Ploszczyca K, Foltyn J, Goliniewski J. et al. Seasonal changes in gross efficiency and aerobic capacity in well-trained road cyclists. Isokinet Exerc Sci 2019; 27: 193-202 DOI: 10.3233/IES-192115.
• 22 Zapico AG, Calderón 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
• 23 Leo P, Spragg J, Mujika I. et al. Power profiling in U23 professional cyclists during a competitive season. Int J Sports Physiol Perform 2021; 16: 881-889 DOI: 10.1123/ijspp.2020-0200.
• 24 Mujika I, Rønnestad BR, Martin DT. Effects of increased muscle strength and muscle mass on endurance-cycling performance. Int J Sports Physiol Perform 2016; 11: 283-289 DOI: 10.1123/IJSPP.2015-0405.
• 25 Valenzuela APL, Alejo LB, Montalvo-pérez A. et al. Laboratory-based determinants of simulated time trial performance in cyclists. Biol Sport 2023; 40: 1169-1176
• 26 Marín-Pagán C, Dufour S, Freitas TT. et al. Performance profile among age categories in young cyclists. Biology (Basel) 2021; 10: 1196 DOI: 10.3390/biology10111196.
• 27 Lillo-Bevia J, Pallarés J. Validity and reliability of the Cycleops Hammer cycle ergometer. Int J Sports Physiol Perform 2018; 13: 853-859
• 28 Klika RJ, Alderdice MS, Kvale JJ. et al. Efficacy of cycling training based on a power field test. J Strength Cond Res 2007; 21: 265-269 DOI: 10.1249/00005768-200605001-02051.
• 29 Gavin TP, Van Meter JB, Brophy PM. et al. Comparison of a field-based test to estimate functional threshold power and power output at lactate threshold. J Strength Cond Res 2012; 26: 416-421 DOI: 10.1519/JSC.0b013e318220b4eb.
• 30 Sanders D, Taylor RJ, Myers T. et al. A field-based cycling test to assess predictors of endurance performance and establishing training zones. J Strength Cond Res 2020; 34: 3482-3488 DOI: 10.1519/JSC.0000000000001910.
• 31 Valenzuela PL, Gil-Cabrera J, Talavera E. et al. On- versus off-bike power training in professional cyclists: A randomized controlled trial. IntJ Sports Physiol Perform 2021; 16: 674-681 DOI: 10.1123/ijspp.2020-0305.
• 32 Martínez-Cava A, Hernandez-Belmonte A, Couriel-Ibáñez J. et al. Reliability of technologies to measure the barbell velocity: Implications for monitoring resistance training. PLoS One 2020; 15: e0232465 DOI: 10.1371/journal.pone.0232465.
• 33 de Hoyo M, Núñez FJ, Sañudo B. et al. Predicting loading intensity measuring velocity in barbell hip thrust exercise. J Strength Cond Res 2021; 35: 2075-2081 DOI: 10.1519/JSC.0000000000003159.
• 34 Conceição F, Fernandes J, Lewis M. et al. Movement velocity as a measure of exercise intensity in three lower limb exercises. J Sports Sci 2016; 34: 1099-1106 DOI: 10.1080/02640414.2015.1090010.
• 35 Hopkins W, Marshall SW, Batterham AM. et al. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc 2009; 41: 3-12 DOI: 10.1249/MSS.0b013e31818cb278.
• 36 Svendsen IS, Tønnesen E, Tjelta LI. et al. Training, performance, and physiological predictors of a successful elite senior career in junior competitive road cyclists. Int J Sports Physiol Perform 2018; 13: 1287-1292 DOI: 10.1123/ijspp.2017-0824.
• 37 Menaspà P, Sassi A, Impellizzeri FM. Aerobic fitness variables do not predict the professional career of young cyclists. Med Sci Sports Exerc 2010; 42: 805-812 DOI: 10.1249/MSS.0b013e3181ba99bc.
• 38 Valenzuela PL, Alejo LB, Lucia A. et al. What does it take to become a professional cyclist? A laboratory-based longitudinal analysis in competitive young riders. Int J Sports Physiol Perform 2023; 18: 1275-1282 DOI: 10.1123/ijspp.2023-0083.
• 39 Gil-Cabrera J, Valenzuela PL, Alejo LB. et al. Traditional versus optimum power load training in professional cyclists: A randomized controlled trial. Int J Sports Physiol Perform 2021; 16: 496-503 DOI: 10.1123/IJSPP.2020-0130.