CrossFit Overview: Systematic Review and Meta-analysis

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Abstract

Background

CrossFit is recognized as one of the fastest growing high-intensity functional training modes in the world. However, scientific data regarding the practice of CrossFit is sparse. Therefore, the objective of this study is to analyze the findings of scientific literature related to CrossFit via systematic review and meta-analysis.

Methods

Systematic searches of the PubMed, Web of Science, Scopus, Bireme/MedLine, and SciELO online databases were conducted for articles reporting the effects of CrossFit training. The systematic review followed the PRISMA guidelines. The Oxford Levels of Evidence was used for all included articles, and only studies that investigated the effects of CrossFit as a training program were included in the meta-analysis. For the meta-analysis, effect sizes (ESs) with 95% confidence interval (CI) were calculated and heterogeneity was assessed using a random-effects model.

Results

Thirty-one articles were included in the systematic review and four were included in the meta-analysis. However, only two studies had a high level of evidence at low risk of bias. Scientific literature related to CrossFit has reported on body composition, psycho-physiological parameters, musculoskeletal injury risk, life and health aspects, and psycho-social behavior. In the meta-analysis, significant results were not found for any variables.

Conclusions

The current scientific literature related to CrossFit has few studies with high level of evidence at low risk of bias. However, preliminary data has suggested that CrossFit practice is associated with higher levels of sense of community, satisfaction, and motivation.

Electronic supplementary material

The online version of this article (10.1186/s40798-018-0124-5) contains supplementary material, which is available to authorized users.

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Most cited references 60

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Measuring inconsistency in meta-analyses.

Julian P. T. Higgins, Simon Thompson, Jonathan Deeks … (2003)

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Meta-analysis in clinical trials

Rebecca DerSimonian, Nan Laird (1986)

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Is Open Access

Estimating the mean and variance from the median, range, and the size of a sample

Stela Pudar Hozo, Benjamin Djulbegovic, Iztok Hozo (2005)

Background Usually the researchers performing meta-analysis of continuous outcomes from clinical trials need their mean value and the variance (or standard deviation) in order to pool data. However, sometimes the published reports of clinical trials only report the median, range and the size of the trial. Methods In this article we use simple and elementary inequalities and approximations in order to estimate the mean and the variance for such trials. Our estimation is distribution-free, i.e., it makes no assumption on the distribution of the underlying data. Results We found two simple formulas that estimate the mean using the values of the median (m), low and high end of the range (a and b, respectively), and n (the sample size). Using simulations, we show that median can be used to estimate mean when the sample size is larger than 25. For smaller samples our new formula, devised in this paper, should be used. We also estimated the variance of an unknown sample using the median, low and high end of the range, and the sample size. Our estimate is performing as the best estimate in our simulations for very small samples (n ≤ 15). For moderately sized samples (15 70), the formula range/6 gives the best estimator for the standard deviation (variance). We also include an illustrative example of the potential value of our method using reports from the Cochrane review on the role of erythropoietin in anemia due to malignancy. Conclusion Using these formulas, we hope to help meta-analysts use clinical trials in their analysis even when not all of the information is available and/or reported.

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Author and article information

Contributors

João Gustavo Claudino: claudinojgo@usp.br

Tim J. Gabbett: tim@gabbettperformance.com.au

Frank Bourgeois: fabourgeoisii@gmail.com

Helton de Sá Souza: heltondesasouza@gmail.com

Rafael Chagas Miranda: rafaelchagasmiranda@gmail.com

Bruno Mezêncio: mezencio@usp.br

Rafael Soncin: rafaelsoncin@usp.br

Carlos Alberto Cardoso Filho: carlos.filho@usp.br

Martim Bottaro: martim@unb.br

Arnaldo Jose Hernandez: ajhernandez@uol.com.br

Alberto Carlos Amadio: acamadio@usp.br

Julio Cerca Serrão: jcserrao@usp.br

Journal

Journal ID (nlm-ta): Sports Med Open

Journal ID (iso-abbrev): Sports Med Open

Title: Sports Medicine - Open

Publisher: Springer International Publishing (Cham )

ISSN (Print): 2199-1170

ISSN (Electronic): 2198-9761

Publication date (Electronic): 26 February 2018

Publication date PMC-release: 26 February 2018

Publication date Collection: December 2018

Volume: 4

Electronic Location Identifier: 11

Affiliations

[1 ]ISNI 0000 0004 1937 0722, GRID grid.11899.38, School of Physical Education and Sport, Laboratory of Biomechanics, , University of São Paulo, ; São Paulo, Brazil

[2 ]GRID grid.441787.9, Faculty of Physical Education, , University of Itaúna, ; Itaúna, Brazil

[3 ]Gabbett Performance Solutions, Brisbane, Australia

[4 ]ISNI 0000 0004 0473 0844, GRID grid.1048.d, Institute for Resilient Regions, , University of Southern Queensland, ; Ipswich, Australia

[5 ]ISNI 0000 0001 0705 7067, GRID grid.252547.3, Sport Performance Research Institute New Zealand, , Auckland University of Technology, ; Auckland, New Zealand

[6 ]ISNI 0000 0001 0514 7202, GRID grid.411249.b, Department of Psychobiology, , Federal University of São Paulo, ; São Paulo, Brazil

[7 ]ISNI 0000 0001 2238 5157, GRID grid.7632.0, College of Physical Education, , University of Brasília, ; Brasília, Brazil

[8 ]ISNI 0000 0004 1937 0722, GRID grid.11899.38, Orthopedics and Traumatology Institute, , University of São Paulo, ; São Paulo, Brazil

Article

Publisher ID: 124

DOI: 10.1186/s40798-018-0124-5

PMC ID: 5826907

PubMed ID: 29484512

SO-VID: 6f7d5b80-0df2-4551-9656-c48116497077

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

History

Date received : 26 June 2017

Date accepted : 7 February 2018

Custom metadata

Keywords: high-intensity functional training,high-intensity interval training,training load

Data availability:

Keywords: high-intensity functional training, high-intensity interval training, training load

CrossFit Overview: Systematic Review and Meta-analysis

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Abstract

Background

Methods

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Conclusions

Electronic supplementary material

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Road 2 Open Meta

Most cited references 60

Measuring inconsistency in meta-analyses.

Meta-analysis in clinical trials

Estimating the mean and variance from the median, range, and the size of a sample

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