Determination of a CrossFit® Benchmark Performance Profile

The physical demands and physiological responses to CrossFit®: a scoping review with evidence gap map and meta-correlation

BACKGROUND

CrossFit® combines different types of activities (weightlifting, gymnastics, and cardiovascular training) that challenge aerobic and anaerobic pathways. Over the last few years, the scientific interest in CrossFit® has increased considerably. However, there have been no published reviews characterizing the physical demands and physiological responses to CrossFit®. The present study synthesizes current evidence on the physical demands and physiological responses to CrossFit®.

METHODS

The search was performed in three electronic databases (PubMed, Scopus, and Web of Science). Manuscripts related to the physical and physiological performance of adult CrossFit® participants written in English, Portuguese, and Spanish were retrieved for the analysis.

RESULTS

In addition, a meta-correlation was conducted to examine the predictors of CrossFit® performance. A total of 68 papers were included in the review. Physical and physiological markers differed between the different workouts analyzed. In addition, 48 to 72 h are needed to recover from a CrossFit® challenge. Specific tests that involve CrossFit® movements were more related to CrossFit® performance than non-specific.

CONCLUSION

Although the characterization of CrossFit® is dependent on the workout examined, the benefits of muscle hypertrophy are aligned with the recent findings of concurrent training. The characterization of CrossFit® entire sessions and appropriate recovery strategies should be considered in future studies to help coaches manipulate and adjust the training load.

Exploring CrossFit performance prediction and analysis via extensive data and machine learning

BACKGROUND

The analysis of athletic performance has always aroused great interest from sport scientist. This study utilized machine learning methods to build predictive models using a comprehensive CrossFit (CF) dataset, aiming to reveal valuable insights into the factors influencing performance and emerging trends.

METHODS

Random forest (RF) and multiple linear regression (MLR) were employed to predict performance in four key weightlifting exercises within CF: clean and jerk, snatch, back squat, and deadlift. Performance was evaluated using R-squared (R2) values and mean squared error (MSE). Feature importance analysis was conducted using RF, XGBoost, and AdaBoost models.

RESULTS

The RF model excelled in deadlift performance prediction (R2=0.80), while the MLR model demonstrated remarkable accuracy in clean and jerk (R2=0.93). Across exercises, clean and jerk consistently emerged as a crucial predictor. The feature importance analysis revealed intricate relationships among exercises, with gender significantly impacting deadlift performance.

CONCLUSIONS

This research advances our understanding of performance prediction in CF through machine learning techniques. It provides actionable insights for practitioners, optimize performance, and demonstrates the potential for future advancements in data-driven sports analytics.

Assessment of Cardiorespiratory and Metabolic Contributions in an Extreme Intensity CrossFit® Benchmark Workout

Our purpose was to characterize the oxygen uptake kinetics (VO2), energy systems contributions and total energy expenditure during a CrossFit® benchmark workout performed in the extreme intensity domain. Fourteen highly trained male CrossFitters, aged 28.3 ± 5.4 years, with height 177.8 ± 9.4 cm, body mass 87.9 ± 10.5 kg and 5.6 ± 1.8 years of training experience, performed the Isabel workout at maximal exertion. Cardiorespiratory variables were measured at baseline, during exercise and the recovery period, with blood lactate and glucose concentrations, including the ratings of perceived exertion, measured pre- and post-workout. The Isabel workout was 117 ± 10 s in duration and the VO2 peak was 47.2 ± 4.7 mL·kg-1·min-1, the primary component amplitude was 42.0 ± 6.0 mL·kg-1·min-1, the time delay was 4.3 ± 2.2 s and the time constant was 14.2 ± 6.0 s. The accumulated VO2 (0.6 ± 0.1 vs. 4.8 ± 1.0 L·min-1) value post-workout increased substantially when compared to baseline. Oxidative phosphorylation (40%), glycolytic (45%) and phosphagen (15%) pathways contributed to the 245 ± 25 kJ total energy expenditure. Despite the short ~2 min duration of the Isabel workout, the oxygen-dependent and oxygen-independent metabolism energy contributions to the total metabolic energy release were similar. The CrossFit® Isabel requires maximal effort and the pattern of physiological demands identifies this as a highly intensive and effective workout for developing fitness and conditioning for sports.

Simulation analysis of low back forces in Snatch and Clean & Jerk movements via digital human modelling

BACKGROUND

Weightlifting is an Olympic sport for dynamic strength and power, and requires the execution of different lifting techniques It is important to analyze the forces subjected to the lower back during weightlifting movements to prevent injuries. Digital Human Modeling (DHM) is a powerful tool that can be used to analyze and optimize the performance of humans while doing their work or activities.

OBJECTIVE

The purpose of this study is to present a simulation analysis of the lower back forces during the execution of two weightlifting techniques: Snatch (SN) and Clean & Jerk (CJ), with different loads and for both genders.

METHODS

Digital Human modelling through JACK simulation package was used analyze the forces exerted on the lumbosacral area (L5-S1) of the lower back in order to determine the risk for low back injuries. The level of compression and shear forces recommended by the literature have been set as thresholds. The simulaitons were performed in male and female models, with loads from 20-100 kg.

RESULTS

The results show that any weight higher than 60 kg in both movements poses risk for the weightlifters in terms of compression and shear forces. It has been observed that weightlifters can lift greater loads in the CJ technique compared to the SN technique. Furthermore, females are able to lift higher loads with lower risk of injuries.

CONCLUSION

Weightlifting is a high-risk activity due to the high levels of shear and compression forces that the body is exposed to during the lifting techniques. Digital Human Modeling holds significant value due to their ability to facilitate the exploration of diverse conditions within a safe environment, devoid of any potential harm to human subjects.

CrossFit®: 'Unknowable' or Predictable?-A Systematic Review on Predictors of CrossFit® Performance

The functional fitness training program CrossFit^® is experiencing fast-growing and widespread popularity with day-to-day varying 'Workouts of the Day' (WOD). Even among tactical athletes, the training program is widely applied. Nevertheless, there is a lack of data on which parameters influence CrossFit^® performance. For this reason, the purpose of this study is to conduct a systematic review of the existing literature to identify and summarize predictors of CrossFit^® performance and performance enhancement. In accordance with the PRISMA guidelines, a systematic search of the following databases was conducted in April 2022: PubMed, SPORTDiscus, Scopus, and Web of Science. Using the keyword 'CrossFit', 1264 entries are found, and 21 articles are included based on the eligibility criteria. In summary, the studies show conflicting results, and no specific key parameter was found that predicts CrossFit^® performance regardless of the type of WOD. In detail, the findings indicate that physiological parameters (in particular, body composition) and high-level competitive experience have a more consistent influence than specific performance variables. Nevertheless, in one-third of the studies, high total body strength (i.e., CrossFit^® Total performance) and trunk strength (i.e., back squat performance) correlate with higher workout scores. For the first time, this review presents a summary of performance determinants in CrossFit^®. From this, a guiding principle for training strategies may be derived, suggesting that a focus on body composition, body strength, and competition experience may be recommended for CrossFit^® performance prediction and performance enhancement.

Purchasing behavior and use of digital sports offers by CrossFit® and weightlifting athletes during the first SARS-CoV-2 lockdown in Germany

BACKGROUND

To combat the spread of SARS-CoV-2, CrossFit® training centers, and fitness studios were closed during the first lockdown in Germany from mid-March until June 2020, and as a result, CrossFit® (CFA) or weightlifting athletes (WLA) faced a major challenge for the first time. Therefore, this study aimed to investigate the impact of the first lockdown on the training behavior and to analyze the way the athletes dealt with the new situation. In detail, we focus on habits of purchase and examine the acceptance of digital sports offers between CFA and WLA in response to the restrictions of the nationwide lockdown.

METHODS

An online survey was used to characterize the purchasing behavior and use of digital sports offers of CFA and WLA. In total, 484 volunteers (192 women, 290 men, 2 diverse) responded to the online questionary, allowing us to identify changes in training behavior and differences between the sports disciplines.

RESULTS

Our data shows both CFA and WLA purchase new equipment for a home gym and the use of digital sports increased significantly across all age groups. A comparison during the lockdown even showed that within the CFA, one group (n = 142) reported losing 5 kg or more of body mass, while the value of the WLA remained constant. On the one hand, the results indicate that despite the restrictions during the lockdown, CFA were may able to enhance health aspects by improving their body composition. On the other hand, this study shows that the training habits of both groups of athletes have changed significantly with the use of digital sports offers.

CONCLUSIONS

We suppose that the great openness and the expansion of online sports offers during the first lockdown may change the sports industry in the future.