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Rapid weight loss or “weight cutting” is a common but potentially harmful practice used in mixed martial arts competition. Following the official weigh-in, competitors refeed and rehydrate themselves in a process known as rapid weight gain (RWG) to realize a potential competitive advantage. While data from surveys and small series have indicated the majority of mixed martial arts athletes engage in rapid weight loss, there is a lack of officially collected data from sanctioning organizations describing its prevalence. The present investigation represents a summary of the data collected between December 2015 and January 2018 by the California State Athletic Commission. In total, 512 professional mixed martial artists (455 males and 57 females) were included. Of these, 503 (98%) athletes gained body mass between weigh-in and their bouts. Total RWG between weigh-in and competition was 5.5 ± 2.5 kg, corresponding to an 8.1% ± 3.6% body mass increase. Total RWG was 5.6 ± 2.5 kg (8.1% ± 3.6%) for males and 4.5 ± 2.3 kg (8.0% ± 3.8%) for females. More than one quarter of men and one third of women gained >10% body mass between weigh-in and competition. Athletes from leading international promotions gained more absolute, but not relative, body mass than those from regional promotions. Our findings indicate RWG is nearly ubiquitous in professional , with a similar prevalence in male and female athletes. Trends based on promotion suggest a larger magnitude of RWG in presumably more experienced and/or successful mixed martial artists from leading international promotions.

Purpose: To examine the recovery kinetics of exercise-induced muscle damage (EIMD), neuromuscular fatigue, and performance following small-sided games (SSGs) of different densities in soccer. Methods: Ten male players randomly completed 3 trials: a control trial (no SSGs), 4v4 SSGs (62.5 m²/player), and 8v8 SSGs (284.4 m²/player). External and internal load were monitored using GPS technology, heart-rate monitors, and rating of perceived exertion. Delayed-onset muscle soreness (DOMS), creatine kinase (CK), isokinetic strength, countermovement jump (CMJ), and sprint were determined at baseline, as well as at 24, 48, and 72 hours post-SSGs. Neuromuscular fatigue was assessed at baseline and at 1, 2, and 3 hours post-SSGs. Results: DOMS increased (P < .05) in 4v4 for 72 hours and in 8v8 for 24 hours with that of knee flexors being more pronounced than that of extensors. CK increased (P < .05) in 4v4 for 72 hours and in 8v8 for 24 hours. Neuromuscular fatigue increased (P < .05) in 4v4 for 2 hours and in 8v8 for 3 hours. Strength declined (P < .05) in 4v4 for 48 hours and in 8v8 for 72 hours. CMJ decreased (P < .05) in 4v4 for 24 hours and in 8v8 for 48 hours. Sprint decreased (P < .05) for 48 hours in 4v4 and for 72 hours in 8v8. Conclusions: SSGs are associated with a prolonged rise of EIMD and induce short-term neuromuscular fatigue and slow recovery kinetics of strength, jump, and sprinting performance. The time for complete recovery is longer for SSGs of lower density.

Purpose: Women’s professional cycling has grown in popularity, and this increase is also apparent in Brazil, which has increased its female cycling calendar in recent years. The aim of this observational study was to (1) determine training and competition loads of a top-level Brazilian female cycling team, (2) evaluate nutrition and clinical health, and (3) measure whether exercise capacity changed throughout the season. Methods: Training and competition data were collected over the season using global positioning system monitors, while laboratory-based physiological and performance measures (incremental cycling test, 30-s Wingate, 4-km time trial) and clinical and nutritional analyses were performed at time points throughout the season. Results: Total distance covered over the year was 11,124 (2895) km (7382–14,698 km). Endurance capacity was reduced over the season (P = .005) but not anaerobic power (all P > .05). Nutrition and stress markers remained largely unchanged throughout the season, although there were some individual fluctuations in some measures, and testosterone concentration was low for some. Median estimated energy availability ranged between 32.3 and 56.8 kcal·kgLBM⁻¹·d⁻¹ during training and 26.4 and 53.8 kcal·kgLBM⁻¹·d⁻¹ during competition. Percentage of training spent in optimal estimated energy availability was generally low, with 3 athletes spending <35% within the optimal intake. Conclusions: Substantial training and competition loads of the monitored professional Brazilian female cyclists may have reduced exercise capacity toward the end of the season, indicative of a grueling yearlong schedule. Several athletes may have had suboptimal energy availability during the season, potentially affecting testosterone concentration. These data demonstrate the difficulties in maintaining optimal nutrition, health, and performance throughout a season in professional female cycling and highlight the need for quality sport-science support for this type of top-level athlete.

Purpose: Elite athletes experience chronic sleep insufficiency due to training and competition schedules. However, there is little research on sleep and caffeine use of elite youth athletes and a need for a more nuanced understanding of their sleep difficulties. This study aimed to (1) examine the differences in sleep characteristics of elite youth athletes by individual and team sports, (2) study the associations between behavioral risk factors associated with obstructive sleep apnea and caffeine use with sleep quality, and (3) characterize the latent sleep profiles of elite youth athletes to optimize the sleep support strategy. Methods: A group (N = 135) of elite national youth athletes completed a self-administered questionnaire consisting of the Pittsburgh Sleep Quality Index (PSQI) and questions pertaining to obstructive sleep apnea, napping behavior, and caffeine use. K-means clustering was used to characterize unique sleep characteristic subgroups based on PSQI components. Results: Athletes reported 7.0 (SD = 1.2) hours of sleep. Out of the total group, 45.2% of the athletes had poor quality sleep (PSQI global >5), with team-sport athletes reporting significantly poorer sleep quality than individual-sport athletes. Multiple logistic regression analysis indicated that sport type significantly correlated with poor sleep quality. The K-means clustering algorithm classified athletes’ underlying sleep characteristics into 4 clusters to efficiently identify athletes with similar underlying sleep issues to enhance interventional strategies.Conclusion: These findings suggest that elite youth team-sport athletes are more susceptible to poorer sleep quality than individual-sport athletes. Clustering methods can help practitioners characterize sleep-related problems and develop efficient athlete support strategies.

Purpose: Little is known about the effect of sleep restriction (SR) on different domains of athletes’ physical performance. Therefore, the aim of this randomized, counterbalanced, and crossover study was to evaluate the effect of acute SR on sport-specific technical and athletic performance in male junior tennis players. Methods: Tennis players (N = 12; age 15.4 ± 2.6 y) were randomly allocated to either a sleep-restriction condition (SR, n = 6), where they experienced acute sleep restriction the night before the test session (≤5 h of sleep), or to a control condition (CON, n = 6), where they followed their habitual sleep–wake routines. Testing procedures included 20 left and right serves, 15 forehand and backhand crosscourt shots, and a repeated-sprint-ability test (RSA). The accuracy of serves and shots was considered for further analysis. One week later, players of SR joined CON, and players of CON experienced SR, and all test procedures were repeated. Results: Significant decrease in the accuracy of right (−17.5%, P = .010, effect size [ES] = 1.0, moderate) and left serve (−14.1%, P = .014, ES = 1.2, large), crosscourt backhand (−23.9%, P = .003, ES ≥ 2.0, very large), and forehand shot (−15.6%, P = .014, ES = 1.1, moderate) were observed in SR compared to CON, while RSA was similar in both conditions. Conclusion: Coaches and athletes at the team and individual level should be aware that 1 night of SR affects sport-specific but not athletic performance in tennis players.

Purpose: To compare the cardiorespiratory responses of a traditional session of high-intensity interval training session with that of a session of similar duration and average load, but with decreasing workload within each bout in cyclists and runners. Methods: A total of 15 cyclists (maximal oxygen uptake [$\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$] 62 [6] mL·kg⁻¹·min⁻¹) and 15 runners ($\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ 58 [4] mL·kg⁻¹·min⁻¹) performed both sessions at the maximal common tolerable load on different days. The sessions consisted of four 4-minute intervals interspersed with 3 minutes of active recovery. Power output was held constant for each bout within the traditional day, whereas power started 40 W (2 km·h⁻¹) higher and finished 40 W (2 km·h⁻¹) lower than average within each bout of the decremental session. Results: Average oxygen uptake during the high-intensity intervals was higher in the decremental session in cycling (89 [4]% vs 86 [5]% of $\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$, P = .002) but not in running (91 [4]% vs 90 [4]% of $\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$, P = .38), as was the time spent >90% of $\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ and the time spent >90% of peak heart rate. Average heart rate (P < .001), pulmonary ventilation (P < .001), and blood lactate concentration (P < .001) were higher during the decremental sessions in both cycling and running. Conclusions: Higher levels of physiological perturbations were achieved during decremental sessions in both cycling and running. These differences were, however, more prominent in cycling, thus making cycling a more attractive modality for testing the effects of a training intervention.

Background: Compared with normoxia, repeated short (5–10 s) sprints (>10 efforts) with incomplete recovery (≤30 s) in hypoxia likely cause substantial performance reduction accompanied by larger metabolic disturbances and magnitude of neuromuscular fatigue. However, the effects of hypoxia on performance of repeated long (30 s) “all-out” efforts with near complete recovery (4.5 min) and resulting metabolic and neuromuscular adjustments remain unclear. Purpose: The intention was to compare acute performance, metabolic, and neuromuscular responses across repeated Wingates between hypoxia and normoxia. Methods: On separate visits, 6 male participants performed 4 × 30-second Wingate efforts with 4.5-minute recovery in either hypoxia (fraction of inspired oxygen: 0.145) or normoxia. Responses to exercise (muscle and arterial oxygenation trends, heart rate, and blood lactate concentration) and the integrity of neuromuscular function in the knee extensors were assessed for each exercise bout. Results: Mean (P = .80) and peak (P = .92) power outputs, muscle oxygenation (P = .88), blood lactate concentration (P = .72), and perceptual responses (all Ps > .05) were not different between conditions. Arterial oxygen saturation was significantly lower, and heart rate higher, in hypoxia versus normoxia (P < .001). Maximal voluntary contraction force and peripheral fatigue indices (peak twitch force and doublets at low and high frequencies) decreased across efforts (all Ps < .001) irrespective of conditions (all Ps > .05). Conclusion: Despite heightened arterial hypoxemia and cardiovascular solicitation, hypoxic exposure during 4 repeated 30-second Wingate efforts had no effect on performance and accompanying metabolic and neuromuscular adjustments.

Purpose: The aim of this study was to investigate changes in the power profile of U23 professional cyclists during a competitive season based on maximal mean power output (MMP) and derived critical power (CP) and work capacity above CP (W′) obtained during training and racing. Methods: A total of 13 highly trained U23 professional cyclists (age = 21.1 [1.2] y, maximum oxygen consumption = 73.8 [1.9] mL·kg^–1·min^–1) participated in this study. The cycling season was split into pre-season and in-season. In-season was divided into early-, mid-, and late-season periods. During pre-season, a CP test was completed to derive CP_test and W′_test. In addition, 2-, 5-, and 12-minute MMP during in-season were used to derive CP_field and W′_field. Results: There were no significant differences in absolute 2-, 5-, and 12-minute MMP, CP_field, and W′_field between in-season periods. Due to changes in body mass, relative 12-minute MMP was higher in late-season compared with early-season (P = .025), whereas relative CP_field was higher in mid- and late-season (P = .031 and P = .038, respectively) compared with early-season. There was a strong correlation (r = .77–.83) between CP_test and CP_field in early- and mid-season but not late-season. Bland–Altman plots and standard error of estimates showed good agreement between CP_test and in-season CP_field but not between W′_test and W′_field. Conclusion: These findings reveal that the power profile remains unchanged throughout the in-season, except for relative 12-minute MMP and CP_field in late-season. One pre-season and one in-season CP test are recommended to evaluate in-season CP_field and W′_field.

Purpose: To quantify and describe relationships between subjective and external measures of training load in professional youth soccer players. Methods: Data from differential ratings of perceived exertion (dRPE) and 7 measures of external training load were collected from 20 professional youth soccer players over a 46-week season. Relationships were described by repeated-measures correlation, principal component analysis, and factor analysis with oblimin rotation. Results: Significant positive (.44 ≤ r ≤ .99; P < .001) within-individual correlations were obtained across dRPE and all external training load measures. Correlation magnitudes were found to decrease when training load variables were expressed per minute. Principal component analysis provided 2 components, which described 83.3% of variance. The first component, which described 72.9% of variance, was heavily loaded by all measures of training load, while the second component, which described 10.4% of the variance, appeared to have a split between objective and subjective measures of volume and intensity. Exploratory factor analysis identified 4 theoretical factors, with correlations between factors ranging from .5 to .8. These factors could be theoretically described as objective volume, subjective volume, objective running, and objective high-intensity measures. Removing dRPE measures from the analysis altered the structure of the model, providing a 3-factor solution. Conclusions: The dRPE measures are significantly correlated with a range of external training load measures and with each other. More in-depth analysis showed that dRPE measures were highly related to each other, suggesting that, in this population, they would provide practitioners with similar information. Further analysis provided characteristic groupings of variables.