High Intensity Crossfit Training Compared to High Intensity Swimming: A Pre-Post Trial to Assess the Impact on Body Composition, Muscle Strength and Resting Energy Expenditure


avatar Simone Perna 1 , avatar Chiara Bologna 1 , avatar Irene Degli Agosti 2 , avatar Mariangela Rondanelli 1

1University of Pavia, Department of Public Health, Experimental and Forensic Medicine, Section of Human Nutrition, Endocrinology and Nutrition Unit, Azienda di Servizi Alla Persona, Pavia, Italy
Azienda di Servizi alla Persona, Pavia, Italy, Pavia, Italy
Warning: No corresponding author defined!

how to cite: Perna S, Bologna C, Degli Agosti I, Rondanelli M. High Intensity Crossfit Training Compared to High Intensity Swimming: A Pre-Post Trial to Assess the Impact on Body Composition, Muscle Strength and Resting Energy Expenditure. Asian J Sports Med. 2018;9(1):e13843. https://doi.org/10.5812/asjsm.13843.



The aim of this study was to evaluate the impact of two different forms of high intensity training i.e. power CrossFit and intermittent swimming, on body composition markers, max strength and resting energy expenditure.


This pre-post trial was conducted on twenty three subjects (14 female, 9 male; mean age = 31.74 ± 7.46 years; BMI = 23.665 ± 2.994 kg/m2). They were assigned into interventions of CrossFit training or swimming (CrossFit/Swimming: 10/13) for 8-weeks (60 min, 3 times per week). Using dual X-ray energy absorptiometry (DXA), we measured body mass composition markers such as body weight, total free fat mass, total fat mass, arms and legs free fat mass, and percentage of android and gynoid fat mass. Also muscle strength and resting energy expenditure were measured at baseline and immediately after 8 weeks of training intervention. Feasibility measures of recruitment and injury were also assessed. These variables were measured at baseline and after 8 weeks and compared within and between groups, using paired t-tests and linear regression models, to detect significant changes.


Between groups, data comparisons (pre-post intervention training) demonstrated a significant effect of CrossFit on gynoid fat (β = -1.42%; CI 95% -2.81; -0.03; P = 0.047), and suggestive but not significant variations in decreasing for total fat mass (β -1427 g, CI 95%: -2861, 7, 31; P = 0.051) and android fat (β = -2.64%, CI 95%: -5.36, 0.08; P = 0.056).


This study showed the potential benefits of high intensity training in improvement of body composition markers. In particular, CrossFit is more effective than swimming in losses of total fat mass, specifically of gynoid and android fat mass. Further research is needed to understand the potential of CrossFit training on health.

1. Background

High-intensity interval training (HIIT) has been used as an alternative to traditional aerobic training (1).

HIIT is practical for many individuals due to the minimum time required when compared to traditional continuous endurance training. In the last few years a new variation of HIIT that combines high-intensity endurance training with varied and multiple-joint movements has become popular. High-intensity power training (HIPT) differs from traditional HIIT in that it does not include rest periods but it focuses on sustained high power output and resistance (2).

Depending on the age and fitness level of the subjects as well as the duration and intensity of the intervention; improvements of muscle mass, strength and VO2 max have been reported (3-5). In literature there are different studies that evaluated the effects of high-intensity sports on different aspects of body composition and health. In particular, it was concluded that HIPT is an effective training strategy to improve cardiovascular health, physical performance (6), basal metabolic rate and body composition (1).

Previously, in literature the high intensity training was compared with low to medium-intensity, with good results. Currently, there are no studies that compare various forms of training at high intensity. The aim of this study is to compare the effects of two high intensity training methods on body composition markers, muscle strength and basal metabolic rate.

2. Methods

2.1. Study Participants

We evaluated both male and female subjects, with a history of moderate training activity, from May 2014 to June 2015, at metabolic and body composition lab of the University of Pavia.

Fourty participants were recruited. Twenty participants received the high intensity power CrossFit training group voluntarily, the other twenty performed high intensity intermittent swimming training (controls).

The inclusion criteria were that participants must be aged above 18 years and below 45 years, available to complete all exercise sessions and with a previous history of moderate training, such as fitness or gym activity. Subjects were included in this study if they were physically active and healthy. A preliminary visit was conducted to detect the lack of inclusion criteria. Participants were excluded if they participated in regular physical high intensity training previously, and if they had unstable medical conditions such as cardiovascular disorders, musculoskeletal injury, and cardiopulmonary disorders that might affect the performance. Each athlete had given informed consent to participate in this research.

2.2. Instruments

2.2.1. Body Composition Markers

Body composition was measured by dual-energy X-ray densitometry (DXA) using a Lunar Prodigy DEXA (GE Medical Systems, Waukesha, Wisconsin). The in vivo coefficients of variation were 0.89% and 0.48% for fat and muscle mass, respectively. Free fat mass (FFM; in grams), % free fat mass (%FFM), fat mass (FM; in grams), % fat mass (%FM), android fat %, gynoid fat % were evaluated on the basis of total body scan data. Absolute DXA data were compared to previously established age- and sex-matched reference values (7).

2.2.2. Maximal Handgrip Strength Test

The handgrip strength of the dominant hand was assessed using a Jamar dynamometer (Sammons Preston, Mississauga, ON, Canada). Standard assessment procedures were followed as described in a previous study (8). Briefly, participants squeezed the dynamometer as hard as possible three times, and the highest grip-strength value was used for analysis. The inter- and intra-rater reliabilities of this test have been reported to be good to excellent (ICC = 0.94 - 0.98) in adults.

2.3. Training Protocol

Subjects participated in a high intensity training program three non-consecutive days (Monday, Wednesday and Friday) per week throughout the entire eight-week training period. All training was performed under the supervision of the principal investigators and trained HPL research assistants.

The CrossFit group utilized training for 60-minute sessions. Nine movements (i.e. air squat, front squat, overhead squat, press, push press push jerk, deadlift, sumo deadlift high pull, and medicine ball clean) were introduced in sessions 1 - 2. Remaining sessions included warm-up and stretching (10 - 15 minutes), instruction and technique practice (10 - 20 minutes), workout (5 - 30 minutes), and cool-down and stretching (5 minutes). Swimmers group completed session lasted 60 minutes and consisted of 20 60 seconds all-out free-style swimming (front crawl) intervals interspersed by 2 min of passive recovery after training principles previously described.

2.4. Resting Energy Expenditure

Resting energy expenditure (REE) was measured using a ventilated hood and open-circuit calorimeter (QUARK RMR, Cosmed, Rome, Italy). Before each evaluation, the device was calibrated to 95% O2 and 5% CO2. All subjects underwent the test in the morning after a 12-hours overnight fast. The subjects were instructed to avoid any intense physical activity during the 24-hours period before REE measurement. Oxygen consumption (VO2) and carbon dioxide production (VCO2) were continuously measured for 25 minutes. The first 5 minutes were discarded to ensure adequate acclimation. The subjects were instructed to avoid hyperventilation, fidgeting, or falling asleep during the test.

2.5. Statistical Analysis

For each outcome, one-way ANOVA was performed for assessment differences between intervention groups at baseline. Analysis of covariance (ANCOVA) was performed using of covariate sex, percentage of fat mass, percentage of gynoid fat and fat mass in grams were used to compare crossfitter and swimmer over the time (i.e. mean differences between treatments over the time). The statistical package SPSS version 20 was used for statistical analysis (9). A value of P < 0.05 was considered statistically significant.

3. Results

Participant flow diagram is shown in Figure 1. Fifteen athletes received CrossFit training, and another fifteen athletes received swimming training. In either groups there were drop outs. The reasons were injuries, 5 among crossfitters and 2 among swimmers. Recruitment was performed from May 2014 to June 2015. The follow-up was 8 weeks.

Table 1 shows the baseline outcomes in both groups. There were no significant difference between-groups.

Nevertheless, as showed in Table 2, the between-treatment comparisons (over the time) by analysis of covariance (ANCOVA) demonstrated a significant effect of CrossFit. Between groups, data comparisons (pre-post intervention training) demonstrated a significant effect of CrossFit on gynoid fat (β = -1.42%; CI 95% -2.81; -0.03; P = 0.047), and suggestive but not significant variations in decreasing for total fat mass (β= -1427 g, CI 95%: -2861, 7,31; P = 0.051) and android fat (β=-2.64%, CI 95%: -5.36, 0.08; P = 0.056).

Table 1. Sample Characteristics at Baselinea
VariableCrossfitters (N = 10)Swimmers (N = 13)P ValueTotal (N = 23)
Age, y33.10 ±9.82630.69 ± 5.1860.77831.74 ± 7.460
Weight, kg71.66 ± 19.13468.88 ± 8.4830.97570.09 ± 13.821
BMI, kg/m224.71 ± 4.29122.86 ± 1.0070.31523.67 ± 2.994
Arms free fat mass, g6.99 ± 3.0436.67 ± 1.6100.7046.81 ± 2.287
Legs free fat mass, g19.81 ± 6.67320.11 ± 3.8590.48019.98 ± 5.135
Free fat mass, g53.18 ± 16.55754.0 ± 9.4040.41653.64 ± 12.681
Fat mass, g15.80 ± 7.20312.03 ± 4.4120.16713.67 ± 5.958
Gynoid fat, %31.26 ± 11.18324.91 ± 10.0110.17427.67 ± 10.779
Android fat, %27.69 ± 11.09025.67 ± 8.9520.28626.55 ± 9.750
A/G ratio0.89 ± 0.2391.08 ± 0.2900.0991.00 ± 0.281
Tissue, g68.97 ± 17.96266.02 ± 7.9950.93867.31 ± 13.004
Free fat mass, %76.54 ± 9.05281.47 ± 7.3560.17979.33 ± 8.323
Fat mass, %23.46 ± 9.05218.53 ± 7.3560.17920.67 ± 8.323
Handgrip strenght (right), kg47.80 ± 13.93548.92 ± 13.5430.85448.35 ± 13.739
Resting energy exp., kcal1401.50 ± 440.661475.08 ± 264.720.3431438.29 ± 352.69
Table 2. Between-Treatment Changes (Baseline to 8 Weeks)a,b
VariablesPre - Training (Swimmers)Post - Training (Swimmers)**Pre - Training (CrossFit)Post - Training (CrossFit)**Mean Difference and CI 95% (Crossfitters Minus Swimmersc)FP Value
Weight, kg71.66 ± 19.1369.70 ± 0.4768.88 ± 8.4868.10 ± 0.54-0.70 (-2.33; 0.92)0.840.372
BMI, kg/m224.710 ± 4.29123.29 ± 0.2022.862 ± 1.0123.54 ± 0.240.25 (-0.50; 0.98)0.490.493
Arms free fat mass, g6995 ± 30436804 ± 1316668 ± 16106757 ± 155-46.50 (-536.53; 443.54)0.040.843
Legs free fat mass, g19810 ± 667320029 ± 24720112 ± 385920248 ± 286219.10 (-631.93; 1070,13)0.300.593
Free fat mass, g53176 ± 1655753548 ± 50853999 ± 940454410 ± 590861.19 (-909.08; 2631.46)1.060.318
Fat mass, g15797 ± 720313229 ± 42612025 ± 441211801 ± 492-1427.24 (-2861.79; 7.31)4.410.051
Gynoid Fat, %31.26 ± 11.18327.30 ± 0.4124.91 ± 10.01125.88 ± 0.48-1.42 (-2.81; -0.03)4.630.047
Android fat, %27.69 ± 11.09026.33 ± 0.7425.67 ± 8.95223.69 ± 0.88-2.64 (-5.36; 0.08)4.240.056
Free fat mass, %76.542 ± 9.05279.33 ± 0.1681.469 ± 7.35679.36 ± 0.190.03 (-0.52; 0.57)0.010.922
Fat mass, %23.458 ± 9.05220.32 ± 0.5018.531 ± 7.35618.66 ± 0.58-1.66 (-3.35; 0.03)4.300.054
Handgrip strength (right), kg47.80 ± 13.93548.94 ± 0.8048.92 ± 13.54347.49 ± 0.93-1.45 (-4.17; 1.27)1.280.274
Resting energy exp., kcal1401.50 ± 440.6611616.38 ± 79.461475.08 ± 264.7231483.31 ± 92.0-133.07 (-405.22; 139.08)1.070.315

4. Discussion

This study shows that 8-weeks of high power training CrossFit program, based on progressive resistance, power strength and functional training, improved resting energy expenditure, and all body composition parameters. It’s particularly more effective in total fat mass losses compared to high intermittent swimming training.

No differences were found in changes on arms and legs free fat mass between the two groups, but in a medium long time, CrossFit seems to have important effects on legs free fat mass. This potential of CrossFit, specifically in legs improvement, has been described previously in a study performed by Glassman in 2006 (10). Our data showed that CrossFit also increased muscle strength. But no significant differences between trainings were found. Thus, regarding the REE data, the CrossFit was no more effective than swimming in increasing this parameter. This last data might explain the association with the decrease in total fat mass and the increasing in free fat mass. This relationship has been reported in several studies on the general population (11, 12).

Limitations included the small size of the sample and a previous history of moderate training for all participants. Finally, because of the small sample size, these results may not generalize outside of these participants. This study included a control condition but no randomization were made.

Study strengths included adherence: drop-out rates of 13 % (2/15) for swimmer and 33% (5/15) for crossfitter participants. Only one crossfitter subject experienced a serious injury (these episodes are typical in CrossFit as described in literature (13).

The CrossFit training was safely conducted among participants of our study. Another point of strength was the assessment of body composition using DXA, which is the gold standard for body composition evaluation.

Future research could include a larger sample with extended follow-up to determine the effects of CrossFit high intensity power during a medium long time period.

4.1. Conclusions

Overall, both forms of training appear to have caused the same improvements in the body composition of all athletes, but between thegroups, the crossfitter group have seen more effects on fat mass decrease, specifically in the reduction of gynoid and android fat. Future approaches of CrossFit training: it could also be administered in conjunction with nutrition intervention in the hope of eliciting changes to body composition in a large samples of overweight or obese participants.



  • 1.

    Smith MM, Sommer AJ, Starkoff BE, Devor ST. Crossfit-based high-intensity power training improves maximal aerobic fitness and body composition. J Strength Cond Res. 2013;27(11):3159-72. doi: 10.1519/JSC.0b013e318289e59f. [PubMed: 23439334].

  • 2.

    Gillen JB, Gibala MJ. Is high-intensity interval training a time-efficient exercise strategy to improve health and fitness?. Appl Physiol Nutr Metab. 2014;39(3):409-12. doi: 10.1139/apnm-2013-0187. [PubMed: 24552392].

  • 3.

    Burgomaster KA, Howarth KR, Phillips SM, Rakobowchuk M, Macdonald MJ, McGee SL, et al. Similar metabolic adaptations during exercise after low volume sprint interval and traditional endurance training in humans. J Physiol. 2008;586(1):151-60. doi: 10.1113/jphysiol.2007.142109. [PubMed: 17991697].

  • 4.

    Helgerud J, Hoydal K, Wang E, Karlsen T, Berg P, Bjerkaas M, et al. Aerobic high-intensity intervals improve VO2max more than moderate training. Med Sci Sports Exerc. 2007;39(4):665-71. doi: 10.1249/mss.0b013e3180304570. [PubMed: 17414804].

  • 5.

    Perry CG, Heigenhauser GJ, Bonen A, Spriet LL. High-intensity aerobic interval training increases fat and carbohydrate metabolic capacities in human skeletal muscle. Appl Physiol Nutr Metab. 2008;33(6):1112-23. doi: 10.1139/H08-097. [PubMed: 19088769].

  • 6.

    Mohr M, Nordsborg NB, Lindenskov A, Steinholm H, Nielsen HP, Mortensen J, et al. High-intensity intermittent swimming improves cardiovascular health status for women with mild hypertension. Biomed Res Int. 2014;2014:728289. doi: 10.1155/2014/728289. [PubMed: 24812628].

  • 7.

    Baim S, Binkley N, Bilezikian JP, Kendler DL, Hans DB, Lewiecki EM, et al. Official Positions of the International Society for Clinical Densitometry and executive summary of the 2007 ISCD Position Development Conference. J Clin Densitom. 2008;11(1):75-91. doi: 10.1016/j.jocd.2007.12.007. [PubMed: 18442754].

  • 8.

    Fong SSM, Guo X, Cheung APM, Jo ATL, Lui GKW, Mo DKC. Elder chinese martial art practitioners have higher radial bone strength, hand-grip strength, and better standing balance control. ISRN Rehabil. 2013;2013. doi: 10.1155/2013/185090.

  • 9.

    R Foundation for Statistical Computing. Vienna, Austria; 2013.Development Core Team R: a language and environment for statistical computing.

  • 10.

    Glassman G. Validity of crossfit tested. CrossFit J. 2006;41.

  • 11.

    Wang Z, Heshka S, Gallagher D, Boozer CN, Kotler DP, Heymsfield SB. Resting energy expenditure-fat-free mass relationship: new insights provided by body composition modeling. Am J Physiol Endocrinol Metab. 2000;279(3):E539-45. [PubMed: 10950820].

  • 12.

    Aristizabal JC, Freidenreich DJ, Volk BM, Kupchak BR, Saenz C, Maresh CM, et al. Effect of resistance training on resting metabolic rate and its estimation by a dual-energy X-ray absorptiometry metabolic map. Eur J Clin Nutr. 2015;69(7):831-6. doi: 10.1038/ejcn.2014.216. [PubMed: 25293431].

  • 13.

    Larsen C, Jensen MP. [Rhabdomyolysis in a well-trained woman after unusually intense exercise]. Ugeskr Laeger. 2014;176(25). [PubMed: 25352283].

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