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

BMC Sports Science, Medicine and Rehabilitation volume 16, Article number: 196 (2024) Cite this article

Abstract

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.

Peer Review reports

Background

CrossFit® includes the training and practice of weightlifting (e.g., snatch, clean, and jerk), gymnastics (e.g., handstand walk, ring muscle pull-up), and cardiovascular activities (e.g., running, rowing, cycling) [1, 2]. The practice of CrossFit® focuses on improving different components of fitness: cardiorespiratory, stamina, strength, flexibility, power, speed, coordination, agility, balance, and accuracy [3]. Different types of workouts are prescribed: Different types of workouts are prescribed: rounds for time (RFT), performing as many rounds as possible within a given time (AMRAP), and completing repetitions of exercises in a given number of minutes (every minute EMOM). For example, the “Angie” workout consists of completing 100 pull-ups, 100 push-ups, and 100 squats as quickly as possible; the “Chelsea” workout involves performing 5 pull-ups, 10 push-ups, and 15 squats every 60 s for 30 min; and the “Nate” workout involves the completion of as many rounds as possible in 20 min of muscle-ups, handstand push-ups, and kettlebell swing [4]. Given the varied nature of CrossFit®, there are many variables (number of repetitions, sets, load, rest between sets, movement, type of workout) that can influence the response to a stimulus and, consequently, have an impact on training adaptations [2, 5].

The organization of training sessions and the adjustments of training load are central to the modality since the CrossFit® Open is a competition that allows everyone to participate in three weeks of competition. Every week, an online challenge is proposed, and CrossFit® participants should perform, record, and submit their scores [6, 7]. Subsequently, the best twenty-five percent of CrossFit® Open results advanced to the subsequent phase of competition. Then, the top forty of each region (Europe, Africa, Asia, South America, North America East, North America West, and Oceania) advance to the semifinals. After the last phase, the top 40 athletes of both sexes are selected for the CrossFit® Games [6]. Coaches organize training sessions to optimize the three essential characteristics (i.e., weightlifting, gymnastics, and cardiovascular) that constitute the CrossFit® in order to prepare the participants for ‘unknown and unknowable events [1, 2, 8]. At CrossFit® Games, athletes engage in unspecified events until right before the competition begins. The founder of CrossFit® recognized that methodological guidelines are empirical, but more evidence-based, measurable, observable, and repeatable data are needed [1].

Over the last decade, scientific interest has increased in CrossFit®, most notably in physical performance and physiology [9,10,11], injuries [12], psychology [13], and nutrition [14]. Reviews about CrossFit® often combine those who practice CrossFit® or are involved in functional or resistance training or those who are classified as healthy or sedentary participants, impacting the interpretation of physical, physiological, and performance data. Moreover, previous reviews focused on physical and physiological aspects [9,10,11] and did not examine the demands induced by each type of CrossFit® workout (i.e., AMRAP, RFT, EMOM). Given the variability of movements, training sessions, and, consequently, the metabolic demands imposed by CrossFit® workouts, a review focused on the physical and physiological outcomes of exclusively CrossFit® participants is lacking. Another challenge is which physical protocols should be applied to predict CrossFit® performance [9]. The present scoping review aims to summarize: (1) the physical and physiological demands in CrossFit®; (2) interpret the literature that explains different activity profiles within the CrossFit® context; (3) examine the association between CrossFit® performance and physical or physiological assessments; and (4) identify literature gaps and point suggestions for further research.

Methods

The current review followed the Cochrane instructions [15], PRISMA 2020 guidelines [16], and the respective extension for scoping reviews [16]. The protocol was developed by three expert elements and registered by an expert author on the Open Science Framework at https://doi.org/10.37766/inplasy2024.5.0063.

Eligibility criteria

Original studies published in peer-reviewed journals and written in English, Portuguese, and Spanish were included in the present review. There were no defined restrictions regarding the year of publication. The inclusion criteria were defined considering the Participants, Intervention, Comparator, Outcomes, and Study Design (PICOS) framework as follows: Participants – adult CrossFit® participants with previous training experience (minimal training experience or reported training practice had to be reported for inclusion); Intervention – any outcome, observation, intervention, or exposure associated with CrossFit® participation; Comparator – optional, other sporting activities, physical active; Outcomes – physical measures (for example, performance or body composition assessments); physiological outputs (such as maximal oxygen uptake, heart rate, rate of perceived exertion, hormonal levels); Study Design – no restrictions were applied to the type of studies included in the present review. Reviews and meta-analysis were not included.

Information sources, source strategy and selection process

Three databases were consulted (Pubmed, Scopus, and Web of Science) on 23rd February 2024 using the following search terms: (CrossFit OR “CrossFit Games” OR “workout of the day” OR “WOD$”) AND (“training load” OR train* OR physiolog* OR perform* OR energ* OR metabol* OR nutrition* OR “body composition”). Originally, the initial purpose of this review was also to include CrossFit® studies that had a nutritional component; however, a decision was taken not to include these studies in the present review since there was a wealth of data available on physical and physiological parameters in CrossFit® participants. The inclusion of nutritional studies would have reduced the focus of the current review. After extracting the papers, they were combined into a reference management software (EndNoteTM 21.0, ClarivateTM). The omissions of duplicates followed a two-step process: (1) automatically removed and (2) manually checked to ensure that duplicates were removed with precision. One author (XXX) performed the entire screening process. Two authors (XXX/XX) examined the titles and abstracts to check if the studies met the eligibility criteria. The processes were then repeated for the full texts of the included papers. When discrepancies occurred, a third author (HS) was contacted to ensure concordance by consensus.

Data extraction and itsem

The two first authors (XXX/XX) organized a predefined template to collect relevant data about physical and physiological parameters. A third author (XX) confirmed the data extraction. The final summary of data comprised information on the different papers, considering the study's purpose and design. The data were categorized into the following topics: (1) characterization of physical (e.g., body fat percentage, lean body mass, weight lifted in CrossFit® movements) and physiological outcomes (e.g., VO2max, heart rate, blood lactate); (2) assessments of the acute effects of different CrossFit® workouts on physical performance measurements; (3) data pertaining to the chronic effects of CrossFit® participation; (4) comparisons of CrossFit® participation with other types of training or no training; (5) interventional studies with participants of different competitive levels; (6) comparisons of different competitive levels in physical and physiological outcomes; (7) predictors of CrossFit® performance. Corresponding authors were individually contacted when the information was unavailable. In case the authors did not respond and the data were presented graphically, a specific software was used (GetData Graph Digitizer; http://www.getdata-graph-digitizer.com). This software has been shown to be accurate and precise [17] to extract mean and standard deviation from graphs.

Statistical analysis

Data analyses were conducted using Comprehensive Meta-Analysis software (CMA, version 2.2.064, Biostat, NJ, USA). The main outcome was performance, which was divided considering three different groups: (1) global (overall performance or ranking), (2) studies focused on a challenge from a CrossFit® competition, only reported the challenge as workout of the day, rounds for time or as many rounds as possible, or combined different challenges, or (3) studies using particular workouts (e.g., Donkey Kong, Fran, Grace, Murph, Nancy). Different protocols were used to examine the relationship with CrossFit® performance and were classified according to the methodologies reported: specific from CrossFit® (e.g., snatch, bench press, squat) and non-specific (e.g., VO2max, Wingate test). The statistical method for examining the relationship between two continuous variables over numerous studies is meta-correlation, also known as meta-analysis of r correlation coefficient. From each study, the coefficient of correlation and the number of participants were retrieved. The magnitude of correlation coefficients was interpreted as follows [18]: trivial (r < 0.10), small (0.10 ≤ r < 0.30), moderate (0.30 ≤ r < 0.50), large (0.50 ≤ r < 0.70), very large (0.70 ≤ r < 0.90), and nearly perfect (r ≥ 0.90). The I2 informed about the proportion of variance in correlation coefficients that was due to heterogeneity instead of chance. The cut-off values for I2 values were interpreted as follows [19]: low (I2 < 25%), moderate (25% ≥ I2 > 75%), and high (I2 ≥ 75%). Subgroup differences between different types of protocols were tested, and statistical significance was determined at a two-sided level of p < 0.05.

Results

Study identification and selection

The initial search was conducted in three databases, and 2238 records were detected. Of these, 818 were identified as duplicates, and once removed, 1520 papers were screened according to title and abstract. This process resulted in full-text screenings of 158 studies. Nine reasons were identified to exclude 117 studies: nutrition (n = 38), training experience (i.e., average or minimal experience) (n = 54), not an original paper (n = 8), not physical or physiological outputs (n = 6), papers about injuries (n = 5), combined different sports activities (n = 2), studies that examined music effects on performance (n = 2), detraining (n = 1), and psychological outputs (n = 1). Finally, 68 studies were included in the current review and were retrieved for the analysis, as shown in Fig. 1.

Fig. 1
figure 1

Prisma flow diagram of the study identification and selection to the present review

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Study characteristics

Table 1 summarizes the primary information extracted from each study, the origin of the corresponding author, the inclusion criteria to be considered as a CrossFit® participant, sampling characteristics, aim, main methodologies or variables analyzed, as well as findings of each study [10, 20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86]. Twelve studies focused on the comparison between CrossFit® workouts or contrasted CrossFit® with types of training [22, 24, 25, 35, 41, 45, 50, 54, 63, 68, 74, 85]. Five studies distinguished participants of CrossFit® across different competitive levels [27, 31, 34, 43, 84]. Five studies focused on recovery [20, 38, 43, 71, 83] while seven papers centered on testing the data quality of protocols that can be useful for CrossFit® [32, 44, 46,47,48, 55, 75]. More than 50% of papers (n = 38) focused on the physical and physiological characterization of CrossFit® participants. Twenty-three (~ 34%) of the studies were organized by Brazilian authors, and American authors led 19 studies (~ 28%). Among European countries, Spanish authors were interested in research in CrossFit® (n = 12 manuscripts, ~ 18%) – Fig. 2, panel A. Data regarding the training experience in CrossFit® varied across the studies (Fig. 2, panel B). Most of the studies included participants with at least one year of experience (n = 24, ~ 35%), and the 6-month cut-off value to describe training experience was used in 15 studies (~ 22%). Three years of training experience was uniquely considered in four studies (n = 4).

Table 1 Summary of sampling, aim, methodologies and results of studies that investigated physical and physiological dimensions of CrossFit® participants
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Fig. 2
figure 2

Frequencies of studies published considering the origin of the first author (A) and the athletes' experience (B)

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Results of individual studies

As shown in Fig. 3 (panel A), regarding the evaluation of maximal oxygen uptake (i.e., VO2max) during different workouts (i.e., Fran, Isabel, Cindy), the values varied within the same training workout and across metabolic challenges. Considering the Fran workout exclusively, the VO2max reported in 20 participants with more than three years of practice was 49.2 ml.kg−1.min−1 [20], whilst the value was substantially lower in 10 participants with one year of CrossFit® experience (29.1 ml.kg−1.min−1) [85]. The VO2max of other metabolic challenges (i.e., Isabel and Cindy) was also substantially different than the values obtained in response to the Fran Workout [21, 85]. The overall mean of maximal heart combining the data points was 184 beats per minute, with individual values of each study ranging from 177 beats per minute on an as many rounds as possible workout [87] to 189 on rounds for time workout [45]. Figure 3 (panel B) indicates considerable variation between subjects. Mean heart rate differs substantially when the workouts are compared (Fig. 3, panel C). Higher mean heart rate values were observed in response to Fran's (179 ± 8 beats.min−1) and Cindy's (182 ± 7 beats.min−1) workouts. The mean value registered for the Murph workout was 169 ± 6 beats.min−1. Comparing the lactate measured immediately after CrossFit® sessions, the highest mean values were observed following rounds for time workouts. The lowest lactate values were noted following Cindy, Fran, and Murph's workouts (Fig. 3, panel D). The values of the ratings of perceived exertion, considering exclusively the studies that used the Borg scale 1–10, showed that rounds for time tend to be classified as the most physically demanding (Fig. 4). Body composition data indicated that male participants (overall mean considering two studies: 10.5%) presented lower values of fat mass percentage than females (overall mean considering two studies: 16.3%) (Fig. 5).

Fig. 3
figure 3

Physiological indicators reported according to type of CrossFit® challenge

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Fig. 4
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Rate of perceived exertion considering different types of CrossFit® challenges

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Fig. 5
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Mean values of fat mass percentage splitting by sex

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Recovery was assessed following a number of different CrossFit® workouts, with Fran [20, 46], Karen [43], Fight Gone Bad [10], Cindy [41], and Isabel [21] being the most examined workouts, while other studies classified the workouts as many rounds as possible or rounds for time [68]. Recovery was assessed after each workout using measures of physical performance (i.e., jumping, plank time) or physiological outcomes (i.e., heart rate, lactate, CPK). One study investigated the jumping and plank performance until 24 h after the Fran workout [20]. Physiological parameters were measured immediately-, 10-, 20-, and 30-min post-Fran workout [76]. As shown in Fig. 6, 30 min and 24 h after are not sufficient for the physical and physiological parameters to return to the baseline. The recovery process was also examined following Karen's workout using jumping height and creatine kinase as measures of recovery [43]. Jumping performance was comparable between baseline and 72 h after the Karen workout, whilst creatine kinase was substantially higher 72 h post-Karen workout (baseline: 151.1 ± 68.3; 72-h post-training: 223.0 ± 86.3, Fig. 7). Heart rate and lactate values 30 min after Open 18.4, Fight Gone Bad, and Cindy workouts were also higher than baseline [41, 76]. The same trend was noted in heart rate 5 min after the Isabel workout [21] (Figs. 8, 9, and 10). Although a separate study did not report the differences between the types of workouts, two different types of workouts were used – as many rounds as possible and rounds for time [68]. Physical performance returned to baseline 48 h post-workouts, while the physiological markers were comparable to baseline 72 h after the workout (Figs. 11 and 12).

Fig. 6
figure 6

Physical and physiological variation before and after Fran challenge

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Fig. 7
figure 7

Physical and physiological variation before and after Karen challenge

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Fig. 8
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Physical and physiological variation before and after Open 18.4 challenge

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Fig. 9
figure 9

Physiological variation before and after Fight Gone Bad challenge

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Fig. 10
figure 10

Physiological variation before and after Cindy challenge

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Fig. 11
figure 11

Physical and physiological variation before and after as many rounds as possible workouts

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Fig. 12
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Physical and physiological variation before and after rounds for time workouts

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Table 2 summarizes the studies assessing the chronic effects of CrossFit® participation [67, 69]. Six weeks was not sufficient to improve the physical performance outputs, but 8 weeks of CrossFit® participation resulted in increases in testosterone and decreases in cortisol levels. Using the same participants, only one interventional study assessed the change in physiological response (blood lactate, heart rate, ratings of perceived exertion, and ratings of discomfort) to different types of training (Cindy workout vs. continuous running) [23]. The mean heart rate was comparable in both groups. However, the maximal heart rate, blood lactate, ratings of perceived exertion, and ratings of perceived discomfort were higher during Cindy's workout. Separate investigations compared CrossFit® athletes with other groups, including runners [22], sedentary populations [25], athletes completing cross-training exercise [54], physically active cohorts [25], and resistance training participants [12, 84] in relation to changes in body composition, physiological responses, and physical performance.

Table 2 Studiesa that described the chronic effects of CrossFit® participation
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Table 3 presents data for body composition and VO2max. CrossFit® participants had lower values of fat mass percentage than sedentary [25], cross-training [54], or physically active individuals [65]. The VO2max of CrossFit® athletes was higher than the sedentary group [25] and lower than the runners [22]. CrossFit® participants also demonstrated superior physical performance (jumping performance, box-jump, pull-ups, push-ups, burpees, maximum speed) than cross-training [54] and resistance training participants [84].

Table 3 Observational studies comparing Crossfit® with other activities or control group
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Interventional [27, 34, 62] and observational studies [10, 28, 60] examined variation by competitive level. Two studies compared the mean and maximal heart rate in experienced and less experienced participants after implementing different training sessions [27] and Cindy workouts [62]. Mean values were equivalent between groups. Cognition variables were measured in 32 CrosssFit® athletes classified as elite, advanced, and beginner participants after Fran workouts [34]. All groups differed significantly between pre- and post-Fran workout. Additionally, two observational studies showed that CrossFit® participants of elite or advanced levels had lower fat mass values than those at the middle or recreational level [28, 60]. Strength variables were also affected by the competitive level of participants, with more experienced athletes showing higher values in the maximal repetition test [60], isometric mid-thigh pull assessment [28], rate of force development [28], and power [10].

Meta-correlation

Challenges focused on completing as many rounds as possible

Studies that used specific protocols of CrossFit® (back squat, front squat, snatch, clean and jerk) found a positive and moderate relationship between challenges focused on completing as many rounds as possible (r = 0.33; 95% CI:—0.09 to 0.65), which means that participants who lift more weight on strength protocols complete more rounds on the workouts. The magnitude of correlation increased when the specific protocols were expressed per kilogram of body mass (r = 0.38; 95% CI: 0.03 to 0.65; p = 0.03) (Figs. 13 and 14). In contrast, non-specific protocols were not associated with performance in as many rounds as possible workout. The values of heterogeneity were high and moderate for specific (I2 = 77.1%) and specific protocols relativize for body mass (I2 = 67%), respectively.

Fig. 13
figure 13

Meta-correlation between specific protocols and CrossFit® workouts classified as many rounds as possible. F (female); M (male). Note: positive indicates positive performance

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Fig. 14
figure 14

Meta-correlation between specific protocols normalized for body weight and CrossFit® workouts classified as many rounds as possible. F (female); M (male). Note: positive indicates positive performance

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Challenges focused on completing the workout fasting as possible

When the workouts focused on completing the challenge as fast as possible, time and specific CrossFit® protocols were not associated, as shown in Fig. 15. The overall correlation coefficient was positive (r = -0.18; 95% CI: -0.36 to 0.01; p = 0.07), demonstrating that athletes who were faster on challenge performed less strength on specific protocols. Similar results were obtained for non-specific CrossFit® protocols.

Fig. 15
figure 15

Fig. 14. Meta-correlation between specific protocols and CrossFit® workouts classified as time to complete. F (female); M (male). Note: positive indicates positive performance

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Specific workouts

Studies with correlation data between protocols and performance were available for the following workouts: Cindy, Fran, Donkey Kong, Grace, Murph, and Nancy. For Murph and Nancy workouts, data was limited, and for this reason, a meta-correlation was not conducted.

For the Cindy workout (which comprises completing as many rounds as possible in 20 min of five pull-ups, ten push-ups, and fifteen air squats), only one study (Butcher et al., 2015) tested the association between the number of rounds complete and non-specific (Wingate test and VO2max) and specific protocols (CrossFit®). As shown in Supplementary Material 1, the combination of different protocols resulted in a non-significant association with rounds performed on Cindy workout (r = 0.14; 95% CI: -0.15 to 0.41; p = 0.33).

The Fran challenge involves completing as fast as possible three rounds of 21, 15, and 9 repetitions of two exercises: thrusters and pull-ups. The meta-correlation of sub-groups (specific and non-specific) noted significant magnitudes of associations between types of protocol and performance (Fig. 16). A negative correlation coefficient indicated that more time to complete the challenge was associated with better performance in protocols. A small and non-significant association between Fran performance and non-specific protocols (r = 0.24, p = 0.19) was found. In opposition, specific protocols were moderately associated with performance in the Fran challenge (r = -0.44; 95% CI: 0.22 to 0.54; p < 0.05). The value of heterogeneity was high (I2 = 75%).

Fig. 16
figure 16

Meta-correlation between specific, non-specific protocols and Fran performance. Note: positive indicates positive performance

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On the Donkey Kong challenge, CrossFit® participants should complete as fast as possible three rounds of 21, 15, and 9 repetitions of burpees, kettlebell swings, and box jumps. After each exercise, participants need to perform six lunges. A negative correlation coefficient indicated that less time to complete the challenge was associated with a better performance in Donkey Kong challenge. Only one study examined the relationship between different protocols and performance in this challenge (Gomez-Landero et al., 2020). For non-specific protocols, the study included four different protocols: sit-ups, hand dynamometers, VO2max estimated from a shuttle-run test, and peak power derived from countermovement jumps. Specific protocols used were pull-ups, bench presses, and squats. Non-specific protocols were significantly related with performance, while the overall magnitude of correlation in specific protocols was small and non-significant (r = -0.26; 95% CI: -0.06 to 0.53; p = 0.11) (Supplementary Material 2).

The Grace challenge consists of performing 30 repetitions of clean and jerk as fast as possible, which means a negative correlation represents less time to complete the workout. Non-specific protocols were not associated with performance on Grace performance. On the other hand, total strength, strict press, deadlift, and back squat exercises were related to Grace performance (r = 0.478; 95% CI: 0.177 to 0.697; p = 0.003). The heterogeneity across studies was moderate (I2 = 62%) (Fig. 17).

Fig. 17
figure 17

Meta-correlation between specific, non-specific protocols and Grace performance. Note: positive indicates positive performance

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Discussion

The aim of this scoping review was to characterize the physical demands and physiological responses to CrossFit®. The findings and potential gaps in the scientific literature that emerged from the current review were as follows: (1) CrossFit® studies have mainly been undertaken in North and South America; (2) the definition of a CrossFit® athlete is not clear in the literature with different cut-off values being used to include participants in the studies; (3) a limited number of studies focused on characterizing the physiological and physical parameters of different workouts; (4) body composition data suggest that males have less fat mass percentage than females; (5) recovery strategies for CrossFit® should be investigated in order to optimize weekly performance and physiological markers; (6) the literature relation to the chronic effects of CrossFit® is scarce, although the study that did exist in this area demonstrated that six weeks was not sufficient to promote significant changes in physical and physiological parameters, while eight weeks led to increases in testosterone and decrements in cortisol; (7) in comparison to other exercise modalities (i.e., resistance training, endurance), CrossFit® elicits greater benefits to body composition and maximal oxygen uptake; (8) it was not possible to determine a unique predictor of CrossFit® performance; (9) movements specific to CrossFit® seem to be more related to CrossFit® performance than non-specific protocols.

 An early study compared three groups across ten weeks, with participants either undertaking aerobic training (n = 8), resistance training (n = 8), or concurrent training (n = 7) [88]. Concurrent training involves the inclusion of resistance training (to gain strength, hypertrophy, and power) combined with aerobic exercise (to enhance endurance) [89]. A reduction in lower body strength was found in the concurrent training group compared to resistance exercise. It was hypothesized that aerobic training negatively impacted the resistance training adaptations, termed the “interference effect” [66]. In a meta-analysis that combined 21 studies, resistance training promoted higher gains in hypertrophy, strength, and power than concurrent protocols [90]. This study also concluded that the type and volume of endurance training impact the “interference effects” of resistance training [90]. While in the meta-analysis, the details of studies were not presented, the participants of the concurrent training group included in the original research about the “interference effect” completed the resistance training and endurance protocol separated by two hours [88]. The training sessions of CrossFit® incorporated both endurance and resistance exercises within the same session, which suggests the “interference effect” cannot be generalized for CrossFit® participants. Nevertheless, more recent studies about the short-term effects of concurrent training on muscle hypertrophy showed contradictory findings questioning the “interference effect” theory [87, 91, 92]. A parallel study compared two different conditions (resistance training in isolation vs. concurrent training [i.e., combining cycling activities with resistance training]) across a 7-week program on muscle size and specific indicators of protein synthesis and degradation [91]. Muscle fiber area increased significantly in the concurrent training group, whilst negligible changes were noted in the resistance training group. The levels of the mechanistic target of rapamycin (i.e., an indicator of muscle mass development) were also raised in concurrent training, highlighting the anabolic effects when endurance and resistance activities were combined [91]. In the present review, CrossFit® participants had higher mean values of lean mass in comparison to physically active [65] and resistance training [74] participants, suggesting the combination of resistance training and endurance exercise within the same training session could potentiate the development of muscle mass. Participation in CrossFit® sessions over 8 weeks also demonstrated increases in testosterone and decreases in cortisol.

The main findings pertaining to concurrent training are modulated by the training status of participants and the methodologies used to assess changes in muscle strength and hypertrophy [5]. Three to nine months were proposed to classify an athlete as “trained” for a resistance and endurance athlete [5]. Although most of the studies included in the present review attained these criteria, the literature focused exclusively on the best athletes was scarce. Strength, power, and body composition distinguished elite athletes from lower competitive levels [10, 28, 60]. Moreover, considering the CrossFit® Open allows everyone to participate, significant variability in physical performance is expected. Future studies should focus on examining participants considering the different phases of CrossFit® competition: CrossFit® Open, quarterfinals, semi-finals, and CrossFit® Games. It might then be possible to discriminate participants according to training status and not focus exclusively on training time. Specificity was another concept claimed to define training status, particularly task-specific activities related to maximal strength. Hypertrophy is not exercise-dependent [5, 93], while changes in strength are exercise- and intensity-dependent (i.e., specific).

The relationship between CrossFit® performance and different protocols seems specific, with movements often carried out during CrossFit® sessions (e.g., back squat, front, deadlift, clean, clean and jerk) being more related to performance. In contrast, non-specific protocols were rarely associated with CrossFit® performance. For those who train CrossFit® athletes, the application of specific protocols has more relevance. Whereas, on the other hand, the results of the current meta-correlation also showed that it is difficult to generalize a particular test for all types of workouts. This is in line with a systematic review that described back squat and total body strength as the main variables to explain performance. However, variation in the results across the 21 studies included indicates that a consensus about predictors could not be generalized [9]. Considering the variability of CrossFit® in terms of exercises and intensity, this point is not surprising. The variability of physical and physiological indicators to explain the performance in CrossFit® workouts was noted, which indicates that the predictors of a typical endurance workout should not be generalized for a resistance workout [21, 28, 42, 66, 79]. In order to support coaches in the monitoring, quantification, and regulation of training load, future studies need to investigate the physical and physiological characteristics of other specific workouts.

A considerable number of studies used physical and physiological outputs to examine recovery after a CrossFit® workout [20, 41, 43, 68, 88]. In general, 48–72 h were insufficient to obtain the baseline values of physical and physiological markers, and these studies only focused on a specific challenge. A typical CrossFit® session includes other components rather than the workout of the day (e.g., strength, mobility, stability, skill). In order to prevent fatigue and optimize performance, research is needed on which recovery strategies for CrossFit® athletes are needed [6, 14].

The inclusion of papers solely written in English, Portuguese, and Spanish is a limitation of the current review. Studies about injuries or psychological variables were not considered since these topics were previously discussed in the literature [13, 94]. The non-uniform criteria relating to training experience required to qualify as a CrossFit® athlete is the major limitation of the eligible studies. Consequently, the participants' level must be discriminated in the sampling description. In most of the studies, a specific workout's physical or physiological description did not always consider the entire training session. Consequently, future studies should investigate training sessions' physical and physiological aspects individually or combined in microcycles or mesocycles.

Conclusion

CrossFit® seems to align with the recent benefits described in concurrent training, although they are modulated by training status and specificity of exercise. The definition of CrossFit® athlete needs to be considered in future studies since everyone can perform CrossFit Open®. In this competition, significant variability in performance and participants' characteristics are observed, influencing the interpretation of results. The correct manipulation of the training load is an additional issue for coaches in order to optimize performance and prevent fatigue. Coaches should be aware that the design and implementation of CrossFit® programs require specific information about the metabolic demands of each workout. Consequently, they should use training tools to control the volume and intensity of training to manage the training load. In order to interpret performance, protocols with specific CrossFit® movements should be routinely applied, even though it was challenging to obtain a test for all workouts. Therefore, further research needs to be conducted to characterize workouts that induce distinct physical and physiological responses.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information file.

References

  1. Glassman G. Understanding CrossFit CrossFit J. 2007;56:1–2.

    Google Scholar 

  2. Schlegel P. CrossFit® Training Strategies from the Perspective of Concurrent Training: A Systematic Review. J Sports Sci Med. 2020;19(4):670–80.

    PubMed  PubMed Central  Google Scholar 

  3. Glassman G. What is fitness CrossFit J. 2002;3:1–11.

    Google Scholar 

  4. Glassman G. Benchmark workouts CrossFit J. 2003;13:1–5.

    Google Scholar 

  5. Fyfe JJ, Loenneke JP. Interpreting Adaptation to Concurrent Compared with Single-Mode Exercise Training: Some Methodological Considerations. Sports Med. 2018;48(2):289–97.

    Article  PubMed  Google Scholar 

  6. Halson SL. Monitoring training load to understand fatigue in athletes. Sports Med. 2014;44 Suppl 2((Suppl 2)):S139–47.

    Article  PubMed  Google Scholar 

  7. About the Games. 2023. [https://games.crossfit.com/about-the-games].

  8. Edmonds W. Is the CrossFit Open the biggest sporting competition on Earth? CNN Sports. 2018.

    Google Scholar 

  9. Meier N, Schlie J, Schmidt A. CrossFit®: “Unknowable” or Predictable?-A Systematic Review on Predictors of CrossFit® Performance. Sports (Basel). 2023;11(6):112.

    Article  PubMed  Google Scholar 

  10. Bellar D, Hatchett A, Judge LW, Breaux ME, Marcus L. The relationship of aerobic capacity, anaerobic peak power and experience to performance in CrossFit exercise. Biol Sport. 2015;32(4):315–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Meyer J, Morrison J, Zuniga J. The Benefits and Risks of CrossFit: A Systematic Review. Workplace Health Saf. 2017;65(12):612–8.

    Article  PubMed  Google Scholar 

  12. Ángel Rodríguez M, García-Calleja P, Terrados N, Crespo I, Del Valle M, Olmedillas H. Injury in CrossFit®: A Systematic Review of Epidemiology and Risk Factors. Phys Sportsmed. 2022;50(1):3–10.

    Article  PubMed  Google Scholar 

  13. Dominski FH, Serafim TT, Siqueira TC, Andrade A. Psychological variables of CrossFit participants: a systematic review. Sport Sci Health. 2021;17(1):21–41.

    Article  PubMed  Google Scholar 

  14. de Souza RAS, da Silva AG, de Souza MF, Roschel H, Silva SF, Saunders B. A Systematic Review of CrossFit® Workouts and Dietary and Supplementation Interventions to Guide Nutritional Strategies and Future Research in CrossFit®. Int J Sport Nutr Exerc Metab. 2021;31(2):187–205.

    Article  PubMed  Google Scholar 

  15. Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane handbook for systematic reviews of interventions. 2nd Edition. Chichester: Wiley; 2019.

  16. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372: n71.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Wojtyniak JG, Britz H, Selzer D, Schwab M, Lehr T. Data Digitizing: Accurate and Precise Data Extraction for Quantitative Systems Pharmacology and Physiologically-Based Pharmacokinetic Modeling. CPT Pharmacometrics Syst Pharmacol. 2020;9(6):322–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc. 2009;41(1):3–13.

    Article  PubMed  Google Scholar 

  19. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58.

    Article  PubMed  Google Scholar 

  20. Rios M, Becker KM, Monteiro AS, Fonseca P, Pyne DB, Reis VM, Moreira-Gonçalves D, Fernandes R. Effect of the Fran CrossFit Workout on Oxygen Uptake Kinetics, Energetics, and Postexercise Muscle Function in Trained CrossFitters. Int J Sports Physiol Perform. 2024;19(3):299–306.

    Article  PubMed  Google Scholar 

  21. Rios M, Becker KM, Cardoso F, Pyne DB, Reis VM, Moreira-Gonçalves D, Fernandes RJ. Assessment of Cardiorespiratory and Metabolic Contributions in an Extreme Intensity CrossFit® Benchmark Workout. Sensors (Basel). 2024;24(2):513.

    Article  PubMed  Google Scholar 

  22. Carvalho LL, Costa DA, N, Mansour KM, Simonis JG, Teixeira L, Gonçalves DP, Rekziegel MB, Possuelo LG, Valim ARDE. Effects of Crossfit® and street running practice on anthropometric, lipids parameters, cardiorespiratory fitness and sleep quality. J Sports Med Phys Fitness. 2024;64(1):1–6.

    PubMed  Google Scholar 

  23. Santos DAT, Morais NS, Viana RB, Costa GCT, Andrade MS, Vancini RL, Weiss K, Knechtle B, de Lira CAB. Comparison of physiological and psychobiological acute responses between high intensity functional training and high intensity continuous training. Sports MedHealth Sci. 2023. https://doi.org/10.1016/j.smhs.2023.10.006.

  24. Rios M, Zacca R, Azevedo R, Fonseca P, Pyne DB, Reis VM, Moreira-Gonçalves D, Fernandes RJ. Bioenergetic Analysis and Fatigue Assessment During the Fran Workout in Experienced Crossfitters. Int J Sports Physiol Perform. 2023;18(7):786–92.

    Article  PubMed  Google Scholar 

  25. Pearson RC, Olenick AA, Jenkins NT. Metabolic response during high-intensity interval exercise and resting vascular and mitochondrial function in CrossFit participants. Kinesiology. 2023;55(2):228–44.

    Article  Google Scholar 

  26. Párraga-Montilla JA, Cabrera Linares JC, Jiménez Reyes P, Moyano López M, Serrano Huete V, Morcillo Losa JA, Latorre Román PA. Force–velocity profiles in CrossFit athletes: A cross-sectional study considering sex, age, and training frequency. Balt J Health Phys Act. 2023;15(1):5.

    Article  Google Scholar 

  27. Meier N, Sietmann D, Schmidt A. Comparison of cardiovascular parameters and internal training load of different 1-h training sessions in non-elite CrossFit® athletes. J Sci Sport Exerc. 2023;5(2):130–41.

    Article  Google Scholar 

  28. Manrique JEH, Chavarría WEBM, Sánchez WGV, Velásquez CAA. Are there differences in maximal strength, flexibility, and body composition in CrossFit® competitors according to their category? Retos. 2023;47:866–75.

    Article  Google Scholar 

  29. Mangine GT, Zeitz EK, Dexheimer JD, Hines A, Lively B, Kliszczewicz BM. Pacing Strategies Differ by Sex and Rank in 2020 CrossFit® Open Tests. Sports (Basel). 2023;11(10):199.

    Article  PubMed  Google Scholar 

  30. Mangine GT, Grundlingh N, Feito Y. Normative Scores for CrossFit® Open Workouts: 2011–2022. Sports (Basel). 2023;11(2):24.

    Article  PubMed  Google Scholar 

  31. Mangine GT, Grundlingh N, Feito Y. Differential improvements between men and women in repeated CrossFit open workouts. PLoS ONE. 2023;18(11): e0283910.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Linhares M, Façanha C, Teixeira M, Alves KS, Pires T, Coswig V, Cabido C, Fermino RC, Oliveira S, de Frota Souza TM, Aidar FJ, Acioli T, Cirilo-Sousa MS, de Rabello Lima LC, Bertú F, Assumpção CO, Banja T. Examining strength, muscular power, and maximal performance in the power clean among CrossFit® practitioners. J Phys Educ Sport. 2023;23(11):3119–26.

    Google Scholar 

  33. da Silveira Castanheira LF, Penna EM, Franco ECS, Coswig VS. Training load monitoring and physiological responses to RX CrossFit® training. J Phys Educ Sport. 2023;23(5):1076–85.

    Google Scholar 

  34. Brito MA, Fernandes JR, De Carvalho PHB, Brito CJ, Aedo-Muñoz E, Soto DAS, Miarka B. Acute Effect of a Cross-Training Benchmark on Psychophysiological Factors of Cross-Training According to Performance. Sport Mont. 2023;21(2):9–15.

    Article  Google Scholar 

  35. Barreto AC, Medeiros AP, da Silva AG, de Souza Vale RG, Vianna JM, Alkimin R, Serra R, Leitão L, Reis VM, da Silva Novaes J. Heart rate variability and blood pressure during and after three CrossFit® sessions. Retos. 2023;47:311–6.

    Article  Google Scholar 

  36. Schlegel P, Křehký A. Performance Sex Differences in CrossFit®. Sports (Basel). 2022;10(11):165.

    Article  PubMed  Google Scholar 

  37. Menargues-Ramírez R, Sospedra I, Holway F, Hurtado-Sánchez JA, Martínez-Sanz JM. Evaluation of Body Composition in CrossFit® Athletes and the Relation with Their Results in Official Training. Int J Environ Res Public Health. 2022;19(17):11003.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Martínez-Gómez R, Valenzuela PL, Lucia A, Barranco-Gil D. Comparison of Different Recovery Strategies After High-Intensity Functional Training: A Crossover Randomized Controlled Trial. Front Physiol. 2022;13: 819588.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Mangine GT, McDougle JM, Feito Y. Relationships Between Body Composition and Performance in the High-Intensity Functional Training Workout “Fran” are Modulated by Competition Class and Percentile Rank. Front Physiol. 2022;13: 893771.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Mangine GT, Seay TR. Quantifying CrossFit®: Potential solutions for monitoring multimodal workloads and identifying training targets. Front Sports Act Living. 2022;4: 949429.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Forte LDM, Freire YGC, Júnior JSDS, Melo DA, Meireles CLS. Physiological responses after two different CrossFit workouts. Biol Sport. 2022;39(2):231–6.

    Article  PubMed  Google Scholar 

  42. Dias MR, Vieira JG, Pissolato JC, Heinrich KM, Vianna JM. Training load through heart rate and perceived exertion during CrossFit®. Rev Bra Med Esporte. 2022;28:315–9.

    Article  Google Scholar 

  43. Sousa Neto IVD, Sousa NMFD, Neto FR, Falk Neto JH, Tibana RA. Time course of recovery following CrossFit® Karen Benchmark workout in trained men. Front Physiol. 2022;2022(13): 899652.

    Article  Google Scholar 

  44. Conde TF, Silva MRDS, Caobianco J, Robalino J, Ferreira JC. Sensitivity of operational tests to training load in Crossfit®. J Phys Educ Sport. 2022;22(6):1493–8.

    Google Scholar 

  45. Toledo R, Dias MR, Toledo R, Erotides R, Pinto DS, Reis VM, Novaes JS, Vianna JM, Heinrich KM. Comparison of Physiological Responses and Training Load between Different CrossFit® Workouts with Equalized Volume in Men and Women. Life (Basel). 2021;11(6):586.

    PubMed  Google Scholar 

  46. Tibana RA, de Sousa Neto IV, Sousa NMF, Romeiro C, Hanai A, Brandão H, Dominski FH, Voltarelli FA. Local Muscle Endurance and Strength Had Strong Relationship with CrossFit® Open 2020 in Amateur Athletes. Sports (Basel). 2021;9(7):98.

    Article  PubMed  Google Scholar 

  47. Schlegel P, Režný L, Fialová D. Pilot study: Performance-ranking relationship analysis in Czech crossfiters. J Hum Sport Exerc. 2021;16(1):187–98.

    Google Scholar 

  48. Ponce-García T, Benítez-Porres J, García-Romero JC, Castillo-Domínguez A, Alvero-Cruz JR. The Anaerobic Power Assessment in CrossFit® Athletes: An Agreement Study. Int J Environ Res Public Health. 2021;18(16):8878.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Peña J, Moreno-Doutres D, Peña I, Chulvi-Medrano I, Ortegón A, Aguilera-Castells J, Buscà B. Predicting the Unknown and the Unknowable. Are Anthropometric Measures and Fitness Profile Associated with the Outcome of a Simulated CrossFit® Competition? Int J Environ Res Public Health. 2021;18(7):3692.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Mota MR, Brandao HCP, Alencastro G, Elias R, Ribeiro A, de Araujo Ribeiro AL, Chaves SN, Cleto F, Silva AS, Clael S. Glycemia Analysis in Two Different CrossFit [R] Benchmark Protocols. J Exerc Physiol Online. 2021;24(2):1–9.

    Google Scholar 

  51. Meier N, Rabel S, Schmidt A. Determination of a CrossFit® Benchmark Performance Profile. Sports (Basel). 2021;9(6):80.

    Article  PubMed  Google Scholar 

  52. Mangine GT, Feito Y, Tankersley JE, McDougle JM, Kliszczewicz BM. Workout Pacing Predictors of Crossfit® Open Performance: A Pilot Study. J Hum Kinet. 2021;78:89–100.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Leitão L, Dias M, Campos Y, Vieira JG, San’t Ana L, Telles LG, Tavares C, Mazini M, Noves J, Vianna J. Physical and Physiological Predictors of FRAN CrossFit® WOD Athlete’s Performance. Int J Environ Res Public Health. 2021;18(8):4070.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Fernando W, Santos W, Barbieri J, de Medeiros Lima LE, Miguel H, Guedes D Jr, Silva RP, Marchioni E, Moriggi JRR. Physical capacities and anthropometric measures between crossfit® practitioners and crosstraining. Multidiscip Res J. 2021;3(2): e2021006.

    Google Scholar 

  55. Fernández-Lázaro D, Mielgo-Ayoulso J, Novo DFZS, Lázaro-Asensio MP, Sánchez-Serrano N, Fernández-Lázaro CI. Athletic, muscular and hormonal evaluation in CrossFit® athletes using the Elevation Training Mask. Arch Med Deporte. 2021;38:274–81.

    Article  Google Scholar 

  56. Bustos-Viviescas BJ, Luna LAD, Osorio RDM, Parra AJO, Acevedo-Mindiola AA, García Yerena CE. High-intensity functional training: Association of body fat with cardiorespiratory fitness. Revista Cubana de Medicina Militar. 2021;50(2):1–13.

  57. Pritchard HJ, Keogh JW, Winwood PW. Tapering practices of elite CrossFit athletes. Int J Sports Sci Coach. 2020;15(5–6):753–61.

    Article  Google Scholar 

  58. Martínez-Gómez R, Valenzuela PL, Alejo LB, Gil-Cabrera J, Montalvo-Pérez A, Talavera E, Lucia A, Moral-González S, Barranco-Gil D. Physiological Predictors of Competition Performance in CrossFit Athletes. Int J Environ Res Public Health. 2020;17(10):3699.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Mangine GT, Tankersley JE, McDougle JM, Velazquez N, Roberts MD, Esmat TAM. Predictors of CrossFit Open Performance. Sports (Basel). 2020;8(7):102.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Mangine GT, Stratton MT, Almeda CG, Roberts MD, Esmat TA, VanDusseldorp T, Feito Y. Physiological differences between advanced CrossFit athletes, recreational CrossFit participants, and physically-active adults. PLoS ONE. 2020;15(4): e0223548.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Gómez-Landero LA, Frías-Menacho JM. Analysis of Morphofunctional Variables Associated with Performance in Crossfit® Competitors. J Hum Kinet. 2020;73:83–91.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Gomes JH, Mendes RR, Franca CS, Silva-Grigoletto MES, Silva DRP, Antoniolli AR, Oliveira E, Silva AM, Quintans-Júnior L. Acute leucocyte, muscle damage, and stress marker responses to high-intensity functional training. PLoS One. 2020;15(12):e0243276.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Faelli E, Bisio A, Codella R, Ferrando V, Perasso M, Panascì M, Saverino D, Ruggeri P. Acute and Chronic Catabolic Responses to CrossFit® and Resistance Training in Young Males. Int J Environ Res Public Health. 2020;17(19):7172.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Dexheimer JD, Schroeder ET, Sawyer BJ, Pettitt RW, Torrence WA. Total Body Strength Predicts Workout Performance in a Competitive Fitness Weightlifting Workout. J Exerc Physiol Online. 2020;23(4):95–104.

  65. Cavedon V, Milanese C, Marchi A, Zancanaro C. Different amount of training affects body composition and performance in High-Intensity Functional Training participants. PLoS ONE. 2020;15(8): e0237887.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Carreker JD, Grosicki GJ. Physiological Predictors of Performance on the CrossFit “Murph” Challenge. Sports (Basel). 2020;8(7):92.

    Article  PubMed  Google Scholar 

  67. Cardeñosa AC, Andrada RT, Cardeñosa MC, Flores SG, Camacho GJO, Serrano MM. Six-months CrossFit training improves metabolic efficiency in young trained men. Culto Ciência Esporte. 2020;15(45):421–7.

    Google Scholar 

  68. Timón R, Olcina G, Camacho-Cardeñosa M, Camacho-Cardenosa A, Martinez-Guardado I, Marcos-Serrano M. 48-hour recovery of biochemical parameters and physical performance after two modalities of CrossFit workouts. Biol Sport. 2019;36(3):283–9.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Poderoso R, Cirilo-Sousa M, Júnior A, et al. Gender Differences in Chronic Hormonal and Immunological Responses to CrossFit®. Int J Environ Res Public Health. 2019;16(14):2577.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Martínez-Gómez R, Valenzuela PL, Barranco-Gil D, Moral-González S, García-González A, Lucia A. Full-Squat as a Determinant of Performance in CrossFit. Int J Sports Med. 2019;40(9):592–6.

    Article  PubMed  Google Scholar 

  71. Feito Y, Giardina MJ, Butcher S, Mangine GT. Repeated anaerobic tests predict performance among a group of advanced CrossFit-trained athletes. Appl Physiol Nutr Metab. 2019;44(7):727–35.

    Article  CAS  PubMed  Google Scholar 

  72. Dexheimer JD, Schroeder ET, Sawyer BJ, Pettitt RW, Aguinaldo AL, Torrence WA. Physiological Performance Measures as Indicators of CrossFit® Performance. Sports (Basel). 2019;7(4):93.

    Article  PubMed  Google Scholar 

  73. de Oliveira FTO, Ramos ACC, Almeida CN, Dos Santos CPC, Oliveira IAA, Mendel MDR, Santos ECL, Dias CMCC. Modulation of heart rate variability in CrossFit® practitioners. Revista Pesquisa em Fisioterapia. 2019;9(3):353–60.

    Google Scholar 

  74. Barbieri JF, Figueiredo GTDC, Castano LAA, Guimaraes PDS, Ferreira RR, Ahmadi S, Gaspari AF, De Moraes AC. A comparison of cardiorespiratory responses between CrossFit® practitioners and recreationally trained individual. J Phys Educ Sport. 2019;19(3):1606–11.

    Google Scholar 

  75. Alsamir Tibana R, de Manuel Frade Sousa N, Prestes J, Nascimento DC, Ernesto C, Neto JHF, Kennedy MD, Voltarelli FA. Is Perceived Exertion a Useful Indicator of the Metabolic and Cardiovascular Responses to a Metabolic Conditioning Session of Functional Fitness? Sports (Basel). 2019;7(7):161.

    Article  PubMed  Google Scholar 

  76. Tibana RA, De Sousa NMF, Prestes J, Voltarelli FA. Lactate, Heart Rate and Rating of Perceived Exertion Responses to Shorter and Longer Duration CrossFit® Training Sessions. J Funct Morphol Kinesiol. 2018;3(4):60.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Tibana RA, de Sousa NMF, Cunha GV, Prestes J, Fett C, Gabbet TJ, Voltarelli FA. Validity of Session Rating Perceived Exertion Method for Quantifying Internal Training Load during High-Intensity Functional Training. Sports (Basel). 2018;6(3):68.

    Article  PubMed  Google Scholar 

  78. Tibana RA, Farias DL, Nascimento DC, Silva-Grigoletto ME, Prestes J. Correlation of muscle strength with weightlifting performance in CrossFit® practitioners. Rev Andaluza Med Deporte. 2016;11(2):84–8.

    Article  Google Scholar 

  79. Serafini PR, Feito Y, Mangine GT. Self-reported Measures of Strength and Sport-Specific Skills Distinguish Ranking in an International Online Fitness Competition. J Strength Cond Res. 2018;32(12):3474–84.

    Article  PubMed  Google Scholar 

  80. Mangine GT, Van Dusseldorp TA, Feito Y, Holmes AJ, Serafini PR, Box AG, Gonzalez AM. Testosterone and Cortisol Responses to Five High-Intensity Functional Training Competition Workouts in Recreationally Active Adults. Sports (Basel). 2018;6(3):62.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Mangine GT, Cebulla B, Feito Y. Normative Values for Self-Reported Benchmark Workout Scores in CrossFit® Practitioners. Sports Med Open. 2018;4(1):39.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Prado Dantas TS, Aidar FJ, de Souza RF, de Matos GD, Pires Ferreira AR, de Almeida BN, Santos MDM, Barros GO, Santos CRR, da Silva Júnior WM. Evaluation of a CrossFit® Session on Post-Exercise Blood Pressure. J Exerc Physiol Online. 2018;21(1):44–51.

    Google Scholar 

  83. Ouellette KA, Brusseau TA, Davidson LE, Ford CN, Hatfield DL, Shaw JM, Eisenman PA. Comparison of the Effects of Seated, Supine, and Walking Interset Rest Strategies on Work Rate. J Strength Cond Res. 2016;30(12):3396–404.

    Article  PubMed  Google Scholar 

  84. de Sousa AF, dos Santos GB, dos Reis T, Valerino AJ, Del Rosso S, Boullosa DA. Differences in Physical Fitness between Recreational CrossFit® and Resistance Trained Individuals. J Exerc Physiol Online. 2016;19(5):112–22.

    Google Scholar 

  85. Fernández JF, Solana RS, Moya D, Marin JMS, Ramón MM. Acute physiological responses during crossfit® workouts. European Journal of Human Movement. 2015;35:114–24.

    Google Scholar 

  86. Butcher SJ, Neyedly TJ, Horvey KJ, Benko CR. Do physiological measures predict selected CrossFit(®) benchmark performance? Open Access J Sports Med. 2015;6:241–7.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Lundberg TR, Fernandez-Gonzalo R, Gustafsson T, Tesch PA. Aerobic exercise does not compromise muscle hypertrophy response to short-term resistance training. J Appl Physiol (1985). 2013;114(1):81–9.

    Article  PubMed  Google Scholar 

  88. Hickson RC. Interference of strength development by simultaneously training for strength and endurance. Eur J Appl Physiol Occup Physiol. 1980;45(2–3):255–63.

    Article  CAS  PubMed  Google Scholar 

  89. Berryman N, Mujika I, Bosquet L. Concurrent Training for Sports Performance: The 2 Sides of the Medal. Int J Sports Physiol Perform. 2019;14(3):279–85.

    Article  PubMed  Google Scholar 

  90. Wilson JM, Marin PJ, Rhea MR, Wilson SM, Loenneke JP, Anderson JC. Concurrent training: a meta-analysis examining interference of aerobic and resistance exercises. J Strength Cond Res. 2012;26(8):2293–307.

    Article  PubMed  Google Scholar 

  91. Kazior Z, Willis SJ, Moberg M, et al. Endurance Exercise Enhances the Effect of Strength Training on Muscle Fiber Size and Protein Expression of Akt and mTOR. PLoS ONE. 2016;11(2): e0149082.

    Article  PubMed  PubMed Central  Google Scholar 

  92. Murach KA, Bagley JR. Skeletal Muscle Hypertrophy with Concurrent Exercise Training: Contrary Evidence for an Interference Effect. Sports Med. 2016;46(8):1029–39.

    Article  PubMed  Google Scholar 

  93. Buckner SL, Mouser JG, Jessee MB, Dankel SJ, Mattocks KT, Loenneke JP. What does individual strength say about resistance training status? Muscle Nerve. 2017;55(4):455–7.

    Article  PubMed  Google Scholar 

  94. Summitt RJ, Cotton RA, Kays AC, Slaven EJ. Shoulder Injuries in Individuals Who Participate in CrossFit Training. Sports Health. 2016;8(6):541–6.

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. University of Coimbra, Research Unit for Sport and Physical Activity, Faculty of Sport Sciences and Physical Education, Coimbra, Portugal

    Diogo V. Martinho, Renato Costa, Alex S. Ribeiro, Catarina Amorim & Hugo Sarmento

  2. Laboratory of Robotics and Engineering Systems, Interactive Technologies Institute, Funchal, Portugal

    Diogo V. Martinho & Élvio R. Gouveia

  3. CIDEFES, Centro de Investigação em Desporto, Educação Física e Exercício e Saude, Universidade Lusófona, Lisbon, Portugal

    André Rebelo

  4. COD, Center of Sports Optimization, Sporting Clube de Portugal, Lisbon, Portugal

    André Rebelo

  5. Department of Physical Education and Sport, University of Madeira, Funchal, Portugal

    Élvio R. Gouveia

  6. Department of Sport and Exercise Science, Manchester Metropolitan University, Manchester, United Kingdom

    Adam Field

  7. Research Group in Physiology and Physical Activity, University of Northern Paraná, Londrina, Brazil

    Juliano Casonatto

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DS, DVM, ERG, HS conceptualized the manuscript. DS, DVM, ERG, HS wrote the protocol and methodology. DVM, AR, CA, DSB, NS used and managed the software. DVM, ERG, AS, AF, HS organized the data. DVM, AR, CA, NS, AS writing the original draft. DS, DVM, CM, AF, ERG, HS wrote and reviewed the manuscript.

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Martinho, D.V., Rebelo, A., Gouveia, É.R. et al. The physical demands and physiological responses to CrossFit®: a scoping review with evidence gap map and meta-correlation. BMC Sports Sci Med Rehabil 16, 196 (2024). https://doi.org/10.1186/s13102-024-00986-3

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BMC Sports Science, Medicine and Rehabilitation

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