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Background Mechanical power output is recognized as a critical characteristic of an athlete with regard to superior performance during a competition. It seems fully justified that ballistic exercises, in which the external load is projected into a flight phase, as in the bench press throw (BPT), are the most commonly prescribed exercises for the development of power output. In addition, the muscular phenomenon known as post-activation performance enhancement (PAPE), which is an acute improvement in strength and power performance as a result of recent voluntary contractile history, has become the focus of many strength and conditioning training programs. Although the PAPE phenomenon is widely used in the upper-body training regimens, there are still several issues regarding training variables that facilitate the greatest increase in power output and need to be resolved. Objective The purposes of this meta-analysis were to determine the effect of performing a conditioning activity (CA) on subsequent BPT performr effect than a multiple set (ES = 0.29). Moderate rest intervals induced a slightly greater PAPE effect for intensity below 85% 1RM (5-7 min, ES = 0.48) than shorter (0.15-4 min, ES = 0.4) and longer (≥8 min, ES = 0.36) intra-complex rest intervals. Considering an intensity above 85% 1RM during the CA, a moderate rest interval resulted in a similar PAPE effect (5-7 min, ES = 0.3) compared with longer (8 min, ES = 0.29) intra-complex rest interval, whereas shorter rest intervals resulted in a negative effect on BPT performance (0.15-4 min, ES = -0.13). learn more Conclusion The presented meta-analysis shows that performing a CA induces a small PAPE effect for the BPT performance in resistance-trained men. Individuals seeking to improve their BPT performance should consider preceding them with a single set of the BP exercise at moderate intensity (60-84% 1RM), performed 5-7 min before the explosive activity.Severe cold exercise involves the irisin response, and may be related to body composition. We aimed to investigate changes in circulating irisin after ice swimming (IS), as well as to evaluate the correlation between body composition and the change in irisin caused by IS (Δirisin). 81 ice swimmers were recruited to perform IS activities. Blood samples were drawn 30 min before and 30 min after IS, and the serum levels of irisin and the ice swimmers' body composition were measured. As results, circulating irisin declined significantly during the recovery period following IS exercise (P less then 0.001). The afternoon baseline circulating irisin level and Δirisin in response to IS were correlated with body fat characteristics rather than muscle parameters in ice swimmers. Δirisin subgroup analyses showed that the Δirisin ascending group (Δirisin+) subjects had a higher fat composition and higher basal irisin levels than the Δirisin descending group (Δirisin-). Furthermore, the decrease in irisin was negatively correlated with fat components in Δirisin- subjects, whereas no correlation was observed between the increase in irisin and body composition in Δirisin + subjects. Finally, a non-linear association analysis suggested that body fat indicators had obvious curvilinear relationships with Δirisin. In conclusion, IS caused a significant decrease in irisin. Statistical and curvilinear associations suggested that the correlation between fat tissue and Δirisin caused by IS is dimorphic and the underlying mechanisms may be due to the different metabolic states of subjects.The short-term scaling exponent alpha1 of detrended fluctuation analysis (DFA a1), a nonlinear index of heart rate variability (HRV) based on fractal correlation properties, has been shown to steadily change with increasing exercise intensity. To date, no study has specifically examined using the behavior of this index as a method for defining a low intensity exercise zone. The aim of this report is to compare both oxygen intake (VO2) and heart rate (HR) reached at the first ventilatory threshold (VT1), a well-established delimiter of low intensity exercise, to those derived from a predefined DFA a1 transitional value. Gas exchange and HRV data were obtained from 15 participants during an incremental treadmill run. Comparison of both VO2 and HR reached at VT1 defined by gas exchange (VT1 GAS) was made to those parameters derived from analysis of DFA a1 reaching a value of 0.75 (HRVT). Based on Bland Altman analysis, linear regression, intraclass correlation (ICC) and t testing, there was strong agreement between VT1 GAS and HRVT as measured by both HR and VO2. Mean VT1 GAS was reached at 39.8 ml/kg/min with a HR of 152 bpm compared to mean HRVT which was reached at 40.1 ml/kg/min with a HR of 154 bpm. Strong linear relationships were seen between test modalities, with Pearson's r values of 0.99 (p less then 0.001) and.97 (p less then 0.001) for VO2 and HR comparisons, respectively. Intraclass correlation between VT1 GAS and HRVT was 0.99 for VO2 and 0.96 for HR. In addition, comparison of VT1 GAS and HRVT showed no differences by t testing, also supporting the method validity. In conclusion, it appears that reaching a DFA a1 value of 0.75 on an incremental treadmill test is closely associated with crossing the first ventilatory threshold. As training intensity below the first ventilatory threshold is felt to have great importance for endurance sport, utilization of DFA a1 activity may provide guidance for a valid low training zone.We address the problem of efficiently and informatively quantifying how multiplets of variables carry information about the future of the dynamical system they belong to. In particular we want to identify groups of variables carrying redundant or synergistic information, and track how the size and the composition of these multiplets changes as the collective behavior of the system evolves. In order to afford a parsimonious expansion of shared information, and at the same time control for lagged interactions and common effect, we develop a dynamical, conditioned version of the O-information, a framework recently proposed to quantify high-order interdependencies via multivariate extension of the mutual information. The dynamic O-information, here introduced, allows to separate multiplets of variables which influence synergistically the future of the system from redundant multiplets. We apply this framework to a dataset of spiking neurons from a monkey performing a perceptual discrimination task. The method identifies synergistic multiplets that include neurons previously categorized as containing little relevant information individually.