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Workload characteristics and race performance of U23 and elite cyclists during an UCI 2. Pro multistage race (Tour of the Alps)

Workload characteristics and race performance of U23 and elite cyclists during an UCI 2. Pro
multistage race (Tour of the Alps)
Peter Leo1,3, Andrea Giorgi4, Dan Lorang5, James Spragg6, Iñigo Mujika2 and Justin Lawley1
1 University of Innsbruck, Sports Science Department
2University of the Basque Country
3Tirol KTM Cycling Team U23 development team
4 Androni Giocattoli Sidermec Professional Cycling Team
5Bora Hansgrohe Professional Cycling Team
6Spragg Cycle Coaching
Background: Professional multistage cycle racing is characterised by complex team tactics and
race strategies, but the primary predictor of race success is still a rider’s individual physical
capacity1,2. In their development towards elite cycling, U23 cyclists are required to
progressively improve their physical qualities and adapt to the elite race formats, which in
itself is a selection process. Increasing the workloads in training and racing for better
performance outcome requires a well conceptualized training load quantification system3,4
as well as a carefully managed training intensity distribution5. The aim of this study was to
compare workload parameters and racing performance in U23 and elite cyclists across two
editions of a UCI 2. Pro multistage; Tour of the Alps. In this mountainous five-day stage race
the riders complete an average total distance of >700 km and climb ~13000 m. Methods:
Fourteen U23 cyclists from an UCI continental team (mean ± SD age 20.8 ± 0.9 years; body
mass 69.3 ± 6.2 kg; height 181.6 ± 5.6 cm; BMI 20.9 ± 1.2 kg.m-2) and 11 elite cyclists from a
UCI pro continental and a world tour team (mean ± SD age 28.9 ± 4.0 years; body mass 62.2
± 4.4 kg; height 177.1 ± 4.9 cm; BMI 19.8 ± 0.9 kg.m-2) participated in this study. All riders
completed the same UCI 2. Pro multistage race in either 2018 or 2019, except two riders who
raced in both years. (add how many completed both). Relative Maximum aerobic power
(MAP)6, 20min mean maximum power (MMP), 20min MMP after 2000 kilojoules (kJ), average
power (AP) and normalized power (NP) were recorded in all stages. Workload was quantified
via total work, total training stress score7 (TSS), Lucia’s training impulse8 (TRIMP) and the ratio
of TSS/km as well as kJ/km3. Metcalfe’s relative power distribution method9 was used to
classify percentage of overall race time at; <1.9, 2.0-4.9, 5.07.9 and >8.0 Race performance was expressed as final general classification (GC) position, absolute
time difference to the winner, and percentage time difference to the winner. Independent-
samples t-tests were conducted to compare U23 and elite categories. In addition, multiple
regression analyses were performed to assess the influence of anthropometrics, relative
power output, power output distribution, and workload parameters on race performance in
each group.
Results: Anthropometric data including body mass, BMI, and body surface area (BSA) were
lower in elite riders compared to U23 (p<0.05). MAP, 20min MMP, 20min MMP after 2000 kJ,
AP, and NP between the two groups indicated higher peak values in elite riders (p<0.05).
Percentage of overall race time at 2.0-4.9, 5.07.9 and >8.0 was higher
in the elite group (p<0.05), while the U23 group performed a higher percentage of total race
time at <1.9 Workload parameters including TSS, TRIMP, total work, TSS/km and
kJ/km were not significantly different between the two groups. A multiple regression analysis
compared the influence of MAP, 20min MMP, 20min MMP after 2000 kJ, AP, and NP on GC
position and found MAP to be the strongest predictor (F(1,14)=26.534, p0.001). Furthermore,
multiple regression analyses were performed for anthropometrics, relative power
distribution, and workloads, on final GC position. BSA (F(1,16)=5.978, p = 0.026) for
anthropometrics, percentage of overall race time at 5.07.9 (F(1,14)=13.595, p = 0.002)
and TRIMP and kJ/km (F(2,10)=11.397, p = 0.003) for workloads, were the strongest
determinants of GC (see Figure 1).
Figure 1: Differences in MAP, BSA and percentage of overall race time at 5.0-7.9 between U23 and elite cyclists
Conclusion: Differences in MAP, BSA, percentage of overall race time at 5.07.9,
TRIMP, and kJ/km were the best predictors of GC. In addition, these results suggest that U23
cyclists need to improve in several areas; body composition, physical conditioning, and race
tactics during their maturation to the elite level. Further research is recommended to better
understand the complex mechanisms that underpin performance in professional road cycling.
Keywords: cycling, racing, workload, performance
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... Thus, PRO and U23 races differ only in duration and not external intensity: PRO cyclists do not produce higher POs than U23, but they produce the same PO for longer durations than U23 cyclists. Accordingly, Leo et al 24 showed that during a 5-day cycling multistage race including both PRO and U23 teams, absolute and relative RPOs were not different between professional and U23 cyclists but, interestingly, professional cyclists showed higher relative RPOs after a certain amount of work (1000-3000 kJ) than U23 cyclists. From this perspective, our findings could suggest that fatigue resistance (ie, the ability to decrease the PO as little as possible after a prior amount of exercise) could be a peculiar feature that differentiates PRO from U23 cyclists. ...
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Purpose: To compare the race demands of junior (JUN), under 23 (U23), and professional (PRO) road cyclists. Methods: Thirty male cyclists, divided into 3 age-related categories (JUN, n = 10; U23, n = 10; and PRO, n = 10), participated in this study. Race data collected during the 2019 competitive season were retrospectively analyzed for race characteristics, external, and internal competition load. Results: Higher annual and per race duration, distance, elevation gain, Edward's training impulse, total work, and work per hour were observed in PRO versus U23 and JUN, and U23 versus JUN (P < .01). PRO and U23 recorded higher mean maximal power (RPOs) between 5 and 180 minutes compared with JUN (P < .01). Edward's training impulse per hour was higher in JUN than PRO and U23 (P < .01). Accordingly, JUN spent a higher percentage of racing time in high internal intensity zones compared with U23 and PRO, while these 2 categories spent more time at low internal intensity zones (P < .01). Conclusions: JUN races were shorter and included less elevation gain per distance unit compared to U23 and PRO races, but more internally demanding. JUN produced less power output in the moderate-, heavy-, and severe-intensity exercise domains compared with U23 and PRO (RPOs: 5-180 min). U23 and PRO races presented similar work demands per hour and RPOs, but PRO races were longer than U23.
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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.
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Purpose:: The relationship between various training load (TL) measures in professional cycling is not well explored. This study investigates the relationship between mechanical energy spent (in kJ), sRPE, LuTRIMP and TSS in training, races and time trials (TT). Methods:: From 4 consecutive years field data was collected from 21 professional cyclists and categorized as being collected in training, racing or TT's. kJ spent, sRPE, LuTRIMP and TSS were calculated and the correlations between the various TL's were made. Results:: 11,655 sessions were collected from which 7,596 sessions had heart rate (HR) data and 5,445 sessions had an RPE-score available. The r between the various TL's during training was almost perfect. The r between the various TL's during racing was almost perfect or very large. The r between the various TL's during TT's was almost perfect or very large. For all relationships between TSS and one of the other measurements of TL (kJ spent, sRPE and LuTRIMP) a significant different slope was found. Conclusions:: kJ spent, sRPE, LuTRIMP and TSS have all a large or almost perfect relationship with each other during training, racing and TT's but during racing both sRPE and LuTRIMP have a weaker relationship with kJ spent and TSS. Further, the significant different slope of TSS versus the other measurements of TL during training and racing has the effect that TSS collected in training and road-races differ by 120% while the other measurements of TL (kJ spent, sRPE and LuTRIMP) differ by only 73%, 67%, and 68% respectively).
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Purpose: The aim of this study was to assess the dose-response relationships between different training load methods and aerobic fitness and performance in competitive road cyclists. Method: Training data from 15 well-trained competitive cyclists were collected during a 10-week (December - March) pre-season 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's TRIMP (bTRIMP), Edwards' TRIMP (eTRIMP), individualized TRIMP (iTRIMP), Lucia's TRIMP (luTRIMP) and session-RPE (sRPE). External load was measured using Training Stress Score™ (TSS). Results: Large to very large relationships (r = 0.54-0.81) between training load and changes in submaximal fitness variables (power at 2 and 4 mmol·L(-1)) were observed for all training load calculation methods. The strongest relationships with changes in aerobic fitness variables were observed for iTRIMP (r = 0.81 [95% CI: 0.51 to 0.93, r = 0.77 [95% CI 0.43 to 0.92]) and TSS (r = 0.75 [95% CI 0.31 to 0.93], r = 0.79 [95% CI: 0.40 to 0.94]). The highest dose-response relationships with changes in the 8MT performance test were observed for iTRIMP (r = 0.63 [95% CI 0.17 to 0.86]) and luTRIMP (r = 0.70 [95% CI: 0.29 to 0.89). Conclusions: The results show that 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.
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In cycling, the maximal aerobic power (MAP) is an important parameter for the coaches in the training process and the monitoring of the cyclist’s aerobic potential. However, there is no common procedure that would determine the MAP since it is dependent on the test protocol in laboratory and field. The purpose of this study was to propose a methodology from field data to determine both a field MAP, the time that MAP can be sustained (TMAP) and an aerobic endurance index (AEI) in professional and elite cyclists. Twenty-eight cyclists trained and raced with mobile power meter devices fixed to their bikes during two consecutive seasons. The Record Power Profile (RPP) of each cyclist was determined from the maximal power output realised by the cyclists (i.e. record PO) on different durations between 1 second and 4 hours. The method of MAP determination was to define the upper limit of the aerobic metabolism from the relationship between the record PO (from 3 min to 4 h) and the logarithm of time. From this method, the average values of MAP and TMAP were 456 ± 42 W (6.87 ± 0.5 (95%CI = 439 - 473 W) and 4.13 ± 0.7 min (95%CI = 3.84 - 4.42 min), respectively. All the AEI were ranged between -8.34 and -11.33 (mean AEI = -9.53 ± 0.7, 95%CI = -9.24 / -9.82). The most important finding of this study is the possible determination of MAP, TMAP and AEI on the field from the RPP. Compared to the elite cyclists, the professionals presented a higher MAP (+9.9%, p<0.05) and shorter TMAP (-13.5%, p<0.05) with no difference in AEI. Several practical applications of this field method may be relevant and suitable for the coaches in the training monitoring of their cyclists.
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To compare the total exercise loads (intensity x volume) of the Vuelta a España and Tour de France during the last year. Seven professional road cyclists (28 +/- 1 yr; [OV0312]O(2max): 74.6 +/- 2.2 who participated in both Tour and Vuelta during the years 1997, 1999, 2000, or 2001 were collected as subjects. They wore a heart rate (HR) telemeter during each stage of the two races, and exercise intensity was divided into three phases according to the reference HR values obtained during a previous ramp cycle-ergometer test: phase I (<ventilatory threshold (VT)), phase II (between VT and the respiratory compensation point (RCP)) and phase III (>RCP). Total volume and intensity were integrated as a single variable. The score for volume x intensity in each phase was computed by multiplying the accumulated duration in this phase by a multiplier for this particular phase. The total score for Tour and Vuelta was obtained by summating the results of the three phases. The total loads (volume x intensity) did not significantly differ between the two races (P > 0.05), despite a significantly longer total exercise time of the Tour (P < 0.05) (5552 +/- 176 vs 5086 +/- 290 min). The physiological loads imposed on cyclists' bodies do not differ between the Tour and Vuelta, despite the longer duration of daily stages in the former race.
Purpose: To describe the within-season external workloads of professional male road cyclists for optimal training prescription. Methods: Training and racing of four international competitive professional male cyclists (age 24 ± 2 y, body mass 77.6 ± 1.5 kg) were monitored for 12 months prior to the world team time trial championships. Three within-season phases leading up to the team time trial world championships on 20(th) Sept 2015 were defined as phase one (Oct - Jan), phase two (Feb - May) and phase three (June - Sept). Distance and time were compared between training and racing days and over each of the various phases. Time spent within absolute (<100 W, 100 to 300 W, 400 to 500 W, >500W) and relative (0 to 1.9 W·kg(-1), 2.0 to 4.9 W·kg(-1), 5.0 to 7.9 W·kg(-1), >8 W·kg(-1)) power zones were also compared for the whole season and between phases one to three. Results: Total distance (3859 ± 959 vs 10911 ± 620 km) and time (240.5 ± 37.5 vs 337.5 ± 26 h) was lower (P <0.01) in phase one than phase two, respectively. Total distance decreased (P <0.01) from phase two to phase three (10911 ± 620 vs 8411 ± 1399 km, respectively). Mean absolute (236 ± 12.1 vs 197 ± 3 W) and relative (3.1 ± 0 vs 2.5 ± 0 W·kg(-1)) power output was higher (P <0.05) during racing compared with training, respectively. Conclusions: Volume and intensity differed between training and racing over each of three distinct within-seasonal phases.
Science and Application of High-Intensity Interval Training: Solutions to the Programming Puzzle
  • P Laursen
  • M Buchheit
Laursen P, Buchheit M. Science and Application of High-Intensity Interval Training: Solutions to the Programming Puzzle. Human Kinetics; 2019.