A systematic review and net meta-analysis of the effects of different warm-up methods on the acute effects of lower limb explosive strength

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Methods : R software combined with Stata software, version 13.0, was used to analyse the outcome metrics of the 35 included papers. Results : 1) Static combined with dynamic stretching [MD = 1.80, 95% CI: (0.43, 3.20)] and dynamic stretching [MD = 1.60, 95% CI: (0.67, 2.60)] were significantly better than controls in terms of improving reverse long jump performance, and the effect of dynamic stretching was influenced by the duration of stretching (I 2 = 80.4%), study population (I 2 = 77.2%) and age (I 2 = 75.6%) as moderating variables, with the most significant effect size for dynamic stretching time of 7-10 min. 2) Only dynamic stretching [MD = -0.08, 95% CI: (-0.15, -0.008)] was significantly better than the control group in terms of improving sprint performance, while static stretching [MD = 0.07, 95% CI: (0.002, 0.13)] showed a significant, negative effect. 3) No results were available to demonstrate a significant difference between other methods, such as foam axis rolling, and the control group. Conclusion : Static stretching was able to reduce explosive performance, while the 2 warm-up methods, namely dynamic stretching and static combined with dynamic stretching, were able to significantly improve explosive performance, with dynamic stretching being the most stable and moderated by multiple variables and dynamic stretching for 7-10 min producing the best explosive performance. In the future, high-quality studies should be added based on strict adherence to test specifications. warm-up methods explosive lower limb strength acute effects reticulation meta-analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Warming up as a routine activity before a match or training has been popular in the sports training community for centuries. A scientific warm-up not only helps athletes to become fit but also improves joint mobility and muscle contraction to reduce the incidence of sports injuries 1 . With the development of science and technology, new warm-up methods have emerged, but the opinion remains mixed on which is more suitable for athletes' explosive performance. Muscle stretch (MS) has received much attention as an important part of the preexercise warm-up 2 , but the effect of stretching on explosive power remains controversial; for example, a large number of previous studies have shown that static stretching has a negative effect on subsequent performance 3, 4 , while dynamic stretching has a beneficial effect 5 , although in recent years, there have been, in contrast 6, 7 , with the advent of combined stretching methods 8 , some subjective researcher bias between studies, as well as differences in outcome indicators, leading to some variation in the effect values of intervention results, thus affecting the accuracy of the results. The advent of the foam rolling (FR) technique 9 has led to an increasing number of coaches and athletes promoting this technique and abandoning the original stretching method. Therefore, there remains a lack of clarity regarding which warm-up method is more appropriate for explosive performance, what the dose-effect relationship is, and what the effects of different warm-up methods are. The recent maturation of reticulated meta-analysis theory and techniques has provided a method for comparison between multiple interventions, and it has become feasible to analyse the effects of different warm-up methods on the acuity of lower limb explosive strength. Based on this fact, this study used Bayesian reticulated meta-analysis to statistically evaluate the effects of different warm-up methods on the acute effect of explosive strength in the population receiving different warm-up methods to suggest the optimal warm-up method and provide some theoretical basis and reference for the development of precompetition or pretraining preparation activity programmes. Information And Methods Data sources and study selection Databases were searched for literature published from January 2000 to December 2022. Guidelines from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 10 were followed throughout. The Google Scholar, PubMed, Web of Science, Elsevier, Scopus, CNKI and Wanfang databases were searched using terms, such as Static Stretching (SS), Dynamic Stretching (DS), Ballistic Stretching (BS), Proprioceptive Neuromuscular Facilitation (PNF), PNF Stretching, Static Dynamic Stretching (SDS), Combination of Stretching, Foam Rolling (FR), Different Warm-up Methods, Different Stretching Methods, Self-myofascial Release, Lower Extremity Explosive Performance, Vertical Jump (VJ), Countermovement Jump (CMJ), Sprint, Speed, and Acute Effects. The literature was comprehensively searched using different combinations of these terms, with the language limited to Chinese and English. Inclusion and exclusion criteria The criteria for inclusion in the literature were based on the PICOS principles of evidence-based medicine, considering 5 aspects: study subjects, interventions, control group, study outcomes and study design as follows: 1) study subjects -- healthy adolescents with sports experience, free of other injuries and disease conditions prior to the intervention; 2) interventions -- experimental group only different warm-up methods of intervention; 3) control group -- no warm-up exercise or light aerobic running; 4) study outcomes -- the jump index was selected as countermovement jump (CMJ), the sprint index was selected as the 20-m sprint and the 30-m sprint; and 5) study design -- due to the specificity of the warm-up method intervention (short intervention time), the study design included RCTs and an own before-and-after controlled trial design. There were no significant differences between the experimental and control groups at baseline. The exclusion criteria were as follows: 1) literature that did not meet the inclusion criteria; 2) review literature or dissertations; 3) literature that was not in English or Chinese; and 4) literature with incomplete data on outcome indicators, resulting in data that could not be extracted. Literature screen and data extraction First, relevant literature was searched in various databases according to the developed literature search strategy. Then, the literature was uniformly imported into Endnote X9 software for deweighting, followed by an initial screening of literature titles, abstracts and keywords by 2 researchers using an independent double-blind approach according to the above inclusion criteria. Finally, qualitative and quantitative analyses were conducted on the screened eligible literature. The literature that met the criteria was independently extracted and included the following 3 aspects: (1) general information -- first author, year of publication; (2) intervention characteristics -- sample size, study population, intervention content, intervention duration; and (3) outcome indicators -- countermovement jump (CMJ), 20-m sprint and 30-m sprint indicators were selected. Quality evaluation Based on the characteristics of the included literature, this study used the Methodological Index for Non-randomized Studies (MINORS) risk of bias tool 11 to assess the quality of the literature in 12 domains (clear statement of study purpose, consistency of patients included, collection of expected data, whether the outcome indicators reflected the study purpose, whether the outcome indicators were objective, adequacy of follow-up time, failure rate less than 5%, whether sample size was estimated, appropriateness of control group selection, synchronisation of control groups, comparability of baseline between groups, appropriateness of statistical analysis) to evaluate the quality of the literature, with a score of 0–2 for each domain and a maximum score of 24. Statistical analyses Bayesian MeSH meta-analysis was performed using R software running the gemtc package in the R studio environment in conjunction with Stata software, version 13.0. The outcome indicators in this study were continuous variables, and the mean difference (MD) and 95% confidence interval (95% CI) were used as effect size indicators. Each model was set using four Markov chains for initial values, and the number of iterations was set at 20000, with the first 5000 used for annealing. Model inconsistency was diagnosed using R software, and Brooks-Gelman-Rubin diagnostic plots were plotted to quantitatively evaluate the convergence of the models. Local inconsistency was tested using the nodal separation method. Finally, heterogeneity was tested using I 2 to measure the magnitude of heterogeneity under the random-effects model, with I 2 ≤ 50% indicating low interstudy heterogeneity and I 2 > 50% indicating high interstudy heterogeneity. The network relationships were mapped using Stata software, version 13.0, and analysed for risk of publication bias; the metrics were ranked by surface under the cumulative ranking (SUCRA), where 0 ≤ SUCRA ≤ 100%, 100% representing the most effective warm-up method and 0 the worst and least effective. Finally, subgroup analyses were conducted to explore the effects of moderating variables. Results Results of the literature screening A total of 3255 relevant documents were retrieved from the seven selected search libraries, including 72 documents in Chinese and 3183 documents in English. A total of 2078 duplicate publications were excluded, 1029 documents were excluded on the basis of reading the titles and abstracts, and 110 documents were excluded on the basis of reading the full texts. Thirty-eight documents were finally included in the literature in English, and the screening flow chart is shown in Fig. 1 . Basic characteristics of the included studies Among the included papers, all articles were published in English 12–46 . All original studies included in the trial were RCTs and had a before-and-after controlled trial design. The interventions provided to the experimental group included static stretching, dynamic stretching, ballistic stretching, PNF stretching, static-dynamic stretching and foam rolling, while the interventions provided to the control group were mild aerobic exercise or no sports. The basic characteristics of the included studies are shown in Table 1 . Table 1 Basic characteristics of the included studies. First author Year Sample size Age (years) Intervention methods Time(min) Indicators Experimental group Control group Experimental group Control group Ari(1) 12 2021 11 11 11 24.4 ± 5.1 Static Stretching No sports 8 min a Dynamic Stretching Fortier 13 2013 15 15 22.8 ± 2.0 Static Stretching No sports 4.5 min a Paradisis 14 2014 47 47 47 14.6 ± 1.7 Static Stretching Dynamic Stretching No sports 6 min a, b Loughran 15 2017 8 8 18–30 Static Stretching No sports 10 min a, b 8 Static-Dynamic Stretching 20 min Ryan 16 2014 26 26 22.2 ± 1.3 Dynamic Stretching No sports 6.7 min a Fletcher(1) 17 2010 24 24 21.0 ± 0.3 Dynamic Stretching No sports NR a Curry 18 2009 24 24 26.0 ± 3.0 Static Stretching Mild aerobic 10 min a 24 Dynamic Stretching Nagle 19 2010 14 15 18–24 Static Stretching No sports 12 min a 13 Dynamic Stretching Baumgart 20 2019 20 20 26.6 ± 2.7 Foam Rolling No sports 10 min a Henning 21 2019 19 19 M:21.5 ± 1.8 Foam Rolling No sports 15 min a W:20.2 ± 1.5 Franco 22 2019 15 15 EG:24.1 ± 4.2 Foam Rolling Mild aerobic 8 min a CG:25.0 ± 4.7 Kopec 23 2017 20 20 22.5 ± 4.0 Dynamic Stretching No sports 3 min a 20 Foam Rolling Oliveira 24 2017 12 12 17.7 ± 0.9 Ballistic Stretching No sports 15 min a, b 12 PNF Stretching Unick 25 2005 16 16 19.2 ± 1.0 Static Stretching No sports 6 min a 16 Ballistic Stretching Kruse 26 2013 11 11 20.0 ± 1.6 Static Stretching No sports 7 min a 11 Dynamic Stretching Morrin 27 2013 10 10 27.0 ± 5.0 Static Stretching No sports 8 min a 10 Dynamic Stretching 10 Static Dynamic Stretching Yildiz 28 2020 35 35 23.6 ± 1.3 Static Stretching Mild aerobic NR a Chaouachi 29 2010 22 22 20.6 ± 1.2 Static Stretching No sports 10 min a, c 22 Dynamic Stretching 22 Static Dynamic Stretching Christensen 30 2008 68 68 20.5 ± 1.4 Dynamic Stretching Mild aerobic NR a 68 PNF Stretching Fletcher(2) 31 2010 27 27 20.5 ± 2.2 Static Stretching Mild aerobic 6 min a, b 27 Dynamic Stretching Pagaduan 32 2012 29 29 19.4 ± 1.1 Static Stretching No sports 7 min a 29 Dynamic Stretching 29 Static-Dynamic Stretching Byrne 33 2014 29 29 20.8 ± 4.4 Dynamic Stretching Mild aerobic 11 min b Bafghi 34 2013 15 15 24.7 ± 4.6 Static Stretching No sports NR a 15 Dynamic Stretching Shi Huang 35 2022 14 14 22.6 ± 1.7 Dynamic Stretching No sports 8 min b Baskurt 36 2017 15 15 22.9 ± 1.3 Static Stretching No sports NR b Ari(2) 37 2021 8 8 15.4 ± 1.1 Static Stretching No sports 8 min a 8 Dynamic Stretching 8 Static-Dynamic Stretching Dallias 38 2019 26 26 22.4 ± 3.7 Dynamic Stretching No sports NR b Utku 39 2017 12 12 15.0 ± 0.5 Static Stretching No sports 14 min b 12 PNF Stretching 12 Ballistic Stretching Jaggers 40 2008 20 20 24.8 ± 3.0 Dynamic Stretching No sports NR a 20 Ballistic Stretching Vetter 41 2007 26 26 M:21.7 ± 1.2 Static Stretching Mild aerobic NR c 26 W:22.3 ± 1.6 Dynamic Stretching Gelen 42 2010 26 26 23.3 ± 3.2 Static Stretching No sports 10 min c 26 Dynamic Stretching 10 min 26 Static-Dynamic Stretching 20 min Nelson 43 2005 16 16 M:21.0 ± 2.0 W:19.0 ± 1.0 Static Stretching No sports NR b Adam 44 2008 10 10 18–29 Static Stretching Mild aerobic NR c Marinho 45 2017 16 16 22.0 ± 1.6 Static Stretching No sports 8–10 min b 16 Dynamic Stretching Perrier 46 2011 21 21 21 24.4 ± 4.5 Static Stretching Dynamic Stretching No sports 13.8 min a Table Note: “NR” indicates not reported; “a” indicates countermovement jump (CMJ); “b” indicates 20-m sprint; “c” indicates 30-m sprint. Results of the methodological quality evaluation The 2 researchers scored each of the 12 domain entries according to the (Methodological Index for Non-randomized Studies, MINORS) scale, and the threshold of disagreement was referred to another author for judgement, resulting in an overall final score for the literature, as shown in Table 2 , from which the results of the literature quality evaluation found that the quality scores of the literature for all included studies were relatively high, with risky entries occurring in the use of blinding and whether sample sizes were estimated. Table 2 Results of the methodological quality evaluation. First author 1 2 3 4 5 6 7 8 9 10 11 12 Scores Ari(1) 12 2 2 2 2 0 2 2 0 2 0 2 2 18 Fortier 13 2 2 2 2 0 2 2 0 0 0 2 2 16 Paradisis 14 2 2 2 2 0 2 2 0 1 0 2 2 17 Loughran 15 2 2 2 2 1 2 2 0 2 2 2 2 21 Ryan 16 2 2 2 2 1 2 2 0 0 0 2 2 17 Fletcher(1) 17 2 2 2 2 0 2 2 0 0 0 1 2 15 Curry 18 2 2 2 2 1 2 2 0 2 0 2 2 19 Nagle 19 2 2 2 2 0 2 2 0 1 2 2 2 19 Baumgart 20 2 2 2 2 0 2 2 0 2 0 2 2 18 Henning 21 2 2 2 2 0 2 2 0 0 0 2 2 16 Franco 22 2 2 2 2 0 2 2 0 2 2 2 2 20 Kopec 23 2 2 2 2 0 2 2 0 0 0 2 2 16 Oliveira 24 2 2 2 2 0 2 2 0 1 0 2 2 17 Unick 25 2 2 2 2 0 2 2 0 1 0 2 2 17 Kruse 26 2 2 2 2 0 2 2 0 2 0 2 2 18 Morrin 27 2 2 2 2 0 2 2 0 2 0 2 2 18 Yildiz 28 2 2 2 2 0 2 2 0 1 0 1 2 16 Chaouachi 29 2 2 2 2 0 2 2 0 1 0 2 2 17 Christensen 30 2 2 2 2 0 2 2 0 0 0 1 2 15 Fletcher(2) 31 2 2 2 2 0 2 2 0 2 0 2 2 18 Pagaduan 32 2 2 2 2 0 2 2 0 0 0 2 2 16 Byrne 33 2 2 2 2 0 2 2 0 2 0 2 2 18 Bafghi 34 2 2 2 2 0 2 2 0 2 0 1 2 17 Shi Huang 35 2 2 2 2 0 2 2 0 2 0 2 2 18 Baskurt 36 2 2 2 2 0 2 2 0 1 2 1 2 18 Ari(2) 37 2 2 2 2 0 2 2 0 2 0 2 2 18 Dallias 38 2 2 2 2 0 2 2 0 2 0 1 2 17 Utku 39 2 2 2 2 0 2 2 0 0 0 2 2 16 Jaggers 40 2 2 2 2 0 2 2 0 1 0 1 2 16 Vetter 41 2 2 2 2 0 2 2 0 1 0 1 2 16 Gelen 42 2 2 2 2 0 2 2 0 2 0 2 2 18 Nelson 43 2 2 2 2 0 2 2 0 1 0 1 2 16 Adam 44 2 2 2 2 0 2 2 0 2 0 1 2 17 Marinho 45 2 2 2 2 0 2 2 0 1 0 2 2 17 Perrier 46 2 2 2 2 0 2 2 0 2 0 2 2 18 Table Note: “1” indicates that the purpose of the study was clearly given; “2” indicates the consistency of the patients included; “3” indicates the expected data collection; “4” indicates whether the outcome indicators reflected the purpose of the study; “5” indicates whether the trial was blinded; “6” indicates whether the follow-up period was adequate; “7” indicates whether the loss of follow-up rate was less than 5%; “8” indicates whether the sample size was estimated; “9” indicates whether the selection of the control group was appropriate; “10” indicates whether the control groups indicated whether the control groups are synchronised; “11” indicates whether the baselines were comparable between groups; “12” indicates whether the statistical analysis was appropriate. Results of the reticulated meta-analysis Network relations map A total of 25 studies in the overall included literature used the CMJ metric, and 15 studies used the sprint metric to evaluate the effect of different warm-up methods on the acute impact of lower limb explosive strength. The reticulated relationship graphs generally show a star-like structure centred on the control group, and all formed a triangular closed loop, as shown in Fig. 2 . These figures show that there is evidence of both direct and indirect comparisons between different intervention methods in the lower limb explosive strength index; therefore, the basic conditions for a reticulated meta-analysis are present. From the figure, it can be seen that the thickest line between DS, SS and the control group represents the most direct comparison studies, reflecting from the side that the 2 are the most controversial warm-up methods in current sports training, and their mechanism of action on explosive strength has yet to be clarified. Overall consistency test and model convergence diagnosis Overall consistency test The reticulated meta-analysis is based on the assumption of consistency. First, we must judge whether the data are consistent or not, record the DIC 1 value under the consistency model fit in R software, and then record the DIC 2 value under the inconsistency model fit to judge the global consistency. If the difference between the two is less than five, the data are generally consistent. CMJ indicators: DIC 1 = 105.316 under the consistency model fit was recorded in R software, and then DIC 2 = 108.412 under the inconsistency model fit was recorded, with a difference less than five, indicating that there was no inconsistency between the intervention methods under direct and indirect comparisons and that the consistency model could be used for analysis. Sprint indicator: DIC 1 = 79.577 under the consistency model fit was recorded in R software, and then DIC 2 = 79.969 under the inconsistency model fit was recorded, with a difference less than five, indicating that the intervention modality was not inconsistent under direct and indirect comparisons and that the consistency model could be used for analysis. Model convergence diagnosis The Brooks-Gelman-Rubin diagnostic map was used to quantify the degree of convergence of the diagnostic model, as shown in Fig. 3 . The results of the images show that the median value of the reduction factor, 97.5% of the reduction factor and the potential scale reduction factor (PSFR) of this study's model are close to one after iterative calculation, and the model converges strongly, proving that the results of the mesh meta-analysis are reliable. Direct meta-analysis comparison of results The study was conducted by plotting direct comparison forest plots to analyse the effects of different warm-up methods on the explosive power of the lower limbs and, by examining at whether the 95% CI crossed the 0 scale, to determine whether the difference was statistically significant. (1) CMJ indicators The results of the reticulated meta-analysis of CMJ indicators for direct comparison between each warm-up method and the control group are shown in Fig. 4 . The acute effects of SDS [MD = 1.80, 95% CI: (0.43, 3.20)], DS [MD = 1.60, 95% CI: (0.67, 2.60)] on CMJ indicators were superior to those of the control group. There was no evidence that BS [MD = 0.30, 95% CI: (-2.30, 2.90)], FR [MD = 0.63, 95% CI: (-1.60, 2.80)], PNF [MD = -0.94, 95% CI: (-3.80, 1.90)], or SS [MD = -0.75, 95% CI: (-1.70, 0.18)] were significantly different from the control group. (2) Sprint indicators The results of the net meta-analysis of sprint metrics for direct comparison between each warm-up method and the control group are shown in Fig. 5 . The acute effect of DS [MD = -0.08, 95% CI: (-0.15, -0.008)] on sprint metrics was better than that of the control group, while the acute effect of SS [MD = 0.07, 95% CI: (0.002, 0.13)] on sprint metrics was less than that of the control group. There was no evidence of significant differences among BS [MD = 0.04, 95% CI: (-0.11, 0.18)], PNF stretch [MD = -0.06, 95% CI: (-0.09, 0.21)], SDS [MD = -0.06, 95% CI: (-0.16, 0.05)] and controls. Local consistency check The local inconsistency test (local inconsistency) was performed by the node-splitting method to test whether there was a difference between the results of direct and indirect comparisons when there was a closed loop between the direct and indirect comparisons, and if the difference was not statistically significant, the results of direct and indirect comparisons were considered to be consistent. (1) CMJ indicators As shown in Table 3 , in all closed loops, the differences between direct and indirect comparisons were not significantly different between the groups (P > 0.05), and overall, there was good agreement between direct and indirect interventions where closed loops existed. Table 3 CMJ indicator node splitting method test Intervention methods Direct MD(95%CI) Indirect MD(95%CI) Network MD(95%CI) P DS vs. BS 0.24(-7.50, 8.00) 1.70(-1.30, 4.60) 1.40(-1.30, 4.10) 0.73 PNF vs. BS -2.10(-6.20, 2.00) 0.48(-4.90, 5.80) -1.20(-4.50, 2.10) 0.46 SS vs. BS 0.20(-4.50, 4.90) -1.50(-5.00, 1.90) -1.00(-3.70, 1.70) 0.56 FR vs. DS -0.66(-8.30, 7.00) -1.00(-3.60, 1.50) -1.00(-3.40, 1.40) 0.92 PNF vs. DS -0.08(-4.90, 4.70) -4.20(-8.10, 0.25) -2.60(-5.50, 0.29) 0.19 SS vs. DS -2.30(-3.30, -1.40) -0.85(-2.60, 4.30) 0.17(-1.20, 1.60) 0.56 SDS vs. DS -0.28(-1.90, 1.40) 0.85(-2.60, 4.30) 0.17(-1.20, 1.60) 0.56 (2) Sprint indicators As shown in Table 4 , the differences between direct and indirect comparisons were not significantly different between the groups in all of the closures (P > 0.05), and overall, there was good agreement between direct and indirect interventions for the closures. Table 4 Sprint indicator node splitting method test Intervention methods Direct MD(95%CI) Indirect MD(95%CI) Network MD(95%CI) P SS vs. BS 0.03(-0.23, 0.29) 0.03(-0.19, 0.25) 0.03(-0.12, 0.19) 0.99 SS vs. DS 0.18(0.06, 0.29) 0.09(-0.09, 0.27) 0.14(0.07, 0.23) 0.38 SDS vs. DS 0.07(-0.03, 0.16) 0.04(-0.09, 0.16) 0.02(-0.09, 0.13) 0.68 SS vs. PNF 0.001(-0.26, 0.26) 0.01(-0.21, 0.25) 0.01(-0.15, 0.17) 0.95 Cumulative probability ranking results The cumulative probability ranking diagram and SUCRA values were used to comprehensively evaluate the order of merit of different warm-up methods on the acute effects of lower limb explosive strength to screen for the best warm-up method. (1) CMJ indicators The results of the ranking of the effects of different warm-up methods on the CMJ index are shown in Fig. 6 . The results show that SDS (87.6%) > DS (83.5%) > FR (55.5%) > BS (48.1%) > CON (40.2%) > PNF stretch (19.5%) > SS (15.6%). (2) Sprint indicators The results of the ranking of the effects of different warm-up methods on sprint indices are shown in Fig. 7 . The results show that DS (91.1%) > SDS (79.6%) > CON (51.9%) > BS (36.4%) > PNF stretch (25.0%) > SS (16.1%). Publication bias analysis The 35 included studies were tested for risk of publication bias, and the funnel plot is shown in Fig. 8 . The results of the image show that the majority of studies had effect sizes concentrated at the top of the funnel plot, but given that there are still a few studies located at the outside and bottom of the funnel plot, it suggests that there might be a slight publication bias, as well as a small sample effect in this network of studies. A test of the moderating effect of dynamic stretching on explosive power Considering the outcomes together, DS produced the most consistent effect on the acute effect of explosive strength compared to other warm-up methods, and significant heterogeneity in the results of comparisons between DS and controls was found through R software analysis. Further analysis using Stata software yielded the results of the overall effect test, as shown in Table 5 , reflecting the possibility of the existence of potential moderating variables. In light of this finding, a moderating effect test was conducted on the 17 DS studies in the total included literature to examine the roles of six moderating variables in the effects of DS on the acute effect of lower limb explosive strength, as shown in Table 6 . Table 5 Overall effect test for the acute effect of dynamic stretching on explosive power Studies Heterogeneity test Two-tailed test MD(95%CI) I 2 P Z P Random effects model 17 75.6 0.00 3.16 0.00 1.77(0.68, 2.87) 1) Stretching time. A total of 12 studies were included in this conditioning group, and there was heterogeneity in the effect sizes of the three groups (I 2 = 80.9%), indicating a moderating effect of stretching time on the acute effects of explosive force. The stretching time of 7–10 min had an effect size MD = 2.90 (95% CI (1.07, 4.73), P 0.05), suggesting that the acute effect of stretching time of 7–10 min on explosive strength was the most significant. 2) Mean age. A total of 17 studies were included in this conditioning group, and there was heterogeneity in effect size across the 3 groups (I 2 = 75.6%), suggesting that there was some moderation of the effect size of age on the acute effect of explosive strength. The effect size MD = 1.95 (95% CI (0.20, 3.70), P 0.05 for 21–25 years of age as well as older than 25 years of age), suggesting that the mean age of the study population was the most significant intervention effect for those younger than 21 years of age. 3) Study population. A total of 16 studies were included in this conditioning group, and there was heterogeneity in effect size between the 2 groups (I 2 = 77.2%), suggesting that there was some moderation of the acute effect of explosive strength with the effect size of the study subjects, with an effect size MD = 2.00 (95% CI (0.15, 3.85), P < 0.05 for bodybuilders and an effect size MD = 1.67, 95% CI (0.31, 3.04) for athletes, P < 0.05), which showing the most significant effect size for gym goers. 4) Sample size. A total of 17 studies were included in this moderation group, and there was heterogeneity in the effect sizes of the 3 groups (I 2 = 75.6%), suggesting that there was some moderation of the effect size of the sample size on the acute effect of explosive strength. The effect size MD = 2.11 (95% CI (0.37, 3.84), P < 0.05 for up to 20 cases, MD = 1.93, 95% CI (0.28, 3.59), P 0.05). The results suggest that the sample size was most significant for sample sizes of less than 30 cases. 5) Year of publication. The moderation group included a total of 17 studies in this moderation group, and there was heterogeneity in the effect sizes of the three groups (I 2 = 75.6%), suggesting that there was some moderation of the effect size for year of publication on the acute effect of explosiveness (effect size MD = 2.53, 95% CI (0.89, 4.18), P 0.05), showing that the effect size for 2011–2015 was the most significant. 6) Quality of literature. The moderation group included a total of 17 studies in this moderation group, and there was heterogeneity in the effect sizes of the three groups (I 2 = 75.6%), suggesting that there was some moderation of the effect size of literature quality on the acute effect of explosive force. (effect size MD = 2.02, 95% CI (0.47, 3.56), P < 0.01 for 18–19 scores, and P 0.05), and the results suggest that the effect size for scores of 18–19 was the most significant. Table 6 Tests of the moderating effect of dynamic stretching on the acute effect of explosive power Adjustment variables Heterogeneity test Category Effect value&95%CI Two-tailed test Number of studies * I 2 P Z P Stretching time 80.9 0.00 Less than 7 min 0.44(-1.64, 2.52) 0.41 0.68 3 7−10 min 2.90(1.07, 4.73) 3.11 0.00 5 More than 10 min 1.40(-0.55, 3.35) 1.41 0.16 4 Average age 75.6 0.00 Less than 21 years 1.95(0.20, 3.70) 2.18 0.03 8 21−25 years 1.39(-0.41, 3.20) 1.51 0.13 7 More than 25 years 2.52(-0.26, 5.30) 1.78 0.08 2 Subjects 77.2 0.00 athletes 1.67(0.31, 3.04) 2.40 0.02 10 gymgoers 2.00(0.15, 3.85) 2.12 0.03 6 Sample size 75.6 0.00 Less than 20 cases 2.11(0.37, 3.84) 2.38 0.02 7 21–30 cases 1.93(0.28, 3.59) 2.29 0.02 8 More than 31 cases 0.07(-0.28, 2.23) 0.07 0.95 2 Years 75.6 0.00 2005−2010 years 0.77(-0.73, 2.28) 1.01 0.31 7 2011−2015 years 2.53(0.89, 4.18) 3.01 0.003 7 2016–2021 years 1.55(-1.10, 4.21) 1.15 0.25 3 Quality of the literature 75.6 0.00 14−15 scores 0.28(-2.85, 3.40) 0.17 0.862 2 16–17 scores 1.71(-0.31, 3.73) 1.66 0.10 7 18−19 scores 2.02(0.47, 3.56) 2.56 0.009 8 Table Note:∗ indicates that there are differences in the classification of some independent samples, resulting in a total number of studies with some moderating variables not equal to the total number of included studies. Discussion This study explored the acute effects of different warm-up methods on lower limb explosive strength from an evidence-based medical perspective. The results of a net meta-analysis showed that SDS and DS were able to have a positive effect on explosive strength overall, while SS showed a negative effect, with a smaller but still statistically significant effect size and no results yet proving the effectiveness of other warm-up methods. This finding is largely in line with previous research findings 47,48 . However, a recent meta-analysis was inconsistent with the results of this study 49 , and the analysis suggests that this inconsistency might be related to the study methodology on the one hand. This study used a traditional meta-analysis, which was only able to analyse the relative validity of the effects of different warm-up methods on the acute effects of lower limb explosive strength, and it lacked a comprehensive comparative analysis between studies, whereas this study used a continuum analysis to test the stability and reliability of the relative evidence between different warm-up methods, and the indirect comparative modelling approach provided more accurate statistical analysis 50 , which would allow relevant coaches and athletes alternatives to different warm-up methods, rather than in a traditional meta-analysis of individual warm-up method studies. Therefore, it is speculated that the study method might have influenced the results. Another aspect could be related to publication bias in the literature. Publication bias in meta-analyses can lead to lower actual effect sizes and can also increase the risk of no and negative effect sizes 51 , so-called false 'positive' or false 'negative' results. The study did not test for the risk of publication bias, so there is a potential risk of publication bias. The study used a qualitative (funnel plot) test of bias to ensure the accuracy of the results to a certain extent. Analysis of the effect of different warm-up methods on the acute effect of lower limb explosive power Dynamic stretching affects the effect The results of the study showed that the DS group had a better effect size than the control group for both CMJ performance [MD = 1.60, 95% CI: (0.67, 2.60)] and sprint performance [MD = -0.08, 95% CI: (-0.15, -0.008)], and the results of the SUCRA ranking chart indicated that, although slightly lower than the SDS group for CMJ performance, its effect on explosive power was an acute effect that was significantly better than the other warm-up methods. Regarding the mechanism by which DS promotes explosive strength, some scholars believe that it might be related to DS increasing body and muscle temperature. During stretching, muscles actively contract and stretch, increasing temperature while decreasing viscosity; at the same time, increased muscle temperature can cause increased neuroreceptor sensitivity, and this increased sensitivity suggests that neuromuscles might show stronger motor unit activation through increased motor unit recruitment 52 , thereby improving muscle contractile performance. For example, Fletcher et al. showed 53 that DS was able to significantly elevate muscle temperature and ultimately reverse longitudinal jump height compared to SS. Furthermore, some scholars have found that the ability of DS to enhance subsequent explosive performance could be related to the preactivation of movement patterns 54, 55 . Studies have shown that it might stimulate the muscle shuttle to increase muscle reflex activity, thus allowing the muscle to better complete active contractions based on the characteristics of the subsequent movement and ultimately inducing a postactivation potentiation effect (PAP) 56 . DS has long been recommended as an essential component of warm-up activities, and its effects on improving joint mobility and preventing sports injuries are well established, but the effect on subsequent explosive power remains controversial, and the reasons for this controversy might be influenced by the duration of stretching, the study population and other factors. The results of the study showed significant heterogeneity (I 2 = 75.6%) in the results of the comparison between the DS and control groups, suggesting that the variation caused by real differences in effect sizes accounted for 75.6% of the total variation, which can be considered a large degree of dispersion in the effect sizes of the individual studies; therefore, it is necessary to introduce moderating variables to investigate the heterogeneity in depth. (1) In terms of stretch time. The test found that a stretch time of 7–10 min produced the largest effect size. This finding is largely in line with previous findings. A study by Mcmillian et al. 57 found that DS with a total time duration of 10 min was able to enhance subsequent jump performance compared to the SS and control groups. Similarly, Behm et al. 58 clearly suggested that a 7 min and 10 min DS could have a positive effect on subsequent explosive power. The reason for this finding might be that shorter periods of stretching do not make effective use of the compliance effects on tendon units, while longer periods can cause fatigue in the body, thus not maximising the benefits of dynamic stretching. (2) Study population and mean age. In terms of the study population, fitness enthusiasts produced a larger effect size than athletes. The analysis suggests that athletes have better muscle mass and physical function than fitness enthusiasts due to long-term systematic training and that there could be a saturation effect on their physical function. This age group is in the adolescent stage, a sensitive period for neuromuscular development 59 , and is at the peak of natural growth in both speed and explosive ability; therefore, the stimulatory response to dynamic stretching could be higher in this stage than in the adult stage. (3) In terms of sample size, years of publication and quality of literature. In terms of sample size, samples with fewer than 30 cases were the most significant, consistent with the ease of good results in trials with fewer subjects found in the meta-analysis by Kang Yujie et al. 60 . However, if the sample size is too small, potential errors due to random error factors cannot be excluded, regardless of whether the treatment effect survives as valid. Conversely, a sample size that is too large can result in a waste of resources. Therefore, the selection of the optimal sample size should consider both clinically significant and statistically significant differences in efficacy, as well as factors such as financial budget. In terms of the year of publication of the literature, the effect size of the studies between 2011 and 2015 was significant, consistent with the statement by Liu et al. 2 that "a large number of studies prior to 2016 demonstrated some facilitation of subsequent exercise performance by DS, after which many opposing views emerged that DS does not necessarily facilitate exercise performance". The reason for this outcome could be related to the relatively stable methodological quality of studies prior to 2016 61 . In terms of the quality of the literature, only two of the 17 DS-related studies reported the use of blinding, which to some extent affects the quality of the literature, although a meta-analysis indicated that there was no significant correlation between the efficacy of physical methods and adequate blinding 62 . However, adherence to the 'blinding principle' can improve the internal consistency of trials and reduce bias due to the expectations of subjects, intervention implementers or outcome evaluation. Static stretching affects the effect SS has become one of the most widely used warm-ups in sports due to its simplicity and controllability and low muscle damage 2 . Although SS can significantly improve joint mobility 63 , its effect on subsequent explosive power must be further confirmed 64 . The results of this study showed that the effect size in terms of sprint performance was MD = 0.07 [95% CI: (0.002, 0.13)], which was significantly different from the control group, and the effect size in terms of CMJ performance was MD = -0.75 [95% CI: (-1.70, 0.18)], which was not significantly different from the control group, but from the SUCRA ranking graph, it can be seen that the percentage of its area under the curve was 15.6%, which was much smaller than that of the control group, indicating that SS was able to negatively affect subsequent explosive performance. The reason for the lack of significant differences between SS and controls in terms of CMJ performance could be that, compared to the complexity and coordination required for the short sprint, the measure of jumping ability is one dimensional; therefore, the effect of SS on relatively single-movement ability might be lower than that on relatively complex movement ability 65 , and although the reverse vertical jump also requires a coordinated body effort, the time to complete the movement is relatively short, at least compared to the short sprint, and any change in the session might not have a significant effect on the outcome metrics. Physiological and neurological studies have provided insight into the reasons for the negative impact of SS on explosive power. It has been found that prolonged static stretching of muscles affects the sensitivity of the muscle spindle (MS), which functions to encode information about the length of muscle extension as nerve impulses to the centre, reflexively generates and maintains muscle tension, and participates in the casual regulation of movement 66 through the coactivation of alpha-gamma motor neurons to ensure high sensitivity of the MS during muscle contraction 67 . However, prolonged SS can cause deactivation of the sensitivity of the γ system in MS, resulting in the inability of MS to transmit the actual length of the muscle to the superior centre, reducing the number of motor units excited out of neuroprotective inhibition and ultimately leading to a decrease in explosive power 68 . Another part of the study suggested that SS leads to a decrease in muscle-tendon unit (MTU) stiffness. The lower limb muscle-tendon union acts as a carrier of elastic energy storage and utilisation. In a state of constant muscle length, greater stiffness helps the muscle to generate more force during centripetal contraction 69 . Therefore, it has been suggested that SS might reduce muscle length and tone 70 , thus preventing the muscle from being in an activated state, in turn leading to a decrease in stiffness 71 and ultimately having a negative impact on explosive power. Static combined with dynamic stretching to influence the effect Since the strengths and weaknesses of SS are so obvious, it has been asked whether a combined approach of SS and DS could be used to take advantage of the improved joint mobility of SS while avoiding the detrimental effects of SS on explosive power through subsequent DS. The results of this study showed that, in terms of CMJ performance, the SDS effect size was MD = 1.80 [95% CI: (0.43, 3.20)], which was significantly different from the control group; the effect size in terms of sprint performance was MD = -0.06 [95% CI: (-0.16, 0.05)], suggesting that SDS can have some effect on subsequent CMJ performance. No results were available to demonstrate the effect of SDS on sprint performance. For the lack of significant differences between SDS and controls in terms of sprint performance, the analysis suggests that this difference could be related to the number of included papers. Of the 35 included papers, only three 15, 29, 42 examined the effect of SDS on sprint performance; in other words, the network comparison analysis was based on only these three papers, so the strength of the relevant evidence findings was significantly reduced. For example, only Chaouachi et al. 29 of the 3 papers showed no significant difference in SDS regarding subsequent sprint performance because the study was conducted with physical education students, and the trained population was less susceptible to the acute effects of stretching than the untrained population, as confirmed by Egan et al. 72 . Regarding the mechanism by which SDS can enhance explosive performance, the present study suggested that it could be related to the reactivation of γ motor neurons in MS. Although the sensitivity of γ-motor neurons is inactivated after SS, the subsequent stimulation of the muscle shuttle by DS could "reawaken" the sensitivity of γ-motor neurons, and muscle contraction reverts to the coactivation pattern of α-γ-motor neurons 68 , thus exploiting the advantages of SS and DS while avoiding their disadvantages. However, since few studies have been conducted, the results must be interpreted with caution, and further evaluation is needed. Other warm-up methods affect the results In addition to the above warm-up methods, there are no results showing that foam axis rolling, PNF stretching and bouncy stretching can have effects on subsequent explosive power. (1) Foam axis rolling. Also known as self-fascial relaxation, foam axis rolling is an emerging warm-up and relaxation technique in recent years. The practitioner uses self-weight to give the target muscle a certain amount of pressure to roll back and forth on the foam axis to improve the stretch and flexibility of the outer connective tissue of the muscle fibres 73 , promote blood circulation, and increase myocyte oxygen and energy metabolism. The results of the present study remain largely consistent with previous studies and with a meta-analysis of FR by Wiewelhove et al. 49 showing that FR does not significantly affect subsequent explosive performance and that it is more suitable for postexercise relaxation than preexercise warm-up since it has been shown to be effective in relieving exercise-related muscle soreness. The few studies that have shown FR to significantly improve exercise performance suggested that FR could disrupt myofascial trigger-points (MTrP), which are nodules produced by transitional stress in skeletal muscle that can lead to muscle fatigue and stiffness 74 , while Huang Haojie et al. 24 suggested that the stress produced by the foam axis on the muscle activated the Golgi Tdon organs (GTOs), which when active inhibit the muscle shuttle, causing a muscle relaxation response, a decrease in muscle tension, a decrease in muscle adhesion and an increase in muscle performance. Other studies have suggested that it could be related to the psychological factors of the participants since they believed that FR would improve their exercise performance 75 . (2) PNF stretching. The full name is proprioceptive neuromuscular facilitation (PNF), which first originated in the field of rehabilitation medicine for the treatment of diseases such as neuromuscular paralysis by activating the autonomic and cross-inhibitory effects of muscles to improve the function of specific muscles and later began to be widely used in the field of competitive sports, mostly used to prevent and treat sports injuries and improve joint mobility 76 . However, the stretching process requires the assistance of a professional to apply the force and is time-consuming and could be more suitable for professional athletes from a simple and economic point of view. The results of the MeSH meta-analysis showed that PNF stretching had a negative impact on explosive power, although the difference was not statistically significant, consistent with previous studies. 77 Therefore, Bradley et al. 78 suggested that PNF stretching should not be performed prior to explosive sports. The mechanism of the effect of PNF stretching on explosive strength has been considered by most scholars to be the same as that of static stretching and can be explained by affecting the sensitivity of sarcolemmal receptors. (3) Stretching with elastic shock. BS has been gradually marginalised due to its special stretching mechanism -- forcing the target muscle to elongate by means of rapid rebound, which most scholars believe causes a strong stretch reflex that in turn causes the muscle to contract to a shorter length than before the stretch and is therefore more likely to trigger muscle damage 79 . The results of the present study suggested that BS has a positive effect on subsequent explosive performance, although this effect was not statistically significant. Mariscal et al. 80 suggested that BS, by stimulating neuromuscular activity, could activate the stretch-shortening cycle (SSC), thereby improving subsequent sprint performance. The outcome indicators included in this study, both CMJ in situ and short-distance sprinting, required centrifugal muscle elongation to store energy, followed by centripetal contraction to improve subsequent performance, which could, to some extent, explain why BS could have a positive effect on subsequent explosive performance. Limitations of the study. (1) The search of the literature for this study did not include unpublished literature, and some literature was not included due to the absence of a control group or incomplete data about outcome indicators, which might have affected the comprehensiveness of the information to some extent. (2) The small number of static combined with dynamic stretching and foam axis rolling related literature included might have weakened the argument to some extent. (3) Due to the warm-up method intervention short duration and other peculiarities, most studies did not use randomised, controlled studies but their own before-and-after controlled studies to avoid the influence of individual differences on the study results. Conclusion (1) Static stretching reduces subsequent explosive performance, while dynamic stretching and static stretching combined with dynamic stretching are the two warm-up methods that significantly improve subsequent explosive performance, with dynamic stretching being the most stable and moderated by a variety of variables. (2) The dynamic stretching time of 7–10 min produced the best explosive performance, and the intervention effect was also influenced by modifying variables, such as study population and age. (3) The quality and quantity of the included literature affects the overall effect of the intervention, and it is recommended that subsequent studies add high-quality studies based on strict adherence to the trial specifications. Declarations Ethics approval and consent to participate Ethics was approved by Beijing Normal University ethics committee. No consent to participate required. Consent for publication Not applicable Availability of dataset and materials The datasets used and/or analysed during the current study is available from the corresponding author on reasonable request. Competing interests The author declare no competing interests. Funding This systematic review is funded by two grant programs, The first is the Beijing Social Science Foundation(20YTB012) and the second is the Beijing Normal University Young Teachers' Fund(2020NTSS39). Author Contributions Fuyou LI and Chenggen GUO wrote the main manuscript text and thesis proofing;Huashuai LI and Haoran XU prepared figures 1-8 and tables 1-6;Sun PU adjusted and modified the structure and content of the first draft. Acknowledgements Immense gratitude goes to Pu SUN, Beijing Normal University, College of P.E And Sports for his continued support throughout this revision process. Authors' information Beijing Normal University, College of Physical Education and Sports; Research area is physical training and health promotion. References Xie YM, Gu JQ, Wang SW, et al. Study on the best stretching methods in pre-game preparation activities for female professional basketball players[J]. Journal of Beijing Sport University,2018, 41(12):117-122. Liu Xiaoyang,Chen Leqin. Research progress on the acute effects and mechanisms of muscle stretching on sports performance[J]. Journal of Hebei Institute of Physical Education,2021,35(02):72-82. Gong Jianfang,Li Shan,Wang Baofeng. 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International journal of sports physical therapy, 2015, 10(6): 827-838. Bonci A S, Oswald S. Physiology: Barrier Trigger Points And Muscle Performance[J]. Strength & Conditioning Journal, 1993, 15(6): 39-43. Jo E, Juache G A, Saralegui D E, et al. The acute effects of foam rolling on fatigue-related impairments of muscular performance[J]. Sports, 2018, 6(4): 112-120. Zhang BG, Wang SD. Improving limb flexibility and muscle strength:the application of proprioceptive neuromuscular facilitation in the field of exercise[J]. Chinese Tissue Engineering Research and Clinical Rehabilitation,2010,14(37):6995-6998. Wang S.D.,Zhang B.G. Effects of PNF stretching method on muscle strength, flexibility and bioelectric properties[J]. Chinese Journal of Sports Medicine,2011,30(04):387-391. Bradley P S, Olsen P D, Portas M D. The effect of static, ballistic, and proprioceptive neuromuscular facilitation stretching on vertical jump performance[J]. The Journal of Strength & Conditioning Research, 2007, 21(1): 223-226. Wang WX. Theory and practice of physical fitness training [M]. Beijing: Higher Education Press, 2012: 177-178. Mariscal S L, Garcia V S, Fernández-García J C, et al. Acute Effects of Ballistic vs. Passive Static Stretching Involved in a Prematch Warm-up on Vertical Jump and Linear Sprint Performance in Soccer Players[J]. The Journal of Strength & Conditioning Research, 2021, 35(1): 147-153. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Aug, 2023 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted Editorial decision: Major revision 07 Mar, 2023 Reviews received at journal 23 Feb, 2023 Reviewers agreed at journal 18 Feb, 2023 Reviewers agreed at journal 09 Feb, 2023 Reviewers invited by journal 02 Feb, 2023 Editor assigned by journal 02 Feb, 2023 Editor invited by journal 02 Feb, 2023 Submission checks completed at journal 02 Feb, 2023 First submitted to journal 16 Jan, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2483758","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":172724369,"identity":"1bc3755b-37d2-4fe5-8a55-837a6d1a6dcd","order_by":0,"name":"Fuyou LI","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fuyou","middleName":"","lastName":"LI","suffix":""},{"id":172724370,"identity":"877199e5-8ae7-4874-80f1-c16b6306d592","order_by":1,"name":"Chenggen GUO","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chenggen","middleName":"","lastName":"GUO","suffix":""},{"id":172724371,"identity":"a747336c-6392-46f3-aaed-63f69d9184f9","order_by":2,"name":"Huashuai LI","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huashuai","middleName":"","lastName":"LI","suffix":""},{"id":172724372,"identity":"9e6eaacf-141c-4079-8399-132023c9d127","order_by":3,"name":"Haoran XU","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haoran","middleName":"","lastName":"XU","suffix":""},{"id":172724373,"identity":"8088e523-28d0-48c8-846f-829881e6a5db","order_by":4,"name":"Pu SUN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYNCCigNgSoIELWdI1sLYRooW+Rm5xx7zzruTZ3CA+eBtHga7PIJaDG7kpRvzbntWbHCALdmahyG5mLAWiRwzad5thxM3HOAxk+ZhOJDYQNhhIC1zQFr4vxGnheEGSEsD2BY24rQYnHljbjjn2LPEmYfZjC3nGCQT4bD2HLMHb2ruJPYdb354402FHREOY2Bgg1DMYEuJUI/QMgpGwSgYBaMAFwAAD4U7agvBsrgAAAAASUVORK5CYII=","orcid":"","institution":"Beijing Normal University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Pu","middleName":"","lastName":"SUN","suffix":""}],"badges":[],"createdAt":"2023-01-16 12:29:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2483758/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2483758/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13102-023-00703-6","type":"published","date":"2023-08-29T15:10:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":32444033,"identity":"ff3c65ea-3c9b-4d5b-80f5-516d42b59bdb","added_by":"auto","created_at":"2023-02-03 15:58:41","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":427358,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram regarding article selection for meta-analysis.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2483758/v1/b4777d12a6d6f772eafdec8f.jpeg"},{"id":32444039,"identity":"dbb6cb3b-ac07-43b2-9b5e-a23f95fe435e","added_by":"auto","created_at":"2023-02-03 15:58:41","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":68354,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork diagram of the relationship between different warm-up methods on the explosive strength indicators of the lower limbs\u003c/p\u003e\n\u003cp\u003eFigure Note: “SS” indicates static stretching, “DS” indicates dynamic stretching, “BS” indicates ballistic stretching, “PNF” indicates PNF stretching, “FR” indicates foam rolling, “SDS” indicates static-dynamic stretching, and “CON” indicates control group.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2483758/v1/c4e9c172f5d0fb0edb6cb2da.jpg"},{"id":32444708,"identity":"91618fc9-200e-4d9f-b3f7-13c1596edf69","added_by":"auto","created_at":"2023-02-03 16:06:41","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":101125,"visible":true,"origin":"","legend":"\u003cp\u003eBrooks-Gelman-Rubin diagnostic chart\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2483758/v1/a6dfbcd08014538f377a47f0.jpg"},{"id":32445061,"identity":"c39d6c2a-d204-485a-8491-deddb1880fc4","added_by":"auto","created_at":"2023-02-03 16:14:41","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":39865,"visible":true,"origin":"","legend":"\u003cp\u003eCMJ indicator forest chart\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2483758/v1/ff82b6bd491ad37a2aa1215b.jpg"},{"id":32444032,"identity":"22ae15c0-8998-4112-8e28-dece581ca60b","added_by":"auto","created_at":"2023-02-03 15:58:41","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":45201,"visible":true,"origin":"","legend":"\u003cp\u003eSprint indicator forest chart\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2483758/v1/5384ad87084a59352d77694c.jpg"},{"id":32444036,"identity":"99ab4b76-558b-4546-ae70-1a7ec36f52f7","added_by":"auto","created_at":"2023-02-03 15:58:41","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":80591,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative ranking probability graph for CMJ indicators\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2483758/v1/0d6e41a45487706a153d7467.jpg"},{"id":32444707,"identity":"74191444-68e0-4c5a-83a3-62ec8807f04f","added_by":"auto","created_at":"2023-02-03 16:06:41","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":76421,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative ranking probability chart for sprint indicators\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2483758/v1/2cfda9cf1735589147c1c1bc.jpg"},{"id":32444034,"identity":"306b175e-9132-4709-b099-7ec4bbeff40d","added_by":"auto","created_at":"2023-02-03 15:58:41","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":38535,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot of lower limb explosive strength-related indicators\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2483758/v1/ac70314cb3a6ef8aa7f46148.jpg"},{"id":42782242,"identity":"1a952b35-0e21-484c-82c3-56d4e28570fb","added_by":"auto","created_at":"2023-09-07 15:16:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1171850,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2483758/v1/7f740f30-5887-4139-b400-9a22cd96dce5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A systematic review and net meta-analysis of the effects of different warm-up methods on the acute effects of lower limb explosive strength","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWarming up as a routine activity before a match or training has been popular in the sports training community for centuries. A scientific warm-up not only helps athletes to become fit but also improves joint mobility and muscle contraction to reduce the incidence of sports injuries \u003csup\u003e1\u003c/sup\u003e. With the development of science and technology, new warm-up methods have emerged, but the opinion remains mixed on which is more suitable for athletes' explosive performance.\u003c/p\u003e \u003cp\u003eMuscle stretch (MS) has received much attention as an important part of the preexercise warm-up \u003csup\u003e2\u003c/sup\u003e, but the effect of stretching on explosive power remains controversial; for example, a large number of previous studies have shown that static stretching has a negative effect on subsequent performance \u003csup\u003e3, 4\u003c/sup\u003e, while dynamic stretching has a beneficial effect \u003csup\u003e5\u003c/sup\u003e, although in recent years, there have been, in contrast \u003csup\u003e6, 7\u003c/sup\u003e, with the advent of combined stretching methods \u003csup\u003e8\u003c/sup\u003e, some subjective researcher bias between studies, as well as differences in outcome indicators, leading to some variation in the effect values of intervention results, thus affecting the accuracy of the results. The advent of the foam rolling (FR) technique \u003csup\u003e9\u003c/sup\u003e has led to an increasing number of coaches and athletes promoting this technique and abandoning the original stretching method. Therefore, there remains a lack of clarity regarding which warm-up method is more appropriate for explosive performance, what the dose-effect relationship is, and what the effects of different warm-up methods are.\u003c/p\u003e \u003cp\u003eThe recent maturation of reticulated meta-analysis theory and techniques has provided a method for comparison between multiple interventions, and it has become feasible to analyse the effects of different warm-up methods on the acuity of lower limb explosive strength. Based on this fact, this study used Bayesian reticulated meta-analysis to statistically evaluate the effects of different warm-up methods on the acute effect of explosive strength in the population receiving different warm-up methods to suggest the optimal warm-up method and provide some theoretical basis and reference for the development of precompetition or pretraining preparation activity programmes.\u003c/p\u003e"},{"header":"Information And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources and study selection\u003c/h2\u003e \u003cp\u003eDatabases were searched for literature published from January 2000 to December 2022. Guidelines from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) \u003csup\u003e10\u003c/sup\u003e were followed throughout. The Google Scholar, PubMed, Web of Science, Elsevier, Scopus, CNKI and Wanfang databases were searched using terms, such as Static Stretching (SS), Dynamic Stretching (DS), Ballistic Stretching (BS), Proprioceptive Neuromuscular Facilitation\u003c/p\u003e \u003cp\u003e(PNF), PNF Stretching, Static Dynamic Stretching (SDS), Combination of Stretching, Foam Rolling (FR), Different Warm-up Methods, Different Stretching Methods, Self-myofascial Release, Lower Extremity Explosive Performance, Vertical Jump (VJ), Countermovement Jump (CMJ), Sprint, Speed, and Acute Effects. The literature was comprehensively searched using different combinations of these terms, with the language limited to Chinese and English.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eThe criteria for inclusion in the literature were based on the PICOS principles of evidence-based medicine, considering 5 aspects: study subjects, interventions, control group, study outcomes and study design as follows: 1) study subjects -- healthy adolescents with sports experience, free of other injuries and disease conditions prior to the intervention; 2) interventions -- experimental group only different warm-up methods of intervention; 3) control group -- no warm-up exercise or light aerobic running; 4) study outcomes -- the jump index was selected as countermovement jump (CMJ), the sprint index was selected as the 20-m sprint and the 30-m sprint; and 5) study design -- due to the specificity of the warm-up method intervention (short intervention time), the study design included RCTs and an own before-and-after controlled trial design. There were no significant differences between the experimental and control groups at baseline.\u003c/p\u003e \u003cp\u003eThe exclusion criteria were as follows: 1) literature that did not meet the inclusion criteria; 2) review literature or dissertations; 3) literature that was not in English or Chinese; and 4) literature with incomplete data on outcome indicators, resulting in data that could not be extracted.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLiterature screen and data extraction\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFirst, relevant literature was searched in various databases according to the developed literature search strategy. Then, the literature was uniformly imported into Endnote X9 software for deweighting, followed by an initial screening of literature titles, abstracts and keywords by 2 researchers using an independent double-blind approach according to the above inclusion criteria. Finally, qualitative and quantitative analyses were conducted on the screened eligible literature. The literature that met the criteria was independently extracted and included the following 3 aspects: (1) general information -- first author, year of publication; (2) intervention characteristics -- sample size, study population, intervention content, intervention duration; and (3) outcome indicators -- countermovement jump (CMJ), 20-m sprint and 30-m sprint indicators were selected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eQuality evaluation\u003c/h2\u003e \u003cp\u003eBased on the characteristics of the included literature, this study used the Methodological Index for Non-randomized Studies (MINORS) risk of bias tool \u003csup\u003e11\u003c/sup\u003e to assess the quality of the literature in 12 domains (clear statement of study purpose, consistency of patients included, collection of expected data, whether the outcome indicators reflected the study purpose, whether the outcome indicators were objective, adequacy of follow-up time, failure rate less than 5%, whether sample size was estimated, appropriateness of control group selection, synchronisation of control groups, comparability of baseline between groups, appropriateness of statistical analysis) to evaluate the quality of the literature, with a score of 0\u0026ndash;2 for each domain and a maximum score of 24.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eBayesian MeSH meta-analysis was performed using R software running the gemtc package in the R studio environment in conjunction with Stata software, version 13.0. The outcome indicators in this study were continuous variables, and the mean difference (MD) and 95% confidence interval (95% CI) were used as effect size indicators. Each model was set using four Markov chains for initial values, and the number of iterations was set at 20000, with the first 5000 used for annealing. Model inconsistency was diagnosed using R software, and Brooks-Gelman-Rubin diagnostic plots were plotted to quantitatively evaluate the convergence of the models. Local inconsistency was tested using the nodal separation method. Finally, heterogeneity was tested using I\u003csup\u003e2\u003c/sup\u003e to measure the magnitude of heterogeneity under the random-effects model, with I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026le;\u0026thinsp;50% indicating low interstudy heterogeneity and I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;50% indicating high interstudy heterogeneity. The network relationships were mapped using Stata software, version 13.0, and analysed for risk of publication bias; the metrics were ranked by surface under the cumulative ranking (SUCRA), where 0\u0026thinsp;\u0026le;\u0026thinsp;SUCRA\u0026thinsp;\u0026le;\u0026thinsp;100%, 100% representing the most effective warm-up method and 0 the worst and least effective. Finally, subgroup analyses were conducted to explore the effects of moderating variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003eResults of the literature screening\u003c/h2\u003e\n \u003cp\u003eA total of 3255 relevant documents were retrieved from the seven selected search libraries, including 72 documents in Chinese and 3183 documents in English. A total of 2078 duplicate publications were excluded, 1029 documents were excluded on the basis of reading the titles and abstracts, and 110 documents were excluded on the basis of reading the full texts. Thirty-eight documents were finally included in the literature in English, and the screening flow chart is shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003eBasic characteristics of the included studies\u003c/h2\u003e\n \u003cp\u003eAmong the included papers, all articles were published in English \u003csup\u003e12\u0026ndash;46\u003c/sup\u003e. All original studies included in the trial were RCTs and had a before-and-after controlled trial design. The interventions provided to the experimental group included static stretching, dynamic stretching, ballistic stretching, PNF stretching, static-dynamic stretching and foam rolling, while the interventions provided to the control group were mild aerobic exercise or no sports. The basic characteristics of the included studies are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab1\" style=\"text-align: inherit;\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBasic characteristics of the included studies.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFirst author\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eIntervention methods\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTime(min)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIndicators\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExperimental group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExperimental group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl group\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAri(1) \u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e8 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFortier \u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.5 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParadisis \u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea, b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLoughran \u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e18\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ea, b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic-Dynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 min\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRyan \u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.7 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFletcher(1) \u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCurry \u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e26.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMild aerobic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e10 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNagle \u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e18\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e12 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaumgart \u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFoam Rolling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHenning \u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM:21.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFoam Rolling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e15 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW:20.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFranco \u003csup\u003e22\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEG:24.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFoam Rolling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMild aerobic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e8 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCG:25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eKopec \u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e3 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFoam Rolling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOliveira \u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e17.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBallistic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e15 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ea, b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePNF Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eUnick \u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e6 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBallistic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eKruse \u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e20.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e7 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMorrin \u003csup\u003e27\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e8 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Dynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYildiz \u003csup\u003e28\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild aerobic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eChaouachi \u003csup\u003e29\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e20.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e10 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ea, c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Dynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eChristensen \u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e20.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMild aerobic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePNF Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFletcher(2) \u003csup\u003e31\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e20.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMild aerobic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e6 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ea, b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ePagaduan \u003csup\u003e32\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e19.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e7 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic-Dynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eByrne \u003csup\u003e33\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild aerobic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eBafghi \u003csup\u003e34\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e24.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShi Huang \u003csup\u003e35\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaskurt \u003csup\u003e36\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAri(2) \u003csup\u003e37\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e15.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e8 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic-Dynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDallias \u003csup\u003e38\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eUtku \u003csup\u003e39\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e15.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e14 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePNF Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBallistic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eJaggers \u003csup\u003e40\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBallistic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVetter \u003csup\u003e41\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM:21.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMild aerobic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW:22.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGelen \u003csup\u003e42\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 min\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic-Dynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 min\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNelson \u003csup\u003e43\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM:21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\n \u003cp\u003eW:19.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdam \u003csup\u003e44\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild aerobic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMarinho \u003csup\u003e45\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e22.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e8\u0026ndash;10 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerrier \u003csup\u003e46\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatic Stretching\u003c/p\u003e\n \u003cp\u003eDynamic Stretching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo sports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.8 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cspan style=\"text-align: inherit;\"\u003eTable Note: \u0026ldquo;NR\u0026rdquo; indicates not reported; \u0026ldquo;a\u0026rdquo; indicates countermovement jump (CMJ); \u0026ldquo;b\u0026rdquo; indicates 20-m sprint; \u0026ldquo;c\u0026rdquo; indicates 30-m sprint.\u003c/span\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003eResults of the methodological quality evaluation\u003c/h2\u003e\n \u003cp\u003eThe 2 researchers scored each of the 12 domain entries according to the (Methodological Index for Non-randomized Studies, MINORS) scale, and the threshold of disagreement was referred to another author for judgement, resulting in an overall final score for the literature, as shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, from which the results of the literature quality evaluation found that the quality scores of the literature for all included studies were relatively high, with risky entries occurring in the use of blinding and whether sample sizes were estimated.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\"\u003e\u003cstrong style=\"text-align: inherit;\"\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003cspan style=\"text-align: inherit;\"\u003eResults of the methodological quality evaluation.\u003c/span\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eFirst author\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"9.782608695652174%\"\u003e\n \u003cp\u003eScores\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eAri(1)\u003csup\u003e\u0026nbsp;12\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eFortier\u003csup\u003e\u0026nbsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eParadisis\u003csup\u003e\u0026nbsp;14\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eLoughran \u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eRyan \u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eFletcher(1) \u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eCurry \u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eNagle \u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eBaumgart \u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eHenning \u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eFranco \u003csup\u003e22\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eKopec \u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eOliveira \u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eUnick \u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eKruse \u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eMorrin \u003csup\u003e27\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eYildiz \u003csup\u003e28\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eChaouachi \u003csup\u003e29\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eChristensen \u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eFletcher(2)\u003csup\u003e\u0026nbsp;31\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003ePagaduan \u003csup\u003e32\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eByrne \u003csup\u003e33\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eBafghi \u003csup\u003e34\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eShi Huang \u003csup\u003e35\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eBaskurt \u003csup\u003e36\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eAri(2) \u003csup\u003e37\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eDallias \u003csup\u003e38\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eUtku \u003csup\u003e39\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eJaggers \u003csup\u003e40\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eVetter \u003csup\u003e41\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eGelen \u003csup\u003e42\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eNelson \u003csup\u003e43\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eAdam \u003csup\u003e44\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003eMarinho \u003csup\u003e45\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.47826086956522%\"\u003e\n \u003cp\u003ePerrier \u003csup\u003e46\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.260869565217391%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.695652173913043%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.608695652173913%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eTable Note: \u0026ldquo;1\u0026rdquo; indicates that the purpose of the study was clearly given; \u0026ldquo;2\u0026rdquo; indicates the consistency of the patients included; \u0026ldquo;3\u0026rdquo; indicates the expected data collection; \u0026ldquo;4\u0026rdquo; indicates whether the outcome indicators reflected the purpose of the study; \u0026ldquo;5\u0026rdquo; indicates whether the trial was blinded; \u0026ldquo;6\u0026rdquo; indicates whether the follow-up period was adequate; \u0026ldquo;7\u0026rdquo; indicates whether the loss of follow-up rate was less than 5%; \u0026ldquo;8\u0026rdquo; indicates whether the sample size was estimated; \u0026ldquo;9\u0026rdquo; indicates whether the selection of the control group was appropriate; \u0026ldquo;10\u0026rdquo; indicates whether the control groups indicated whether the control groups are synchronised; \u0026ldquo;11\u0026rdquo; indicates whether the baselines were comparable between groups; \u0026ldquo;12\u0026rdquo; indicates whether the statistical analysis was appropriate.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec11\"\u003e\n \u003ch2\u003eResults of the reticulated meta-analysis\u003c/h2\u003e\n \u003cdiv class=\"Section3\" id=\"Sec12\"\u003e\n \u003ch2\u003eNetwork relations map\u003c/h2\u003e\n \u003cp\u003eA total of 25 studies in the overall included literature used the CMJ metric, and 15 studies used the sprint metric to evaluate the effect of different warm-up methods on the acute impact of lower limb explosive strength. The reticulated relationship graphs generally show a star-like structure centred on the control group, and all formed a triangular closed loop, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. These figures show that there is evidence of both direct and indirect comparisons between different intervention methods in the lower limb explosive strength index; therefore, the basic conditions for a reticulated meta-analysis are present. From the figure, it can be seen that the thickest line between DS, SS and the control group represents the most direct comparison studies, reflecting from the side that the 2 are the most controversial warm-up methods in current sports training, and their mechanism of action on explosive strength has yet to be clarified.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec14\"\u003e\n \u003ch2\u003eOverall consistency test and model convergence diagnosis\u003c/h2\u003e\n \u003cdiv class=\"Section3\" id=\"Sec15\"\u003e\n \u003ch2\u003eOverall consistency test\u003c/h2\u003e\n \u003cp\u003eThe reticulated meta-analysis is based on the assumption of consistency. First, we must judge whether the data are consistent or not, record the DIC\u003csub\u003e1\u003c/sub\u003e value under the consistency model fit in R software, and then record the DIC\u003csub\u003e2\u003c/sub\u003e value under the inconsistency model fit to judge the global consistency. If the difference between the two is less than five, the data are generally consistent.\u003c/p\u003e\n \u003cp\u003eCMJ indicators: DIC\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;105.316 under the consistency model fit was recorded in R software, and then DIC\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;108.412 under the inconsistency model fit was recorded, with a difference less than five, indicating that there was no inconsistency between the intervention methods under direct and indirect comparisons and that the consistency model could be used for analysis.\u003c/p\u003e\n \u003cp\u003eSprint indicator: DIC\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;79.577 under the consistency model fit was recorded in R software, and then DIC\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;79.969 under the inconsistency model fit was recorded, with a difference less than five, indicating that the intervention modality was not inconsistent under direct and indirect comparisons and that the consistency model could be used for analysis.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec16\"\u003e\n \u003ch2\u003eModel convergence diagnosis\u003c/h2\u003e\n \u003cp\u003eThe Brooks-Gelman-Rubin diagnostic map was used to quantify the degree of convergence of the diagnostic model, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The results of the images show that the median value of the reduction factor, 97.5% of the reduction factor and the potential scale reduction factor (PSFR) of this study\u0026apos;s model are close to one after iterative calculation, and the model converges strongly, proving that the results of the mesh meta-analysis are reliable.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec17\"\u003e\n \u003ch2\u003eDirect meta-analysis comparison of results\u003c/h2\u003e\n \u003cp\u003eThe study was conducted by plotting direct comparison forest plots to analyse the effects of different warm-up methods on the explosive power of the lower limbs and, by examining at whether the 95% CI crossed the 0 scale, to determine whether the difference was statistically significant.\u003c/p\u003e\n \u003cp\u003e(1) CMJ indicators\u003c/p\u003e\n \u003cp\u003eThe results of the reticulated meta-analysis of CMJ indicators for direct comparison between each warm-up method and the control group are shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The acute effects of SDS [MD\u0026thinsp;=\u0026thinsp;1.80, 95% CI: (0.43, 3.20)], DS [MD\u0026thinsp;=\u0026thinsp;1.60, 95% CI: (0.67, 2.60)] on CMJ indicators were superior to those of the control group. There was no evidence that BS [MD\u0026thinsp;=\u0026thinsp;0.30, 95% CI: (-2.30, 2.90)], FR [MD\u0026thinsp;=\u0026thinsp;0.63, 95% CI: (-1.60, 2.80)], PNF [MD = -0.94, 95% CI: (-3.80, 1.90)], or SS [MD = -0.75, 95% CI: (-1.70, 0.18)] were significantly different from the control group.\u003c/p\u003e\n \u003cp\u003e(2) Sprint indicators\u003c/p\u003e\n \u003cp\u003eThe results of the net meta-analysis of sprint metrics for direct comparison between each warm-up method and the control group are shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The acute effect of DS [MD = -0.08, 95% CI: (-0.15, -0.008)] on sprint metrics was better than that of the control group, while the acute effect of SS [MD\u0026thinsp;=\u0026thinsp;0.07, 95% CI: (0.002, 0.13)] on sprint metrics was less than that of the control group. There was no evidence of significant differences among BS [MD\u0026thinsp;=\u0026thinsp;0.04, 95% CI: (-0.11, 0.18)], PNF stretch [MD = -0.06, 95% CI: (-0.09, 0.21)], SDS [MD = -0.06, 95% CI: (-0.16, 0.05)] and controls.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec18\"\u003e\n \u003ch2\u003eLocal consistency check\u003c/h2\u003e\n \u003cp\u003eThe local inconsistency test (local inconsistency) was performed by the node-splitting method to test whether there was a difference between the results of direct and indirect comparisons when there was a closed loop between the direct and indirect comparisons, and if the difference was not statistically significant, the results of direct and indirect comparisons were considered to be consistent.\u003c/p\u003e\n \u003cp\u003e(1) CMJ indicators\u003c/p\u003e\n \u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, in all closed loops, the differences between direct and indirect comparisons were not significantly different between the groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and overall, there was good agreement between direct and indirect interventions where closed loops existed.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCMJ indicator node splitting method test\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIntervention methods\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDirect MD(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndirect MD(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNetwork MD(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDS vs. BS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24(-7.50, 8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.70(-1.30, 4.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40(-1.30, 4.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePNF vs. BS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.10(-6.20, 2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48(-4.90, 5.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.20(-4.50, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSS vs. BS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20(-4.50, 4.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.50(-5.00, 1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.00(-3.70, 1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFR vs. DS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.66(-8.30, 7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.00(-3.60, 1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.00(-3.40, 1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePNF vs. DS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.08(-4.90, 4.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.20(-8.10, 0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.60(-5.50, 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSS vs. DS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.30(-3.30, -1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.85(-2.60, 4.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17(-1.20, 1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSDS vs. DS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.28(-1.90, 1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85(-2.60, 4.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17(-1.20, 1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e(2) Sprint indicators\u003c/p\u003e\n \u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the differences between direct and indirect comparisons were not significantly different between the groups in all of the closures (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and overall, there was good agreement between direct and indirect interventions for the closures.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSprint indicator node splitting method test\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIntervention methods\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDirect MD(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndirect MD(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNetwork MD(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSS vs. BS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03(-0.23, 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03(-0.19, 0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03(-0.12, 0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSS vs. DS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18(0.06, 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09(-0.09, 0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14(0.07, 0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSDS vs. DS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07(-0.03, 0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04(-0.09, 0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02(-0.09, 0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSS vs. PNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001(-0.26, 0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01(-0.21, 0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01(-0.15, 0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec19\"\u003e\n \u003ch2\u003eCumulative probability ranking results\u003c/h2\u003e\n \u003cp\u003eThe cumulative probability ranking diagram and SUCRA values were used to comprehensively evaluate the order of merit of different warm-up methods on the acute effects of lower limb explosive strength to screen for the best warm-up method.\u003c/p\u003e\n \u003cp\u003e(1) CMJ indicators\u003c/p\u003e\n \u003cp\u003eThe results of the ranking of the effects of different warm-up methods on the CMJ index are shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. The results show that SDS (87.6%)\u0026thinsp;\u0026gt;\u0026thinsp;DS (83.5%)\u0026thinsp;\u0026gt;\u0026thinsp;FR (55.5%)\u0026thinsp;\u0026gt;\u0026thinsp;BS (48.1%)\u0026thinsp;\u0026gt;\u0026thinsp;CON (40.2%)\u0026thinsp;\u0026gt;\u0026thinsp;PNF stretch (19.5%)\u0026thinsp;\u0026gt;\u0026thinsp;SS (15.6%).\u003c/p\u003e\n \u003cp\u003e(2) Sprint indicators\u003c/p\u003e\n \u003cp\u003eThe results of the ranking of the effects of different warm-up methods on sprint indices are shown in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. The results show that DS (91.1%)\u0026thinsp;\u0026gt;\u0026thinsp;SDS (79.6%)\u0026thinsp;\u0026gt;\u0026thinsp;CON (51.9%)\u0026thinsp;\u0026gt;\u0026thinsp;BS (36.4%)\u0026thinsp;\u0026gt;\u0026thinsp;PNF stretch (25.0%)\u0026thinsp;\u0026gt;\u0026thinsp;SS (16.1%).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec20\"\u003e\n \u003ch2\u003ePublication bias analysis\u003c/h2\u003e\n \u003cp\u003eThe 35 included studies were tested for risk of publication bias, and the funnel plot is shown in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. The results of the image show that the majority of studies had effect sizes concentrated at the top of the funnel plot, but given that there are still a few studies located at the outside and bottom of the funnel plot, it suggests that there might be a slight publication bias, as well as a small sample effect in this network of studies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec21\"\u003e\n \u003ch2\u003eA test of the moderating effect of dynamic stretching on explosive power\u003c/h2\u003e\n \u003cp\u003eConsidering the outcomes together, DS produced the most consistent effect on the acute effect of explosive strength compared to other warm-up methods, and significant heterogeneity in the results of comparisons between DS and controls was found through R software analysis. Further analysis using Stata software yielded the results of the overall effect test, as shown in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, reflecting the possibility of the existence of potential moderating variables. In light of this finding, a moderating effect test was conducted on the 17 DS studies in the total included literature to examine the roles of six moderating variables in the effects of DS on the acute effect of lower limb explosive strength, as shown in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOverall effect test for the acute effect of dynamic stretching on explosive power\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eStudies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHeterogeneity test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTwo-tailed test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMD(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandom effects model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.77(0.68, 2.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cspan style=\"text-align: inherit;\"\u003e1) Stretching time. A total of 12 studies were included in this conditioning group, and there was heterogeneity in the effect sizes of the three groups (I\u003c/span\u003e\u003csup style=\"text-align: inherit;\"\u003e2\u003c/sup\u003e\u003cspan style=\"text-align: inherit;\"\u003e\u0026thinsp;=\u0026thinsp;80.9%), indicating a moderating effect of stretching time on the acute effects of explosive force. The stretching time of 7\u0026ndash;10 min had an effect size MD\u0026thinsp;=\u0026thinsp;2.90 (95% CI (1.07, 4.73), P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while the 95% CI for less than 7 min and more than 10 min passed the 0 point (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that the acute effect of stretching time of 7\u0026ndash;10 min on explosive strength was the most significant.\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e2) Mean age. A total of 17 studies were included in this conditioning group, and there was heterogeneity in effect size across the 3 groups (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;75.6%), suggesting that there was some moderation of the effect size of age on the acute effect of explosive strength. The effect size MD\u0026thinsp;=\u0026thinsp;1.95 (95% CI (0.20, 3.70), P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for younger than 21 years of age, and 95% CI past 0 points, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for 21\u0026ndash;25 years of age as well as older than 25 years of age), suggesting that the mean age of the study population was the most significant intervention effect for those younger than 21 years of age.\u003c/p\u003e\n \u003cp\u003e3) Study population. A total of 16 studies were included in this conditioning group, and there was heterogeneity in effect size between the 2 groups (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;77.2%), suggesting that there was some moderation of the acute effect of explosive strength with the effect size of the study subjects, with an effect size MD\u0026thinsp;=\u0026thinsp;2.00 (95% CI (0.15, 3.85), P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for bodybuilders and an effect size MD\u0026thinsp;=\u0026thinsp;1.67, 95% CI (0.31, 3.04) for athletes, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which showing the most significant effect size for gym goers.\u003c/p\u003e\n \u003cp\u003e4) Sample size. A total of 17 studies were included in this moderation group, and there was heterogeneity in the effect sizes of the 3 groups (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;75.6%), suggesting that there was some moderation of the effect size of the sample size on the acute effect of explosive strength. The effect size MD\u0026thinsp;=\u0026thinsp;2.11 (95% CI (0.37, 3.84), P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for up to 20 cases, MD\u0026thinsp;=\u0026thinsp;1.93, 95% CI (0.28, 3.59), P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for 21\u0026ndash;30 cases, and MD\u0026thinsp;=\u0026thinsp;0.07, 95% CI past 0 points, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The results suggest that the sample size was most significant for sample sizes of less than 30 cases.\u003c/p\u003e\n \u003cp\u003e5) Year of publication. The moderation group included a total of 17 studies in this moderation group, and there was heterogeneity in the effect sizes of the three groups (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;75.6%), suggesting that there was some moderation of the effect size for year of publication on the acute effect of explosiveness (effect size MD\u0026thinsp;=\u0026thinsp;2.53, 95% CI (0.89, 4.18), P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for 2011\u0026ndash;2015, and 95% CI for 2005\u0026ndash;2010 and 2016\u0026ndash;2021 were both past the 0 point, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), showing that the effect size for 2011\u0026ndash;2015 was the most significant.\u003c/p\u003e\n \u003cp\u003e6) Quality of literature. The moderation group included a total of 17 studies in this moderation group, and there was heterogeneity in the effect sizes of the three groups (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;75.6%), suggesting that there was some moderation of the effect size of literature quality on the acute effect of explosive force. (effect size MD\u0026thinsp;=\u0026thinsp;2.02, 95% CI (0.47, 3.56), P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for 18\u0026ndash;19 scores, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for 14\u0026ndash;15 and 16\u0026ndash;17 scores both had 95% CIs past the 0 point, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and the results suggest that the effect size for scores of 18\u0026ndash;19 was the most significant.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab6\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTests of the moderating effect of dynamic stretching on the acute effect of explosive power\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAdjustment variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHeterogeneity test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEffect value\u0026amp;95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTwo-tailed test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNumber of studies\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eStretching time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e80.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than 7 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44(-1.64, 2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u0026minus;10 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.90(1.07, 4.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore than 10 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40(-0.55, 3.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAverage age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e75.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than 21 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95(0.20, 3.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u0026minus;25 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39(-0.41, 3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore than 25 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.52(-0.26, 5.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSubjects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e77.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eathletes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67(0.31, 3.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egymgoers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00(0.15, 3.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e75.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than 20 cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.11(0.37, 3.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u0026ndash;30 cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.93(0.28, 3.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore than 31 cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07(-0.28, 2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eYears\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e75.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2005\u0026minus;2010 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77(-0.73, 2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2011\u0026minus;2015 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.53(0.89, 4.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u0026ndash;2021 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55(-1.10, 4.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eQuality of the literature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e75.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u0026minus;15 scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28(-2.85, 3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u0026ndash;17 scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71(-0.31, 3.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026minus;19 scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.02(0.47, 3.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cspan style=\"text-align: inherit;\"\u003eTable Note:\u0026lowast; indicates that there are differences in the classification of some independent samples, resulting in a total number of studies with some moderating variables not equal to the total number of included studies.\u003c/span\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study explored the acute effects of different warm-up methods on lower limb explosive strength from an evidence-based medical perspective. The results of a net meta-analysis showed that SDS and DS were able to have a positive effect on explosive strength overall, while SS showed a negative effect, with a smaller but still statistically significant effect size and no results yet proving the effectiveness of other warm-up methods. This finding is largely in line with previous research findings \u003csup\u003e47,48\u003c/sup\u003e. However, a recent meta-analysis was inconsistent with the results of this study \u003csup\u003e49\u003c/sup\u003e, and the analysis suggests that this inconsistency might be related to the study methodology on the one hand. This study used a traditional meta-analysis, which was only able to analyse the relative validity of the effects of different warm-up methods on the acute effects of lower limb explosive strength, and it lacked a comprehensive comparative analysis between studies, whereas this study used a continuum analysis to test the stability and reliability of the relative evidence between different warm-up methods, and the indirect comparative modelling approach provided more accurate statistical analysis \u003csup\u003e50\u003c/sup\u003e, which would allow relevant coaches and athletes alternatives to different warm-up methods, rather than in a traditional meta-analysis of individual warm-up method studies. Therefore, it is speculated that the study method might have influenced the results. Another aspect could be related to publication bias in the literature. Publication bias in meta-analyses can lead to lower actual effect sizes and can also increase the risk of no and negative effect sizes \u003csup\u003e51\u003c/sup\u003e, so-called false 'positive' or false 'negative' results. The study did not test for the risk of publication bias, so there is a potential risk of publication bias. The study used a qualitative (funnel plot) test of bias to ensure the accuracy of the results to a certain extent.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalysis of the effect of different warm-up methods on the acute effect of lower limb explosive power\u003c/b\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eDynamic stretching affects the effect\u003c/h2\u003e \u003cp\u003eThe results of the study showed that the DS group had a better effect size than the control group for both CMJ performance [MD\u0026thinsp;=\u0026thinsp;1.60, 95% CI: (0.67, 2.60)] and sprint performance [MD = -0.08, 95% CI: (-0.15, -0.008)], and the results of the SUCRA ranking chart indicated that, although slightly lower than the SDS group for CMJ performance, its effect on explosive power was an acute effect that was significantly better than the other warm-up methods. Regarding the mechanism by which DS promotes explosive strength, some scholars believe that it might be related to DS increasing body and muscle temperature. During stretching, muscles actively contract and stretch, increasing temperature while decreasing viscosity; at the same time, increased muscle temperature can cause increased neuroreceptor sensitivity, and this increased sensitivity suggests that neuromuscles might show stronger motor unit activation through increased motor unit recruitment \u003csup\u003e52\u003c/sup\u003e, thereby improving muscle contractile performance. For example, Fletcher et al. showed \u003csup\u003e53\u003c/sup\u003e that DS was able to significantly elevate muscle temperature and ultimately reverse longitudinal jump height compared to SS. Furthermore, some scholars have found that the ability of DS to enhance subsequent explosive performance could be related to the preactivation of movement patterns \u003csup\u003e54, 55\u003c/sup\u003e. Studies have shown that it might stimulate the muscle shuttle to increase muscle reflex activity, thus allowing the muscle to better complete active contractions based on the characteristics of the subsequent movement and ultimately inducing a postactivation potentiation effect (PAP) \u003csup\u003e56\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDS has long been recommended as an essential component of warm-up activities, and its effects on improving joint mobility and preventing sports injuries are well established, but the effect on subsequent explosive power remains controversial, and the reasons for this controversy might be influenced by the duration of stretching, the study population and other factors. The results of the study showed significant heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;75.6%) in the results of the comparison between the DS and control groups, suggesting that the variation caused by real differences in effect sizes accounted for 75.6% of the total variation, which can be considered a large degree of dispersion in the effect sizes of the individual studies; therefore, it is necessary to introduce moderating variables to investigate the heterogeneity in depth.\u003c/p\u003e \u003cp\u003e(1) In terms of stretch time. The test found that a stretch time of 7\u0026ndash;10 min produced the largest effect size. This finding is largely in line with previous findings. A study by Mcmillian et al. \u003csup\u003e57\u003c/sup\u003e found that DS with a total time duration of 10 min was able to enhance subsequent jump performance compared to the SS and control groups. Similarly, Behm et al. \u003csup\u003e58\u003c/sup\u003e clearly suggested that a 7 min and 10 min DS could have a positive effect on subsequent explosive power. The reason for this finding might be that shorter periods of stretching do not make effective use of the compliance effects on tendon units, while longer periods can cause fatigue in the body, thus not maximising the benefits of dynamic stretching.\u003c/p\u003e \u003cp\u003e(2) Study population and mean age. In terms of the study population, fitness enthusiasts produced a larger effect size than athletes. The analysis suggests that athletes have better muscle mass and physical function than fitness enthusiasts due to long-term systematic training and that there could be a saturation effect on their physical function. This age group is in the adolescent stage, a sensitive period for neuromuscular development \u003csup\u003e59\u003c/sup\u003e, and is at the peak of natural growth in both speed and explosive ability; therefore, the stimulatory response to dynamic stretching could be higher in this stage than in the adult stage.\u003c/p\u003e \u003cp\u003e(3) In terms of sample size, years of publication and quality of literature. In terms of sample size, samples with fewer than 30 cases were the most significant, consistent with the ease of good results in trials with fewer subjects found in the meta-analysis by Kang Yujie et al. \u003csup\u003e60\u003c/sup\u003e. However, if the sample size is too small, potential errors due to random error factors cannot be excluded, regardless of whether the treatment effect survives as valid. Conversely, a sample size that is too large can result in a waste of resources. Therefore, the selection of the optimal sample size should consider both clinically significant and statistically significant differences in efficacy, as well as factors such as financial budget. In terms of the year of publication of the literature, the effect size of the studies between 2011 and 2015 was significant, consistent with the statement by Liu et al. \u003csup\u003e2\u003c/sup\u003e that \"a large number of studies prior to 2016 demonstrated some facilitation of subsequent exercise performance by DS, after which many opposing views emerged that DS does not necessarily facilitate exercise performance\". The reason for this outcome could be related to the relatively stable methodological quality of studies prior to 2016 \u003csup\u003e61\u003c/sup\u003e. In terms of the quality of the literature, only two of the 17 DS-related studies reported the use of blinding, which to some extent affects the quality of the literature, although a meta-analysis indicated that there was no significant correlation between the efficacy of physical methods and adequate blinding \u003csup\u003e62\u003c/sup\u003e. However, adherence to the 'blinding principle' can improve the internal consistency of trials and reduce bias due to the expectations of subjects, intervention implementers or outcome evaluation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eStatic stretching affects the effect\u003c/h2\u003e \u003cp\u003eSS has become one of the most widely used warm-ups in sports due to its simplicity and controllability and low muscle damage \u003csup\u003e2\u003c/sup\u003e. Although SS can significantly improve joint mobility \u003csup\u003e63\u003c/sup\u003e, its effect on subsequent explosive power must be further confirmed \u003csup\u003e64\u003c/sup\u003e. The results of this study showed that the effect size in terms of sprint performance was MD\u0026thinsp;=\u0026thinsp;0.07 [95% CI: (0.002, 0.13)], which was significantly different from the control group, and the effect size in terms of CMJ performance was MD = -0.75 [95% CI: (-1.70, 0.18)], which was not significantly different from the control group, but from the SUCRA ranking graph, it can be seen that the percentage of its area under the curve was 15.6%, which was much smaller than that of the control group, indicating that SS was able to negatively affect subsequent explosive performance. The reason for the lack of significant differences between SS and controls in terms of CMJ performance could be that, compared to the complexity and coordination required for the short sprint, the measure of jumping ability is one dimensional; therefore, the effect of SS on relatively single-movement ability might be lower than that on relatively complex movement ability \u003csup\u003e65\u003c/sup\u003e, and although the reverse vertical jump also requires a coordinated body effort, the time to complete the movement is relatively short, at least compared to the short sprint, and any change in the session might not have a significant effect on the outcome metrics.\u003c/p\u003e \u003cp\u003ePhysiological and neurological studies have provided insight into the reasons for the negative impact of SS on explosive power. It has been found that prolonged static stretching of muscles affects the sensitivity of the muscle spindle (MS), which functions to encode information about the length of muscle extension as nerve impulses to the centre, reflexively generates and maintains muscle tension, and participates in the casual regulation of movement \u003csup\u003e66\u003c/sup\u003e through the coactivation of alpha-gamma motor neurons to ensure high sensitivity of the MS during muscle contraction \u003csup\u003e67\u003c/sup\u003e. However, prolonged SS can cause deactivation of the sensitivity of the γ system in MS, resulting in the inability of MS to transmit the actual length of the muscle to the superior centre, reducing the number of motor units excited out of neuroprotective inhibition and ultimately leading to a decrease in explosive power \u003csup\u003e68\u003c/sup\u003e. Another part of the study suggested that SS leads to a decrease in muscle-tendon unit (MTU) stiffness. The lower limb muscle-tendon union acts as a carrier of elastic energy storage and utilisation. In a state of constant muscle length, greater stiffness helps the muscle to generate more force during centripetal contraction \u003csup\u003e69\u003c/sup\u003e. Therefore, it has been suggested that SS might reduce muscle length and tone \u003csup\u003e70\u003c/sup\u003e, thus preventing the muscle from being in an activated state, in turn leading to a decrease in stiffness \u003csup\u003e71\u003c/sup\u003e and ultimately having a negative impact on explosive power.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003eStatic combined with dynamic stretching to influence the effect\u003c/h2\u003e \u003cp\u003eSince the strengths and weaknesses of SS are so obvious, it has been asked whether a combined approach of SS and DS could be used to take advantage of the improved joint mobility of SS while avoiding the detrimental effects of SS on explosive power through subsequent DS. The results of this study showed that, in terms of CMJ performance, the SDS effect size was MD\u0026thinsp;=\u0026thinsp;1.80 [95% CI: (0.43, 3.20)], which was significantly different from the control group; the effect size in terms of sprint performance was MD = -0.06 [95% CI: (-0.16, 0.05)], suggesting that SDS can have some effect on subsequent CMJ performance. No results were available to demonstrate the effect of SDS on sprint performance. For the lack of significant differences between SDS and controls in terms of sprint performance, the analysis suggests that this difference could be related to the number of included papers. Of the 35 included papers, only three \u003csup\u003e15, 29, 42\u003c/sup\u003e examined the effect of SDS on sprint performance; in other words, the network comparison analysis was based on only these three papers, so the strength of the relevant evidence findings was significantly reduced. For example, only Chaouachi et al. \u003csup\u003e29\u003c/sup\u003e of the 3 papers showed no significant difference in SDS regarding subsequent sprint performance because the study was conducted with physical education students, and the trained population was less susceptible to the acute effects of stretching than the untrained population, as confirmed by Egan et al. \u003csup\u003e72\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRegarding the mechanism by which SDS can enhance explosive performance, the present study suggested that it could be related to the reactivation of γ motor neurons in MS. Although the sensitivity of γ-motor neurons is inactivated after SS, the subsequent stimulation of the muscle shuttle by DS could \"reawaken\" the sensitivity of γ-motor neurons, and muscle contraction reverts to the coactivation pattern of α-γ-motor neurons \u003csup\u003e68\u003c/sup\u003e, thus exploiting the advantages of SS and DS while avoiding their disadvantages. However, since few studies have been conducted, the results must be interpreted with caution, and further evaluation is needed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eOther warm-up methods affect the results\u003c/h2\u003e \u003cp\u003eIn addition to the above warm-up methods, there are no results showing that foam axis rolling, PNF stretching and bouncy stretching can have effects on subsequent explosive power.\u003c/p\u003e \u003cp\u003e(1) Foam axis rolling. Also known as self-fascial relaxation, foam axis rolling is an emerging warm-up and relaxation technique in recent years. The practitioner uses self-weight to give the target muscle a certain amount of pressure to roll back and forth on the foam axis to improve the stretch and flexibility of the outer connective tissue of the muscle fibres \u003csup\u003e73\u003c/sup\u003e, promote blood circulation, and increase myocyte oxygen and energy metabolism. The results of the present study remain largely consistent with previous studies and with a meta-analysis of FR by Wiewelhove et al. \u003csup\u003e49\u003c/sup\u003e showing that FR does not significantly affect subsequent explosive performance and that it is more suitable for postexercise relaxation than preexercise warm-up since it has been shown to be effective in relieving exercise-related muscle soreness. The few studies that have shown FR to significantly improve exercise performance suggested that FR could disrupt myofascial trigger-points (MTrP), which are nodules produced by transitional stress in skeletal muscle that can lead to muscle fatigue and stiffness \u003csup\u003e74\u003c/sup\u003e, while Huang Haojie et al. \u003csup\u003e24\u003c/sup\u003e suggested that the stress produced by the foam axis on the muscle activated the Golgi Tdon organs (GTOs), which when active inhibit the muscle shuttle, causing a muscle relaxation response, a decrease in muscle tension, a decrease in muscle adhesion and an increase in muscle performance. Other studies have suggested that it could be related to the psychological factors of the participants since they believed that FR would improve their exercise performance \u003csup\u003e75\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e(2) PNF stretching. The full name is proprioceptive neuromuscular facilitation (PNF), which first originated in the field of rehabilitation medicine for the treatment of diseases such as neuromuscular paralysis by activating the autonomic and cross-inhibitory effects of muscles to improve the function of specific muscles and later began to be widely used in the field of competitive sports, mostly used to prevent and treat sports injuries and improve joint mobility \u003csup\u003e76\u003c/sup\u003e. However, the stretching process requires the assistance of a professional to apply the force and is time-consuming and could be more suitable for professional athletes from a simple and economic point of view. The results of the MeSH meta-analysis showed that PNF stretching had a negative impact on explosive power, although the difference was not statistically significant, consistent with previous studies. \u003csup\u003e77\u003c/sup\u003e Therefore, Bradley et al. \u003csup\u003e78\u003c/sup\u003e suggested that PNF stretching should not be performed prior to explosive sports. The mechanism of the effect of PNF stretching on explosive strength has been considered by most scholars to be the same as that of static stretching and can be explained by affecting the sensitivity of sarcolemmal receptors.\u003c/p\u003e \u003cp\u003e(3) Stretching with elastic shock. BS has been gradually marginalised due to its special stretching mechanism -- forcing the target muscle to elongate by means of rapid rebound, which most scholars believe causes a strong stretch reflex that in turn causes the muscle to contract to a shorter length than before the stretch and is therefore more likely to trigger muscle damage \u003csup\u003e79\u003c/sup\u003e. The results of the present study suggested that BS has a positive effect on subsequent explosive performance, although this effect was not statistically significant. Mariscal et al. \u003csup\u003e80\u003c/sup\u003e suggested that BS, by stimulating neuromuscular activity, could activate the stretch-shortening cycle (SSC), thereby improving subsequent sprint performance. The outcome indicators included in this study, both CMJ in situ and short-distance sprinting, required centrifugal muscle elongation to store energy, followed by centripetal contraction to improve subsequent performance, which could, to some extent, explain why BS could have a positive effect on subsequent explosive performance.\u003c/p\u003e \u003cp\u003eLimitations of the study. (1) The search of the literature for this study did not include unpublished literature, and some literature was not included due to the absence of a control group or incomplete data about outcome indicators, which might have affected the comprehensiveness of the information to some extent. (2) The small number of static combined with dynamic stretching and foam axis rolling related literature included might have weakened the argument to some extent. (3) Due to the warm-up method intervention short duration and other peculiarities, most studies did not use randomised, controlled studies but their own before-and-after controlled studies to avoid the influence of individual differences on the study results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003e(1) Static stretching reduces subsequent explosive performance, while dynamic stretching and static stretching combined with dynamic stretching are the two warm-up methods that significantly improve subsequent explosive performance, with dynamic stretching being the most stable and moderated by a variety of variables.\u003c/p\u003e \u003cp\u003e(2) The dynamic stretching time of 7\u0026ndash;10 min produced the best explosive performance, and the intervention effect was also influenced by modifying variables, such as study population and age.\u003c/p\u003e \u003cp\u003e(3) The quality and quantity of the included literature affects the overall effect of the intervention, and it is recommended that subsequent studies add high-quality studies based on strict adherence to the trial specifications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics was approved by Beijing Normal University ethics committee. No consent to participate required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of dataset and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study is available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis systematic review is funded by two grant programs, The first is the Beijing Social Science Foundation(20YTB012) and the second is the Beijing Normal University Young Teachers\u0026apos; Fund(2020NTSS39).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFuyou LI and Chenggen GUO wrote the main manuscript text and thesis proofing;Huashuai LI and Haoran XU prepared figures 1-8 and tables 1-6;Sun PU adjusted and modified the structure and content of the first draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmense gratitude goes to Pu SUN, Beijing Normal University, College of P.E And Sports for his continued support throughout this revision process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBeijing Normal University, College of Physical Education and Sports; Research area is physical training and health promotion.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eXie YM, Gu JQ, Wang SW, et al. 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Chinese Tissue Engineering Research and Clinical Rehabilitation,2010,14(37):6995-6998.\u003c/li\u003e\n\u003cli\u003eWang S.D.,Zhang B.G. Effects of PNF stretching method on muscle strength, flexibility and bioelectric properties[J]. Chinese Journal of Sports Medicine,2011,30(04):387-391.\u003c/li\u003e\n\u003cli\u003eBradley P S, Olsen P D, Portas M D. The effect of static, ballistic, and proprioceptive neuromuscular facilitation stretching on vertical jump performance[J]. The Journal of Strength \u0026amp; Conditioning Research, 2007, 21(1): 223-226.\u003c/li\u003e\n\u003cli\u003eWang WX. Theory and practice of physical fitness training [M]. Beijing: Higher Education Press, 2012: 177-178.\u003c/li\u003e\n\u003cli\u003eMariscal S L, Garcia V S, Fern\u0026aacute;ndez-Garc\u0026iacute;a J C, et al. Acute Effects of Ballistic vs. Passive Static Stretching Involved in a Prematch Warm-up on Vertical Jump and Linear Sprint Performance in Soccer Players[J]. The Journal of Strength \u0026amp; Conditioning Research, 2021, 35(1): 147-153.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"warm-up methods, explosive lower limb strength, acute effects, reticulation meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-2483758/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2483758/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: To evaluate the effects of different warm-up methods on the acute effect of lower limb explosive strength with the help of a reticulated meta-analysis system and to track the optimal method.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: R software combined with Stata software, version 13.0, was used to analyse the outcome metrics of the 35 included papers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: 1) Static combined with dynamic stretching [MD = 1.80, 95% CI: (0.43, 3.20)] and dynamic stretching [MD = 1.60, 95% CI: (0.67, 2.60)] were significantly better than controls in terms of improving reverse long jump performance, and the effect of dynamic stretching was influenced by the duration of stretching (I\u003csup\u003e2\u003c/sup\u003e = 80.4%), study population (I\u003csup\u003e2\u003c/sup\u003e = 77.2%) and age (I\u003csup\u003e2\u003c/sup\u003e = 75.6%) as moderating variables, with the most significant effect size for dynamic stretching time of 7-10 min. 2) Only dynamic stretching [MD = -0.08, 95% CI: (-0.15, -0.008)] was significantly better than the control group in terms of improving sprint performance, while static stretching [MD = 0.07, 95% CI: (0.002, 0.13)] showed a significant, negative effect. 3) No results were available to demonstrate a significant difference between other methods, such as foam axis rolling, and the control group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Static stretching was able to reduce explosive performance, while the 2 warm-up methods, namely dynamic stretching and static combined with dynamic stretching, were able to significantly improve explosive performance, with dynamic stretching being the most stable and moderated by multiple variables and dynamic stretching for 7-10 min producing the best explosive performance. In the future, high-quality studies should be added based on strict adherence to test specifications.\u003c/p\u003e","manuscriptTitle":"A systematic review and net meta-analysis of the effects of different warm-up methods on the acute effects of lower limb explosive strength","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-02-03 15:58:36","doi":"10.21203/rs.3.rs-2483758/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-03-07T05:35:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-02-23T13:11:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1bdfc94d-5de8-43b1-a751-f7bf5d38db65","date":"2023-02-18T09:08:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59ad0f4e-837d-456f-80d1-2c86efb91bab","date":"2023-02-09T06:03:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-02-02T09:41:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-02-02T09:26:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-02-02T09:17:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-02-02T09:14:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Sports Science, Medicine and Rehabilitation","date":"2023-01-16T12:18:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c2f82b16-85f3-4277-9f6a-3dbc769bf93a","owner":[],"postedDate":"February 3rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-09-07T15:14:19+00:00","versionOfRecord":{"articleIdentity":"rs-2483758","link":"https://doi.org/10.1186/s13102-023-00703-6","journal":{"identity":"bmc-sports-science-medicine-and-rehabilitation","isVorOnly":false,"title":"BMC Sports Science, Medicine and Rehabilitation"},"publishedOn":"2023-08-29 15:10:07","publishedOnDateReadable":"August 29th, 2023"},"versionCreatedAt":"2023-02-03 15:58:36","video":"","vorDoi":"10.1186/s13102-023-00703-6","vorDoiUrl":"https://doi.org/10.1186/s13102-023-00703-6","workflowStages":[]},"version":"v1","identity":"rs-2483758","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2483758","identity":"rs-2483758","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}