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Purpose: To investigate the relationship of field-derived power and physical performance parameters with competition success in road cycling climbing specialists of age-related categories and to explore cross-sectional differences between high-ranked (HIGHR) climbing specialists of each category. Methods: Fifty-three male climbers participated in this study (junior [JUN], n = 15; under 23 [U23], n = 21; professional [PRO], n = 17). Training and racing data collected during the 2016–19 competitive seasons were retrospectively analyzed for record power outputs (RPOs) and RPOs after prior accumulated work. Results: In JUN, body mass, absolute RPOs, and relative RPOs were higher in HIGHR compared with low ranked (d = 0.97–2.20, large; P =.097–.001); in U23 and PRO, the percentage decrease in RPOs after 20, 30, 40, and 50 kJ·kg−1 was less in HIGHR compared with low ranked (d = 0.77–1.74, moderate–large; P =.096–.004). JUN HIGHR presented lower absolute and relative RPO-20 min ( η p 2 =.34 −.38 , large; P =.099–.001) and higher percentage decrease in RPOs after prior accumulated work compared with U23 and PRO HIGHR ( η p 2 =.28 −.68 , large; P =.060–.001); percentage decrease in RPOs after prior accumulated work was the only parameter differentiating U23 and PRO HIGHR, with PRO declining less in relative RPO-1 min, RPO-5 min, and RPO-20 min after 20 to 50 kJ·kg−1 ( η p 2 =.28 −.68 , large; P =.090–.001). Conclusions: Superior absolute and relative RPOs characterize HIGHR JUN climbing specialists. Superior fatigue resistance differentiates HIGHR U23 and PRO climbers compared with low ranked, as well as PRO versus U23 climbers. [ABSTRACT FROM AUTHOR]

Purpose: The present study aimed to determine the influence of fatigue on the record power profile of professional male cyclists. We also assessed whether fatigue could differently affect cyclists of 2 competition categories. Methods: We analyzed the record power profile in 112 professional cyclists (n = 46 and n = 66 in the ProTeam [PT] and WorldTour [WT] category, respectively; age 29 [6] y, 8 [5] y experience in the professional category) during 2013–2021 (8 [5] seasons/cyclist). We analyzed their mean maximal power (MMP) values for efforts lasting 10 seconds to 120 minutes with no fatigue (after 0 kJ·kg−1) and with increasing levels of fatigue (after 15, 25, 35, and 45 kJ·kg−1). Results: A significant (P <.001) and progressive deterioration of all MMP values was observed from the lowest levels of fatigue assessed (ie, −1.6% to −3.0% decline after 15 kJ·kg−1, and −6.0% to −9.7% after 45 kJ·kg−1). Compared with WT, PT cyclists showed a greater decay of MMP values under fatigue conditions (P <.001), and these differences increased with accumulating levels of fatigue (decay of −1.8 to −2.9% [WT] with reference to 0 kJ·kg−1 vs −1.1% to −4.4% [PT] after 15 kJ·kg−1 and of −4.7% to −8.8% [WT] vs −7.6% to −11.6% [PT] after 45 kJ·kg−1). No consistent differences were found between WT and PT cyclists in MMP values assessed in nonfatigue conditions (after 0 kJ·kg−1), but WT cyclists attained significantly higher MMP values with accumulating levels of fatigue, particularly for long-duration efforts (≥5 min). Conclusions: Our findings highlight the importance of considering fatigue when assessing the record power profile of endurance athletes and support the ability to attenuate fatigue-induced decline in MMP values as a determinant of endurance performance. [ABSTRACT FROM AUTHOR]

Purpose: To present normative data for the record power profile of male professional cyclists attending to team categories and riding typologies. Methods: Power output data registered from 4 professional teams during 8 years (N = 144 cyclists, 129,262 files, and 1062 total seasons [7 (5) per cyclist] corresponding to both training and competition sessions) were analyzed. Cyclists were categorized as ProTeam (n = 46) or WorldTour (n = 98) and as all-rounders (n = 65), time trialists (n = 11), climbers (n = 50), sprinters (n = 11), or general classification contenders (n = 7). The record power profile was computed as the highest maximum mean power (MMP) value attained for different durations (1 s to 240 min) in both relative (W·kg−1) and absolute units (W). Results: Significant differences between ProTeam and WorldTour were found for both relative (P =.002) and absolute MMP values (P =.006), with WT showing lower relative, but not absolute, MMP values at shorter durations (30–60 s). However, higher relative and absolute MMP values were recorded for very short- (1 s) and long-duration efforts (60 and 240 min for relative MMP values and ≥5 min for absolute ones). Differences were also found regarding cyclists' typologies for both relative and absolute MMP values (P <.001 for both), with sprinters presenting the highest relative and absolute MMP values for short-duration efforts (5–30 s) and general classification contenders presenting the highest relative MMP values for longer efforts (1–240 min). Conclusions: The present results––obtained from the largest cohort of professional cyclists assessed to date—could be used to assess cyclists' capabilities and indicate that the record power profile can differ between cyclists' categories and typologies. [ABSTRACT FROM AUTHOR]

Context: Although skin-temperature assessment has received much attention in recent years as a possible internal-load measurement, scientific evidence is scarce. Purpose: To analyze baseline skin temperature and its rewarming through means of a cold-stress test before and after performing a marathon and to study the association between skin temperature and internal/external-load measurements. Methods: A total of 16 runners were measured 48 and 24 h before and 24 and 48 h after completing a marathon. The measurements on each day of testing included urine biomarkers of oxidative stress, pain and fatigue perception, skin temperature (at baseline and after a cold-stress test), and jump performance. Results: Reduced jump performance (P <.01 and effect size [ES] = 0.5) and higher fatigue and pain perception were observed 24 h after the marathon (P <.01 and ES > 0.8). Although no differences in baseline skin temperature were observed between the 4 measuring days, posterior legs presented lower constant (P <.01 and ES = 1.4) and higher slope (P =.04 and ES = 1.1) parameters in the algorithmic equations fitted for skin-temperature recovery after the cold-stress test 24 h after the marathon than on the day before the marathon. Regressions showed that skin-temperature parameters could be predicted by the ratio of ortho-tyrosine isomer to phenylalanine (oxidative stress biomarker) and body fat composition, among others. Conclusions: Although baseline skin temperature was not altered 24 or 48 h after a marathon, the application of cold stress after the marathon would appear to be a good method for providing information on vasoconstriction and a runner's state of stress. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the differences between clusters obtained by the acute fatigue effect following an ultra-endurance event on the internal and external load of cyclists. 26 volunteers participated in the study, divided into the experimental group (N = 18; height: 177 ± 8 cm; body mass: 78.6 ± 10.3 kg) and the control group (N = 8; height: 176 ± 10 cm; body mass: 78.0 ± 15.7 kg). The experimental group completed a 12 h non-stop cycling event. Jump height, lactate, plasma antioxidant capacity, pain perception and fatigue perception were measured before and after the event. Cyclists of the experimental group were classified taking into account their training characteristics (recreational vs. competitive) and conducting non-supervised Kmeans clustering. The differentiation of cyclists according to training characteristics resulted in a lower distance covered by recreational cyclists than competitive cyclists (279.4 ± 39.7 km vs. 371.0 ± 71.7 km; ES = 0.8; p < 0.01), although no differences were observed in the other variables between groups (p > 0.05). The clustering analysis resulted in two clusters. Cluster 2 suffered a greater jump height decrease (-3.3 ± 1.6 vs. 1.2 ± 0.8; ES = 0.8; p < 0.001) and increased pain and fatigue perception (ES = 0.5; p < 0.05) after the race than Cluster 1. In conclusion, countermovement jump and fatigue/pain perception can differentiate the fatigue produced by a cycling ultra-endurance event and therefore, these non-invasive measurements are useful in fatigue monitoring and recovery planning. [ABSTRACT FROM AUTHOR]

Power output is considered one of the best tools to control external loads in cycling, but the relationship between a target power output and the physiological responses may suffer from the effects of road gradient, which is also affected by cyclist specialization. The objective was to determine the effects of cyclist specialization on effort perception and physiological response (heart rate and lactate concentration) while sustaining efforts at similar power output but riding on two different road gradients. Nineteen male competitive road cyclists performed two randomized trials of 10 min at 0% (velodrome) and 10 min at 6% road gradient (field uphill), at an intensity of 10% ± 3% below the individual's functional threshold power. Cadence was kept between 75 and 80 rpm in both trials and posture remained unchanged during the tests. Heart rate, speed, cadence, power output, blood lactate, and rate of perceived effort were measured for each trial. K-means cluster analyses differentiate uphill (n = 10) and flat specialists (n = 9) according to lactate responses. Flat specialists presented lower heart rate (p < 0.001 and ES = 0.2), perceived exertion (p < 0.01 and ES = 0.7), and blood lactate concentration (p < 0.001 and ES = 0.7) riding on the flat than uphill. Uphill specialists presented lower perceived exertion (p < 0.01 and ES = 0.8) and blood lactate concentration (p < 0.01 and ES = 0.5) riding uphill than on the flat. In conclusion, the combination of cyclist specialization and road gradient affects physiological and effort perception parameters in response to a similar power output demand. These factors deserve attention in training schedules and monitoring performance using power output data. [ABSTRACT FROM AUTHOR]