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

Background The application of artificial intelligence (AI) opens an interesting perspective for predicting injury risk and performance in team sports. A better understanding of the techniques of AI employed and of the sports that are using AI is clearly warranted. The purpose of this study is to identify which AI approaches have been applied to investigate sport performance and injury risk and to find out which AI techniques each sport has been using. Methods Systematic searches through the PubMed, Scopus, and Web of Science online databases were conducted for articles reporting AI techniques or methods applied to team sports athletes. Results Fifty-eight studies were included in the review with 11 AI techniques or methods being applied in 12 team sports. Pooled sample consisted of 6456 participants (97% male, 25 ± 8 years old; 3% female, 21 ± 10 years old) with 76% of them being professional athletes. The AI techniques or methods most frequently used were artificial neural networks, decision tree classifier, support vector machine, and Markov process with good performance metrics for all of them. Soccer, basketball, handball, and volleyball were the team sports with more applications of AI. Conclusions The results of this review suggest a prevalent application of AI methods in team sports based on the number of published studies. The current state of development in the area proposes a promising future with regard to AI use in team sports. Further evaluation research based on prospective methods is warranted to establish the predictive performance of specific AI techniques and methods. Electronic supplementary material The online version of this article (10.1186/s40798-019-0202-3) contains supplementary material, which is available to authorized users.