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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.

BackgroundStudies involving chronic creatine supplementation in elite soccer players are scarce. Therefore, the aim of this study was to examine the effects of creatine monohydrate supplementation on lower-limb muscle power in Brazilian elite soccer players (n = 14 males) during pre-season training.FindingsThis was a randomized, double-blind, placebo-controlled parallel-group study. Brazilian professional elite soccer players participated in this study. During the pre-season (7 weeks), all the subjects underwent a standardized physical and specific soccer training. Prior to and after either creatine monohydrate or placebo supplementation, the lower-limb muscle power was measured by countermovement jump performance. The Jumping performance was compared between groups at baseline (p = 0.99). After the intervention, jumping performance was lower in the placebo group (percent change = - 0.7%; ES = - 0.3) than in the creatine group (percent change = + 2.4%; ES = + 0.1), but it did not reach statistical significance (p = 0.23 for time x group interaction). Fisher’s exact test revealed that the proportion of subjects that experienced a reduction in jumping performance was significantly greater in the placebo group than in the creatine group (5 and 1, respectively; p = 0.05) after the training. The magnitude-based inferences demonstrated that placebo resulted in a possible negative effect (50%) in jumping performance, whereas creatine supplementation led to a very likely trivial effect (96%) in jumping performance in the creatine group.ConclusionsCreatine monohydrate supplementation prevented the decrement in lower-limb muscle power in elite soccer players during a pre-season progressive training.

BackgroundSleep quality is an essential component of athlete’s recovery. However, a better understanding of the parameters to adequately quantify sleep quality in team sport athletes is clearly warranted.ObjectiveTo identify which parameters to use for sleep quality monitoring in team sport athletes.MethodsSystematic searches for articles reporting the qualitative markers related to sleep in team sport athletes were conducted in PubMed, Scopus, SPORTDiscus and Web of Science online databases. The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. For the meta-analysis, effect sizes with 95% CI were calculated and heterogeneity was assessed using a random-effects model. The coefficient of variation (CV) with 95% CI was also calculated to assess the level of instability of each parameter.ResultsIn general, 30 measuring instruments were used for monitoring sleep quality. A meta-analysis was undertaken on 15 of these parameters. Four objective parameters inferred by actigraphy had significant results (sleep efficiency with small CV and sleep latency, wake episodes and total wake episode duration with large CV). Six subjective parameters obtained from questionnaires and scales also had meaningful results (Pittsburgh Sleep Quality Index (sleep efficiency), Likert scale (Hooper), Likert scale (no reference), Liverpool Jet-Lag Questionnaire, Liverpool Jet-Lag Questionnaire (sleep rating) and RESTQ (sleep quality)).ConclusionsThese data suggest that sleep efficiency using actigraphy, Pittsburgh Sleep Quality Index, Likert scale, Liverpool Jet-Lag Questionnaire and RESTQ are indicated to monitor sleep quality in team sport athletes.PROSPERO registration numberCRD42018083941.

The purpose of this study was to assess the effect of training load regulation, using the CMJ at the beginning of the session, on the total plyometric training load and the vertical jump performance. 44 males were divided into 4 groups: No Regulation Group (nRG), Regulation Group (RG), Yoked Group (YG) and Control Group (CG). The nRG received 6 weeks of plyometric training, with no adjustment in training load. The RG underwent the same training; however, the training load was adjusted according to the CMJ performance at the beginning of each session. The adjustment made in RG was replicated for the volunteers from the corresponding quartile in the YG, with no consideration given to the YG participant's condition at the beginning of its session. At the end of the training, the CMJ and SJ performance of all of the participants was reassessed. The total training load was significantly lower (p=0.036; ES=0.82) in the RG and the YG (1905±37 jumps) compared to the nRG (1926±0 jumps). The enhancement in vertical jump performance was significant for the groups that underwent the training (p<0.001). Vertical jump performance, performed at the beginning of the session, as a tool to regulate the training load resulted in a decrease of the total training load, without decreasing the long-term effects on vertical jump performance.

Claudino, JG, Cronin, JB, Mezêncio, B, Pinho, JP, Pereira, C, Mochizuki, L, Amadio, AC, and Serrão, JC. Autoregulating jump performance to induce functional overreaching. J Strength Cond Res 30(8): 2242-2249, 2016-The purpose of this study was to determine whether autoregulating jump performance using the minimal individual difference (MID) associated with countermovement jump (CMJ) height could be used to regulate and monitor a training phase that elicited functional overreaching and tapering in team sport athletes. The participants were familiarized with the jump and then the CMJ height reliability was quantified to determine the MID. Countermovement jump height was assessed in the pretesting session (T0), at the end of 4 weeks of intensified training (T1), and after 2 weeks of tapering (T2). Eighteen national level U17 male futsal players were randomly allocated into the regulated group (RG; n = 9) and the control group (CG; n = 9). The RG performed 6 weeks of training with the training load regulated by mean height of CMJ with MID, whereas the CG performed the preplanned training. The differences between groups and across time points were compared by a 2-way analysis of variance. In the RG, the MID loading was increased in weeks 3 and 4 (8.2 and 14.5%, respectively; p < 0.001) compared with the preplanned loading of the CG during the overreaching phase. In the jump results, the RG significantly (p ≤ 0.05) reduced CMJ height during T1 (effect size [ES] = -0.31; 95% confidence interval [CI]: -0.58 to -0.02); however, there were no significant changes in the CG jump height at T1 and T2. At T2, the RG significantly increased CMJ height above baseline (ES = 0.30; 95% CI: 0.09 to 0.51). Researchers and practitioners could use this autoregulating method to regulate and monitor training load to achieve functional overreaching in youth futsal players.

BackgroundThe ability to jump has been related to muscle strength and power, speed and amplitude of the lower limbs movements, and specifically for the elderly, the vertical jump has been shown to be a good predictor of functional capacity and risk of falling. The use of a mobile application (App) which can measure the vertical jump (i.e., iPhone App My Jump) has recently emerged as a simple, cheap and very practical tool for evaluation of jump ability. However, the validity of this tool for the elderly population has not been tested yet. The elderly usually perform very low jumps and therefore the signal-to-noise ratio may compromise the validity and reliability of this method. Thus, the aim of the current study was to verify the validity and reliability of the iPhone App “My Jump” for the evaluation of countermovement jump (CMJ) height within an elderly population.MethodsAfter familiarization, 41 participants performed three CMJs assessed via a contact mat and the My Jump App. The intraclass correlation coefficient (ICC) was used to verify the relative reliability, while the coefficient of variation (CV%) and the typical error of measurement (TEM) were used to verify the absolute reliability. Pearson’s correlation coefficient was used to verify the strength of the relationship between methods (i.e., concurrent validity), a Bland–Altman plot to show their agreement, and the Student’s t-test to identify systematic bias between them. For reliability analyses, all jumps were considered (i.e., 123). All jumps (i.e., 123), the average height of each attempt (i.e., 41), and the highest jump, were considered for validity analyses.ResultsThe CMJ height of the highest jump was 10.78 ± 5.23 cm with contact mat, and 10.87 ± 5.32 with My Jump App, with an identified systematic bias of 0.096 cm (P = 0.007). There was a nearly perfect correlation between methods (r = 0.999; P = 0.000, in all cases) with a very good agreement observed (0.3255 to −0.5177 cm, 0.2797 to −0.5594 cm, and 0.3466 to −0.6264 cm, for highest jump height, average jump height, and all jump heights, respectively). The ICC of the My Jump App was 0.948, the TEM was 1.150 cm, and the CV was 10.10%.ConclusionOur results suggest that the My Jump App is a valid and reliable tool compared to the contact mat for evaluating vertical jump performance in the elderly. Therefore, it allows a simple and practical assessment of lower limbs’ power in this population. For the elderly, as well as for other populations with low jumping heights, the highest jump height and the average jump height could be used indistinctly.