Potential Moderators of the Effects of Blood Flow Restriction Training on Muscle Strength and Hypertrophy: A Meta-Analysis Based on a Comparison with High-Load Resistance Training

Potential Moderators of the Effects of Blood Flow Restriction Training on Muscle Strength and Hypertrophy: A Meta-Analysis Based on a Comparison with High-Load Resistance Training | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Potential Moderators of the Effects of Blood Flow Restriction Training on Muscle Strength and Hypertrophy: A Meta-Analysis Based on a Comparison with High-Load Resistance Training Yu Geng, Xueping Wu, Yong Zhang, Meng Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2987684/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 May, 2024 Read the published version in Sports Medicine-Open → Version 1 posted 4 You are reading this latest preprint version Abstract Background Although, it has been examined whether there are similar magnitudes of muscle strength and hypertrophy adaptations between low-load resistance training combined with blood-flow restriction training (BFR-RT) and high-load resistance training (HL-RT), some important potential moderators (e.g., age, gender, upper and lower limbs, frequency and duration etc.) have yet to be analyzed further. Furthermore, training status, specificity of muscle strength tests (dynamic versus isometric or isokinetic) and specificity of muscle mass assessments (locations of muscle hypertrophy assessments) seem to exhibit different effects on the results of the analysis. The role of these influencing factors, therefore, remains to be elucidated. Objectives The aim of this meta-analysis was to compare the effects of BFR- versus HL-RT on muscle adaptations, when considering the influence of population characteristics (training status, gender and age), protocol characteristics (upper or lower limbs, duration and frequency) and test specificity. Methods Studies were searched through database based on the following inclusion criteria: (1) pre- and post-training assessment of muscular strength; (2) pre- and post-training assessment of muscular hypertrophy; (3) comparison of BFR-RT vs. HL-RT; (4) score ≥ 4 on PEDro scale; (5) means and standard deviations (or standard errors) are reported or allow estimation from graphs. In cases where the fifth criterion was not met, the data were requested directly from the authors. Results The main finding of the present study was that training status was an important influencing factor in the effects of BFR-RT. The trained individuals may gain greater muscle strength and hypertrophy with BFR-RT as compared to HL-RT. However, the results showed that the untrained individuals experienced similar muscle mass gains and superior muscle strength gains in with HL-RT compared to BFR-RT. Conclusion Compared to HL-RT, training status is an important factor influencing the effects of the BFR-RT, in which trained can obtain greater muscle strength and hypertrophy gains in BFR-RT, while untrained individuals can obtain greater strength gains and similar hypertrophy in HL-RT. Blood flow restriction training High-load resistance training Training effect Strength Hypertrophy Training status Protocol characteristics Test specificity Meta-analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Key Points Training status is an important factor influencing the effects of the BFR-RT. Trained individuals may obtain greater muscle strength and hypertrophy gains in BFR-RT compared to HL-RT. Untrained individuals may experience a smaller increase in strength and a similar increase in hypertrophy with BFR-RT compared to HL-RT. 1 Background High-load resistance training (HL-RT) has long been considered as the “golden standard” protocol to increase muscle strength and mass. It has been suggested that ≥ 65% one-repetition maximum (1RM) is required to increase strength and hypertrophy[ 1 – 3 ]. However, mounting evidence indicates that the use of low-load resistance training (< 50% 1RM) combined with blood flow restriction (BFR-RT) results in strength and morphological responses[ 4 ]. The results of previous study showed that low load resistance training with and without blood flow resulted in similar adaptations when sets of exercise were taken to failure[ 5 ], however the results of multiple studies showed the superiority of BFR-RT in terms of gains in muscle strength and hypertrophy when compared with similar low-load resistance training without blood flow restriction [ 6 – 8 ]. However, the literature is controversial about the magnitude of the adaptations when comparing BFR-RT to HL-RT. For example, some studies have reported greater increases in muscle strength for HL-RT when comparing to BFR-RT[ 9 – 13 ], while others have suggested similar gains between the two exercise protocols[ 14 – 17 ]. Moreover, some studies have reported that BFR-RT has higher muscle strength gains than HL-RT[ 18 , 19 ]. Some studies compared the effects of BFR-RT and HL-RT through meta-analysis[ 20 , 21 ]. In the meta-analysis of Lixandrão et al., they observed that BFR-RT and HL-RT have similar gains in muscle hypertrophy, while HL-RT is more effective in increasing muscle strength[ 20 ]. However, limited by the number of studies comparing BFR and HL-RT at that time, some important potential moderators (e.g., training frequency, etc.) could not been further explored[ 20 ]. The increase in muscle strength is the result of the coordination of nerve and muscle systems[ 22 ]. It was believed that neural adaptation dominates early in the training programme, later, as neural adaptations reach a plateau, muscular adaptation (hypertrophy) dominates[ 23 ]. At this stage, in intermediate and advanced training progress is limited to the extent of muscular adaptation that can be achieved[ 23 ]. It has been believed that BFR-RT provides a potential time-effective approach to stimulate muscle adaptations[ 8 , 24 , 25 ], and even well-trained athletes may benefit from BFR-RT[ 26 ]. Thus, compared to HL-RT, training status and training duration may be a key factor affecting the effectiveness of BFR-RT. Additionally, the results of several studies have showed that compared with HL-RT, BFR-RT induced less hypertrophy in the proximal region [ 17 , 27 , 28 ]. Therefore, this regional specificity may be another influencing factor. Finally, HL-RT implies high-load exercise, which is similar to specific strength assessments (i.e., 1RM test), while during BFR-RT, participants are never exposed to high loads[ 29 ]. Thus, non-specific strength assessments (i.e., isometric or isokinetic tests) may more accurately reflect the response to the low-load training protocols[ 29 ]. In this regard, test specificity may also affect the results. In short, there inconsistencies in the literature regarding effects of BFR-RT compared with HL-RT on the muscle strength and hypertrophy justify the need for synthesis and a comprehensive review of the available evidence. Therefore, based on previous studies, the purpose of this study was to conduct a meta-analysis comparing the responses of BFR-RT and HL-RT on muscle strength and hypertrophy. To further explore the effects of muscle strength and hypertrophy among these schemes, we will also consider potential influence factors such as population characteristics (i.e., training status, gender and age), protocol characteristics (i.e., upper or lower limbs, duration and frequency), test specificity (i.e., 1RM and spacing or equal speed testing), and region-specific adaptations in muscle mass. 2 Methods 2.1 Search Strategy and Study Selection The articles were identified through the English databases Web of Knowledge, PubMed, EBSCO-SPORTDiscus from the earliest record up to February 2024, and the Chinese database WANFANG DATA, CNKI from the earliest record up to February 2024. The search strategy combined the English and Chinese terms (see Electronic Supplementary Material, Table S1 ). Two reviewers (GY and ZY) evaluated the titles and abstracts of the retrieved articles to assessed their eligibility for the meta-analysis. In case of differences, a consensus was adopted. If necessary, the third reviewer (WXP) evaluated the article. If the abstract did not provide sufficient information about the inclusion criteria, the reviewers read the full text. 2.2 Eligibility Criteria Studies were considered for inclusion if they met the following criteria: (1) articles were published in English or Chinese; (2) subjects were healthy people, (3) pre- and post-intervention assessment of muscular strength (i.e., dynamic, isometric or isokinetic test); (4) pre- and post-intervention assessment of muscle hypertrophy (i.e., magnetic resonance imaging, computerized tomography, or ultrasonography); (5) comparisons between HL-RT (i.e., > 65%1RM) and BFR-RT (i.e., < 50%1RM); (6) score ≥ 4 on the Physiotherapy Evidence Database (PEDro) scale。 2.3 Study Quality The quality of the study was determined by using the PEDro scale, based on the Delphi list[ 30 ]. Studies with a score ≥ 4 were included in this meta-analysis (see Electronic Supplementary Material, Table S2 ). For each of the items (2–11) of the PEDro scale, two reviewers (GY and ZY) assessed the studies independently. In case of disagreement, a consensus was adopted or a third reviewer (WXP) evaluated the study. 2.4 Data extraction After screening of the studies, all included studies were assessed for eligibility based on their full texts. Tow reviewers (GY and ZY) extracted data from the articles independently, in case of disagreement, if no consensus could be reached, a third reviewer (WXP) was consulted. The data extracted were recorded relating to (1) population characteristics (i.e., age, gender and training status); (2) intervention protocol characteristics (i.e., duration, frequency, training load, volume, exercises etc.); (3) pre- and post-intervention assessment of muscle strength (i.e., dynamic, isometric, or isokinetic test); (4) pre- and post-intervention assessment of muscle hypertrophy (cross section area, muscle thickness and muscle mass). The trained individuals were defined as athletes or individuals who participated in regular resistance training protocols before the intervention. The untrained individuals were defined as individuals who were sedentary or not participated in regular resistance training protocols before the interventions. In case of incomplete data availability, we extrapolated the data from figures or contacted the corresponding author. The graphical data were extracted using the OriginPro 2021 (Version 2021. OriginLab Corporation, Northampton, MA, USA) graphical digitizing tool. Only the last was included as the post-intervention value for analysis, when intervention effects were assessed at multiple time points. When intervention effects were measured through multiple measurement methods (e.g, CSA and muscle thickness for muscle size), the multiple outcomes were combined (i.e., using the mean of the outcomes) [ 31 ]. The combination was performed by Comprehensive Meta-Analysis software (version 3.3, Biostat, Inc., Englewood, NJ, USA). The extracted data of included studies were depicted in Tables 1 and 2 . 2.5 Statistical Analyses All analyses were performed using Comprehensive Meta-Analysis software (version 3.3, Biostat, Inc., Englewood, NJ, USA). The comparisons (BFR-RT vs. HL-RT) were calculated as the effect size difference (ES diff ) using the difference in pre- and post- intervention mean and standard deviation values of muscle strength and mass, sample size and correlation between pre- and post- test for all groups. If the studies included in the meta-analysis did not report correlation between pre- and post- test, the following formula was used for estimation [ 20 , 72 ]: $$r=\frac{{S}_{pre}^{2}+{S}_{post}^{2}-{SD}^{2}}{2\times {S}_{pre}\times {S}_{post}}$$ Where S pre and S post are the standard deviation of pre-test and post-test, respectively. SD is the standard deviation of difference between pre- and post-test calculated using the following formula [ 1 , 20 ]: $$SD=\sqrt{\frac{{S}_{pre}^{2}}{n}+\frac{{S}_{post}^{2}}{n}}$$ Using the correction factor to correct the small sample size bias of all ES diff [ 20 , 31 ]. The correction factor is given by: $$\text{Correction factor}=1-\frac{3}{4\times \left({n}_{1}+{n}_{2}-2\right)-1}$$ The subjects of the studies included in present meta-analysis came from different populations. Moreover, different training protocols and various strength and hypertrophy measurements and variables were utilized in these studies. All factors may have an impact on the effect of the intervention. Thus, the random-effects model was used to perform the meta-analysis[ 73 ]. The I 2 statistics was used to assess heterogeneity. I 2 values of 25%, 50% and 75% were set as low, moderate and high levels of heterogeneity, respectively.[ 74 ]. The first step was to compare the effects of BFR-RT and HL-RT on muscle strength and muscle mass. Subsequently, subgroup analyses were conducted to examine the effects of training status (trained vs. untrained individuals)., gender, age, upper and lower limbs, test specificity (i.e. 1RM test vs. isometric or isokinetic tests) and region-specific adaptations of muscle hypertrophy. Based on the average age reported by the included studies, the age subgroups were divided into young (≤ 33 year) and old (≥ 57 year). Finally, according to the measured position reported by the studies, the results of muscle hypertrophy were categorized into three subgroups: proximal, middle and distal, which were 50% of the length of the femur or humerus, respectively. To identify the presence of highly influential studies that might bias the analyses, a sensitivity analysis was performed. The analysis was therefore conducted by removing one study at a time and then examining its effect on comparisons. If removal changed the significance level of ES diff (i.e., from P ≤ 0.05 to P > 0.05, or vice versa), it was considered as influential. This method has been used elsewhere[ 75 ]. The funnel plot, and Begg and Egger’s test were used to consider and assess publication bias, respectively. All data are presented as mean ± standard error. The significance level was set to P ≤ 0.05. 3 Results The initial search retrieved 2801 English studies and Chinese 361studies. Afterwards, 723 duplicated studies were excluded. After evaluation titles and abstracts, 2376 studies were removed, while the remaining 63 studies were assessed through full texts. Finally, 53 studies were considered to meet the inclusion criteria (Fig. 1), 51 of which were included in the muscle strength analysis (Table 1) and 28 in the muscle hypertrophy analysis (Table 2). In addition, by contacting the authors, the muscle hypertrophy data of a study was obtained[28]. However, after multiple attempts to contact the author, the muscle strength and hypertrophy data for one study were not included as the author did not respond[76]. 3.1 Muscle Strength Fifty-one studies involving 1164 participants were included in present meta-analysis to compare muscle strength gains. In the studies investigated the trained population, only one study adopted 9 weeks of training duration and one study adopted a training frequency of 5 sessions per week. The overall ES diff demonstrated significantly lower gains in muscle strength for BFR-RT compared with HL-RT (ES diff = -0.335±0.092, 95% confidence interval [CI] -0.515 to -0.156) (Fig. 1 and Table 3). However, when considering training status, the differences between trained and untrained subgroups were significant (Q=29.39, P<0.01) (Table 3). Significantly higher strength gains for BFR-RT were observed compared with HL-RT in the trained group (ES diff =0.491±0.172, 95% CI 0.154 to 0.827) (Fig.3 and Table 3). In contrast, the strength gains of HL-RT were significantly higher than that of BFR-RT in the untrained group(ES diff = -0.552±0.087,95%CL -0.722 to -0.382) (Fig.3 and Table 3). In trained individuals, there were no significant differences between the different gender, limbs, durations, frequency and test type (Table 3 and Fig. S1-S5). In untrained individuals, there were also no significant differences between the different gender, age, limbs, training duration and frequency (Table 3 and Fig. S6-S11). The sensitivity analysis conducted by deleting one study at a time and re-analyzing the data showed that none of the studies will have a significant impact on muscle strength results. Inspection of the funnel plots indicated no evidence of publication bias (Fig. S12). The results of the Begg test show that Kendall’s tau with continuity correction was equal to -0.02 (P=0.84), and Egger’s regression intercept was equal to 1.09 (P =0.41). 3.2 Muscle Hypertrophy Twenty-eight studies involving 703 participants were included in present meta-analysis to compare muscle hypertrophy gains. However, only three studies investigated the trained population. In addition, of the studies that investigated the untrained population, only one included female subjects, and only one adopted a training frequency of 4 sessions per week. The overall ES diff suggested similar gains in muscle mass between BFR-RT and HL-RT (ES diff = -0.067±0.070, 95%CI -0.205 to 0.071) (Fig. 4 and Table 4). However, when considering training status, the differences between trained and untrained subgroups were significant (Q=9.41, P<0.01) (Table 4). Significantly higher muscle hypertrophy gains for BFR-RT were observed compared with HL-RT in the trained subgroup(ES diff = 0.695±0.258, 95% CI 0.189 to 1.200). In contrast, the muscle mass gains of BFR-RT were similar to HL-RT in the untrained subgroup (ES diff = -0.128±0.073, 95%CL -0.272 to 0.015) (Fig. 5 and Table 4). However, in untrained individuals, there were no significant differences between the different age, limbs, duration and frequency, and region-specific adaptations in muscle mass (Fig. S13-S18 and Table 4). The sensitivity analysis showed that muscle hypertrophic adaptation was not affected by any particular study. Inspection of the funnel plots indicated no evidence of publication bias (Fig. S19). The results of the Begg test show that Kendall’s tau with continuity correction was equal to 0.17 (P=0.16), and Egger’s regression intercept was equal to 0.52 (P =0.65). 4 Discussion The purpose of the current study was to compare the effects of BFR-RT and HL-RT on the muscle strength and hypertrophy, using HL-RT as a control to evaluate the effects and characteristics of BFR-RT. The main finding of the present study was that training status was an important influencing factor in the effects of BFR-RT. The trained individuals will get greater muscle strength and hypertrophy gains from BFR-RT as compared with HL-RT. However, in the untrained individuals, the results demonstrated that superior gains in muscle strength and similar muscle mass for HL-RT as compared with BFR-RT. 4.1 Effect of BFR-RT on Trained Individuals The analysis results of trained individuals (ES diff = 0.491) suggested that in the comparison of these two training modalities, 69% of the trained individual may obtain greater gains in muscle strength with BFR-RT[ 77 ]. Training is initially characterized by neural adaptations, however, later as neural adaptations reach a plateau, muscular adaptation (i.e., hypertrophy) dominates[ 23 , 78 ]. In intermediate and advanced training, the progress of strength training is limited to the degree of muscle adaptation that can be achieved[ 23 ]. Hakkinen et al. reported that during one-year traditional strength training, advanced weight-lifers show limited potential for further neural adaptations, and the total mean muscle fiber area did not increase significantly[ 79 ]. However, mounting research indicates that BFR-RT can promote muscle hypertrophy of athletes[ 18 , 26 , 43 , 80 , 81 ], even in elite powerlifters the muscle fiber cross-sectional area (CSA) increased more in the BFR-RT compared to the HL-RT[ 26 ]. Metabolic stress was believed to be one of the factors promoting muscle hypertrophy.[ 82 ]. Compared with other strength training protocols, BFR-RT was believed to produce a higher level of metabolic stress[ 83 ]. Studies suggested that compared with normoxic conditions, resistance training under the hypoxic condition caused greater metabolic and hormonal responses [ 84 , 85 ], whereas it was believed that blood flow restriction could cause similar muscle hypoxia as compared with systemic hypoxia[ 86 ]. In this study, the results (Table 4 ) showed that in the trained individuals, superior muscle hypertrophy gains were observed for BFR-RT as compared with HL-RT. Put another way, 76% of the trained population may obtain greater gains in muscle hypertrophy with BFR-RT[ 77 ]. In fact, our results showed that the average relative strength change [(pre-training – post-training)/pre-training ×100] of BFR-RT (8.4%±1.09) was twice that of HL-RT (3.96%±0.66) in the trained individuals. 4.2 Effect of BFR-RT on Untrained Individuals Being different from the trained subjects, the analysis results of the untrained individuals (ES diff = -0.552) suggested that about 70% of the untrained individuals may experience greater gains in muscle strength with HL-RT[ 77 ]. Although, previous studies have found that the muscle activation level of BFR-RT was higher than that of the same intensity (low load) resistance exercise[ 87 – 90 ], its muscle activation level is still low as compared with HL-RT[ 91 – 93 ]. For example, Cook et al.[ 92 ] reported that muscle activation level (used surface electromyography )was greater in the HL-RT at the beginning and end of exercise compared with the BFR-RT. It has been suggested that increasing the occlusion pressure (from 40–60% occlusive pressure) could increase the activation level of muscle[ 94 ], but recent research showed that even with higher occlusive pressure (80%), the activation level of BFR-RT on muscle was also significantly lower than HL-RT[ 91 ]. Although these findings were based on surface electromyography, BFR-RT may not achieve the same level of muscle activation and produce the same neural stimulation as HL-RT[ 83 , 95 ]. 4.3 Limitations The current meta-analysis has some limitations. The lack of studies including females limited generalizability of the findings. Because of the sparse number of studies, the results comparing muscle hypertrophy in trained individuals should be interpreted with caution. In addition, the data from one study was not included[ 76 ]. However, the sensitivity analysis revealed that no single study had a significant impact on the analysis results. Therefore, the absence of these data would unlikely to have affected the current results and their interpretation. 5 Conclusion The present meta-analysis indicates that training status is an important factor influencing the effects of BFR-RT. Compared to HL-RT, trained individuals can obtain greater strength and hypertrophy gains from BFR-RT. However, in untrained individuals, the results demonstrate that superior muscle strength and similar mass gains for HL-RT. From a practical standpoint, BFR-RT could be a beneficial supplemental training protocol for trained population. It has been demonstrated that the combination of BFR- and HL-RT was more beneficial for the increase of muscle strength[ 97 ]. Thus, healthy individuals or athletes are likely to maximize their training adaptations by combining these two training methods [ 4 , 98 ]. Finally, it is important to highlight that BFR-RT remains a valid and effective alternative for people who cannot perform high-load resistance training. Abbreviations 1RM One repetition maximum BFR-RT Low-load resistance training (<50% 1RM) combined with blood flow restriction CI Confidence interval CSA Cross-sectional area ES diff Effect size difference HL-RT High-load resistance training PEDro Physiotherapy evidence database Declarations Acknowledgements Not applicable. Author contributions YG and XPW contributed to the study concept and design. YG, XPW and YZ performed the literature search. YG and YZ performed the data extraction and quality assessment. YG wrote the first draft of the manuscript. XPW, Y Z and MZ critically revised the draft of the manuscript. All authors read and approved the final manuscript. Funding No funding was received for this project. Availability of Data and Materials The datasets analysed during the current study are available from the corresponding author on reasonable request. Ethics approval and Consent to participate Not applicable. Consent for Publication Not applicable. Competing of interests Yu Geng, Xueping Wu, Yong Zhang and Meng Zhang declare that they have no conflict of interests relevant to the content of this review. References Schoenfeld BJ, Wilson JM, Lowery RP, Krieger JW. Muscular adaptations in low- versus high-load resistance training: A meta-analysis. Eur J Sport Sci. 2016;16:1–10. Kraemer WJ, Ratamess NA. Fundamentals of resistance training: progression and exercise prescription. Med Sci Sports Exerc. 2004;36:674–88. Campos GER, Luecke TJ, Wendeln HK, Toma K, Hagerman FC, Murray TF, et al. Muscular adaptations in response to three different resistance-training regimens: specificity of repetition maximum training zones. 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Tables Table 1 Characteristics of studies included in the meta-analysis of muscle strength adaptations References Age Training Status N Exercise Protocol Weeks （ times/wk ） Exercise load Occlusion pressure Muscle strength assessment Bjornsen et al.[26] 24 Trained 9 SQ BFR-RT:30-15-12-8 2(5) 24-30%1RM 120mmHg Dynamic SQ Isometric KE 26 8 HL-RT:6-7×1-6 2(5) 74-76%1RM Buckner et al.[32] 18-35 Untrained 20 BC BFR-RT1:4×failure 8(2) 15%1RM 57mmHg Dynamic BC Isometric BC Isokinetic BC (60°/s, 180°/s) 18-35 20 BFR-RT2:4×failure 8(2) 15%1RM 110mmHg 18-35 20 HL-RT:4×failure 8(2) 70%1RM Centner et al.[33] 27 Untrained M11 CR BFR-RT:30-15-15-15 14(3) 20-35%1RM 50%AOP Isometric CR 26 M14 HL-RT:3×6-12 14(3) 70-80%1RM Centner et al.[28] 28 Untrained M14 LP, KE, CR BFR-RT:30-15-15-15 14(3) 20-35%1RM 50%AOP Dynamic LP & KE 28 M15 HL-RT:3×6-12 14(3) 70-85%1RM Clark et al.[14] 24 Untrained 9 KE BFR-RT:3×30-50 4(3) 30%1RM 170mmHg Isometric KE 24 7 HL-RT:3×8-12 4(3) 80%1RM Cook et al.[34] 77 Untrained 12 KE, KF, LP BFR-RT: ~3×30 12(2) 30%1RM 184±25mmHg Dynamic KE, KF & LP Isometric KE 77 12 HL-RT: ~3×15 12(2) 70%1RM Cook et al.[35] 18-22 Untrained 6 KE, LP BFR-RT:2×25,1×failure 6(3) 20%1RM 180-200mmHg Dynamic KE Isometric KE 18-22 6 HL-RT:2×10,1×failure 6(3) 70%1RM Davids et al.[36] 24 Trained 11 SQ, LP, KE, SSQ BFR-RT:30+2-3×15 9(3) 30-40%1RM 60%AOP Dynamic SQ Isometric KE & KF 24 10 HL-RT:3-4×8 9(3) 75-80%1RM de Lemos Muller et al.[37] 24 Untrained M13 BC, KE BFR-RT:4×22 8(3) 30%1RM 110-150mmHg Dynamic BC & KE 25 M13 HL-RT:4×8 8(3) 80%1RM Ellefsen et al.[17] 23 Untrained M12 KE BFR-RT:5×failure 12(2) 30%1RM 90-100mmHg Dynamic KE 23 M12 HL-RT:3×6-10 12(2) 75-90%1RM Fernandes et al.[38] 20 Untrained F14 GS BFR-RT:3×15-25 4(3) 45%1RM 160mmHg Isometric GS 20 F14 HL-RT:3×8-12 4(3) 75%1RM Jessee et al.[39] 21 Untrained 10 KE BFR-RT1:4×failure 8(2) 15%1RM 40%AOP Dynamic KE Isometric KE Isokinetic KE (60°/s, 180°/s) 21 10 BFR-RT2:4×failure 8(2) 15%1RM 80%AOP 21 10 HL-RT:4×failure 8(2) 70%1RM Karabulut et al.[9] 57 Untrained M13 LP, KE BFR-RT:30-15-15 6(3) 20%1RM 205mmHg Dynamic LP & KE 57 M13 HL-RT:3×8 6(3) 80%1RM Kim et al.[40] 26 Untrained M10 LP, KE, KF BFR-RT:2×10 3(3) 20%1RM 178±20mmHg Dynamic LP, KE & KF 22 M10 HL-RT:2×10 3(3) 80%1RM Kim et al.[41] 21 Untrained M9 BC BFR-RT:30-15-15-15 8(3) 30%1RM 72±11mmHg Dynamic BC Isometric BC 21 M9 HL-RT:3×10 8(3) 75%1RM Kim et al.[42] 63 Untrained 9 GS BFR-RT:3×failure 4(3) 20%1RM 160mmHg Isometric GS 63 10 HL-RT:3×failure 4(3) 75%1RM Korkmaz et al.[43] 18 Trained M11 KE BFR-RT:30-15-15-15 6(2) 30%1RM 130-150mmHg Isokinetic KE (60°/s, 80°/s) 18 M12 HL-RT:4×12 6(2) 80%1RM Kubo et al.[10] 25 Untrained M9 KE BFR-RT:25-18-15-12 12(3) 20%1RM 180-240mmHg Isometric KE 25 M9 HL-RT:4×10 12(3) 80%1RM Laswati et al.[44] 33 Untrained M6 BC BFR-RT:30-15-15-15 5(2) 30%1RM 50mmHg Isokinetic BC (60°/s) 33 M6 HL-RT:3×12 5(2) 70%1RM Laurentino et al.[15] 20 Untrained M10 KE BFR-RT:3-4×15 8(2) 20%1RM 95±10mmHg Dynamic KE 24 M9 HL-RT:3-4×8 8(2) 80%1RM Letieri et al.[45] 68 Untrained F11 SQ, LP, KE, KF BFR-RT1:30-15-15 16(3) 20-30%1RM 188±5mmHg Isokinetic KE & KF (60°/s) 69 F10 BFR-RT2:30-15-15 16(3) 20-30%1RM 105±7mmHg 67 F11 HL-RT:3-4×6-8 16(3) 70-80%1RM Libardi et al.[46] 64 Untrained 10 LP BFR-RT:30-15-15-15 12(2) 20-30%1RM 67±8mmHg Dynamic LP 65 8 HL-RT:4×10 12(2) 70-80%1RM Lixandrao et al.[47] 26 Untrained M11 KE BFR-RT1:2-3×15 12(2) 20%1RM 56±8mmHg Dynamic KE 29 M14 BFR-RT2:2-3×15 12(2) 20%1RM 110±9mmHg 26 M8 BFR-RT3:2-3×15 12(2) 40%1RM 55±5mmHg 29 M10 BFR-RT4:2-3×15 12(2) 40%1RM 1055±19mmHg 29 M9 HL-RT:2-3×10 12(2) 80%1RM Luebbers et al.[48] 16-17 Trained M8 SQ BFR-RT:30-15-15-15 6(2) 20%1RM Dynamic SQ 16-17 M9 HL-RT:3×10 6(2) 78%1RM Martin-Hernandez et al.[12] 20 Untrained M10 KE BFR-RT1:30-15-15-15 5(2) 20%1RM 110mmHg Dynamic KE Isokinetic KE (60°/s, 80°/s) 21 M10 BFR-RT2:2×(30-15-15-15) 5(2) 20%1RM 110mmHg 21 M11 HL-RT:3×8 5(2) 85%1RM May et al.[49] 24 Untrained M8 KE, KF BFR-RT:30-15-15-15 7(3) 20%1RM 128mmHg Dynamic KE & KF 24 M9 HL-RT:4×8 7(3) 70%1RM Mendonca et al.[50] 22 Untrained 15 CR BFR-RT:30-15-15-15 4(5) 20%1RM 60%AOP Isometric CR 22 15 HL-RT:4×10 4(5) 75%1RM Morley et al.[51] 24 Untrained 7 KE BFR-RT1:3×10-12+1×failure 8(3) 20%1RM 100%AOP Dynamic KE Isometric KE 21 6 BFR-RT2:3×10-12+1×failure 8(3) 20%1RM 50%AOP 21 7 HL-RT:3×10-12+1×failure 8(3) 70%1RM Ozaki et al.[16] 23 Untrained M10 BP BFR-RT:30-15-15-15 6(3) 30%1RM 100-160mmHg Dynamic BP 24 M9 HL-RT:3×10 6(3) 75%1RM Ramis et al.[52] 24 Untrained M15 BC, KE BFR-RT:4×23 8(3) 30%1RM 110-150mmHg Isometric BC & KE 25 M13 HL-RT:4×8 8(3) 80%1RM Isokinetic BC & KE (60°/s) Sharifi et al.[53] 21 Untrained M8 LP, KE, KF, CP, BC BFR-RT:9×20 6(3) 20-30%1RM 110-160mmHg Dynamic LP & CP 19 M8 HL-RT:9×10 6(3) 70-80%1RM 21 M8 BFR-RT:9×20 6(6) 20-30%1RM 110-160mmHg 19 M8 HL-RT:9×10 6(6) 70-80%1RM Shiromaru et al.[54] 23 Untrained M15 KE BFR-RT:3×15 3(4) 30%1RM 80%AOP Dynamic KE 23 M15 HL-RT:3×10 6(2) 80%1RM Sousa et al.[55] 24 Untrained 10 KE BFR-RT:3×failure 6(2) 30%1RM 142±122mmHg Isometric KE 21 11 HL-RT:3×failure 6(2) 80%1RM Sugiarto et al.[56] 26-45 Untrained M6 BC BFR-RT:30-15-15-15 5(2) 30%1RM 50mmHg Isokinetic KE (60°/s, 120°/s, 180°/s) 26-45 M6 HL-RT:3×12 5(2) 75%1RM Teixeira et al.[57] 24 Untrained M8 KE BFR-RT:3×15 8(2) 20%1RM 80%AOP Dynamic KE 24 M8 HL-RT:3×8 8(2) 70%1RM Thiebaud et al.[58] 59 Untrained F6 CP, SR, SHP BFR-RT:30-15-15 8(3) 10-30%1RM 80-120mmHg Dynamic CP, SR & SHP 62 F8 HL-RT:3×10 8(3) 70-90%1RM Vechin et al.[13] 62 Untrained M8 LP BFR-RT:30-15-15-15 12(2) 20-30%1RM 71±9mmHg Dynamic LP 65 M8 HL-RT:4×10 12(2) 70-80%1RM Yasuda et al.[11] 22-32 Untrained M10 BP, EE BFR-RT:30-15-15-15 6(3) 30%1RM 100-160mmHg Dynamic BP Isometric EE 22-32 M10 HL-RT:3×10 6(3) 75%1RM CHE Tongtong et al.[19] 18 Trained F8 SQ BFR-RT:30-15-15-15 6(3) 30%1RM 180mmHg Dynamic SQ Isokinetic KE & KF (60°/s, 180°/s) 17 F8 HL-RT:4×8-10 6(3) 75%1RM LI Shanghua et al.[59] 21 Trained M12 LP BFR-RT:5×12 8(3) 40%1RM 200mmHg Isokinetic KE & KF (60°/s, 180°/s) 22 M12 HL-RT:5×12 8(3) 70%1RM LI Zhiyuan et al.[18] 22 Trained M8 SQ, SSQ, DF BFR-RT:30-15-15-15 4(3) 30%1RM 200mmHg Dynamic SQ Isokinetic KE & KF (60°/s) 22 M8 HL-RT:4×8-12 4(3) 70%1RM LI Zhiyuan et al.[60] 20 Trained M10 BP, SQ BFR-RT:30-15-15-15 6(2) 30%1RM 160-200mmHg Dynamic SQ & BP 20 M10 HL-RT:4×12 6(2) 70%1RM WANG Mingbo et la.[61] 24 Trained M9 SQ, SSQ, DF BFR-RT:4×20-30 8(3) 30%1RM 200-220mmHg Isokinetic KE & KF (60°/s) 24 M9 HL-RT:4×12 8(3) 70%1RM ZHANG Junjie et la.[62] 23 Trained M8 SQ BFR-RT1:5×10 5(2) 20%1RM 252mmHg Dynamic SQ 24 M8 BFR-RT2:5×11 5(2) 40%1RM 252mmHg 23 M8 HL-RT:5×12 5(2) 75%1RM Centner et al.[65] 28 Untrained M14 CR BFR-RT:30-15-15-15 14(3) 20-35%1RM 50%AOP Dynamic CR 28 M15 HL-RT:3×6-12 14(30 70-80%1RM De Araujo et al.[66] 23 Untrained M10 BC BFR-RT:30-15-15-15 6(2) 30%1RM 50%AOP Dynamic BC 23 M10 HL-RT:3×10-12 6(2) 70%1RM Horiuchi et al.[67] 18-30 Untrained M12 LP, KE BFR-RT:4×20 4(4) 30%1RM 130%AOP Dynamic LP & KE 18-30 M12 HL-RT:3×10 4(4) 75%1RM Judd et al.[68] 20 Trained M4 BC BFR-RT: 4×5 6(2) 30%1RM 40%AOP Dynamic BC 20 M4 HL-RT: 4×5 6(2) 80%1RM 20 F4 BFR-RT: 4×5 3(2) 30%1RM 40%AOP 20 F5 HL-RT: 4×5 3(2) 80%1RM Reece et al.[69] 21 Untrained 15 KE BFR-RT: 3×failure 6(3) 30%1RM 50%AOP Dynamic KE 22 15 HL-RT:3×failure 6(3) 80%1RM Sousa-Silva et al.[70] 21 Untrained M9 BC BFR-RT:1×30+2-3×15 8(2) 30%1RM 50%AOP Dynamic BC 21 M9 HL-RT:3-4×10-12 8(2) 70%1RM Wang et al.[71] 20 Trained M8 SQ BFR-RT: 30-15-15-15 4(3) 30%1RM 200mmHg Dynamic SQ 20 M8 HL-RT;4×8-12 4(3) 70%1RM M male, F female BC biceps curls, BP bench press, CP chest press, DF deadlift, EE elbow extension, GS grip strength, KE knee extension, KF knee flexion, LP leg press, SHP seated shoulder press, CR calf raises, SQ squat, SSQ split squat, SR Seated Rowing Table 2 Characteristics of studies included in the meta-analysis of muscle hypertrophy adaptations References Age Training Status N Exercise Protocol Weeks (times/wk) Exercise load Occlusion pressure Muscle mass assessment Method Muscle groups Test site Bjornsen et al.[26] 24 Trained 9 SQ BFR-RT:30-15-12-8 2(5) 24-30%1RM 120mmHg US Rectus femoris Vastus lateralis Vastus medialis Vastus intermedius Distal 26 8 HL-RT: 6-7×1-6 2(5) 74-76%1RM Buckner et al.[32] 18-35 Untrained 20 BC BFR-RT1: 4 × failure 8(2) 15%1RM 57mmHg US Upper arm Mid Distal 18-35 20 BFR-RT2: 4×failure 8(2) 15%1RM 110mmHg 18-35 20 HL-RT: 4×failure 8(2) 70%1RM Centner et al.[33] 27 Untrained M11 CR BFR-RT:30-15-15-15 14(3) 20-35%1RM 50%AOP US Gastrocnemius 26 M14 HL-RT:3×6-12 14(3) 70-80%1RM Centner et al.[28] 28 Untrained M14 LP,KE,CR BFR-RT:30-15-15-15 14(3) 20-35%1RM 50%AOP MRI Rectus femoris, Proximal Mid Distal 27 M15 HL-RT:3×6-12 14(3) 70-85%1RM Cook et al.[34] 76 Untrained 12 KE,KF,LP BFR-RT:~3×30 12(2) 30%1RM 184±25mmHg MRI Quadriceps 77 12 HL-RT:~3×15 12(2) 70%1RM Cook et al.[35] 18-22 Untrained 6 KE,LP BFR-RT:2×25, 1×failure 6(3) 20%1RM 180-200mmHg MRI Quadriceps 18-22 6 HL-RT:2×10, 1×failure 6(3) 70%1RM Davids et al.[36] 24 Trained 11 SQ,LP,KE,SSQ BFR-RT:30+2-3×15 9(3) 30-40%1RM 60%AOP MRI Quadriceps 24 10 HL-RT:3-4×8 9(3) 75-80%1RM Ellefsen et al.[18] 23 Untrained M12 KE BFR-RT: 5×failure 12(2) 30%1RM 90-100mmHg MRI Quadriceps Proximal Distal 23 M12 HL-RT:3×6-10 12(2) 75-90%1RM Jessee et al.[41] 21 Untrained 10 KE BFR-RT1: 4×failure 8(2) 15%1RM 40%AOP US Quadriceps Proximal Mid Distal 21 10 BFR-RT2: 4×failure 8(2) 15%1RM 80%AOP 21 10 HL-RT: 4×failure 8(2) 70%1RM Kataoka et al. [65] 23 Untrained 27 CR BFR-RT:4×16-39 6(3) 30%1RM 40%AOP US Gastrocnemius 23 27 HL-RT:4×12-19 6(3) 70%1RM Soleus Kim et al.[66] 26 Untrained M10 LP,KE,KF BFR-RT:2×10 3(3) 20%1RM 178±20mmHg CT Thigh Mid 22 M10 HL-RT:2×10 3(3) 80%1RM Kim et al.[41] 21 Untrained M9 BC BFR-RT:30-15-15-15 8(3) 30%1RM 72±11mmHg US Upper arm Mid Distal 21 M9 HL-RT:3×10 8(3) 75%1RM Korkmaz et al.[43] 18 Trained M11 KE BFR-RT:30-15-15-15 6(2) 30%1RM 130-150mmHg US Rectus femoris Vastus lateralis Mid 18 M12 HL-RT:4×12 6(2) 80%1RM Kubo et al.[10] 25 Untrained M9 KE BFR-RT:25-18-15-12 12(3) 20%1RM 180-240mmHg MRI Quadriceps 25 M9 HL-RT:4×10 12(3) 80%1RM Laurentino et al.[15] 20 Untrained M10 KE BFR-RT:3-4×15 8(2) 20%1RM 95±10mmHg MRI Quadriceps Mid 23 M9 HL-RT:3-4×8 8(2) 80%1RM Libardi et al.[46] 64 Untrained 10 LP BFR-RT:30-15-15-15 12(2) 20-30%1RM 67±8mmHg MRI Quadriceps Mid 65 8 HL-RT:4×10 12(2) 70-80%1RM Lixandrao et al.[47] 26 Untrained M11 KE BFR-RT1:2-3×15 12(2) 20%1RM 55.5±8mmHg MRI Quadriceps Mid 28 M14 BFR-RT2:2-3×15 12(2) 20%1RM 109±9mmHg 26 M8 BFR-RT3:2-3×15 12(2) 40%1RM 54±5mmHg 28 M10 BFR-RT4:2-3×15 12(2) 40%1RM 105±19mmHg 29 M9 HL-RT:2-3×10 12(2) 80%1RM Martin-Hernandez et al.[12] 20 Untrained M10 KE BFR-RT1:30-15-15-15 5(2) 20%1RM 110mmHg US Rectus femoris Vastus lateralis Mid 21 M10 BFR-RT2:2×(30-15-15-15) 5(2) 20%1RM 110mmHg 20 M11 HL-RT:3×8 5(2) 85%1RM May et al.[49] 24 Untrained M8 KE,KF BFR-RT:30-15-15-15 7(3) 20%1RM 128mmHg CT Quadriceps Hamstrings Mid Distal 24 M9 HL-RT:4×8 7(3) 70%1RM Mendonca et al.[50] 22 Untrained 15 CR BFR-RT:30-15-15-15 4(5) 20%1RM 60%AOP US Soleus 21 15 HL-RT:4×10 4(5) 75%1RM Ozaki et al.[16] 23 Untrained M10 BP BFR-RT:30-15-15-15 6(3) 30%1RM 100-160mmHg MRI Triceps brachii Pectoralis major 24 M9 HL-RT:3×10 6(3) 75%1RM Ramis et al.[52] 23 Untrained M15 BC,KE BFR-RT:4×23 8(3) 30%1RM 110-150mmHg US Biceps brachii 24 M13 HL-RT:4×8 8(3) 80%1RM Quadriceps Shiromaru et al.[54] 22 Untrained M15 KE BFR-RT:3×15 3(4) 30%1RM 80%AOP MRI Quadriceps Mid 22 M15 HL-RT:3×10 6(2) 80%1RM Teixeira et al.[57] 24 Untrained M8 KE BFR-RT:3×15 8(2) 20%1RM 80%AOP MRI Quadriceps Mid 24 M8 HL-RT:3×8 8(2) 70%1RM Thiebaud et al.,[58] 59 Untrained F6 CP,SR,SHP BFR-RT:30-15-15 8(3) 10-30%1RM 80-120mmHg US Biceps brachii Triceps brachii Distal 62 F8 HL-RT:3×10 8(3) 70-90%1RM Vechin et al.[13] 62 Untrained M8 LP BFR-RT:30-15-15-15 12(2) 20-30%1RM 71±9mmHg MRI Quadriceps Mid 65 M8 HL-RT:4×10 12(2) 70-80%1RM Yasuda et al.[11] 22-32 Untrained M10 BP,EE BFR-RT:30-15-15-15 6(3) 30%1RM 100-160mmHg MRI Triceps brachii Mid 22-32 M10 HL-RT:3×10 6(3) 75%1RM Sousa-Silva et al.[70] 21 Untrained M9 BC BFR-RT: 1×30+2-3×15 8(2) 30%1RM 50%AOP US Biceps brachii Distal 21 M9 BC HL-RT:3-4×10-12 8(2) 70%1RM M male, F female 1RM one-repetition maximum, AOP arterial occlusion pressure, CT Computed Tomography, MRI magnetic resonance imaging, US ultrasonography, Distal > 50% of the thigh or upper arm length, Mid =50% of the thigh or upper arm length, Proximal <50% of the thigh or upper arm length BC biceps curls, BP bench press, CP chest press, DF deadlift, EE elbow extension, GS grip strength, KE knee extension, KF knee flexion, LP leg press, SHP seated shoulder press, CR calf raises, SQ squat, SSQ split squat, SR Seated Rowing Table 3 Summary of meta-analysis results for muscle strength 95% Confidence Interval Between Group Heterogeneity Subgroups N ES diff Standard error Lower limit Upper limit P-value Q-value P-value Q-value P-value I 2 Overall effect 63 -0.335 0.092 -0.515 -0.156 <0.01 157.542 <0.01 60.65 Training status Trained 14 0.491 0.172 0.154 0.827 <0.01 29.392 <0.01 27.047 0.01 51.94 Untrained 49 -0.552 0.087 -0.722 -0.382 <0.01 83.634 <0.01 42.61 Trained Gender Female 2 0.682 0.572 -0.440 1.804 0.23 0.014 0.91 3.896 0.05 74.33 Male 10 0.609 0.236 0.146 1.072 0.01 18.791 0.03 52.11 Limbs Lower limb 12 0.391 0.198 0.003 0.779 0.05 1.495 0.22 18.374 0.07 40.13 Upper limb 3 1.011 0.467 0.095 1.926 0.03 8.736 0.01 77.11 Duration ≤4wk 4 0.724 0.371 -0.004 1.452 0.05 0.214 0.64 3.420 0.33 12.28 =5-8wk 9 0.520 0.238 0.053 0.987 0.03 19.563 0.01 59.11 Frequency 2/wk 7 0.660 0.300 0.072 1.248 0.03 0.330 0.57 20.471 0.00 70.69 3/wk 6 0.415 0.303 -0.179 1.010 0.17 5.821 0.32 14.10 Test type Non-specific test 6 0.541 0.282 -0.011 1.094 0.05 0.352 0.55 11.211 0.05 55.40 Specific test 11 0.330 0.218 -0.098 0.758 0.13 20.499 0.02 51.22 Untrained Gender Female 5 -0.638 0.297 -1.220 -0.055 0.03 0.143 0.71 5.529 0.24 27.66 Male 31 -0.516 0.120 -0.751 -0.282 <0.01 68.109 <0.01 55.95 Age Old 8 -0.714 0.205 -1.115 -0.312 <0.01 0.573 0.45 7.159 0.41 2.22 Young 40 -0.544 0.091 -0.721 -0.367 <0.01 69.180 <0.01 43.63 Limbs Lower limb 36 -0.618 0.111 -0.836 -0.400 <0.01 0.514 0.47 73.921 <0.01 52.65 Upper limb 17 -0.477 0.162 -0.794 -0.160 <0.01 38.871 <0.01 58.84 Duration ≤4wk 7 -0.493 0.216 -0.916 -0.070 0.02 0.278 0.87 5.107 0.53 0.00 =5-8wk 28 -0.593 0.116 -0.820 -0.365 <0.01 51.803 <0.01 47.88 ≥9wk 14 -0.507 0.158 -0.816 -0.198 <0.01 25.833 0.02 49.68 Frequency 2/wk 21 -0.596 0.131 -0.853 -0.339 <0.01 0.795 0.67 35.971 0.02 44.40 3/wk 25 -0.560 0.121 -0.797 -0.323 <0.01 44.477 0.01 46.04 4/wk 2 -0.231 0.388 -0.991 0.528 0.55 0.683 0.41 0.00 Test specificity Non-specific test 24 -0.422 0.120 -0.657 -0.188 <0.01 3.573 0.06 33.745 0.07 31.84 Specific test 37 -0.715 0.099 -0.909 -0.522 <0.01 70.945 <0.01 49.26 ES diff effect size difference. Table 4 Summary of meta-analysis results for muscle hypertrophy 95% CI Between Group Heterogeneity N ES diff Standard error Lower limit Upper limit P-value Q-value P-value Q-value P-value I 2 (%) Overall effect 34 -0.067 0.070 -0.205 0.071 0.34 29.578 0.64 0.00 Training Status Trained 3 0.695 0.258 0.189 1.200 0.01 9.413 <0.01 3.048 0.22 34.39 Untrained 31 -0.128 0.073 -0.272 0.015 0.08 17.116 0.97 0.00 Untrained Age Old 4 0.096 0.226 -0.346 0.538 0.67 1.106 0.29 0.265 0.97 0.00 Young 27 -0.155 0.077 -0.306 -0.003 0.05 15.746 0.94 0.00 Limbs Lower limbs 24 -0.053 0.084 -0.217 0.111 0.52 3.398 0.07 10.961 0.98 0.00 Upper limbs 8 -0.354 0.140 -0.628 -0.080 0.01 3.231 0.86 0.00 Duration ≤4wk 3 -0.115 0.220 -0.546 0.317 0.60 0.254 0.88 0.489 0.78 0.00 =5-8wk 18 -0.102 0.095 -0.289 0.084 0.28 12.718 0.75 0.00 ≥9wk 10 -0.185 0.134 -0.448 0.079 0.17 3.656 0.93 0.00 Frequency 2/wk 17 -0.219 0.100 -0.416 -0.022 0.03 2.128 0.15 7.113 0.97 0.00 3/wk 13 0.000 0.112 -0.219 0.220 0.99 7.663 0.81 0.00 Assessment region (Thigh) Distal 5 -0.069 0.184 -0.430 0.291 0.71 1.805 0.41 0.478 0.98 0.00 Mid 16 -0.149 0.107 -0.359 0.062 0.17 4.550 0.99 0.00 Proximal 4 -0.417 0.203 -0.816 -0.018 0.04 0.470 0.93 0.00 Assessment region (Upper-arm) Distal 5 -0.242 0.172 -0.579 0.094 0.16 1.762 0.18 1.912 0.75 0.00 Mid 4 -0.577 0.184 -0.938 -0.216 <0.01 3.881 0.28 22.70 2/wk 2 sessions per week, 3/w 3 sessions per week, ES diff effect size difference. Supplementary Files Supplementaryfiles.pdf Cite Share Download PDF Status: Published Journal Publication published 21 May, 2024 Read the published version in Sports Medicine-Open → Version 1 posted Reviewers invited by journal 19 Mar, 2024 Editor assigned by journal 17 Mar, 2024 First submitted to journal 17 Mar, 2024 Editorial decision: Minor Revision 12 Feb, 2024 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-2987684","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281306199,"identity":"ea5883c8-0166-4815-841c-84130430b6e5","order_by":0,"name":"Yu Geng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYPACGwYG5gNAmo14LWlA1QmkaTlMghZ5997Dr3nbztvzt/EYMHwoO8zAP7sBvxbDM+fSrHnbbjNLHOMxYJxx7jCDxJ0DBLTMyDEzBmphY7jfY8DM23aYwUAigYCW+W9AWs7xyANtYf5LjBZ5CR7jx7xtByQMQFoYidFiwJNjxjjnXLKB4TG2goM959J5JG4QsqX9jPGHN2V29nLHmDc++FFmLcc/g5AtBxjYpHignANAzINHMdSWBgbmjz8IKhsFo2AUjIIRDQDSND7T6f4xMwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0001-3985-6100","institution":"Jiyang College of Zhejiang A and F University: Zhejiang A and F University Jiyang College","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Geng","suffix":""},{"id":281306200,"identity":"aeb90c19-c8e3-49db-ae86-3101bfc6b7c9","order_by":1,"name":"Xueping Wu","email":"","orcid":"","institution":"Shanghai University of Sport","correspondingAuthor":false,"prefix":"","firstName":"Xueping","middleName":"","lastName":"Wu","suffix":""},{"id":281306201,"identity":"02750bd8-3003-4d15-be6e-1e1efcbf3e96","order_by":2,"name":"Yong Zhang","email":"","orcid":"","institution":"Shaoxing University","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Zhang","suffix":""},{"id":281306202,"identity":"43a9300c-5ec2-4a7e-ba6d-436c07f6958b","order_by":3,"name":"Meng Zhang","email":"","orcid":"","institution":"Huzhou University","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2023-05-27 04:01:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2987684/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2987684/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40798-024-00719-3","type":"published","date":"2024-05-22T00:32:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53180478,"identity":"558f3efb-2785-48c2-bb6f-7c116546cc13","added_by":"auto","created_at":"2024-03-21 15:33:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97745,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the search and review process\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2987684/v1/7d7ed6a8493b1963ecf62af0.png"},{"id":53180481,"identity":"c9143bd8-68ae-4df1-9e92-f1eb7109daee","added_by":"auto","created_at":"2024-03-21 15:33:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1280325,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the effect size difference between BFR-RT versus HL-RT for muscle strength. Different capital letters are used to represent different training protocols for the same study. Hedges’g represents effect size difference. Red diamond represents overall Hedges’g. \u003cem\u003e1rm\u003c/em\u003e 1RM test, \u003cem\u003eBFR-RT\u003c/em\u003e blood-flow restriction low-load resistance training, \u003cem\u003eCI\u003c/em\u003econfidence interval, \u003cem\u003eCombined\u003c/em\u003e mean of multiple outcomes from the same training protocol, \u003cem\u003eHL-RT\u003c/em\u003e high-load resistance training, \u003cem\u003emvc\u003c/em\u003e isometric or isokinetic tests.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2987684/v1/1acf26ea16de4403ce7375bd.png"},{"id":53180479,"identity":"ea90ed7e-f4a7-4755-a503-d311393c8c9d","added_by":"auto","created_at":"2024-03-21 15:33:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1366190,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the effect size difference between BFR-RT versus HL-RT for muscle strength according to training status. Different capital letters are used to represent different training protocols for the same study. Hedges’g represents effect size difference. Red diamonds represent overall Hedges’g of subgroups. \u003cem\u003e1rm\u003c/em\u003e 1RM test, \u003cem\u003eBFR-RT\u003c/em\u003e blood-flow restriction low-load resistance training,\u003cem\u003e CI\u003c/em\u003e confidence interval, \u003cem\u003eCombined\u003c/em\u003e mean of multiple outcomes from the same training protocol, \u003cem\u003eHL-RT\u003c/em\u003e high-load resistance training, \u003cem\u003emvc\u003c/em\u003e isometric or isokinetic tests.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2987684/v1/258da665ec6ccbfe192a810c.png"},{"id":53180480,"identity":"d741eb32-a2b8-4fab-8e5a-12a3d896d2d5","added_by":"auto","created_at":"2024-03-21 15:33:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1046511,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the effect size difference between BFR-RT versus HL-RT for muscle hypertrophy. Different capital letters are used to represent different training protocols for the same study. Hedges’g represents effect size difference. Red diamond represents overall Hedges’g. \u003cem\u003eBFR-RT\u003c/em\u003e blood-flow restriction low-load resistance training,\u003cem\u003e CI\u003c/em\u003e confidence interval, \u003cem\u003eCombined\u003c/em\u003e mean of multiple outcomes from the same training protocol, \u003cem\u003ecsa\u003c/em\u003e cross-section area, \u003cem\u003eHL-RT\u003c/em\u003ehigh-load resistance training, \u003cem\u003emt\u003c/em\u003e muscle thickness.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-2987684/v1/60a6d49a0378fb71aff9056f.png"},{"id":53180482,"identity":"1a5178cb-f477-4a12-b4c0-fbd2b37a4b30","added_by":"auto","created_at":"2024-03-21 15:33:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1295076,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the effect size difference between BFR-RT versus HL-RT for muscle hypertrophy according to training status. Different capital letters are used to represent different training protocols for the same study. Hedges’g represents effect size difference. Red diamonds represent overall Hedges’g of subgroups. \u003cem\u003eBFR-RT\u003c/em\u003e blood-flow restriction low-load resistance training,\u003cem\u003eCI\u003c/em\u003e confidence interval, \u003cem\u003eCombined\u003c/em\u003e mean of multiple outcomes from the same training protocol, \u003cem\u003ecsa\u003c/em\u003e cross-section area, \u003cem\u003eHL-RT\u003c/em\u003e high-load resistance training, \u003cem\u003emt\u003c/em\u003e muscle thickness.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-2987684/v1/a292f92b828527682e517707.png"},{"id":56897646,"identity":"1db68f71-e3d7-470f-b28e-8edca26913ab","added_by":"auto","created_at":"2024-05-22 00:33:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5761599,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2987684/v1/f0155778-de8b-49fe-9824-e10369bfaafa.pdf"},{"id":53181438,"identity":"1751b790-fd24-4f7c-a650-4961ba98dc43","added_by":"auto","created_at":"2024-03-21 15:41:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3467606,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2987684/v1/22e2fbf6e2b29a5f66c628d7.pdf"}],"financialInterests":"","formattedTitle":"Potential Moderators of the Effects of Blood Flow Restriction Training on Muscle Strength and Hypertrophy: A Meta-Analysis Based on a Comparison with High-Load Resistance Training","fulltext":[{"header":"Key Points","content":"\u003cp\u003eTraining status is an important factor influencing the effects of the BFR-RT.\u003c/p\u003e\n\u003cp\u003eTrained individuals may obtain greater muscle strength and hypertrophy gains in BFR-RT compared to HL-RT.\u003c/p\u003e\n\u003cp\u003eUntrained individuals may experience a smaller increase in strength and a similar increase in hypertrophy with BFR-RT compared to HL-RT.\u0026nbsp;\u003c/p\u003e"},{"header":"1 Background","content":"\u003cp\u003eHigh-load resistance training (HL-RT) has long been considered as the \u0026ldquo;golden standard\u0026rdquo; protocol to increase muscle strength and mass. It has been suggested that \u0026ge;\u0026thinsp;65% one-repetition maximum (1RM) is required to increase strength and hypertrophy[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, mounting evidence indicates that the use of low-load resistance training (\u0026lt;\u0026thinsp;50% 1RM) combined with blood flow restriction (BFR-RT) results in strength and morphological responses[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The results of previous study showed that low load resistance training with and without blood flow resulted in similar adaptations when sets of exercise were taken to failure[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], however the results of multiple studies showed the superiority of BFR-RT in terms of gains in muscle strength and hypertrophy when compared with similar low-load resistance training without blood flow restriction [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the literature is controversial about the magnitude of the adaptations when comparing BFR-RT to HL-RT. For example, some studies have reported greater increases in muscle strength for HL-RT when comparing to BFR-RT[\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], while others have suggested similar gains between the two exercise protocols[\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Moreover, some studies have reported that BFR-RT has higher muscle strength gains than HL-RT[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Some studies compared the effects of BFR-RT and HL-RT through meta-analysis[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In the meta-analysis of Lixandr\u0026atilde;o et al., they observed that BFR-RT and HL-RT have similar gains in muscle hypertrophy, while HL-RT is more effective in increasing muscle strength[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, limited by the number of studies comparing BFR and HL-RT at that time, some important potential moderators (e.g., training frequency, etc.) could not been further explored[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe increase in muscle strength is the result of the coordination of nerve and muscle systems[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. It was believed that neural adaptation dominates early in the training programme, later, as neural adaptations reach a plateau, muscular adaptation (hypertrophy) dominates[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. At this stage, in intermediate and advanced training progress is limited to the extent of muscular adaptation that can be achieved[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. It has been believed that BFR-RT provides a potential time-effective approach to stimulate muscle adaptations[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and even well-trained athletes may benefit from BFR-RT[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Thus, compared to HL-RT, training status and training duration may be a key factor affecting the effectiveness of BFR-RT. Additionally, the results of several studies have showed that compared with HL-RT, BFR-RT induced less hypertrophy in the proximal region [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Therefore, this regional specificity may be another influencing factor. Finally, HL-RT implies high-load exercise, which is similar to specific strength assessments (i.e., 1RM test), while during BFR-RT, participants are never exposed to high loads[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Thus, non-specific strength assessments (i.e., isometric or isokinetic tests) may more accurately reflect the response to the low-load training protocols[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this regard, test specificity may also affect the results.\u003c/p\u003e \u003cp\u003eIn short, there inconsistencies in the literature regarding effects of BFR-RT compared with HL-RT on the muscle strength and hypertrophy justify the need for synthesis and a comprehensive review of the available evidence. Therefore, based on previous studies, the purpose of this study was to conduct a meta-analysis comparing the responses of BFR-RT and HL-RT on muscle strength and hypertrophy. To further explore the effects of muscle strength and hypertrophy among these schemes, we will also consider potential influence factors such as population characteristics (i.e., training status, gender and age), protocol characteristics (i.e., upper or lower limbs, duration and frequency), test specificity (i.e., 1RM and spacing or equal speed testing), and region-specific adaptations in muscle mass.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Search Strategy and Study Selection\u003c/h2\u003e\n \u003cp\u003eThe articles were identified through the English databases Web of Knowledge, PubMed, EBSCO-SPORTDiscus from the earliest record up to February 2024, and the Chinese database WANFANG DATA, CNKI from the earliest record up to February 2024. The search strategy combined the English and Chinese terms (see Electronic Supplementary Material, Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Two reviewers (GY and ZY) evaluated the titles and abstracts of the retrieved articles to assessed their eligibility for the meta-analysis. In case of differences, a consensus was adopted. If necessary, the third reviewer (WXP) evaluated the article. If the abstract did not provide sufficient information about the inclusion criteria, the reviewers read the full text.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Eligibility Criteria\u003c/h2\u003e\n \u003cp\u003eStudies were considered for inclusion if they met the following criteria: (1) articles were published in English or Chinese; (2) subjects were healthy people, (3) pre- and post-intervention assessment of muscular strength (i.e., dynamic, isometric or isokinetic test); (4) pre- and post-intervention assessment of muscle hypertrophy (i.e., magnetic resonance imaging, computerized tomography, or ultrasonography); (5) comparisons between HL-RT (i.e., \u0026gt;\u0026thinsp;65%1RM) and BFR-RT (i.e., \u0026lt;\u0026thinsp;50%1RM); (6) score\u0026thinsp;\u0026ge;\u0026thinsp;4 on the Physiotherapy Evidence Database (PEDro) scale。\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Study Quality\u003c/h2\u003e\n \u003cp\u003eThe quality of the study was determined by using the PEDro scale, based on the Delphi list[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. Studies with a score\u0026thinsp;\u0026ge;\u0026thinsp;4 were included in this meta-analysis (see Electronic Supplementary Material, Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). For each of the items (2\u0026ndash;11) of the PEDro scale, two reviewers (GY and ZY) assessed the studies independently. In case of disagreement, a consensus was adopted or a third reviewer (WXP) evaluated the study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Data extraction\u003c/h2\u003e\n \u003cp\u003eAfter screening of the studies, all included studies were assessed for eligibility based on their full texts. Tow reviewers (GY and ZY) extracted data from the articles independently, in case of disagreement, if no consensus could be reached, a third reviewer (WXP) was consulted. The data extracted were recorded relating to (1) population characteristics (i.e., age, gender and training status); (2) intervention protocol characteristics (i.e., duration, frequency, training load, volume, exercises etc.); (3) pre- and post-intervention assessment of muscle strength (i.e., dynamic, isometric, or isokinetic test); (4) pre- and post-intervention assessment of muscle hypertrophy (cross section area, muscle thickness and muscle mass). The trained individuals were defined as athletes or individuals who participated in regular resistance training protocols before the intervention. The untrained individuals were defined as individuals who were sedentary or not participated in regular resistance training protocols before the interventions. In case of incomplete data availability, we extrapolated the data from figures or contacted the corresponding author. The graphical data were extracted using the OriginPro 2021 (Version 2021. OriginLab Corporation, Northampton, MA, USA) graphical digitizing tool. Only the last was included as the post-intervention value for analysis, when intervention effects were assessed at multiple time points. When intervention effects were measured through multiple measurement methods (e.g, CSA and muscle thickness for muscle size), the multiple outcomes were combined (i.e., using the mean of the outcomes) [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. The combination was performed by Comprehensive Meta-Analysis software (version 3.3, Biostat, Inc., Englewood, NJ, USA). The extracted data of included studies were depicted in Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Statistical Analyses\u003c/h2\u003e\n \u003cp\u003eAll analyses were performed using Comprehensive Meta-Analysis software (version 3.3, Biostat, Inc., Englewood, NJ, USA). The comparisons (BFR-RT vs. HL-RT) were calculated as the effect size difference (ES\u003csub\u003ediff\u003c/sub\u003e) using the difference in pre- and post- intervention mean and standard deviation values of muscle strength and mass, sample size and correlation between pre- and post- test for all groups. If the studies included in the meta-analysis did not report correlation between pre- and post- test, the following formula was used for estimation [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e]:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$r=\\frac{{S}_{pre}^{2}+{S}_{post}^{2}-{SD}^{2}}{2\\times {S}_{pre}\\times {S}_{post}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere S\u003csub\u003epre\u003c/sub\u003e and S\u003csub\u003epost\u003c/sub\u003e are the standard deviation of pre-test and post-test, respectively. SD is the standard deviation of difference between pre- and post-test calculated using the following formula [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]:\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$SD=\\sqrt{\\frac{{S}_{pre}^{2}}{n}+\\frac{{S}_{post}^{2}}{n}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eUsing the correction factor to correct the small sample size bias of all ES\u003csub\u003ediff\u003c/sub\u003e [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. The correction factor is given by:\u003c/p\u003e\n \u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\text{Correction factor}=1-\\frac{3}{4\\times \\left({n}_{1}+{n}_{2}-2\\right)-1}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe subjects of the studies included in present meta-analysis came from different populations. Moreover, different training protocols and various strength and hypertrophy measurements and variables were utilized in these studies. All factors may have an impact on the effect of the intervention. Thus, the random-effects model was used to perform the meta-analysis[\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e]. The \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e statistics was used to assess heterogeneity. \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e values of 25%, 50% and 75% were set as low, moderate and high levels of heterogeneity, respectively.[\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe first step was to compare the effects of BFR-RT and HL-RT on muscle strength and muscle mass. Subsequently, subgroup analyses were conducted to examine the effects of training status (trained vs. untrained individuals)., gender, age, upper and lower limbs, test specificity (i.e. 1RM test vs. isometric or isokinetic tests) and region-specific adaptations of muscle hypertrophy. Based on the average age reported by the included studies, the age subgroups were divided into young (\u0026le;\u0026thinsp;33\u0026nbsp;year) and old (\u0026ge;\u0026thinsp;57\u0026nbsp;year). Finally, according to the measured position reported by the studies, the results of muscle hypertrophy were categorized into three subgroups: proximal, middle and distal, which were \u0026lt;\u0026thinsp;50%, = 50% and \u0026gt;\u0026thinsp;50% of the length of the femur or humerus, respectively.\u003c/p\u003e\n \u003cp\u003eTo identify the presence of highly influential studies that might bias the analyses, a sensitivity analysis was performed. The analysis was therefore conducted by removing one study at a time and then examining its effect on comparisons. If removal changed the significance level of ES\u003csub\u003ediff\u003c/sub\u003e (i.e., from P\u0026thinsp;\u0026le;\u0026thinsp;0.05 to P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, or vice versa), it was considered as influential. This method has been used elsewhere[\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e]. The funnel plot, and Begg and Egger\u0026rsquo;s test were used to consider and assess publication bias, respectively. All data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error. The significance level was set to P\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eThe initial search retrieved 2801 English studies and Chinese 361studies. Afterwards, 723 duplicated studies were excluded. After evaluation titles and abstracts, 2376 studies were removed, while the remaining 63 studies were assessed through full texts. Finally, 53 studies were considered to meet the inclusion criteria (Fig. 1), 51 of which were included in the muscle strength analysis (Table 1) and 28 in the muscle hypertrophy analysis (Table 2). In addition, by contacting the authors, the muscle hypertrophy data of a study was obtained[28]. However, after multiple attempts to contact the author, the muscle strength and hypertrophy data for one study were not included as the author did not respond[76].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Muscle Strength\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFifty-one studies involving 1164 participants were included in present meta-analysis to compare muscle strength gains. In the studies investigated the trained population, only one study adopted 9 weeks of training duration and one study adopted a training frequency of 5 sessions per week.\u003c/p\u003e\n\u003cp\u003eThe overall ES\u003csub\u003ediff\u003c/sub\u003e demonstrated significantly lower gains in muscle strength for BFR-RT compared with HL-RT (ES\u003csub\u003ediff\u0026nbsp;\u003c/sub\u003e= -0.335\u0026plusmn;0.092, 95% confidence interval [CI] -0.515 to -0.156)\u0026nbsp;(Fig. 1 and Table 3). However, when considering training status, the differences between trained and untrained subgroups were significant (Q=29.39, P\u0026lt;0.01) (Table 3). Significantly higher strength gains for BFR-RT were observed compared with HL-RT in the trained group (ES\u003csub\u003ediff\u0026nbsp;\u003c/sub\u003e=0.491\u0026plusmn;0.172, 95% CI 0.154 to 0.827) (Fig.3 and Table 3). In contrast, the strength gains of HL-RT were significantly higher than that of BFR-RT in the untrained group(ES\u003csub\u003ediff\u0026nbsp;\u003c/sub\u003e= -0.552\u0026plusmn;0.087,95%CL -0.722 to -0.382) (Fig.3 and Table 3). In trained individuals, there were no significant differences between the different gender, limbs, durations, frequency and test type (Table 3 and Fig. S1-S5). In untrained individuals, there were also no significant differences between the different gender, age, limbs, training duration and frequency (Table 3 and Fig. S6-S11). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe sensitivity analysis conducted by deleting one study at a time and re-analyzing the data showed that none of the studies will have a significant impact on muscle strength results. Inspection of the funnel plots indicated no evidence of publication bias (Fig. S12). The results of the Begg test show that Kendall\u0026rsquo;s tau with continuity correction was equal to -0.02 (P=0.84), and Egger\u0026rsquo;s regression intercept was equal to 1.09 (P =0.41). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Muscle Hypertrophy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwenty-eight studies involving 703 participants were included in present meta-analysis to compare muscle hypertrophy gains. However, only three studies investigated the trained population. In addition, of the studies that investigated the untrained population, only one included female subjects, and only one adopted a training frequency of 4 sessions per week.\u003c/p\u003e\n\u003cp\u003eThe overall ES\u003csub\u003ediff\u003c/sub\u003e suggested similar gains in muscle mass between BFR-RT and HL-RT (ES\u003csub\u003ediff\u0026nbsp;\u003c/sub\u003e= -0.067\u0026plusmn;0.070, 95%CI -0.205 to 0.071) (Fig. 4 and Table 4). However, when considering training status, the differences between trained and untrained subgroups were significant (Q=9.41, P\u0026lt;0.01)\u0026nbsp;(Table 4). Significantly higher muscle hypertrophy gains for BFR-RT were observed compared with HL-RT in the trained subgroup(ES\u003csub\u003ediff\u0026nbsp;\u003c/sub\u003e= 0.695\u0026plusmn;0.258, 95% CI 0.189 to 1.200). In contrast, the muscle mass gains of BFR-RT were similar to HL-RT in the untrained subgroup (ES\u003csub\u003ediff\u0026nbsp;\u003c/sub\u003e= -0.128\u0026plusmn;0.073, 95%CL -0.272 to 0.015) (Fig. 5 and Table 4). However, in untrained individuals, there were no significant differences between the different age, limbs, duration and frequency, and region-specific adaptations in muscle mass (Fig. S13-S18 and Table 4).\u003c/p\u003e\n\u003cp\u003eThe sensitivity analysis showed that muscle hypertrophic adaptation was not affected by any particular study. Inspection of the funnel plots indicated no evidence of publication bias (Fig. S19). The results of the Begg test show that Kendall\u0026rsquo;s tau with continuity correction was equal to 0.17 (P=0.16), and Egger\u0026rsquo;s regression intercept was equal to 0.52 (P =0.65).\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe purpose of the current study was to compare the effects of BFR-RT and HL-RT on the muscle strength and hypertrophy, using HL-RT as a control to evaluate the effects and characteristics of BFR-RT. The main finding of the present study was that training status was an important influencing factor in the effects of BFR-RT. The trained individuals will get greater muscle strength and hypertrophy gains from BFR-RT as compared with HL-RT. However, in the untrained individuals, the results demonstrated that superior gains in muscle strength and similar muscle mass for HL-RT as compared with BFR-RT.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Effect of BFR-RT on Trained Individuals\u003c/h2\u003e \u003cp\u003eThe analysis results of trained individuals (ES\u003csub\u003ediff\u003c/sub\u003e = 0.491) suggested that in the comparison of these two training modalities, 69% of the trained individual may obtain greater gains in muscle strength with BFR-RT[\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTraining is initially characterized by neural adaptations, however, later as neural adaptations reach a plateau, muscular adaptation (i.e., hypertrophy) dominates[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. In intermediate and advanced training, the progress of strength training is limited to the degree of muscle adaptation that can be achieved[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Hakkinen et al. reported that during one-year traditional strength training, advanced weight-lifers show limited potential for further neural adaptations, and the total mean muscle fiber area did not increase significantly[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. However, mounting research indicates that BFR-RT can promote muscle hypertrophy of athletes[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], even in elite powerlifters the muscle fiber cross-sectional area (CSA) increased more in the BFR-RT compared to the HL-RT[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMetabolic stress was believed to be one of the factors promoting muscle hypertrophy.[\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. Compared with other strength training protocols, BFR-RT was believed to produce a higher level of metabolic stress[\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Studies suggested that compared with normoxic conditions, resistance training under the hypoxic condition caused greater metabolic and hormonal responses [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e], whereas it was believed that blood flow restriction could cause similar muscle hypoxia as compared with systemic hypoxia[\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. In this study, the results (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003e) showed that in the trained individuals, superior muscle hypertrophy gains were observed for BFR-RT as compared with HL-RT. Put another way, 76% of the trained population may obtain greater gains in muscle hypertrophy with BFR-RT[\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. In fact, our results showed that the average relative strength change [(pre-training \u0026ndash; post-training)/pre-training \u0026times;100] of BFR-RT (8.4%\u0026plusmn;1.09) was twice that of HL-RT (3.96%\u0026plusmn;0.66) in the trained individuals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Effect of BFR-RT on Untrained Individuals\u003c/h2\u003e \u003cp\u003eBeing different from the trained subjects, the analysis results of the untrained individuals (ES\u003csub\u003ediff\u003c/sub\u003e = -0.552) suggested that about 70% of the untrained individuals may experience greater gains in muscle strength with HL-RT[\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough, previous studies have found that the muscle activation level of BFR-RT was higher than that of the same intensity (low load) resistance exercise[\u003cspan additionalcitationids=\"CR88 CR89\" citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e], its muscle activation level is still low as compared with HL-RT[\u003cspan additionalcitationids=\"CR92\" citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. For example, Cook et al.[\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e] reported that muscle activation level (used surface electromyography )was greater in the HL-RT at the beginning and end of exercise compared with the BFR-RT. It has been suggested that increasing the occlusion pressure (from 40\u0026ndash;60% occlusive pressure) could increase the activation level of muscle[\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e], but recent research showed that even with higher occlusive pressure (80%), the activation level of BFR-RT on muscle was also significantly lower than HL-RT[\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. Although these findings were based on surface electromyography, BFR-RT may not achieve the same level of muscle activation and produce the same neural stimulation as HL-RT[\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Limitations\u003c/h2\u003e \u003cp\u003eThe current meta-analysis has some limitations. The lack of studies including females limited generalizability of the findings. Because of the sparse number of studies, the results comparing muscle hypertrophy in trained individuals should be interpreted with caution. In addition, the data from one study was not included[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. However, the sensitivity analysis revealed that no single study had a significant impact on the analysis results. Therefore, the absence of these data would unlikely to have affected the current results and their interpretation.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThe present meta-analysis indicates that training status is an important factor influencing the effects of BFR-RT. Compared to HL-RT, trained individuals can obtain greater strength and hypertrophy gains from BFR-RT. However, in untrained individuals, the results demonstrate that superior muscle strength and similar mass gains for HL-RT.\u003c/p\u003e \u003cp\u003eFrom a practical standpoint, BFR-RT could be a beneficial supplemental training protocol for trained population. It has been demonstrated that the combination of BFR- and HL-RT was more beneficial for the increase of muscle strength[\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. Thus, healthy individuals or athletes are likely to maximize their training adaptations by combining these two training methods [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]. Finally, it is important to highlight that BFR-RT remains a valid and effective alternative for people who cannot perform high-load resistance training.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e1RM \u0026nbsp; \u0026nbsp; \u0026nbsp;One repetition maximum\u003c/p\u003e\n\u003cp\u003eBFR-RT \u0026nbsp; Low-load resistance training (\u0026lt;50% 1RM) combined with blood flow restriction\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Confidence interval\u003c/p\u003e\n\u003cp\u003eCSA \u0026nbsp; \u0026nbsp; \u0026nbsp; Cross-sectional area\u003c/p\u003e\n\u003cp\u003eES\u003csub\u003ediff\u003c/sub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Effect size difference\u003c/p\u003e\n\u003cp\u003eHL-RT \u0026nbsp; \u0026nbsp; High-load resistance training\u003c/p\u003e\n\u003cp\u003ePEDro \u0026nbsp; \u0026nbsp; \u0026nbsp;Physiotherapy evidence database\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYG and XPW contributed to the study concept and design. YG, XPW and YZ performed the literature search. YG and YZ performed the data extraction and quality assessment. YG wrote the first draft of the manuscript. XPW, Y Z and MZ critically revised the draft of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo funding was received for this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability\u003c/strong\u003e \u003cstrong\u003eof Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cstrong\u003eand\u003c/strong\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\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\u003eCompeting of interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYu Geng, Xueping Wu, Yong Zhang and Meng Zhang declare that they have no conflict of interests relevant to the content of this review.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSchoenfeld BJ, Wilson JM, Lowery RP, Krieger JW. 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Eur J Appl Physiol. 2004;92:399\u0026ndash;406.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakarada Y, Nakamura Y, Aruga S, Onda T, Miyazaki S, Ishii N. Rapid increase in plasma growth hormone after low-intensity resistance exercise with vascular occlusion. J Appl Physiol. 2000;88:61\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYasuda T, Brechue W, Fujita T, Shirakawa J, Sato Y, Abe T. Muscle activation during low-intensity muscle contractions with restricted blood flow. J Sports Sci. 2009;27:479\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLauver JD, Cayot TE, Rotarius T, Scheuermann BW. The effect of eccentric exercise with blood flow restriction on neuromuscular activation, microvascular oxygenation, and the repeated bout effect. 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J Sci Med Sport. 2016;19:360\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Characteristics of studies included in the meta-analysis of muscle strength adaptations\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" style=\"width: 9.4578%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReferences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" style=\"width: 8.1967%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" style=\"width: 7.4401%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExercise\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtocol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeeks\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003etimes/wk\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExercise load\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOcclusion pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" style=\"width: 9.5839%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMuscle strength assessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\" style=\"width: 9.4578%;\"\u003e\n \u003cp\u003eBjornsen et al.[26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\" style=\"width: 8.1967%;\"\u003e\n \u003cp\u003eTrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\" style=\"width: 7.4401%;\"\u003e\n \u003cp\u003eSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eBFR-RT:30-15-12-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e2(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e24-30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e120mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\" style=\"width: 9.5839%;\"\u003e\n \u003cp\u003eDynamic SQ\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIsometric KE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eHL-RT:6-7\u0026times;1-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e2(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e74-76%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"3\" valign=\"top\" style=\"width: 9.4578%;\"\u003e\n \u003cp\u003eBuckner et al.[32]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e18-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"3\" style=\"width: 8.1967%;\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"3\" style=\"width: 7.4401%;\"\u003e\n \u003cp\u003eBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eBFR-RT1:4\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e15%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e57mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"3\" valign=\"top\" style=\"width: 9.5839%;\"\u003e\n \u003cp\u003eDynamic BC\u003c/p\u003e\n \u003cp\u003eIsometric BC\u003c/p\u003e\n \u003cp\u003eIsokinetic BC (60\u0026deg;/s, 180\u0026deg;/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e18-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eBFR-RT2:4\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e15%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e110mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e18-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\" style=\"width: 9.4578%;\"\u003e\n \u003cp\u003eCentner et al.[33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\" style=\"width: 8.1967%;\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003eM11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\" style=\"width: 7.4401%;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e14(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e20-35%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e50%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\" style=\"width: 9.5839%;\"\u003e\n \u003cp\u003eIsometric CR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003eM14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;6-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e14(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e70-80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\" style=\"width: 9.4578%;\"\u003e\n \u003cp\u003eCentner et al.[28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\" style=\"width: 8.1967%;\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003eM14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\" style=\"width: 7.4401%;\"\u003e\n \u003cp\u003eLP, KE, CR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e14(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e20-35%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e50%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\" style=\"width: 9.5839%;\"\u003e\n \u003cp\u003eDynamic LP \u0026amp; KE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003eM15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;6-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e14(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e70-85%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\" style=\"width: 9.4578%;\"\u003e\n \u003cp\u003eClark et al.[14]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\" style=\"width: 8.1967%;\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\" style=\"width: 7.4401%;\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eBFR-RT:3\u0026times;30-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e4(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" rowspan=\"2\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e170mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\" style=\"width: 9.5839%;\"\u003e\n \u003cp\u003eIsometric KE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.711252653927813%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.978768577494693%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.182590233545646%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;8-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.169851380042463%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e4(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.957537154989385%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\" style=\"width: 9.4578%;\"\u003e\n \u003cp\u003eCook et al.[34]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\" style=\"width: 8.1967%;\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\" style=\"width: 7.4401%;\"\u003e\n \u003cp\u003eKE, KF, LP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eBFR-RT: ~3\u0026times;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e184\u0026plusmn;25mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\" style=\"width: 9.5839%;\"\u003e\n \u003cp\u003eDynamic KE, KF \u0026amp; LP\u003c/p\u003e\n \u003cp\u003eIsometric KE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eHL-RT: ~3\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\" style=\"width: 9.4578%;\"\u003e\n \u003cp\u003eCook et al.[35]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e18-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\" style=\"width: 8.1967%;\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\" style=\"width: 7.4401%;\"\u003e\n \u003cp\u003eKE, LP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eBFR-RT:2\u0026times;25,1\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e180-200mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\" style=\"width: 9.5839%;\"\u003e\n \u003cp\u003eDynamic KE\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIsometric KE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e18-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eHL-RT:2\u0026times;10,1\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\" style=\"width: 9.4578%;\"\u003e\n \u003cp\u003eDavids et al.[36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\" style=\"width: 8.1967%;\"\u003e\n \u003cp\u003eTrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\" style=\"width: 7.4401%;\"\u003e\n \u003cp\u003eSQ, LP, KE, SSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eBFR-RT:30+2-3\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e9(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e30-40%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e60%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\" style=\"width: 9.5839%;\"\u003e\n \u003cp\u003eDynamic SQ\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIsometric KE \u0026amp; KF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eHL-RT:3-4\u0026times;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e9(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e75-80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\" style=\"width: 9.4578%;\"\u003e\n \u003cp\u003ede Lemos Muller et al.[37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\" style=\"width: 8.1967%;\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003eM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\" style=\"width: 7.4401%;\"\u003e\n \u003cp\u003eBC, KE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eBFR-RT:4\u0026times;22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e110-150mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\" style=\"width: 9.5839%;\"\u003e\n \u003cp\u003eDynamic BC \u0026amp; KE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003eM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\" style=\"width: 9.4578%;\"\u003e\n \u003cp\u003eEllefsen et al.[17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\" style=\"width: 8.1967%;\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003eM12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\" style=\"width: 7.4401%;\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eBFR-RT:5\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e90-100mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" valign=\"top\" style=\"width: 9.5839%;\"\u003e\n \u003cp\u003eDynamic KE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.711286089238845%\" valign=\"top\" style=\"width: 6.8096%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.167979002624672%\" valign=\"top\" style=\"width: 5.5485%;\"\u003e\n \u003cp\u003eM12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.128608923884514%\" valign=\"top\" style=\"width: 20.3026%;\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;6-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.46719160104987%\" valign=\"top\" style=\"width: 12.8625%;\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.335958005249344%\" valign=\"top\" style=\"width: 7.9445%;\"\u003e\n \u003cp\u003e75-90%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.566929133858268%\" valign=\"top\" style=\"width: 11.8537%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62204724409449%\" valign=\"top\" style=\"width: 9.5839%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFernandes et al.[38]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eF14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:3\u0026times;15-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e4(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e45%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e160mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIsometric GS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eF14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;8-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e4(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e75%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eJessee et al.[39]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"3\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"3\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT1:4\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e15%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e40%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eDynamic KE\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIsometric KE\u003c/p\u003e\n \u003cp\u003eIsokinetic KE (60\u0026deg;/s, 180\u0026deg;/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT2:4\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e15%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e80%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eKarabulut et al.[9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eLP, KE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:30-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e205mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic LP \u0026amp; KE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eKim et al.[40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eLP, KE, KF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:2\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e3(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e178\u0026plusmn;20mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic LP, KE \u0026amp; KF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:2\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e3(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eKim et al.[41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e72\u0026plusmn;11mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic BC\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIsometric BC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e75%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eKim et al.[42]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:3\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e4(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e160mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIsometric GS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e4(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e75%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eKorkmaz et al.[43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eTrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e6(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e130-150mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIsokinetic KE (60\u0026deg;/s, 80\u0026deg;/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e6(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eKubo et al.[10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:25-18-15-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e12(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e180-240mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIsometric KE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e12(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLaswati et al.[44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e5(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e50mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIsokinetic BC (60\u0026deg;/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e5(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLaurentino et al.[15]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:3-4\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e95\u0026plusmn;10mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic KE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3-4\u0026times;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eLetieri et al.[45]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"3\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eF11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"3\"\u003e\n \u003cp\u003eSQ, LP, KE, KF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT1:30-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e16(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e20-30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e188\u0026plusmn;5mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eIsokinetic KE \u0026amp; KF (60\u0026deg;/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eF10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT2:30-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e16(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e20-30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e105\u0026plusmn;7mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eF11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3-4\u0026times;6-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e16(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e70-80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLibardi et al.[46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e20-30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e67\u0026plusmn;8mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" valign=\"top\"\u003e\n \u003cp\u003eDynamic LP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.711286089238845%\" valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.167979002624672%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.128608923884514%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.46719160104987%\" valign=\"top\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.335958005249344%\" valign=\"top\"\u003e\n \u003cp\u003e70-80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.566929133858268%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62204724409449%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eLixandrao et al.[47]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"5\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"5\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT1:2-3\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e56\u0026plusmn;8mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eDynamic KE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT2:2-3\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e110\u0026plusmn;9mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT3:2-3\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e40%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e55\u0026plusmn;5mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT4:2-3\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e40%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e1055\u0026plusmn;19mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:2-3\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLuebbers et al.[48]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e16-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eTrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e6(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic SQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e16-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e6(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e78%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMartin-Hernandez et al.[12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"3\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"3\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT1:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e5(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e110mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eDynamic KE\u003c/p\u003e\n \u003cp\u003eIsokinetic KE (60\u0026deg;/s, 80\u0026deg;/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT2:2\u0026times;(30-15-15-15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e5(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e110mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e5(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e85%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMay et al.[49]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eKE, KF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e7(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e128mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic KE \u0026amp; KF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e7(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMendonca et al.[50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e4(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e60%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIsometric CR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e4(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e75%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMorley et al.[51]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"3\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"3\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT1:3\u0026times;10-12+1\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e100%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eDynamic KE\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIsometric KE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT2:3\u0026times;10-12+1\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e50%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;10-12+1\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eOzaki et al.[16]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e100-160mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic BP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e75%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRamis et al.[52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eBC, KE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:4\u0026times;23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e110-150mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" valign=\"top\"\u003e\n \u003cp\u003eIsometric BC \u0026amp; KE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.711286089238845%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.167979002624672%\" valign=\"top\"\u003e\n \u003cp\u003eM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.128608923884514%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.46719160104987%\" valign=\"top\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.335958005249344%\" valign=\"top\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.566929133858268%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.62204724409449%\" valign=\"top\"\u003e\n \u003cp\u003eIsokinetic BC \u0026amp; KE (60\u0026deg;/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eSharifi et al.[53]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"4\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"4\"\u003e\n \u003cp\u003eLP, KE, KF, CP, BC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:9\u0026times;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e20-30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e110-160mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eDynamic LP \u0026amp; CP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:9\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e70-80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:9\u0026times;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e6(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e20-30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e110-160mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:9\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e6(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e70-80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eShiromaru et al.[54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:3\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e3(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e80%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic KE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e6(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSousa et al.[55]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:3\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e6(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e142\u0026plusmn;122mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" valign=\"top\"\u003e\n \u003cp\u003eIsometric KE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.078602620087336%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51528384279476%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.131004366812227%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.296943231441048%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.5764192139738%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37117903930131%\" valign=\"top\"\u003e\n \u003cp\u003e6(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.262008733624453%\" valign=\"top\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.117903930131005%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.650655021834062%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSugiarto et al.[56]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e26-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e5(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e50mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIsokinetic KE (60\u0026deg;/s, 120\u0026deg;/s, 180\u0026deg;/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e26-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e5(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e75%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTeixeira et al.[57]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:3\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e80%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic KE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eThiebaud et al.[58]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eF6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eCP, SR, SHP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:30-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e10-30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e80-120mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic CP, SR \u0026amp; SHP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eF8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e70-90%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVechin et al.[13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\"\u003e\n \u003cp\u003eLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e20-30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e71\u0026plusmn;9mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" valign=\"top\"\u003e\n \u003cp\u003eDynamic LP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.078602620087336%\" valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51528384279476%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.131004366812227%\" valign=\"top\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.296943231441048%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.5764192139738%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37117903930131%\" valign=\"top\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.262008733624453%\" valign=\"top\"\u003e\n \u003cp\u003e70-80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.117903930131005%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.650655021834062%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYasuda et al.[11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e22-32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eBP, EE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e100-160mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic BP\u003c/p\u003e\n \u003cp\u003eIsometric EE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e22-32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e75%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCHE Tongtong\u0026nbsp;et al.[19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eTrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eF8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e180mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic SQ\u003c/p\u003e\n \u003cp\u003eIsokinetic KE \u0026amp; KF (60\u0026deg;/s, 180\u0026deg;/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eF8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;8-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e75%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLI Shanghua et al.[59]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eTrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:5\u0026times;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e40%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e200mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIsokinetic KE \u0026amp; KF (60\u0026deg;/s, 180\u0026deg;/s)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:5\u0026times;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLI Zhiyuan et al.[18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eTrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eSQ, SSQ, DF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e4(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e200mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic SQ\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIsokinetic KE \u0026amp; KF (60\u0026deg;/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;8-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e4(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLI Zhiyuan et al.[60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eTrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eBP, SQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e6(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e160-200mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic SQ \u0026amp; BP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e6(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eWANG Mingbo et la.[61]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eTrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eSQ, SSQ, DF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:4\u0026times;20-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e200-220mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eIsokinetic KE \u0026amp; KF (60\u0026deg;/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eZHANG Junjie et la.[62]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"3\"\u003e\n \u003cp\u003eTrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"3\"\u003e\n \u003cp\u003eSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT1:5\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e5(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e252mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eDynamic SQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT2:5\u0026times;11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e5(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e40%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e252mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:5\u0026times;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e5(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e75%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCentner et al.[65]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e14(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e20-35%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e50%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic CR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;6-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e14(30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e70-80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eDe Araujo et al.[66]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e6(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e50%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic BC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;10-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e6(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eHoriuchi et al.[67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e18-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eLP, KE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:4\u0026times;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e4(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e130%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic LP \u0026amp; KE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e18-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e4(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e75%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eJudd et al.[68]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"4\"\u003e\n \u003cp\u003eTrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"4\"\u003e\n \u003cp\u003eBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT: 4\u0026times;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e6(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e40%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eDynamic BC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT: 4\u0026times;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e6(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eF4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT: 4\u0026times;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e3(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e40%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eF5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT: 4\u0026times;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e3(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eReece et al.[69]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT: 3\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e50%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic KE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.212121212121213%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.818181818181818%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.393939393939394%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.393939393939394%\" valign=\"top\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.242424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.818181818181817%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eSousa-Silva et al.[70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT:1\u0026times;30+2-3\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e50%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic BC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3-4\u0026times;10-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.217046580773042%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eWang et al.[71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.333994053518335%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.730426164519326%\" rowspan=\"2\"\u003e\n \u003cp\u003eTrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.658077304261645%\" valign=\"top\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5322101090188305%\" rowspan=\"2\"\u003e\n \u003cp\u003eSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.956392467789891%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT: 30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.415262636273539%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;4(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.31615460852329%\" valign=\"top\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.000991080277503%\" valign=\"top\"\u003e\n \u003cp\u003e200mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.839444995044598%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDynamic SQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.714776632302405%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.075601374570446%\" valign=\"top\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.66323024054983%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT;4\u0026times;8-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.323024054982817%\" valign=\"top\"\u003e\n \u003cp\u003e4(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.151202749140893%\" valign=\"top\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.072164948453608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eM\u003c/em\u003e male, \u003cem\u003eF\u003c/em\u003e female\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBC\u003c/em\u003e biceps curls, \u003cem\u003eBP\u003c/em\u003e bench press, \u003cem\u003eCP\u003c/em\u003e chest press, \u003cem\u003eDF\u003c/em\u003e deadlift, \u003cem\u003eEE\u003c/em\u003e elbow extension, \u003cem\u003eGS\u003c/em\u003e grip strength, \u003cem\u003eKE\u003c/em\u003e knee extension, \u003cem\u003eKF\u003c/em\u003e knee flexion, \u003cem\u003eLP\u003c/em\u003e leg press, \u003cem\u003eSHP\u003c/em\u003e seated shoulder press, \u003cem\u003eCR\u003c/em\u003e calf raises, \u003cem\u003eSQ\u003c/em\u003e squat, \u003cem\u003eSSQ\u003c/em\u003e split squat, \u003cem\u003eSR\u003c/em\u003e Seated Rowing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Characteristics of studies included in the meta-analysis of muscle hypertrophy adaptations\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"972\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eReferences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eExercise\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtocol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeeks\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(times/wk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eExercise load\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eOcclusion pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.012345679012345%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMuscle mass assessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.404255319148938%\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.808510638297875%\"\u003e\n \u003cp\u003eMuscle groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.78723404255319%\"\u003e\n \u003cp\u003eTest site\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eBjornsen et al.[26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eTrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:30-15-12-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e2(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e24-30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e120mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\" rowspan=\"2\"\u003e\n \u003cp\u003eRectus femoris\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eVastus lateralis\u003c/p\u003e\n \u003cp\u003eVastus medialis\u003c/p\u003e\n \u003cp\u003eVastus intermedius\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" rowspan=\"2\"\u003e\n \u003cp\u003eDistal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.151187904967603%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.151187904967603%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.406047516198704%\"\u003e\n \u003cp\u003eHL-RT: 6-7\u0026times;1-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311015118790497%\"\u003e\n \u003cp\u003e2(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.518358531317496%\"\u003e\n \u003cp\u003e74-76%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.4622030237581%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eBuckner et al.[32]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e18-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"3\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"3\"\u003e\n \u003cp\u003eBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT1:\u003c/p\u003e\n \u003cp\u003e4 \u0026times; failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e15%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e57mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"3\"\u003e\n \u003cp\u003eUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eUpper arm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" rowspan=\"3\"\u003e\n \u003cp\u003eMid\u003c/p\u003e\n \u003cp\u003eDistal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e18-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eBFR-RT2:\u003c/p\u003e\n \u003cp\u003e4\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e15%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e110mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e18-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eHL-RT:\u003c/p\u003e\n \u003cp\u003e4\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCentner et al.[33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e14(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e20-35%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e50%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eGastrocnemius\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003eM14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;6-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e14(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e70-80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCentner et al.[28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eLP,KE,CR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e14(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e20-35%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e50%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eRectus femoris,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" rowspan=\"2\"\u003e\n \u003cp\u003eProximal Mid\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDistal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003eM15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;6-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e14(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e70-85%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCook et al.[34]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eKE,KF,LP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:~3\u0026times;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e184\u0026plusmn;25mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eQuadriceps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.9219440353460975%\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.9219440353460975%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.642120765832107%\"\u003e\n \u003cp\u003eHL-RT:~3\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.394698085419735%\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.991163475699558%\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.316642120765833%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.440353460972016%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCook et al.[35]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e18-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eKE,LP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:2\u0026times;25, 1\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e180-200mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eQuadriceps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.9219440353460975%\"\u003e\n \u003cp\u003e18-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.9219440353460975%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.642120765832107%\"\u003e\n \u003cp\u003eHL-RT:2\u0026times;10, 1\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.394698085419735%\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.991163475699558%\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.316642120765833%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.440353460972016%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" valign=\"top\"\u003e\n \u003cp\u003eDavids et al.[36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\"\u003e\n \u003cp\u003eTrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp\u003eSQ,LP,KE,SSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:30+2-3\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e9(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e30-40%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e60%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eQuadriceps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eHL-RT:3-4\u0026times;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e9(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e75-80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eEllefsen et al.[18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:\u003c/p\u003e\n \u003cp\u003e5\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e90-100mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eQuadriceps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" rowspan=\"2\"\u003e\n \u003cp\u003eProximal\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDistal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003eM12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;6-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e75-90%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eJessee et al.[41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"3\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"3\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT1:\u003c/p\u003e\n \u003cp\u003e4\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e15%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e40%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"3\"\u003e\n \u003cp\u003eUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eQuadriceps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" rowspan=\"3\"\u003e\n \u003cp\u003eProximal\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMid\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDistal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eBFR-RT2:\u003c/p\u003e\n \u003cp\u003e4\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e15%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e80%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eHL-RT:\u003c/p\u003e\n \u003cp\u003e4\u0026times;failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eKataoka et al.\u0026nbsp;[65]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:4\u0026times;16-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e40%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eGastrocnemius\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.9219440353460975%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.9219440353460975%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.642120765832107%\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;12-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.394698085419735%\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.991163475699558%\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.316642120765833%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.440353460972016%\"\u003e\n \u003cp\u003eSoleus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eKim et al.[66]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eLP,KE,KF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:2\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e3(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e178\u0026plusmn;20mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eThigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" rowspan=\"2\"\u003e\n \u003cp\u003eMid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eHL-RT:2\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e3(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" valign=\"top\"\u003e\n \u003cp\u003eKim et al.[41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp\u003eBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e72\u0026plusmn;11mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp\u003eUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eUpper arm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp\u003eMid\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDistal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e75%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eKorkmaz et al.[43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eTrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e6(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e130-150mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\" rowspan=\"2\"\u003e\n \u003cp\u003eRectus femoris\u003c/p\u003e\n \u003cp\u003eVastus lateralis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp\u003eMid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.592321755027422%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.592321755027422%\"\u003e\n \u003cp\u003eM12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.420475319926874%\"\u003e\n \u003cp\u003e6(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.356489945155394%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eKubo et al.[10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:25-18-15-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e12(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e180-240mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eQuadriceps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.9219440353460975%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.9219440353460975%\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.642120765832107%\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.394698085419735%\"\u003e\n \u003cp\u003e12(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.991163475699558%\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.316642120765833%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.440353460972016%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLaurentino et al.[15]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:3-4\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e95\u0026plusmn;10mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eQuadriceps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp\u003eMid\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.9219440353460975%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.9219440353460975%\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.642120765832107%\"\u003e\n \u003cp\u003eHL-RT:3-4\u0026times;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.394698085419735%\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.991163475699558%\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.316642120765833%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.440353460972016%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLibardi et al.[46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp\u003eLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e20-30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e67\u0026plusmn;8mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eQuadriceps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" rowspan=\"2\"\u003e\n \u003cp\u003eMid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.110438729198185%\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.110438729198185%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.984871406959153%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.095310136157337%\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.623298033282905%\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.372163388804841%\"\u003e\n \u003cp\u003e70-80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.733736762481088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eLixandrao et al.[47]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"5\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"5\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT1:2-3\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e55.5\u0026plusmn;8mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"5\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eQuadriceps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" rowspan=\"5\"\u003e\n \u003cp\u003eMid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003eM14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eBFR-RT2:2-3\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e109\u0026plusmn;9mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eBFR-RT3:2-3\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e40%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e54\u0026plusmn;5mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eBFR-RT4:2-3\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e40%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e105\u0026plusmn;19mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eHL-RT:2-3\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMartin-Hernandez et al.[12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"3\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"3\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT1:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e5(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e110mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"3\"\u003e\n \u003cp\u003eUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\" rowspan=\"2\"\u003e\n \u003cp\u003eRectus femoris\u003c/p\u003e\n \u003cp\u003eVastus lateralis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" rowspan=\"3\"\u003e\n \u003cp\u003eMid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.151187904967603%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.151187904967603%\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.406047516198704%\"\u003e\n \u003cp\u003eBFR-RT2:2\u0026times;(30-15-15-15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311015118790497%\"\u003e\n \u003cp\u003e5(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.518358531317496%\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.4622030237581%\"\u003e\n \u003cp\u003e110mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003eM11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e5(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e85%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMay et al.[49]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eKE,KF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e7(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e128mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eQuadriceps\u003c/p\u003e\n \u003cp\u003eHamstrings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" rowspan=\"2\"\u003e\n \u003cp\u003eMid\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDistal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e7(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMendonca et al.[50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e4(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e60%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eSoleus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e4(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e75%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eOzaki et al.[16]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e100-160mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\" rowspan=\"2\"\u003e\n \u003cp\u003eTriceps brachii\u003c/p\u003e\n \u003cp\u003ePectoralis major\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.592321755027422%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.592321755027422%\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.420475319926874%\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\"\u003e\n \u003cp\u003e75%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.356489945155394%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRamis et al.[52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eBC,KE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:4\u0026times;23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e110-150mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eBiceps brachii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003eM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003eQuadriceps\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eShiromaru et al.[54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:3\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e3(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e80%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eQuadriceps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" rowspan=\"2\"\u003e\n \u003cp\u003eMid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003eM15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e6(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTeixeira et al.[57]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eKE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:3\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e20%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e80%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eQuadriceps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" rowspan=\"2\"\u003e\n \u003cp\u003eMid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eThiebaud et al.,[58]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eF6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eCP,SR,SHP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:30-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e10-30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e80-120mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eBiceps brachii\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTriceps brachii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\" rowspan=\"2\"\u003e\n \u003cp\u003eDistal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.899159663865547%\"\u003e\n \u003cp\u003eF8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.579831932773109%\"\u003e\n \u003cp\u003e8(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e70-90%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.18487394957983%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVechin et al.[13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e20-30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e71\u0026plusmn;9mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eQuadriceps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp\u003eMid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.724725943970768%\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.257003654080389%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.724725943970768%\"\u003e\n \u003cp\u003eM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.763702801461632%\"\u003e\n \u003cp\u003eHL-RT:4\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.942752740560293%\"\u003e\n \u003cp\u003e12(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.571254567600487%\"\u003e\n \u003cp\u003e70-80%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66747868453106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.038976857490864%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.077953714981728%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23142509135201%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYasuda et al.[11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e22-32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eBP,EE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\"\u003e\n \u003cp\u003eBFR-RT:30-15-15-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e100-160mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eTriceps brachii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp\u003eMid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.9219440353460975%\"\u003e\n \u003cp\u003e22-32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.9219440353460975%\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.642120765832107%\"\u003e\n \u003cp\u003eHL-RT:3\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.394698085419735%\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.991163475699558%\"\u003e\n \u003cp\u003e75%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.316642120765833%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.440353460972016%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.74485596707819%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSousa-Silva et al.[70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" rowspan=\"2\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.8353909465020575%\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\"\u003e\n \u003cp\u003eBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.62551440329218%\" valign=\"top\"\u003e\n \u003cp\u003eBFR-RT: 1\u0026times;30+2-3\u0026times;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.864197530864198%\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.77366255144033%\"\u003e\n \u003cp\u003e30%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.699588477366255%\"\u003e\n \u003cp\u003e50%AOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.790123456790123%\" rowspan=\"2\"\u003e\n \u003cp\u003eUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003eBiceps brachii\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.641975308641975%\"\u003e\n \u003cp\u003eDistal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.308724832214765%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\"\u003e\n \u003cp\u003eM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.859060402684564%\"\u003e\n \u003cp\u003eBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.167785234899329%\" valign=\"top\"\u003e\n \u003cp\u003eHL-RT:3-4\u0026times;10-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.651006711409396%\"\u003e\n \u003cp\u003e8(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.751677852348994%\"\u003e\n \u003cp\u003e70%1RM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.95973154362416%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.71812080536913%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.275167785234899%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eM\u003c/em\u003e male, \u003cem\u003eF\u003c/em\u003e female\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1RM\u003c/em\u003e one-repetition maximum, \u003cem\u003eAOP\u003c/em\u003e arterial occlusion pressure, \u003cem\u003eCT\u003c/em\u003e Computed Tomography, \u003cem\u003eMRI\u003c/em\u003e magnetic resonance imaging, \u003cem\u003eUS\u003c/em\u003e ultrasonography,\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDistal\u003c/em\u003e \u0026gt; 50% of the thigh or upper arm length, \u003cem\u003eMid\u003c/em\u003e =50% of the thigh or upper arm length, \u003cem\u003eProximal\u003c/em\u003e \u0026lt;50% of the thigh or upper arm length\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBC\u003c/em\u003e biceps curls, \u003cem\u003eBP\u003c/em\u003e bench press, \u003cem\u003eCP\u003c/em\u003e chest press, \u003cem\u003eDF\u003c/em\u003e deadlift, \u003cem\u003eEE\u003c/em\u003e elbow extension, \u003cem\u003eGS\u003c/em\u003e grip strength, \u003cem\u003eKE\u003c/em\u003e knee extension, \u003cem\u003eKF\u003c/em\u003e knee flexion, \u003cem\u003eLP\u003c/em\u003e leg press, \u003cem\u003eSHP\u003c/em\u003e seated shoulder press, \u003cem\u003eCR\u003c/em\u003e calf raises, \u003cem\u003eSQ\u003c/em\u003e squat, \u003cem\u003eSSQ\u003c/em\u003e split squat, \u003cem\u003eSR\u003c/em\u003e Seated Rowing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eSummary of meta-analysis results for muscle strength\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.416666666666666%\" style=\"width: 4.0599%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" style=\"width: 5.1256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.125%\" style=\"width: 1.4209%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" style=\"width: 3.2479%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" style=\"width: 4.5673%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" colspan=\"2\" style=\"width: 7.054%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% Confidence Interval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" style=\"width: 2.8419%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.5%\" colspan=\"2\" style=\"width: 4.7196%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBetween Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"3\" style=\"width: 7.1047%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeterogeneity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.638297872340425%\" style=\"width: 4.0599%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1914893617021276%\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eES\u003csub\u003ediff\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower limit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper limit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.4359%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.2837%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.25531914893617%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eI\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"13\" style=\"width: 40.5478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.638297872340425%\" style=\"width: 4.0599%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1914893617021276%\" valign=\"bottom\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"bottom\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e-0.335\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" valign=\"bottom\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.092\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"bottom\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.515\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"bottom\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.156\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" valign=\"bottom\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" valign=\"bottom\" style=\"width: 2.4359%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" valign=\"bottom\" style=\"width: 2.2837%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"bottom\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e157.542\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" valign=\"bottom\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.25531914893617%\" valign=\"bottom\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e60.65\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"13\" style=\"width: 40.5478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.638297872340425%\" rowspan=\"2\" valign=\"top\" style=\"width: 4.0599%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003eTrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1914893617021276%\" valign=\"bottom\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"bottom\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e0.491\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" valign=\"bottom\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.172\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"bottom\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e0.154\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"bottom\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e0.827\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" valign=\"bottom\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.4359%;\"\u003e\n \u003cp\u003e29.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.2837%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"bottom\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e27.047\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" valign=\"bottom\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.25531914893617%\" valign=\"bottom\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e51.94\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.277777777777779%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" valign=\"bottom\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.722222222222221%\" valign=\"bottom\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e-0.552\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" valign=\"bottom\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.087\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.722\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.382\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e83.634\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\" valign=\"bottom\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e42.61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"13\" valign=\"top\" style=\"width: 40.5478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrained\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.638297872340425%\" rowspan=\"2\" valign=\"top\" style=\"width: 4.0599%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1914893617021276%\" valign=\"bottom\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"bottom\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e0.682\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" valign=\"bottom\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.572\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"bottom\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.440\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"bottom\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e1.804\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" valign=\"bottom\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e0.23\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.4359%;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.2837%;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"bottom\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e3.896\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" valign=\"bottom\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.25531914893617%\" valign=\"bottom\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e74.33\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.277777777777779%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" valign=\"bottom\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.722222222222221%\" valign=\"bottom\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e0.609\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" valign=\"bottom\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.236\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e0.146\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e1.072\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e18.791\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\" valign=\"bottom\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e52.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.638297872340425%\" rowspan=\"2\" valign=\"top\" style=\"width: 4.0599%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLimbs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003eLower limb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1914893617021276%\" valign=\"bottom\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e12\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"bottom\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e0.391\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" valign=\"bottom\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.198\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"bottom\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"bottom\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e0.779\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" valign=\"bottom\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.4359%;\"\u003e\n \u003cp\u003e1.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.2837%;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"bottom\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e18.374\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" valign=\"bottom\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e0.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.25531914893617%\" valign=\"bottom\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e40.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.277777777777779%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003eUpper limb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" valign=\"bottom\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.722222222222221%\" valign=\"bottom\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e1.011\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" valign=\"bottom\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.467\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e0.095\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e1.926\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e8.736\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\" valign=\"bottom\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e77.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.638297872340425%\" rowspan=\"2\" valign=\"top\" style=\"width: 4.0599%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003e\u0026le;4wk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1914893617021276%\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e0.724\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.371\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e1.452\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.4359%;\"\u003e\n \u003cp\u003e0.214\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.2837%;\"\u003e\n \u003cp\u003e0.64\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e3.420\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e0.33\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.25531914893617%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e12.28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.277777777777779%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003e=5-8wk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.722222222222221%\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e0.520\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.238\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e0.053\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e0.987\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e19.563\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e59.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.638297872340425%\" rowspan=\"2\" valign=\"top\" style=\"width: 4.0599%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003e2/wk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1914893617021276%\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e0.660\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.300\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e0.072\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e1.248\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.4359%;\"\u003e\n \u003cp\u003e0.330\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.2837%;\"\u003e\n \u003cp\u003e0.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e20.471\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.25531914893617%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e70.69\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.277777777777779%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003e3/wk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.722222222222221%\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e0.415\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.303\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.179\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e1.010\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e0.17\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e5.821\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e0.32\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e14.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.638297872340425%\" rowspan=\"2\" valign=\"top\" style=\"width: 4.0599%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003eNon-specific test\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1914893617021276%\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e0.541\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.282\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.011\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e1.094\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.4359%;\"\u003e\n \u003cp\u003e0.352\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.2837%;\"\u003e\n \u003cp\u003e0.55\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e11.211\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.25531914893617%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e55.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.277777777777779%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003eSpecific test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.722222222222221%\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e0.330\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.218\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.098\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e0.758\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e20.499\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e51.22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"13\" valign=\"top\" style=\"width: 40.5478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUntrained\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.638297872340425%\" rowspan=\"2\" valign=\"top\" style=\"width: 4.0599%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1914893617021276%\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e-0.638\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.297\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-1.220\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.055\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.4359%;\"\u003e\n \u003cp\u003e0.143\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.2837%;\"\u003e\n 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\u003cp\u003e0.120\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.751\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.282\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e68.109\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e55.95\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.638297872340425%\" rowspan=\"2\" valign=\"top\" style=\"width: 4.0599%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003eOld\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1914893617021276%\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e-0.714\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.205\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-1.115\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.312\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.4359%;\"\u003e\n \u003cp\u003e0.573\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.2837%;\"\u003e\n \u003cp\u003e0.45\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e7.159\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e0.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.25531914893617%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e2.22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.277777777777779%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003eYoung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.722222222222221%\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e-0.544\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.091\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.721\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.367\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e69.180\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e43.63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.638297872340425%\" rowspan=\"2\" valign=\"top\" style=\"width: 4.0599%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLimbs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003eLower limb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1914893617021276%\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e36\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e-0.618\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.111\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.836\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.400\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.4359%;\"\u003e\n \u003cp\u003e0.514\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.2837%;\"\u003e\n \u003cp\u003e0.47\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e73.921\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.25531914893617%\" style=\"width: 2.0807%;\"\u003e\n 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\u003cp\u003e\u0026lt;0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e38.871\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e58.84\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.638297872340425%\" rowspan=\"3\" valign=\"top\" style=\"width: 4.0599%;\"\u003e\n \u003cp\u003eDuration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003e\u0026le;4wk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1914893617021276%\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e-0.493\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.216\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.916\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.070\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"3\" style=\"width: 2.4359%;\"\u003e\n \u003cp\u003e0.278\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"3\" style=\"width: 2.2837%;\"\u003e\n \u003cp\u003e0.87\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e5.107\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e0.53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.25531914893617%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.277777777777779%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003e=5-8wk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.722222222222221%\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e-0.593\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.116\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.820\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.365\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e51.803\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e47.88\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.277777777777779%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003e\u0026ge;9wk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" valign=\"bottom\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.722222222222221%\" valign=\"bottom\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e-0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.158\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.816\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.198\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e25.833\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e49.68\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.638297872340425%\" rowspan=\"3\" valign=\"top\" style=\"width: 4.0599%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003e2/wk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1914893617021276%\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e21\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e-0.596\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.131\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.853\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.339\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"3\" style=\"width: 2.4359%;\"\u003e\n \u003cp\u003e0.795\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"3\" style=\"width: 2.2837%;\"\u003e\n \u003cp\u003e0.67\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e35.971\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.25531914893617%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e44.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.277777777777779%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003e3/wk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.722222222222221%\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e-0.560\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.121\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.797\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.323\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e\u0026lt;0.01 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e44.477\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e46.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.277777777777779%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003e4/wk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" valign=\"bottom\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.722222222222221%\" valign=\"bottom\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e-0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.388\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.991\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e0.528\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e0.55\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e0.683\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e0.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.638297872340425%\" rowspan=\"2\" valign=\"top\" style=\"width: 4.0599%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest specificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003eNon-specific test\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1914893617021276%\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e24\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e-0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.120\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.657\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.188\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.4359%;\"\u003e\n \u003cp\u003e3.573\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" rowspan=\"2\" style=\"width: 2.2837%;\"\u003e\n \u003cp\u003e0.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e33.745\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e0.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.25531914893617%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e31.84\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.277777777777779%\" style=\"width: 5.1256%;\"\u003e\n \u003cp\u003eSpecific test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\" style=\"width: 1.4209%;\"\u003e\n \u003cp\u003e37\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.722222222222221%\" style=\"width: 3.2479%;\"\u003e\n \u003cp\u003e-0.715\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" style=\"width: 4.5673%;\"\u003e\n \u003cp\u003e0.099\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.909\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 3.5524%;\"\u003e\n \u003cp\u003e-0.522\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 2.8419%;\"\u003e\n \u003cp\u003e\u0026lt;0.01 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" style=\"width: 2.8926%;\"\u003e\n \u003cp\u003e70.945\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" style=\"width: 2.1314%;\"\u003e\n \u003cp\u003e\u0026lt;0.01 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.555555555555555%\" style=\"width: 2.0807%;\"\u003e\n \u003cp\u003e49.26\u0026nbsp;\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\u003cem\u003eES\u003csub\u003ediff\u003c/sub\u003e\u003c/em\u003e effect size difference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Summary of meta-analysis results for muscle hypertrophy\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.4085%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.9876%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 2.1634%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.7724%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 4.7971%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBetween Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 6.7254%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeterogeneity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.4085%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.9876%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.1634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eES\u003csub\u003ediff\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower limit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper limit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.4456%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3515%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.9283%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eI\u003csup\u003e2\u003c/sup\u003e(%)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\" style=\"width: 37.5775%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.4085%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.9876%;\"\u003e\n \u003cp\u003e34\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.1634%;\"\u003e\n \u003cp\u003e-0.067\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\n \u003cp\u003e0.070\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.205\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e0.071\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.34\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.4456%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 2.3515%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 2.5397%;\"\u003e\n \u003cp\u003e29.578\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.64\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.9283%;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\" style=\"width: 37.5775%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 5.4085%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\n \u003cp\u003eTrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.9876%;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.1634%;\"\u003e\n \u003cp\u003e0.695\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\n \u003cp\u003e0.258\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e0.189\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e1.200\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 2.4456%;\"\u003e\n \u003cp\u003e9.413\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 2.3515%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5397%;\"\u003e\n \u003cp\u003e3.048\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.9283%;\"\u003e\n \u003cp\u003e34.39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\n \u003cp\u003eUntrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.9876%;\"\u003e\n \u003cp\u003e31\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.1634%;\"\u003e\n \u003cp\u003e-0.128\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\n \u003cp\u003e0.073\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.272\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e0.015\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5397%;\"\u003e\n \u003cp\u003e17.116\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.9283%;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\" valign=\"top\" style=\"width: 37.5775%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUntrained\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 5.4085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\n \u003cp\u003eOld\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.9876%;\"\u003e\n \u003cp\u003e4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.1634%;\"\u003e\n \u003cp\u003e0.096\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\n \u003cp\u003e0.226\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.346\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e0.538\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.67\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 2.4456%;\"\u003e\n \u003cp\u003e1.106\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 2.3515%;\"\u003e\n \u003cp\u003e0.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5397%;\"\u003e\n \u003cp\u003e0.265\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.9283%;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\n \u003cp\u003eYoung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.9876%;\"\u003e\n \u003cp\u003e27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.1634%;\"\u003e\n \u003cp\u003e-0.155\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\n \u003cp\u003e0.077\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.306\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5397%;\"\u003e\n \u003cp\u003e15.746\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.94\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.9283%;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 5.4085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLimbs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\n \u003cp\u003eLower limbs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.9876%;\"\u003e\n \u003cp\u003e24\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.1634%;\"\u003e\n \u003cp\u003e-0.053\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\n \u003cp\u003e0.084\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.217\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e0.111\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.52\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 2.4456%;\"\u003e\n \u003cp\u003e3.398\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 2.3515%;\"\u003e\n \u003cp\u003e0.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5397%;\"\u003e\n \u003cp\u003e10.961\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.98\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.9283%;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\n \u003cp\u003eUpper limbs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.9876%;\"\u003e\n \u003cp\u003e8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.1634%;\"\u003e\n \u003cp\u003e-0.354\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\n \u003cp\u003e0.140\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.628\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.080\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5397%;\"\u003e\n \u003cp\u003e3.231\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.86\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.9283%;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 5.4085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\n \u003cp\u003e\u0026le;4wk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.9876%;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.1634%;\"\u003e\n \u003cp\u003e-0.115\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\n \u003cp\u003e0.220\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.546\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e0.317\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.60\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 2.4456%;\"\u003e\n \u003cp\u003e0.254\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 2.3515%;\"\u003e\n \u003cp\u003e0.88\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5397%;\"\u003e\n \u003cp\u003e0.489\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.78\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.9283%;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\n \u003cp\u003e=5-8wk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.9876%;\"\u003e\n \u003cp\u003e18\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.1634%;\"\u003e\n \u003cp\u003e-0.102\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\n \u003cp\u003e0.095\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.289\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e0.084\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5397%;\"\u003e\n \u003cp\u003e12.718\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.75\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.9283%;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\n \u003cp\u003e\u0026ge;9wk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0.9876%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2.1634%;\"\u003e\n \u003cp\u003e-0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\n \u003cp\u003e0.134\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.448\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e0.079\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.17\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5397%;\"\u003e\n \u003cp\u003e3.656\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.93\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.9283%;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 5.4085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\n \u003cp\u003e2/wk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.9876%;\"\u003e\n \u003cp\u003e17\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.1634%;\"\u003e\n \u003cp\u003e-0.219\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\n \u003cp\u003e0.100\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.416\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.022\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 2.4456%;\"\u003e\n \u003cp\u003e2.128\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 2.3515%;\"\u003e\n \u003cp\u003e0.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5397%;\"\u003e\n \u003cp\u003e7.113\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.9283%;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\n \u003cp\u003e3/wk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.9876%;\"\u003e\n \u003cp\u003e13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.1634%;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\n \u003cp\u003e0.112\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.219\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e0.220\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.99\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5397%;\"\u003e\n \u003cp\u003e7.663\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.81\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.9283%;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 5.4085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssessment region\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Thigh)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\n \u003cp\u003eDistal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.9876%;\"\u003e\n \u003cp\u003e5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.1634%;\"\u003e\n \u003cp\u003e-0.069\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\n \u003cp\u003e0.184\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.430\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e0.291\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.71\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 2.4456%;\"\u003e\n \u003cp\u003e1.805\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 2.3515%;\"\u003e\n \u003cp\u003e0.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5397%;\"\u003e\n \u003cp\u003e0.478\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.98\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.9283%;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\n \u003cp\u003eMid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.9876%;\"\u003e\n \u003cp\u003e16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.1634%;\"\u003e\n \u003cp\u003e-0.149\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\n \u003cp\u003e0.107\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.359\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e0.062\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.17\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5397%;\"\u003e\n \u003cp\u003e4.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.99\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.9283%;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\n \u003cp\u003eProximal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0.9876%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2.1634%;\"\u003e\n \u003cp\u003e-0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\n \u003cp\u003e0.203\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.816\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.018\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5397%;\"\u003e\n \u003cp\u003e0.470\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.93\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.9283%;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 5.4085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssessment region\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Upper-arm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\n \u003cp\u003eDistal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.9876%;\"\u003e\n \u003cp\u003e5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.1634%;\"\u003e\n \u003cp\u003e-0.242\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\n \u003cp\u003e0.172\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.579\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e0.094\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 2.4456%;\"\u003e\n \u003cp\u003e1.762\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 2.3515%;\"\u003e\n \u003cp\u003e0.18\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5397%;\"\u003e\n \u003cp\u003e1.912\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.75\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.9283%;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.6684%;\"\u003e\n \u003cp\u003eMid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.9876%;\"\u003e\n \u003cp\u003e4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.1634%;\"\u003e\n \u003cp\u003e-0.577\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4679%;\"\u003e\n \u003cp\u003e0.184\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.938\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3862%;\"\u003e\n \u003cp\u003e-0.216\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5397%;\"\u003e\n \u003cp\u003e3.881\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.3045%;\"\u003e\n \u003cp\u003e0.28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.9283%;\"\u003e\n \u003cp\u003e22.70\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e2/wk\u003c/em\u003e 2 sessions per week, \u003cem\u003e3/w\u003c/em\u003e 3 sessions per week, \u003cem\u003eES\u003csub\u003ediff\u003c/sub\u003e\u003c/em\u003e effect size difference.\u003c/p\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":"sports-medicine-open","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"smoa","sideBox":"Learn more about [Sports Medicine-Open](http://sportsmedicine-open.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/smoa/default.aspx","title":"Sports Medicine-Open","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Blood flow restriction training, High-load resistance training, Training effect, Strength, Hypertrophy, Training status, Protocol characteristics, Test specificity, Meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-2987684/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2987684/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlthough, it has been examined whether there are similar magnitudes of muscle strength and hypertrophy adaptations between low-load resistance training combined with blood-flow restriction training (BFR-RT) and high-load resistance training (HL-RT), some important potential moderators (e.g., age, gender, upper and lower limbs, frequency and duration etc.) have yet to be analyzed further. Furthermore, training status, specificity of muscle strength tests (dynamic versus isometric or isokinetic) and specificity of muscle mass assessments (locations of muscle hypertrophy assessments) seem to exhibit different effects on the results of the analysis. The role of these influencing factors, therefore, remains to be elucidated.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThe aim of this meta-analysis was to compare the effects of BFR- versus HL-RT on muscle adaptations, when considering the influence of population characteristics (training status, gender and age), protocol characteristics (upper or lower limbs, duration and frequency) and test specificity.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eStudies were searched through database based on the following inclusion criteria: (1) pre- and post-training assessment of muscular strength; (2) pre- and post-training assessment of muscular hypertrophy; (3) comparison of BFR-RT vs. HL-RT; (4) score\u0026thinsp;\u0026ge;\u0026thinsp;4 on PEDro scale; (5) means and standard deviations (or standard errors) are reported or allow estimation from graphs. In cases where the fifth criterion was not met, the data were requested directly from the authors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe main finding of the present study was that training status was an important influencing factor in the effects of BFR-RT. The trained individuals may gain greater muscle strength and hypertrophy with BFR-RT as compared to HL-RT. However, the results showed that the untrained individuals experienced similar muscle mass gains and superior muscle strength gains in with HL-RT compared to BFR-RT.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCompared to HL-RT, training status is an important factor influencing the effects of the BFR-RT, in which trained can obtain greater muscle strength and hypertrophy gains in BFR-RT, while untrained individuals can obtain greater strength gains and similar hypertrophy in HL-RT.\u003c/p\u003e","manuscriptTitle":"Potential Moderators of the Effects of Blood Flow Restriction Training on Muscle Strength and Hypertrophy: A Meta-Analysis Based on a Comparison with High-Load Resistance Training","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-21 15:33:23","doi":"10.21203/rs.3.rs-2987684/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2024-03-19T10:07:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-17T21:56:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Sports Medicine-Open","date":"2024-03-17T05:46:02+00:00","index":"","fulltext":""},{"type":"decision","content":"Minor Revision","date":"2024-02-13T00:49:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"sports-medicine-open","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"smoa","sideBox":"Learn more about [Sports Medicine-Open](http://sportsmedicine-open.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/smoa/default.aspx","title":"Sports Medicine-Open","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f501a11-5bb4-4961-9a26-37323774c9b6","owner":[],"postedDate":"March 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-22T00:32:47+00:00","versionOfRecord":{"articleIdentity":"rs-2987684","link":"https://doi.org/10.1186/s40798-024-00719-3","journal":{"identity":"sports-medicine-open","isVorOnly":false,"title":"Sports Medicine-Open"},"publishedOn":"2024-05-22 00:32:47","publishedOnDateReadable":"May 22nd, 2024"},"versionCreatedAt":"2024-03-21 15:33:23","video":"","vorDoi":"10.1186/s40798-024-00719-3","vorDoiUrl":"https://doi.org/10.1186/s40798-024-00719-3","workflowStages":[]},"version":"v1","identity":"rs-2987684","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2987684","identity":"rs-2987684","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}