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Background: The force–velocity relationship suggests that maximal power (P _max) can only be produced in optimal torque (T _opt) and cadence (C _opt). However, the cadence at which mean maximal power (MMP) is produced has never been studied. This study aimed to determine the individual MMP–cadence relationship from in situ data. Method: We analyzed 1 year of data from 14 under-19 cyclists and calculated the MMP for each cadence between 50 and 120 rpm for 2-, 5-, and 20-minute durations. The MMP–cadence relationship was fit with a second-order polynomial function. The goodness of fit (r ²) and odd-day–even-day absolute and relative reliability were evaluated, respectively, for P _max, T _opt, and C _opt. Results: The goodness of fit was very high for every duration studied. T _opt and P _max, but not C _opt, were significantly higher for shorter durations. P _max was significantly correlated only with T _opt for the 3 durations (r ² = .63, .71, and .64 for 2, 5, and 20 min, respectively). Discussion: Evaluation of the MMP–cadence relationship from in situ data is feasible and reliable for 2-, 5-, and 20-minute durations. This profiling approach would enable better detection of the strengths and weaknesses of cyclists and make it possible to design more effective training interventions. Practical Applications: The analysis makes it possible to identify the torque versus cadence component that individually limits power production. Knowing the C _opt for a given duration of maximal effort could help athletes choose the right gear ratio and regulate cadence during a race in order to maximize performance.

Purpose: To investigate how cycling time-trial (TT) performance changes over a cycling season, both in a “fresh” state and in a “fatigued” state (durability). Additionally, the aim was to explore whether these changes are related to changes in underlying physiological factors such as gross efficiency, energy expenditure (EE), and substrate oxidation (fat oxidation [FatOx] and carbohydrate oxidation [CarbOx]). Methods: Sixteen male semiprofessional cyclists visited the laboratory on 3 occasions during a cycling season (PRE, START, and IN) and underwent a performance test in both fresh and fatigued states (after 38.1 [4.9] kJ/kg), containing a submaximal warm-up for the measurement of gross efficiency, EE, FatOx, and CarbOx and a maximal TT of 1 (TT1min) and 10 minutes (TT10min). Results were compared across states (fresh vs fatigued) and periods (PRE, START, and IN). Results: The average power output (PO) in TT1min decreased (P < .05) from fresh to fatigued state across all observed periods, whereas there was no change in the PO in TT10min. Over the course of the season, the PO in TT1min in the fatigued state improved more compared with the PO in TT1min in the fresh state. Furthermore, while EE did not significantly change, there was an increase in FatOx and a decrease in CarbOx toward the fatigued state. These changes diminished during the cycling season (IN), indicating a greater contribution of CarbOx in the fatigued state. Conclusions: TT1min performance is more sensitive to fatigue compared with TT10min. Also, during a cycling season, durability improves more when compared with fresh maximal POs, which is also observed in the changes in substrate oxidation.

Purpose: To quantify the change in session rating of perceived exertion training impulse (RPE-TRIMP) that may occur in response to increased running distance at 3 running velocity ranges in elite sprinters. Methods: We monitored training load in elite sprinters (women: n = 7; men: n = 11) using wearable Global Positioning System technology and RPE-TRIMP for a total of 681 individual training sessions during a 22-week competition-preparation period. Internal training load was operationalized by RPE-TRIMP, and external training load was operationalized by distance covered in 3 velocity ranges. A linear mixed-effects model with athlete as a random effect was fit to RPE-TRIMP with total distance covered at ≤69.99% (low-velocity running [LVR]), 70% to 84.99% (high-velocity running [HVR]), and 85% to 100% (very-high-velocity running [VHVR]) of individual maximum velocity. Results: Increased running distance in all 3 velocity ranges (LVR, HVR, and VHVR) resulted in a significant (P < .001) increase in RPE-TRIMP. Coefficients (95% CIs) were .10 (.08–.11) for LVR, .23 (.18–.28) for HVR, and .44 (.35–.53) for VHVR. A 50-m increase in running distance covered in the LVR, HVR, and VHVR velocity ranges was associated with increases in RPE-TRIMP of 5, 11.5, and 22 arbitrary units, respectively. Conclusions: Internal training load, calculated as RPE-TRIMP, increased with increases in total distance covered in the LVR, HVR, and VHVR velocity ranges (P < .001). RPE-TRIMP can be a practical solution for monitoring global training-session load in elite sprinters.

Purpose: This study investigated sex differences in self-reported causes, symptoms, and recovery strategies associated with underperformance in endurance athletes. Methods: A total of 82 athletes (40 women) meeting the inclusion criteria (performance level ≥tier 3, used training diaries, and experienced 1 or more periods of underperformance during their career) completed an online questionnaire. The questionnaire encompassed inquiries regarding load monitoring and experiences with underperformance, focusing on causes, symptoms, and recovery strategies. Results: The most frequently reported symptoms associated with underperformance included psychological (31%), physiological (23%), and health-related (12%) symptoms. Notably, female athletes were more likely to report psychological symptoms associated with underperformance (38% vs 25%, P = .01) compared with male athletes. The leading causes of underperformance comprised illness (21%), mental/emotional challenges (20%), training errors (12%), lack of recovery (10%), and nutritional challenges (5%). Female athletes reported nutritional challenges more frequently as the cause of underperformance compared with males (9% vs 1%, P = .01), whereas male athletes more often attributed underperformance to training errors (15% vs 9%, P = .03). Overall, 67% of athletes reported recovering from underperformance, with a tendency for more male than female athletes to recover (76% vs 58%, P = .07). Furthermore, a higher proportion of male than female athletes reported implementing changes in the training process as a recovery strategy (62% vs 35%, P = .02). Conclusions: This study offers valuable insights into sex differences in experiences with underperformance in endurance athletes. The findings could inform coaches and athletes in both the prevention and treatment of such incidents.

Purpose: To investigate the influence of shorter, more frequent rest breaks with per-cooling as an alternative heat-acclimation session on physiological, perceptual, and self-paced maximal cycling performance, compared with continuous heat exposure. Methods: Thirteen participants completed 1 continuous and 3 intermittent-heat-exposure (IHE) maximal self-paced cycling protocols in a random order in heat (36 °C, 80% relative humidity): 1 × 60-minute exercise (CON), 3 × 20-minute exercise with 7.5-minute rest between sets (IHE-20), 4 × 15-minute exercise with 5-minute rest between sets (IHE-15), and 6 × 10-minute exercise with 3-minute rest between sets (IHE-10). Mixed-method per-cooling (crushed-ice ingestion and cooling vest) was applied during rest periods of all IHE protocols. Results: Total distance completed was greater in IHE-10, IHE-15, and IHE-20 than in CON (+11%, +9%, and +8%, respectively), with no difference observed between IHE protocols. Total time spent above 38.5 °C core temperature was longer in CON compared with IHE-15 and IHE-20 (+62% and +78%, respectively) but similar to IHE-10 (+5%). Furthermore, a longer time above 38.5 °C core temperature occurred in IHE-10 versus IHE-15 and IHE-20 (+54% and +69%, respectively). Sweat loss did not differ between conditions. Conclusion: IHE with per-cooling may be a viable alternative heat-acclimation protocol in situations where training quality takes precedence over thermal stimulus or when both factors hold equal priority.

Objective : To (1) establish extensive physiological profiles of highly trained CrossFit® athletes using gold-standard tests and (2) investigate which physiological markers best correlate with CrossFit Open performance. Methods : This study encompassed 60 participants (30 men and 30 women), all within the top 5% of the CrossFit Open, including 7 CrossFit semifinalists and 3 CrossFit Games finalists. Isokinetic dynamometers were employed to measure maximum isometric and isokinetic leg and trunk strength. Countermovement-jump height and maximum isometric midthigh-pull strength were assessed on a force plate. Peak oxygen uptake (VO₂peak) was measured by a cardiopulmonary exercise test, and critical power and W′ were evaluated during a 3-minute all-out test, both on a cycle ergometer. Results: Male and female athletes’ median (interquartile range) VO₂peak was 4.64 (4.43, 4.80) and 3.21 (3.10, 3.29) L·min⁻¹, critical power 314.5 (285.9, 343.6) and 221.3 (200.9, 238.9) W, and midthigh pull 3158 (2690, 3462) and 2035 (1728, 2347) N. Linear-regression analysis showed strong evidence for associations between different anthropometric variables and CrossFit Open performance in men and women, whereas for markers of cardiorespiratory fitness such as VO₂peak, this was only true for women but not men. Conventional laboratory evaluations of strength, however, manifested minimal evidence for associations with CrossFit Open performance across both sexes. Conclusions : This study provides the first detailed insights into the physiology of high-performing CrossFit athletes and informs training optimization. Furthermore, the results emphasize the advantage of athletes with shorter limbs and suggest potential modifications to CrossFit Open workout designs to level the playing field for athletes across different anthropometric characteristics.

Purpose: To compare the standard Yo-Yo intermittent recovery (IR) test and an agility Yo-Yo IR test with a higher number of change-of-direction movements to cardiorespiratory match performance. Methods: The study included 11 professional male handball players (age 24.5 [4.6] y) of a German second-league team. The performance parameters of the players of the seasons 2016–17 to 2018–19 were analyzed. The Yo-Yo IR test was compared to a Yo-Yo IR test with multiple multidirectional changes of direction in response to visual stimuli, which was conducted on a SpeedCourt (Yo-Yo SC IR). Peak oxygen uptake (VO₂peak) and maximum heart rate (HR_max) were measured. Between-methods differences of individual athletes were quantified with Bland–Altman plots. Results: HR_max was not statistically different during the Yo-Yo SC IR test compared to the Yo-Yo IR test (181 [10] vs 188 [8] beats·min⁻¹; P = .16). Agreement between the 2 tests was moderate for HR_max and good for heart rates >180 beats·min⁻¹. Mean average VO₂peak was 51.7 (3.9) and 50.9 (2.8) mL·min^–1·kg^–1 for the Yo-Yo SC IR test and the Yo-Yo IR test (P = .693), respectively. Conclusions: A Yo-Yo test with multidirectional changes of direction in response to visual stimuli yielded good agreement with the frequently used linear running tests and can be used to assess players’ VO₂peak. The HR_max achieved during this test should be used with caution.

Purpose: This study assessed the reliability and load–velocity profiles of 3 different landmine-punch-throw variations (seated without trunk rotation, seated with trunk rotation, and standing whole body) with different loads (20, 22.5, and 25.0 kg), all with the dominant hand and nondominant hand. Methods: In a quasi-randomized order, 14 boxers (24.1 [4.3] y, 72.6 [10.1] kg) performed 3 repetitions of each variation with their dominant hand and their nondominant hand, with maximal effort and 3 minutes of interset rest. Peak velocity was measured via the GymAware Power Tool (Kinetic Performance Technologies). The interclass correlation coefficients and their 95% CIs were used to determine the intrasession reliability of each variation × load × hand combination. Additionally, a 2 (hand) × 3 (variation) repeated-measures analysis of variance assessed the load–velocity profile slope, and a 3 (variation) × 2 (hand) × 3 (load) repeated-measures analysis of variance assessed the peak velocity of each variation. Results: Most variations were highly reliable (intraclass correlation coefficient > .91), with the nondominant hand being as reliable or more reliable than the dominant hand. Very strong linear relationships were observed for the group average for each variation (R ² ≥ .96). However, there was no variation × hand interaction for the slope, and there was no main effect for variation or hand. Additionally, there was no interaction for the peak velocity, but there were main effects for variation, hand, and load (P < .01). Conclusion: Each variation was reliable and can be used to create upper-body ballistic unilateral load–velocity profiles. However, as with other research on load–velocity profile, individual data allowed for more accurate profiling than group average data.