Local Muscle Endurance and Strength Had Strong Relationship with CrossFit® Open 2020 in Amateur Athletes

Wrist Extensor Muscle Fatigue During a Dual Task With Two Muscular and Cognitive Load Levels in Younger and Older Adults

OBJECTIVE

To examine the effect of concurrent physical and cognitive demands as well as age on indicators of muscle fatigue at the wrist.

BACKGROUND

There are few studies examining risk indicators for musculoskeletal disorders associated with work-related physical and cognitive demands that often occur simultaneously in the workplace.

METHODS

Twenty-four gender-balanced older and 24 gender-balanced younger (mean age 60 and 23 years) participants performed four 30 min dual tasks. Tasks differed by the muscular load level during force tracking: 5% and 10% of maximum voluntary contraction force (MVC) and concurrent cognitive demands on the working memory: easy and difficult. Muscle fatigue was assessed by MVC decline and changes in surface electromyography (increased root mean square: RMS, decreased median frequency: MF) at the extensor digitorum (ED) and extensor carpi ulnaris (EU).

RESULTS

A decline in MVC was found in all participants when tracking was performed at 10% MVC (mean ± SD: 137.9 ± 49.2 - 123.0 ± 45.3 N). Irrespective of age, muscular, or cognitive load, RMS increased (ED 12.3 ± 6.5 - 14.1 ± 7.0% MVE, EU 15.4 ± 7.6 - 16.9 ± 8.6% MVE) and MF decreased (ED 85.4 ± 13.6 - 83.2 ± 12.8 Hz, EU 107.2 ± 17.1 - 104.3 ± 16.7 Hz) in both muscles. However, changes in MF of EU tended to be more pronounced in the older group at higher cognitive and lower muscular load, without reaching statistical significance.

CONCLUSION

Maximum voluntary contraction indicated no interaction between muscle fatigue, cognitive load, or age. However, the tendencies toward altered muscle activity due to an increase in cognitive load and older age suggest muscular adaptations while maintaining tracking performance during the onset of fatigue signs in the sEMG signal.

APPLICATION

If the tendencies in muscle activity are confirmed by further studies, ergonomic assessments in industrial workplaces should consider cognitive load and age when describing the risk of musculoskeletal disorders.

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 the relationship between Total Athleticism score and CrossFit® Open Performance in amateur athletes: single measure involving body fat percentage, aerobic capacity, muscle power and local muscle endurance

There has been an increasing interest among CrossFit® coaches and practitioners in identifying indicators of sport performance. This study aimed to examine the correlation between anthropometric measures, cardiorespiratory capacity, power, local muscle endurance, and total athleticism score, with performance in the CrossFit® Open 2021. Fourteen male volunteers (aged 30.3 ± 5.8 years) participated in the study and underwent a series of tests on separate weeks. These tests included assessments of body fat percentage (subcutaneous adipose thickness measured at seven sites), maximal oxygen consumption (2 km test in rowing ergometer), muscle power (one repetition maximum in power clean), and muscle endurance (Tibana test, which included the conclusion of four distinct rounds of work). These results were used to calculate the total score of athleticism, which was then compared to the participants performance during the CrossFit® Open 2021. The athletes presented an average of body fat (8.6 ± 2.0%), maximal oxygen consumption (53.3 ± 2.4 mL. (kg.min)-1), 2km row time (07:00 ± 00:21 mm:ss), 1-Repetition maximum in power clean (125.2 ± 21.2 kg) and Tibana test performance (281.0 ± 35.9 repetitions). Interestingly, the top five athletes with the highest scores also achieved the highest z-scores in the CrossFit® Open 2021. Conversely, the four athletes with the lowest TSA score had the lowest z-scores in the CrossFit® Open. Moreover, almost perfect correlation (r = 0.91; p<0.01) was found between the total athleticism score and z-scores in the CrossFit® Open 2021. The total score may be a single measure and holistic indication of athleticism level in CrossFit®. Furthermore, coaches can potentially apply this useful tool for monitoring athletic performance and designing training sessions that address specific areas of CrossFit® performance.

Fatigue and Performance Rates as Decision-Making Criteria in Pacing Control During CrossFit®

We investigated fatigue and performance rates as decision-making criteria in pacing control during CrossFit®. Thirteen male regional-level competitors completed conditions of all-out (maximum physical work from beginning to end) and controlled-split (controlled physical work in the first two rounds but maximum work in the third round) pacing throughout the Fight Gone Bad workout separated by one week. We assessed benchmarks, countermovement jumps and ratings of fatigue after each round. Benchmarks were lower in round 1 (99 vs. 114, p < .001) but higher in rounds 2 (98 vs. 80, p < .001) and 3 (97 vs. 80, p < .001) for controlled-split compared with all-out pacing. Reductions in countermovement jumps were higher after rounds 1 (-12.6% vs. 1.6%, p < .001) and 2 (-12.7% vs. -4.0%, p = .014) but similar after round 3 (-13.2% vs. -11.3%, p = .571) for all-out compared with controlled-split pacing. Ratings of fatigue were higher after rounds 1 (7 vs. 5 a.u., p < .001) and 2 (8 vs. 7 a.u, p = .023) but similar after round 3 (9 vs. 9 a.u., p = .737) for all-out compared with controlled-split pacing. During all-out pacing, countermovement jump reductions after round 2 correlated with benchmark drops across rounds 1 and 2 (r = .78, p = .002) and rounds 1 and 3 (r = -.77, p = .002) and with benchmark workout changes between pacing strategies (r = -.58, p = .036), suggesting that the larger the countermovement jump reductions the higher the benchmark drops across rounds and workouts. Therefore, benchmarks, countermovement jumps and ratings of fatigue may assess exercise-induced fatigue as decision-making criteria to improve pacing strategy during workouts performed for as many repetitions as possible.

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.

Performance Sex Differences in CrossFit®

CrossFit® has a unique standard for workout of the day for women and men. Scaling is used to set difficulty levels for women in CrossFit® gyms and competitions. This type of scaling is applied for weightlifting (60-82% of men's load); however, there are usually no differences in difficulty settings for gymnastics and monostructural metabolic conditioning. Performance analysis is essential for every sports discipline, and statistical data comparing men's and women's results from athletics, running, swimming, weightlifting, etc., are available. However, CrossFit® lacks these statistics. The aim of our study was to analyze how the performances of men and women differed at the 2021 CrossFit Games®. Our sample comprised 40 female (age 27.8 ± 5.1) and 40 male participants (age 27.2 ± 3.7) competing in the Rx division. Data obtained from all events were analyzed using effect size and percentage. In 14 out of 15 events, men achieved better results than women. Even with the implementation of scaling, women's results differed by 0.1-33.1% (effect size from small to large). Scaling for women is designed according to general strength and power differences; however, primarily because of anatomic and physiological differences, men attain better results. However, CrossFit Games® events are always unique, and the events rarely repeat; therefore, our study does not provide firm conclusions. As our study is the first to compare CrossFit Games® performance between the sexes, further research is needed.

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^®.