Physiological Predictors of Competition Performance in CrossFit Athletes

Physiological Profiles of Male and Female CrossFit Athletes

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.

Standardized vs. Relative Intensity in CrossFit

CrossFit is characterized by being a standardized training program that improves physical performance through the provision of several stimuli regardless of the participant's strength level. This study aimed to compare the acute response in total repetitions as a measurement of performance, jump ability, physiological demand (heart rate and blood lactate), and perceived effort considering the participants' strength level with individualized intensity in CrossFit. Thirty-five participants were assessed and asked to participate on two separate days in a standardized and relative 'As Many Repetitions As Possible' (AMRAP) CrossFit circuit. Both AMRAPs comprised strength, gymnastic and aerobic exercises, although only strength was individualized according to the participant's level. Before the statistical analysis, participants were allocated to higher- or lower-strength groups following the one-repetition maximum-bodyweight ratio in the push press exercise. Results support the existence of a strong relationship between strength level and total repetitions in both AMRAPs. In addition, differences in total repetitions and rate of perceived exertion between strength groups are discarded when AMRAP intensity is individualized while physiological demand and jump ability are maintained. Thus, the higher-strength participants may benefit from similar responses with a lower number of repetitions. Therefore, CrossFit trainers should be encouraged to prescribe strength tasks based on the percentage of 1RM for every training.

The influence of sex-division, experience, and pacing strategy on performance in the 2020 CrossFit® Open

To observe workout pacing strategies and determine which best predicted performance, this retrospective study analyzed recorded efforts from a random selection of 160 high-ranking (top 10,000) men and women (n = 80 each) in the 2020 CrossFit® Open (CFO). Video recordings submitted to the official competition leaderboard for all five tests were analyzed to quantify overall test completion rates (and tie-break time for test 5 only) and within-test repetition completion rate (repetitions × sec-1) for each exercise, as well as the quantity of failed repetitions, break strategy (count and duration), and transition times. Each variable was aggregated into first-half, last-half, and total-test averages, slopes, and coefficient of variation; except on test 5 (total-test only). Spearman's rank correlation coefficients were calculated between test completion rates, each test's respective pacing variables, competitor demographics (height and body mass) and CFO experience (i.e., past participation, consecutive competitions, and ranks). Stepwise regression using significantly (p < 0.05) correlated variables produced two prediction models for test performance (best predictor only and best overall model within 8 variables) in a validation group (50% of valid efforts) and then cross-validated against remaining athletes. When no between-group differences were seen, data were combined and used to create the final prediction models for test 1 (r2adj = 0.64-0.96, SEE = 0.4-1.2 repetitions × sec-1), test 2 (r2adj = 0.28-0.85, SEE = 2.0-4.5 repetitions × sec-1), test 3 (r2adj = 0.49-0.81, SEE = 1.1-1.7 repetitions × sec-1), test 4 (r2adj = 0.63-0.78, SEE = 0.6-0.9 repetitions × sec-1), and test 5 (rate: r2adj = 0.71-0.84, SEE = 1.2-1.6 repetitions × sec-1; tie-break time: r2adj = 0.06-0.62, SEE = 1.4-2.3 min). Across the five 2020 CFO tests, the data suggested that repetition pace, breaking strategy, and/or consistency in completing calisthenic-gymnastics components (when prescribed) was most predictive of performance. However, their influence was affected by the complexity of prescribed resistance training exercises and their relative loads. Athletes should prioritize calisthenic-gymnastics components but divert attention to more complex resistance training exercises when prescribed at higher relative intensity loads. Neither previous competition experience nor sex-division altered the hierarchal importance of these considerations.

Predicting the Clean Movement Technique in Crossfit® Athletes Using an Optimal Upper-Limb Range of Motion: A Prospective Cohort Study

BACKGROUND

The aim of this study was to determine the optimal upper-limb range of motion (ROM) profile for the catch phase of the clean movement (CPCM) and to identify the key ROMs for performing the CPCM in CrossFit^® athletes.

METHODS

A prospective cohort study of twenty CrossFit^® athletes aged 20-36 years was conducted. Data were collected regarding age, anthropometrics, CrossFit^® training experience and upper-limb ROM. The ROM was measured using the ROM-SPORT method. After 7 months, athletes performed a clean movement with a load of 80% one repetition maximum. A Bayesian Student's t-analysis, binary logistic regression analysis and Receiver Operating Characteristic analysis were performed.

RESULTS

The optimal upper-limb ROM profile that predicted correct CPCM performance was 78° in shoulder extension, 173° in shoulder flexion, 107° in shoulder external rotation, 89° in shoulder internal rotation, 153° in elbow flexion, 99° in elbow pronation and 92° in wrist extension (area under the curve ≥ 651; positive predictive value ≥ 80%). Shoulder external rotation, elbow pronation and wrist extension were found to be the most important ROMs for the efficient and safe performance of CPCM (area under the curve ≥ 854; positive predictive value ≥ 85.7%).

CONCLUSION

The upper-limb ROM profile is associated with proper clean performance. Further studies are warranted to determine whether improving flexibility on upper-limb ROM may improve proper clean movement performance.

Time Course of Recovery Following CrossFit® Karen Benchmark Workout in Trained Men

The establishment of fatigue following the acute exercise stimulus is a complex and multi-factorial process, that might arise due to a range of distinct physiological mechanisms. However, a practical method of assessing CrossFit^® athletes’ recovery status has been neglected entirely in real-world sporting practice. The study describes the acute and delayed time course of recovery following the CrossFit^® Benchmark Workout Karen. Eight trained men (28.4 ± 6.4 years; 1RM back squat 139.1 ± 26.0 kg) undertook the Karen protocol. The protocol consists of 150 Wall Balls (9 kg), aiming to hit a target 3 m high. Countermovement jump height (CMJ), creatine kinase (CK), and perceived recovery status scale (PRS) (general, lower and upper limbs) were assessed pre, post-0h, 24, 48 and 72 h after the session. The creatine kinase concentration 24 h after was higher than pre-exercise (338.4 U/L vs. 143.3 U/L; p = 0.040). At 48h and 72 h following exercise, CK concentration had returned to baseline levels (p > 0.05). The general, lower and upper limbs PRS scores were lower in the 24-h post-exercise compared to pre-exercise (general PRS: 4.7 ± 1.5 and 7.7 ± 1.7; p = 0.013; upper limbs PRS: 6.6 ± 1.3 and 7.5 ± 1.3; p = 0.037; lower limbs PRS: 3.9 ± 2.5 and 7.3 ± 0.1; p = 0.046). Our findings provide insights into the fatigue profile and recovery in acute CrossFit^® and can be useful to coaches and practitioners when planning training programs. Moreover, recovery status can be useful to optimize training monitoring and to minimize the potential detrimental effects associated with the performance of repeated high-intensity sessions of CrossFit^®.

The Anaerobic Power Assessment in CrossFit® Athletes: An Agreement Study

Anaerobic power and capacity are considered determinants of performance and are usually assessed in athletes as a part of their physical capacities’ evaluation along the season. For that purpose, many field tests have been created. The main objective of this study was to analyze the agreement between four field tests and a laboratory test. Nineteen CrossFit^® (CF) athletes were recruited for this study (28.63 ± 6.62 years) who had been practicing CF for at least one year. Tests performed were: (1) Anaerobic Squat Test at 60% of bodyweight (AST60); (2) Anaerobic Squat Test at 70% of bodyweight (AST70); (3) Repeated Jump Test (RJT); (4) Assault Bike Test (ABT); and (5) Wingate Anaerobic Test on a cycle ergometer (WG). All tests consisted of 30 s of max effort. The differences among methods were tested using a repeated-measures analysis of variance (ANOVA) and effect size. Agreement between methods was performed using Bland–Altman analysis. Analysis of agreement showed systematic bias in all field test PP values, which varied between −110.05 (AST60_PP—WG_PP) and 463.58 (ABT_PP—WG_PP), and a significant proportional error in ABT_PP by rank correlation (p < 0.001). Repeated-measures ANOVA showed significant differences among PP values (F(1.76,31.59) = 130.61, p =< 0.001). In conclusion, since to our knowledge, this is the first study to analyze the agreement between various methods to estimate anaerobic power in CF athletes. Apart from ABT, all tests showed good agreement and can be used interchangeably in CF athletes. Our results suggest that AST and RJT are good alternatives for measuring the anaerobic power in CF athletes when access to a laboratory is not possible.

Determination of a CrossFit® Benchmark Performance Profile

In the trend sport CrossFit^®, international competition is held at the CrossFit^® Games, known worldwide as the definitive fitness test. Since American athletes are the best in the world regarding CrossFit^®, there might be influencing factors on international competition performance. Here, we characterize the benchmark performance profile of American and German CrossFit^® athletes (n = 162). To collect the common benchmark performance by questionnaire, 66 male and 96 female CrossFit^® athletes (32.6 ± 8.2 years) participated in our survey in both nations. By comparing the individual performance variables, only a significant difference in total power lift performance by males was identified between the nations (p = 0.034). No other significant differences were found in the Olympic lift, running, or the “Girl” Workout of the Day (Fran, Grace, Helen) performance. Very large to extremely large (r = 0.79–0.99, p < 0.01) positive correlations were found between the power lift and Olympic lift variables. Further linear regression analysis predicted the influence of back squat performance on performance in the Olympic lifts, snatch (R² = 0.76) and clean and jerk (R² = 0.84). Our results suggested a dominant role of back squat performance in the assessment of physical fitness of CrossFit^® athletes.

An Analysis of Sport-Specific Pain Symptoms through Inter-Individual Training Differences in CrossFit

Background: CrossFit is one of the fastest growing “high-intensity functional training” methods in recent years. Due to the very demanding motion sequences and high loads, it was initially assumed that there was an extremely high risk of injury. However, studies have shown that injury rates are given between 0.74–3.3 per 1000 h of training, which is not higher than in other individual sports such as weightlifting. The purpose of the study was to estimate the type of pain symptoms that are directly related to CrossFit, to estimate the frequency of injuries that occur within a population of recreational CrossFit athletes, and, finally, to identify the factors influencing the frequency of pain during CrossFit training. Methods: A total of 414 active CrossFit athletes completed an online survey inclusive of 29 items focusing on individual physical characteristics and training behavior, as well as simultaneous or previously practiced sports. Results: There was a significantly higher proportion of knee pain in athletes who had previously or simultaneously played another sport (p = 0.014). The duration, intensity, or type of personal training plan developed, along with personal information such as age, gender, or BMI, had no significant influence on the pain data. We could not find any significant variance between the groups that we formed based on the differently stated one-repetition max (RMs). There were differences in athletes who stated that they did specific accessory exercises for small muscle groups. Above all, athletes performing exercises for the hamstrings and the gluteus medius indicated fewer pain symptoms for the sacro-iliac joint (SIJ)/iliac and lower back locations. Conclusions: It is important not to see CrossFit as a single type of sport. When treating a CrossFit athlete, care should be taken to address inter-individual differences. This underlines the significant differences of this study between the individual athletes with regard to the ability to master certain skills or their previous sporting experience. The mere fact of mastering certain exercises seems to lead to significantly more pain in certain regions. In addition, there seems to be a connection between the previous or simultaneous participation in other sports and the indication of pain in the knee region.

Evaluation of the repeatability and reliability of the cross-training specific Fight Gone Bad workout and its relation to aerobic fitness

Cross-training is a high-intensity functional training (HIFT) with multiple workout modalities. Despite the increasing number of studies in HIFT, there is still no validated test to measure its specific performance. It would also be advisable to determine whether selected cross-training workouts can implement a stimulus corresponding to maximize aerobic work. For these reasons, the purpose of our study was to evaluate the repeatability and reliability of Fight Gone Bad (FGB) workout and to assess its relationship with aerobic fitness. Twenty-one cross-training participants (9 females) finished the study protocol which included three two-day measurement sessions separated by 10 days. During each session, participants had their body composition measured, and they performed two exercise tests. The first test was an incremental cycling test to measure aerobic fitness, and the second was a cross-training specific FGB workout performed the next day. Reliability and repeatability were calculated from the three measurements. The total FGB Score (FGBTOTAL) showed excellent reliability (ICC 0.9, SEM 6%). Moreover, FGBTOTAL was strongly correlated with aerobic fitness (i.e., time to exhaustion (Texh, R2 = 0.72), maximal workload (Wmax, R2 = 0.69), time to gas exchange threshold (TGET, R2 = 0.68), and peak oxygen uptake (VO2peak, R2 = 0.59). We also found that agreement between standardized FGB and standardized aerobic performance indices such as Texh, VO2peak, Wmax, maximum heart rate, TGET, and workload at gas exchange threshold was high by the Bland-Altman method. In conclusion, FGB is a reliable test that can be used in order to measure changes in cross-training performance caused by an intervention. Moreover, FGB is strongly correlated to aerobic fitness.

Soft Tissue Mobilization and Stretching for Shoulder in CrossFitters: A Randomized Pilot Study

Background. The shoulder in CrossFit should have a balance between mobility and stability. Glenohumeral internal rotation deficit and posterior shoulder stiffness are risk factors for overhead shoulder injury. Objective. To determine the effectiveness of instrument-assisted soft tissue mobilization and horizontal adduction stretch in CrossFit practitioners’ shoulders. Methods: Twenty-one regular CrossFitters were allocated to experimental (stretching with isometric contraction and instrument-assisted soft tissue mobilization) or control groups (instrument-assisted soft tissue mobilization). Each session lasted 5 min, 2 days a week, over a period of 4 weeks. Shoulder internal rotation and horizontal adduction (digital inclinometer), as well as posterior shoulder stretch perception (Park scale), were evaluated. Shapiro–Wilk test was used to analyze the distribution of the sample. Parametric Student’s t-test was used to obtain the intragroup differences. The inter- and intra-rater differences were calculated using a repeated measures analysis of variance (ANOVA). Results. Average age was 30.81 years (SD: 5.35), with an average height of 178 (SD: 7.93) cm and average weight of 82.69 (SD: 10.82) kg. Changes were found in the experimental group following intervention (p < 0.05), and when comparing baseline and follow-up assessments (p < 0.05) in all variables. Significant differences were found in the control group following intervention (p < 0.05), in right horizontal adduction and left internal rotation. When comparing the perception of internal rotation and horizontal adduction in both groups, significant differences were found. Conclusions. Instrument-assisted soft tissue mobilization can improve shoulder horizontal adduction and internal rotation. An instrument-assisted soft tissue mobilization technique yields the same results alone as those achieved in combination with post-isometric stretch with shoulder adduction.

The effects of acute consumption of carbohydrate-protein supplement in varied ratios on CrossFit athletes' performance in two CrossFit exercises: a randomized cross-over trial

BACKGROUND

CrossFit is becoming popular over the past few years, and various supplementation ways have been utilized by exercise physiologists to enhance CrossFit athletes' performance. This study aimed to evaluate the effects of consuming preworkout carbohydrate-protein supplements on CrossFit athletes' performance.

METHODS

Well-trained CrossFit athletes (8 men; 25.62±3.02 years) were randomized to a single-blind, placebo controlled, crossover design (7-day washout) to performed six bouts of two CrossFit workouts: Fight Gone Bad (FGB) and Cindy (CI). One hour and immediately before the onset of each bout, the subjects consumed carbohydrate-protein supplement in two ratios (2:2 or 3:1) or placebo (P): FGB+2:2, FGB+3:1, FGB+P, CI+2:2, CI+3:1, and CI+P. To value the differentiation in performances, the performed each subject repetitions in FGB and CI were recorded in the bouts.

RESULTS

Repeated measure analysis of variance was used to analyze the data, and the level of significance set for the study was P≤0.05. No significant difference was observed in the total number of repetitions performed in FGB (P=0.275) or CI (P=0.789) workouts in supplements and placebo groups.

CONCLUSIONS

These results indicate that acute consumption of preworkout carbohydrate-protein supplement may not enhance the CrossFit athletes' performance in FGB and CI workouts.

Predicting Maximal Oxygen Uptake Using the 3-Minute All-Out Test in High-Intensity Functional Training Athletes

Maximal oxygen uptake (VO2max) and critical speed (CS) are key fatigue-related measurements that demonstrate a relationship to one another and are indicative of athletic endurance performance. This is especially true for those that participate in competitive fitness events. However, the accessibility to a metabolic analyzer to accurately measure VO2max is expensive and time intensive, whereas CS may be measured in the field using a 3 min all-out test (3MT). Therefore, the purpose of this study was to examine the relationship between VO2max and CS in high-intensity functional training (HIFT) athletes. Twenty-five male and female (age: 27.6 ± 4.5 years; height: 174.5 ± 18.3 cm; weight: 77.4 ± 14.8 kg; body fat: 15.7 ± 6.5%) HIFT athletes performed a 3MT as well as a graded exercise test with 48 h between measurements. True VO2max was determined using a square-wave supramaximal verification phase and CS was measured as the average speed of the last 30 s of the 3MT. A statistically significant and positive correlation was observed between relative VO2max and CS values (r = 0.819, p < 0.001). Based on the significant correlation, a linear regression analysis was completed, including sex, in order to develop a VO2max prediction equation (VO2max (mL/kg/min) = 8.449(CS) + 4.387(F = 0, M = 1) + 14.683; standard error of the estimate = 3.34 mL/kg/min). Observed (47.71 ± 6.54 mL/kg/min) and predicted (47.71 ± 5.7 mL/kg/min) VO2max values were compared using a dependent t-test and no significant difference was displayed between the observed and predicted values (p = 1.000). The typical error, coefficient of variation, and intraclass correlation coefficient were 2.26 mL/kg/min, 4.90%, and 0.864, respectively. The positive and significant relationship between VO2max and CS suggests that the 3MT may be a practical alternative to predicting maximal oxygen uptake when time and access to a metabolic analyzer is limited.

The Relationship between CrossFit® Performance and Laboratory-Based Measurements of Fitness

To date, research has examined the physiological determinants of performance in standardized CrossFit^® (CF) workouts but not without the influence of CF familiarity. Therefore, the purpose of this present study was to examine the predictive value of aerobic fitness, body composition, and total body strength on performance of two standardized CF workouts in CF-naïve participants. Twenty-two recreationally trained individuals (males = 13, females = 9) underwent assessments of peak oxygen consumption (VO₂ peak), ventilatory thresholds, body composition, and one repetition maximum tests for the back squat, deadlift, and overhead press in which the sum equaled the CF Total. Participants also performed two CF workouts: a scaled version of the CF Open workout 19.1 and a modified version of the CF Benchmark workout Fran to determine scores based on total repetitions completed and time-to-completion, respectively. Simple Pearson’s r correlations were used to determine the relationships between CF performance variables (19.1 and modified Fran) and the independent variables. A forward stepwise multiple linear regression analysis was performed and significant variables that survived the regression analysis were used to create a predictive model of CF performance. Absolute VO₂ peak was a significant predictor of 19.1 performance, explaining 39% of its variance (adjusted R² = 0.39, p = 0.002). For modified Fran, CF Total was a significant predictor and explained 33% of the variance in performance (adjusted R² = 0.33, p = 0.005). These results suggest, without any influence of CF familiarity or experience, that performance in these two CF workouts could be predicted by distinct laboratory-based measurements of fitness.

Predictors of CrossFit Open Performance

The 2018 CrossFit Open (CFO) was the initial stage of an annual competition that consisted of five weekly workouts. Current evidence suggests that a variety of fitness parameters are important for progressing beyond this stage, but little is known about which are the most important. To examine relationships between CFO performance, experience, and physiological fitness, sixteen experienced (>2 years) athletes (30.7 ± 6.9 years, 171 ± 12 cm, 78.0 ± 16.2 kg) volunteered to provide information about their training and competitive history, and then complete a battery of physiological assessments prior to competing in the 2018 CFO. Athletes’ resting energy expenditure, hormone concentrations, body composition, muscle morphology, cardiorespiratory fitness, and isometric strength were assessed on two separate occasions. Spearman correlations demonstrated significant (p < 0.05) relationships between most variables and performance on each workout. Stepwise regression revealed competition experience (R² = 0.31–0.63), body composition (R² = 0.55–0.80), vastus lateralis cross-sectional area (R² = 0.29–0.89), respiratory compensation threshold (R² = 0.54–0.75), and rate of force development (R² = 0.30–0.76) to be the most common predictors. Of these, body composition was the most important. These fitness parameters are known targets with established training recommendations. Though preliminary, athletes may use these data to effectively train for CFO competition.