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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.

Purpose: In this study, we compared the influence of movement specificity during velocity–load jump testing to predict on-ice acceleration performance in elite speed skaters. Methods: Elite long-track speed skaters (N = 27) performed velocity–load testing with 3 external loads during unilateral horizontal jumping, lateral jumping, and bilateral vertical countermovement jumping. For the unilateral tests, external load conditions were set to 10 N, 7.5% and 15% of external load relative to body weight. For the countermovement jumping, load conditions were body weight and 30% and 60% of external load relative to body weight. On-ice performance measures were obtained during maximal 50-m accelerations from a standing start, including maximal skating speed, maximal acceleration capacity, and maximum horizontal power. The 100-m split time from a 500-m race was also obtained. Regularized regression models were used to identify the most important predictors of on-ice acceleration performance. In addition to regularized regression coefficients, Pearson correlation coefficients (r) were calculated for all variables retained by the model to assess interrelationships between single predictors and on-ice performance measures. Results: The countermovement jump with 30% of body mass demonstrated the strongest association with maximal skating speed, maximum horizontal power, and 100-m time (regularized regression coefficient = .16−.49, r = .84−.97, P < .001). Horizontal jump with 15% of body mass was the strongest predictor of maximal acceleration capacity performance (regularized regression coefficient = .08, r = .83, P < .001). Conclusions: The findings of this study suggest that mechanical specificity rather than movement specificity was more relevant for predicting on-ice acceleration performance.