Self-reported Measures of Strength and Sport-Specific Skills Distinguish Ranking in an International Online Fitness Competition

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.

Physical and Physiological Characteristics of Elite CrossFit Athletes

Due to little available research on elite CrossFit athletes, the present investigation was undertaken to provide knowledge about their physical and physiological characteristics. Nineteen international level CrossFit athletes (8 men; 11 women) were tested for maximum oxygen consumption (V˙O2max), Wingate, squat 1 repetition maximum (1RM), countermovement jump (CMJ), lower body force-velocity, and body composition (DXA). To provide perspective, the results were compared to sixteen elite-level Alpinists (8 men; 8 women). There was no significant difference in absolute nor relative V˙O2max between the CrossFit and Alpinist men (p = 0.335 and p = 0.279, respectively). The CrossFit women showed similar absolute but higher relative V˙O2max than the Alpinist women (p = 0.055 and p = 0.005, respectively). Mean anaerobic power was higher in both CrossFit men and women (p = 0.021 and p = 0.008, respectively). There were no significant differences in squat 1RM and CMJ for both men and women (p > 0.05). Both CrossFit men and women showed lesser lower body force production (p = 0.043 and p = 0.034, respectively) but higher power (p = 0.009 and p = 0.003, respectively). The CrossFit men and women had a lower fat mass (p = 0.018 and p = 0.002, respectively) and fat percentage (p = 0.027 and p < 0.001, respectively). These observations show that elite CrossFit athletes possess physical and physiological characteristics comparable to world-class Alpinists.

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.

Effects of high-intensity functional training on physical fitness and sport-specific performance among the athletes: A systematic review with meta-analysis

OBJECTIVE

This study aims to meta-analyze the impact of high-intensity functional training on athletes' physical fitness and sport-specific performance.

METHODS

A systematic search was conducted in five well-known academic databases (PubMed, Scopus, Web of Science, EBSCOhost, and the Cochrane Library) up to July 1, 2023. The literature screening criteria included: (1) studies involving healthy athletes, (2) a HIFT program, (3) an assessment of outcomes related to athletes' physical fitness or sport-specific performance, and (4) the inclusion of randomized controlled trials. The Physical Therapy Evidence Database (PEDro) scale was used to evaluate the quality of studies included in the meta-analysis.

RESULTS

13 medium- and high-quality studies met the inclusion criteria for the systematic review, involving 478 athletes aged between 10 and 24.5 years. The training showed a small to large effect size (ES = 0.414-3.351; all p < 0.05) in improving upper and lower body muscle strength, power, flexibility, and sport-specific performance.

CONCLUSION

High-intensity functional training effectively improves athletes' muscle strength, power, flexibility, and sport-specific performance but has no significant impact on endurance and agility. Future research is needed to explore the impact of high-intensity functional training on athletes' speed, balance, and technical and tactical performance parameters.

Trunk, Mass Grasp, Knee, and Hip Muscle Performance in CrossFit Participants: Reference Values According to Participants' Sex and Limb Dominance

Background

CrossFit is characterized by a diverse range of exercises recruiting different muscles and requiring different muscle functions. A characterization of muscular performance parameters in this population is needed.

Purpose

To determine reference values for various aspects of muscular performance of muscles of the trunk, thigh, hip, and mass grasp in CrossFit participants. Also, this investigation aimed to compare the strength measures between male and female CrossFit participants, as well as between dominant and non-dominant limbs.

Design

Descriptive, Cross-sectional.

Setting

Laboratory.

Methods

Isometric strength of trunk extensors (TE) and mass grasp was measured with handheld and Jamar dynamometer respectively. An isokinetic dynamometer was used to assess the muscle performance of the knee flexors (KF) and extensors (KE) (at 60º/s and 300º/s), and hip flexors (HF), extensors (HE), and abductors (HA) (60º/s and 240º/s ). Reference values for torque, work, power, fatigue, flexor:extensor ratio for the knee (hamstring:quadriceps - H:Q) and hip (HF:HE) joints were calculated. The torque and work values were normalized by the body mass. Mixed multivariate and univariate analyses of variance and independent t-tests were used for statistical analyses to compare between sexes and limbs.

Results

Participants included 111 individuals (58 males and 53 females) with at least one year of experience in CrossFit. Normative data are provided for the outcome variables. Males had greater values of muscular performance parameters than females in most variables (p<0.05). Also, the dominant limb had greater mass grasp strength (p<0.002), greater KE power at 60º/s (p=0.015), lower H:Q ratio at 60º/s (p=0.021) and 300º/s (p=0.008), and lower KE fatigue (p=0.002).

Conclusion

This study provides reference values for the trunk extensors, mass grasp, knee, and hip muscle performance in male and female CrossFit practitioners. Their muscle performance profile was characterized by few inter-limb asymmetries, and males demonstrated greater muscular performance outcomes than females, even after normalization by body mass. These reference values can be used for comparisons in research and clinical settings.

Level of Evidence

3b.

Normative Scores for CrossFit® Open Workouts: 2011-2022

To create normative scores for all CrossFit^® Open (CFO) workouts and compare male and female performances, official scores were collected from the official competition leaderboard for all competitors of the 2011-2022 CFO competitions. Percentiles were calculated for athletes (18-54 years) who completed all workouts within a single year 'as prescribed' and met minimum scoring thresholds. Independent t-tests revealed significant (p < 0.05) sex differences for 56 of 60 workouts. In workouts scored by repetitions completed, men completed more repetitions in 18 workouts by small to large differences (d = 0.22-0.81), whereas women completed more repetitions in 6 workouts by small to medium differences (d = 0.36-0.77). When workouts were scored by time to completion, men were faster in 10 workouts by small to large differences (d = 0.23-1.12), while women were faster in 3 workouts by small differences (d = 0.46). In three workouts scored by load lifted, men lifted more weight by large differences (d = 2.00-2.98). All other differences were either trivial or not significant. Despite adjusted programming for men and women, the persistence of performance differences across all CFO workouts suggests that resultant challenges are not the same. These normative values may be useful for training and research in male and female CrossFit^® athletes.

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.

CrossFit® open performance is affected by the nature of past competition experiences

Purpose

To examine the relationships between past competition performances and 2020 CrossFit® Open (CFO) performance.

Methods

A random selection from the top one thousand athletes (n = 220, 28.5 ± 4.4 years, 178 ± 7 cm, 87.5 ± 10.2 kg) were selected for this study. Overall and weekly performances (including ranks and scores) of the 2020 CFO, as well as overall ranks from all previous CFO, regional, and Games™ competitions in which they competed, were recorded from their publicly available online profile. The highest, lowest, average, and standard deviation (SD) of past rankings, as well as participation statistics (i.e., years since first appearance, total and consecutive appearances, and participation rate), were calculated for each competition stage. Relationships were then assessed between 2020 CFO performance and all past competition experience variables by calculating Kendall’s tau (τ) correlation coefficients and Bayes factors (BF₁₀).

Results

Overall and weekly ranking of the 2020 CFO was extremely favored (p < 0.001, BF₁₀ > 100) to be related to the athlete’s highest previous CFO rank (τ = 0.26–0.39) and individual regional appearances (τ = − 0.26 to − 0.34), as well as individual Games™ appearances (overall and for weeks 1, 3, and 4; τ = − 0.20 to − 0.22, p < 0.001, BF₁₀ > 100). Evidence for all other significant relationships ranged from moderate to very strong (p < 0.05, BF₁₀ = 3–100) and varied among specific 2020 CFO workouts. Few associations were noted for team competition experience, and these were generally limited to Games™ appearances (τ = − 0.12 to − 0.18, p < 0.05, BF₁₀ = 3.3–100).

Conclusions

Although specific relationships were found between 2020 CFO performance and individual appearances at regional and Games™ competitions, the most consistent relationships were seen with participation and ranking in past CFO competitions.

Physical and Physiological Predictors of FRAN CrossFit® WOD Athlete's Performance

CrossFit^® training is one of the fastest-growing fitness activities in the world due to its varied functional movement and competition experience. The performance is present in almost every workout of the day (WOD); however, there is a lack of knowledge in the science that did not allow us to fully understand the performance determinants of CrossFit WOD’s like we do for other individual or team sports. The purpose of this study was to analyze the physical and physiological variables of recreational trained CrossFit athletes during one of the most famous WOD, FRAN, and to identify which variables best determine performance. Methods: Fifteen CrossFit practitioners performed, alone on separate days, 1RM and a maximum of repetitions of pull-ups test, 1RM and a maximum of repetitions of thrusters with 95 lb/43.2 kg, FRAN CrossFit WOD, and 2K Row test. Results: Blood lactate concentrate, HR_max, HR_av, and RPE achieved higher values for 2K Row and maximum repetitions of thrusters. Maximum repetition of thrusters and pull-ups, 1RM of thrusters, and 2K Row resulted in moderate to strong correlation with FRAN performance (r = −0.78; r = −0.58; r = −0.67; r = 0.63, respectively). Conclusions and practical applications: FRAN performance was strongly related to maximal and endurance strength training of thrusters, which should be prioritized.

Workout Pacing Predictors of Crossfit® Open Performance: A Pilot Study

To observe workout repetition and rest interval pacing strategies and determine which best predicted performance during the 2016 CrossFit® Open, five male (34.4 ± 3.8 years, 176 ± 5 cm, 80.3 ± 9.7 kg) and six female (35.2 ± 6.3 years, 158 ± 7 cm, 75.9 ± 19.3 kg) recreational competitors were recruited for this observational, pilot study. Exercise, round, and rest time were quantified via a stopwatch for all competitors on their first attempt of each of the five workouts. Subsequently, pacing was calculated as a repetition rate (repetitions·s^-1) to determine the fastest, slowest, and average rate for each exercise, round, and rest interval, as well as how these changed (i.e., slope, Δ rate / round) across each workout. Spearman’s rank correlation coefficients indicated that several pacing variables were significantly (p < 0.05) related to performance on each workout. However, stepwise regression analysis indicated that the average round rate best predicted (p < 0.001) performance on the first (R² = 0.89), second (R² = 0.99), and fifth (R² = 0.94) workouts, while the competitors’ rate on their slowest round best predicted workout three performance (R² = 0.94, p < 0.001). The wall ball completion rate (R² = 0.89, p = 0.002) was the best predictor of workout four performance, which was improved by 9.8% with the inclusion of the deadlift completion rate. These data suggest that when CrossFit^® Open workouts consist of multiple rounds, competitors should employ a fast and sustainable pace to improve performance. Otherwise, focusing on one or two key exercises may be the best approach.

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.

Physiological differences between advanced CrossFit athletes, recreational CrossFit participants, and physically-active adults

This investigation examined anthropometric, hormonal, and physiological differences between advanced (ADV; n = 8, 27.8 ± 4.2 years, 170 ± 11 cm, 79.8 ± 13.3 kg) and recreational (REC; n = 8, 33.5 ± 8.1 years, 172 ± 14 cm, 76.3 ± 19.5 kg) CrossFit (CF) trained participants in comparison to physically-active controls (CON; n = 7, 27.5 ± 6.7 years, 171 ± 14 cm, 74.5 ± 14.3 kg). ADV and REC were distinguished by their past competitive success. REC and CON were resistance-trained (>2 years) and exercised on 3-5 days·wk-1 for the past year, but CON utilized traditional resistance and cardiovascular exercise. All participants provided a fasted, resting blood sample and completed assessments of resting metabolic rate, body composition, muscle morphology, isometric mid-thigh pull strength, peak aerobic capacity, and a 3-minute maximal cycle ergometer sprint across two separate occasions (separated by 3-7 days). Blood samples were analyzed for testosterone, cortisol, and insulin-like growth factor-1. Compared to both REC and CON, one-way analysis of variance revealed ADV to possess lower body fat percentage (6.7-8.3%, p = 0.007), greater bone and non-bone lean mass (12.5-26.8%, p ≤ 0.028), muscle morphology characteristics (14.2-59.9%, p < 0.05), isometric strength characteristics (15.4-41.8%, p < 0.05), peak aerobic capacity (18.8-19.1%, p = 0.002), and 3-minute cycling performance (15.4-51.1%, p ≤ 0.023). No differences were seen between REC and CON, or between all groups for resting metabolic rate or hormone concentrations. These data suggest ADV possess several physiological advantages over REC and CON, whereas similar physiological characteristics were present in individuals who have been regularly participating in either CF or resistance and cardiovascular training for the past year.

Repeated anaerobic tests predict performance among a group of advanced CrossFit-trained athletes

High-intensity functional training (HIFT) (i.e., CrossFit (CF) training) uses a combination of movements and self-selected time periods of work and rest. However, little is known about the physiological responses to an acute bout of HIFT exercise or about the physical parameters that distinguish performance. The purpose of this study was to examine the physiological responses in advanced CF athletes to consecutive Wingate trials with short, active recovery periods. Twenty-nine advanced-level CF-trained athletes volunteered for this study. The participants were required to complete 4 consecutive Wingate anaerobic tests (WAnTs) and a 15-min CF-style workout. Across the 4 WAnT trials, significant (p < 0.001) changes were observed in oxygen consumption, respiratory exchange ratio, and heart rate. Significant (p ≤ 0.001) differences among WAnT trials were observed in all anaerobic performance measures. Compared with all other trials, greater peak power (p < 0.04), relative peak power (p < 0.02), average power (p < 0.001), relative average power (p < 0.001), and total work (p < 0.001), together with a lower fatigue index (p < 0.01), were observed during WAnT 1. Overall, the 4 consecutive WAnT trials resulted in a significant (F = 177.0, p < 0.001) increase in blood lactate response. Stepwise regression revealed that the ability to predict total repetitions completed during the 15-min trial to complete as many repetitions as possible improved as the participants progressed from the first to the third WAnT trial. Our data suggest that, combined with the ability to better maintain performance across high-intensity exercise bouts, the ability to quickly recover between bouts is the most important factor in CF performance.

Normative Values for Self-Reported Benchmark Workout Scores in CrossFit® Practitioners

Background

CrossFit® practitioners commonly track progress by monitoring their ability to complete a variety of standardized benchmark workouts within a typical class setting. However, objective assessment of progress is challenging because normative data does not currently exist for any of these benchmark workouts. Therefore, the purpose of this study was to develop normative values for five common benchmark workouts (i.e., Fran, Grace, Helen, Filthy-50 [F50], and Fight-Gone-Bad [FGB]).

Methods

Performance data from 133,857 male (_M) and female (_F) profiles located on a publicly available website were collected and sorted by sex (i.e., male [_M] and female [_F]) and competitive age classification (i.e., teen [T], individual [I], or masters [M]) and screened for errors. Subsequently, 10,000 valid profiles were randomly selected for analysis.

Results

Means and standard deviations were calculated for each category for Fran (I_M 250 ± 106 s; I_F 331 ± 181 s; M_M 311 ± 138 s; M_F 368 ± 138 s; T_M 316 ± 136 s; and T_F 334 ± 120 s), Grace (I_M 180 ± 90 s; I_F 213 ± 96 s; M_M 213 ± 93 s; M_F 238 ± 100 s; T_M 228 ± 63 s; and T_F 223 ± 69 s), Helen (I_M 9.5 ± 1.9 min; I_F 11.1 ± 2.4 min; M_M 10.2 ± 2.0 min; M_F 11.5 ± 2.3 min; T_M 9.4 ± 1.6 min; and T_F 12.7 ± 1.9 min), F50 (I_M 24.4 ± 5.9 min; I_F 27.3 ± 6.9 min; M_M 26.7 ± 6.1 min; M_F 28.2 ± 6.0 min; T_M 25.9 ± 7.9 min; and T_F 28.3 ± 8.1 min), and FGB (I_M 335 ± 65 repetitions; I_F 292 ± 62 repetitions; M_M 311 ± 59 repetitions; M_F 280 ± 54 repetitions; T_M 279 ± 44 repetitions; and T_F 238 ± 35 repetitions). These values were then used to calculate normative percentile (in deciles) values for each category within each workout. Separate, one-way analyses of variance revealed significant (p < 0.05) differences between categories for each workout.

Conclusions

These normative values can be used to assess proficiency and sport-specific progress, establish realistic training goals, and for standard inclusion/exclusion criteria for future research in CrossFit® practitioners.

Changes in body composition, bone metabolism, strength, and skill-specific performance resulting from 16-weeks of HIFT

High Intensity Functional Training (HIFT) is a training modality, characterized by multimodal exercises performed at high-intensity. Little is known about the training adaptations that occur as a prolonged training program. The purpose of this study was to examine changes in body composition, bone metabolism, strength, and skill-specific performance over 16-weeks of HIFT. Twenty-six recreationally active adult males (n = 9; 34.2 ± 9.1 y; 91.5 ± 17.7 kg; 178.5 ± 5.4 cm) and females (n = 17 = 36.4 ± 7.9 y; 91.5 ± 17.7 kg; 162.9 ± 7.0 cm) completed pre and post training assessments of body composition (Dual-Energy X-Ray Absorptiometry) and performance measures. Performance was assessed using three HIFT workouts (WOD 1-3) to assess strength, skill, and metabolic performance. Aside from the body composition measurements, all assessments were carried out at the local training facility. Training included participation in HIFT a minimum of twice a week for 16-weeks. Repeated measures analysis of variance revealed a significant gender x time interaction in Bone Mineral Content (BMC) (p = 0.027), where improvements favored women (1.0% ± 1.1%, p = 0.004) over men (-0.1% + 0.8%, p = 0.625). Further, region-specific analysis indicated that women (2.5% ± 3.0%, p < 0.005) experienced greater improvements in the trunk compared to men (-0.3% ± 1.8%, p = 0.621), while changes in leg BMC were comparable between women (0.8% ± 1.0%, p < 0.001) and men (0.3% ± 0.6%, p < 0.001). Although no other interactions were observed, significant performance improvements were noted for all participants in WOD 1 (18.3% ± 16.8%), absolute 5RM (14.4% ± 9.7%), relative 5RM (15.4% ± 9.2%), WOD 2 (5.7% ± 6.5%), and WOD 3 (-17.3% ± 14.7%). These data indicate that 16-weeks of HIFT resulted in positive outcomes in strength, metabolic conditioning performance, and body composition.

Are Changes in Physical Work Capacity Induced by High-Intensity Functional Training Related to Changes in Associated Physiologic Measures?

High-Intensity Functional Training (HIFT) is a novel exercise intervention that may test body systems in a balanced and integrated fashion by challenging individuals’ abilities to complete mechanical work. However, research has not previously determined if physical work capacity is unique to traditional physiologic measures of fitness. Twenty-five healthy men and women completed a six-week HIFT intervention with physical work capacity and various physiologic measures of fitness assessed pre- and post-intervention. At baseline, these physiologic measures of fitness (e.g., aerobic capacity) were significantly associated with physical work capacity and this relationship was even stronger at post-intervention assessment. Further, there were significant improvements across these physiologic measures in response to the delivered intervention. However, the change in these physiologic measures failed to predict the change in physical work capacity induced via HIFT. These findings point to the potential utility of HIFT as a unique challenge to individuals’ physiology beyond traditional resistance or aerobic training. Elucidating the translational impact of increasing work capacity via HIFT may be of great interest to health and fitness practitioners ranging from strength/conditioning coaches to physical therapists.