Skip to main content
Log in

Acute Effects of Cluster and Rest Redistribution Set Structures on Mechanical, Metabolic, and Perceptual Fatigue During and After Resistance Training: A Systematic Review and Meta-analysis

  • Systematic Review
  • Published:
Sports Medicine Aims and scope Submit manuscript

Abstract

Background

The alteration of individual sets during resistance training (RT) is often used to allow for greater velocity and power outputs, reduce metabolite accumulation such as lactate and also reduce perceived exertion which can ultimately affect the resultant training adaptations. However, there are inconsistencies in the current body of evidence regarding the magnitude of the effects of alternative set structures (i.e., cluster sets and rest redistribution) on these acute mechanical, metabolic, and perceptual responses during and after RT.

Objective

This study aimed to systematically review and meta-analyse current evidence on the differences between traditional and alternative (cluster and rest redistribution) set structures on acute mechanical, metabolic, and perceptual responses during and after RT, and to discuss potential reasons for the disparities noted in the literature.

Methods

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and five databases were searched until June 2019. Studies were included when they were written in English and compared at least one acute mechanical, metabolic, or perceptual response between traditional, cluster or traditional and rest redistribution set structures in healthy adults. Random-effects meta-analyses and meta-regressions were performed where possible.

Results

Thirty-two studies were included. Pooled results revealed that alternative set structures allowed for greater absolute mean [standardized mean difference (SMD) = 0.60] and peak velocity (SMD = 0.41), and mean (SMD = 0.33) and peak power (SMD = 0.38) during RT. In addition, alternative set structures were also highly effective at mitigating a decline in velocity and power variables during (SMD = 0.83–1.97) and after RT (SMD = 0.58) as well as reducing lactate accumulation (SMD = 1.61) and perceived exertion (SMD = 0.81). These effects of alternative set structures on velocity and power decline and maintenance during RT were considerably larger than for absolute velocity and power variables. Sub-group analyses controlling for each alternative set structure independently showed that cluster sets were generally more effective than rest redistribution in alleviating mechanical, metabolic, and perceptual markers of fatigue.

Conclusion

Alternative set structures can reduce mechanical fatigue, perceptual exertion, and metabolic stress during and after RT. However, fundamental differences in the amount of total rest time results in cluster sets generally being more effective than rest redistribution in alleviating fatigue-induced changes during RT, which highlights the importance of classifying them independently in research and in practice. Additionally, absolute values (i.e., mean session velocity or power), as well as decline and maintenance of the mechanical outcomes during RT, and residual mechanical fatigue after RT, are all affected differently by alternative set structures, suggesting that these variables may provide distinct information that can inform future training decisions.

Protocol Registration

The original protocol was prospectively registered (CRD42019138954) with the PROSPERO (International Prospective Register of Systematic Reviews).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Suchomel TJ, Nimphius S, Bellon CR, Stone MH. The importance of muscular strength: training considerations. Sports Med. 2018;48(4):765–85.

    PubMed  Google Scholar 

  2. Suchomel TJ, Nimphius S, Stone MH. The importance of muscular strength in athletic performance. Sports Med. 2016;46(10):1419–49.

    PubMed  Google Scholar 

  3. Kraemer WJ, Ratamess NA, French DN. Resistance training for health and performance. Curr Sports Med Rep. 2002;1(3):165–71.

    PubMed  Google Scholar 

  4. O'Connor PJ, Herring MP, Caravalho A. Mental health benefits of strength training in adults. Am J Lifestyle Med. 2010;4(5):377–96.

    Google Scholar 

  5. Feigenbaum MS, Pollock ML. Prescription of resistance training for health and disease. Med Sci Sports Exerc. 1999;31(1):38–45.

    CAS  PubMed  Google Scholar 

  6. Grgic J, Lazinica B, Mikulic P, Krieger JW, Schoenfeld BJ. The effects of short versus long inter-set rest intervals in resistance training on measures of muscle hypertrophy: a systematic review. Eur J Sport Sci. 2017;17(8):983–93.

    PubMed  Google Scholar 

  7. Grgic J, Schoenfeld BJ, Davies TB, Lazinica B, Krieger JW, Pedisic Z. Effect of resistance training frequency on gains in muscular strength: a systematic review and meta-analysis. Sports Med. 2018;48(5):1207–20.

    PubMed  Google Scholar 

  8. Grgic J, Schoenfeld BJ, Skrepnik M, Davies TB, Mikulic P. Effects of rest interval duration in resistance training on measures of muscular strength: a systematic review. Sports Med. 2018;48(1):137–51.

    PubMed  Google Scholar 

  9. García-Ramos A, González-Hernández JM, Baños-Pelegrín E, Castaño-Zambudio A, Capelo-Ramírez F, Boullosa D, et al. Mechanical and metabolic responses to traditional and cluster set configurations in the bench press exercise. J Strength Cond Res. 2020;34(3):663–70.

    PubMed  Google Scholar 

  10. Torrejón A, Janicijevic D, Haff GG, García-Ramos A. Acute effects of different set configurations during a strength-oriented resistance training session on barbell velocity and the force–velocity relationship in resistance-trained males and females. Eur J Appl Physiol. 2019;119(6):1409–17.

    PubMed  Google Scholar 

  11. Hardee JP, Triplett NT, Utter AC, Zwetsloot KA, Mcbride JM. Effect of interrepetition rest on power output in the power clean. J Strength Cond Res. 2012;26(4):883–9.

    PubMed  Google Scholar 

  12. Tufano JJ, Conlon JA, Nimphius S, Brown LE, Seitz LB, Williamson BD, et al. Maintenance of velocity and power with cluster sets during high-volume back squats. Int J Sports Physiol Perform. 2016;11(7):885–92.

    PubMed  Google Scholar 

  13. Tufano JJ, Conlon JA, Nimphius S, Brown LE, Banyard HG, Williamson BD, et al. Cluster sets: permitting greater mechanical stress without decreasing relative velocity. Int J Sports Physiol Perform. 2017;12(4):463–9.

    PubMed  Google Scholar 

  14. Tufano JJ, Conlon JA, Nimphius S, Brown LE, Petkovic A, Frick J, et al. Effects of cluster sets and rest-redistribution on mechanical responses to back squats in trained men. J Hum Kinet. 2017;58(1):35–433.

    PubMed  PubMed Central  Google Scholar 

  15. Tufano JJ, Conlon JA, Nimphius S, Oliver JM, Kreutzer A, Haff GG. Different cluster sets result in similar metabolic, endocrine, and perceptual responses in trained men. J Strength Cond Res. 2019;33(2):346–54.

    PubMed  Google Scholar 

  16. Mora-Custodio R, Rodríguez-Rosell D, Yáñez-García JM, Sánchez-Moreno M, Pareja-Blanco F, González-Badillo JJ. Effect of different inter-repetition rest intervals across four load intensities on velocity loss and blood lactate concentration during full squat exercise. J Sports Sci. 2018;36(24):2856–64.

    PubMed  Google Scholar 

  17. Iglesias-Soler E, Carballeira E, Sánchez-Otero T, Mayo X, Jiménez A, Chapman ML. Acute effects of distribution of rest between repetitions. Int J Sports Med. 2012;33(5):351–8.

    CAS  PubMed  Google Scholar 

  18. González-Hernández JM, García-Ramos A, Castaño-Zambudio A, Capelo-Ramírez F, Marquez G, Boullosa D, et al. Mechanical, metabolic, and perceptual acute responses to different set configurations in full squat. J Strength Cond Res. 2020;34(6):1581–90.

    PubMed  Google Scholar 

  19. Jukic I, Tufano JJ. Shorter but more frequent rest periods: no effect on velocity and power compared to traditional sets not performed to failure. J Hum Kinet. 2019;66:257–68.

    PubMed  PubMed Central  Google Scholar 

  20. Hardee JP, Lawrence MM, Utter AC, Triplett NT, Zwetsloot KA, McBride JM. Effect of inter-repetition rest on ratings of perceived exertion during multiple sets of the power clean. Eur J Appl Physiol. 2012;112(8):3141–7.

    PubMed  Google Scholar 

  21. Mayo X, Iglesias-Soler E, Kingsley JD. Perceived exertion is affected by the submaximal set configuration used in resistance exercise. J Strength Cond Res. 2019;33(2):426–32.

    PubMed  Google Scholar 

  22. Mayo X, Iglesias-Soler E, Fernández-Del-Olmo M. Effects of set configuration of resistance exercise on perceived exertion. Percept Mot Skills. 2014;119(3):825–37.

    PubMed  Google Scholar 

  23. Jukic I, Tufano JJ. Acute effects of shorter but more frequent rest periods on mechanical and perceptual fatigue during a weightlifting derivative at different loads in strength-trained men. Sports Biomech. 2020. https://doi.org/10.1080/14763141.2020.1747530.

    Article  PubMed  Google Scholar 

  24. Morales-Artacho AJ, Padial P, García-Ramos A, Pérez-Castilla A, Feriche B. Influence of a cluster set configuration on the adaptations to short-term power training. J Strength Cond Res. 2018. https://doi.org/10.1519/jsc.0000000000001925.

    Article  PubMed  Google Scholar 

  25. Nicholson G, Ispoglou T, Bissas A. The impact of repetition mechanics on the adaptations resulting from strength-, hypertrophy-and cluster-type resistance training. Eur J Appl Physiol. 2016;116(10):1875–88.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Oliver JM, Jagim AR, Sanchez AC, Mardock MA, Kelly KA, Meredith HJ, et al. Greater gains in strength and power with intraset rest intervals in hypertrophic training. J Strength Cond Res. 2013;27(11):3116–311.

    PubMed  Google Scholar 

  27. Haff G, Nimphius S. Training principles for power. Strength Cond J. 2012;34(6):2–12.

    Google Scholar 

  28. Suchomel TJ, Comfort P, Lake JP. Enhancing the force-velocity profile of athletes using weightlifting derivatives. Strength Cond J. 2017;39(1):10–20.

    Google Scholar 

  29. Suchomel TJ, Comfort P, Stone MH. Weightlifting pulling derivatives: rationale for implementation and application. Sports Med. 2015;45(6):823–39.

    PubMed  Google Scholar 

  30. Pareja-Blanco F, Rodríguez-Rosell D, Sánchez-Medina L, Gorostiaga E, González-Badillo J. Effect of movement velocity during resistance training on neuromuscular performance. Int J Sports Med. 2014;35(11):916–24.

    CAS  PubMed  Google Scholar 

  31. Padulo J, Mignogna P, Mignardi S, Tonni F, D’ottavio S. Effect of different pushing speeds on bench press. Int J Sports Med. 2012;33(05):376–80.

    CAS  PubMed  Google Scholar 

  32. González-Badillo JJ, Rodríguez-Rosell D, Sánchez-Medina L, Gorostiaga EM, Pareja-Blanco F. Maximal intended velocity training induces greater gains in bench press performance than deliberately slower half-velocity training. Eur J Sports Sci. 2014;14(8):772–81.

    Google Scholar 

  33. Tufano JJ, Halaj M, Kampmiller T, Novosad A, Buzgo G. Cluster sets vs traditional sets: levelling out the playing field using a power-based threshold. PLoS ONE. 2018;13(11):e0208035.

    PubMed  PubMed Central  Google Scholar 

  34. Oliver JM, Jenke SC, Mata JD, Kreutzer A, Jones MT. Acute effect of cluster and traditional set configurations on myokines associated with hypertrophy. Int J Sports Med. 2016;37(13):1019–24.

    CAS  PubMed  Google Scholar 

  35. Bogdanis GC, Nevill ME, Boobis LH, Lakomy H. Contribution of phosphocreatine and aerobic metabolism to energy supply during repeated sprint exercise. J Appl Physiol. 1996;80(3):876–84.

    CAS  PubMed  Google Scholar 

  36. Bogdanis G, Nevill M, Lakomy H, Boobis L. Power output and muscle metabolism during and following recovery from 10 and 20 s of maximal sprint exercise in humans. Acta Physiol Scand. 1998;163(3):261–72.

    CAS  PubMed  Google Scholar 

  37. Bogdanis GC, Nevill ME, Boobis LH, Lakomy H, Nevill AM. Recovery of power output and muscle metabolites following 30 s of maximal sprint cycling in man. J Physiol. 1995;482(2):467–80.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Gorostiaga EM, Navarro-Amézqueta I, Calbet JA, Hellsten Y, Cusso R, Guerrero M, et al. Energy metabolism during repeated sets of leg press exercise leading to failure or not. PLoS ONE. 2012;7(7):e40621.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Gorostiaga EM, Navarro-Amézqueta I, Calbet JA, Sánchez-Medina L, Cusso R, Guerrero M, et al. Blood ammonia and lactate as markers of muscle metabolites during leg press exercise. J Strength Cond Res. 2014;28(10):2775–85.

    PubMed  Google Scholar 

  40. Gorostiaga EM, Navarro-Amézqueta I, Cusso R, Hellsten Y, Calbet JA, Guerrero M, et al. Anaerobic energy expenditure and mechanical efficiency during exhaustive leg press exercise. PLoS ONE. 2010;5(10):e13486.

    PubMed  PubMed Central  Google Scholar 

  41. Tufano JJ, Brown LE, Haff GG. Theoretical and practical aspects of different cluster set structures: a systematic review. J Strength Cond Res. 2017;31(3):848–67.

    PubMed  Google Scholar 

  42. Jukic I, Tufano JJ. Rest redistribution functions as a free and ad-hoc equivalent to commonly used velocity-based training thresholds during clean pulls at different loads. J Hum Kinet. 2019;68:5.

    PubMed  PubMed Central  Google Scholar 

  43. Merrigan JJ, Tufano JJ, Oliver JM, White JB, Fields JB, Jones MT. Reducing the loss of velocity and power in women athletes via rest redistribution. Int J Sports Physiol Perform. 2020;15(2):255–61.

    PubMed  Google Scholar 

  44. Tufano JJ, Omcirk D, Malecek J, Pisz A, Halaj M, Scott BR. Traditional sets vs rest-redistribution: a laboratory-controlled study of a specific cluster set configuration at fast and slow velocities. Appl Physiol Nutr Med. 2019;45(4):421–330.

    Google Scholar 

  45. Latella C, Teo W-P, Drinkwater EJ, Kendall K, Haff GG. The acute neuromuscular responses to cluster set resistance training: a systematic review and meta-analysis. Sports Med. 2019;49(12):1861–77.

    PubMed  PubMed Central  Google Scholar 

  46. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4(1):1.

    PubMed  PubMed Central  Google Scholar 

  47. Higgins JP, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD, et al. The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ. 2011;343:d5928.

    PubMed  PubMed Central  Google Scholar 

  48. Miller JR, Van Hooren B, Bishop C, Buckley JD, Willy RW, Fuller JT. A systematic review and meta-analysis of crossover studies comparing physiological, perceptual and performance measures between treadmill and overground running. Sports Med. 2019;49(5):763–82.

    PubMed  Google Scholar 

  49. Van Hooren B, Fuller JT, Buckley JD, Miller JR, Sewell K, Rao G, et al. Is motorized treadmill running biomechanically comparable to overground running? A systematic review and meta-analysis of cross-over studies. Sports Med. 2019;50(4):785–813.

    PubMed Central  Google Scholar 

  50. Davies TB, Kuang K, Orr R, Halaki M, Hackett D. Effect of movement velocity during resistance training on dynamic muscular strength: a systematic review and meta-analysis. Sports Med. 2017;47(8):1603–17.

    PubMed  Google Scholar 

  51. Atkins D, Best D, Briss P, Eccles M, Falck-Ytter Y, Flottorp S, et al. Grading quality of evidence and strength of recommendations. BMJ. 2004;328(7454):1490.

    PubMed  Google Scholar 

  52. Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010;36(3):1–48.

    Google Scholar 

  53. Schwarzer G, Carpenter JR, Rücker G. Meta-analysis with R. Berlin: Springer; 2015.

    Google Scholar 

  54. R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; (2018). https://www.R-project.org/. Accessed 26 May 2020.

  55. Veroniki AA, Jackson D, Viechtbauer W, Bender R, Bowden J, Knapp G, et al. Methods to estimate the between-study variance and its uncertainty in meta-analysis. Res Synth Methods. 2016;7(1):55–79.

    PubMed  Google Scholar 

  56. Borenstein M, Hedges LV, Higgins JP, Rothstein HR. Introduction to meta-analysis. New York: Wiley; 2011.

    Google Scholar 

  57. Elbourne DR, Altman DG, Higgins JP, Curtin F, Worthington HV, Vail A. Meta-analyses involving cross-over trials: methodological issues. Int J Epidemiol. 2002;31(1):140–9. https://doi.org/10.1093/ije/31.1.140.

    Article  PubMed  Google Scholar 

  58. Cohen J. The concepts of power analysis. Statistical power analysis for the behavioral sciences. Hillsdale: L. Erlbaum Associates; 1988. p. 1–17.

    Google Scholar 

  59. Fu R, Gartlehner G, Grant M, Shamliyan T, Sedrakyan A, Wilt TJ, et al. Conducting quantitative synthesis when comparing medical interventions: AHRQ and the Effective Health Care Program. J Clin Epidemiol. 2011;64(11):1187–97.

    PubMed  Google Scholar 

  60. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–60.

    PubMed  PubMed Central  Google Scholar 

  61. Sterne JA, Egger M, Moher D. Chapter 10: Addressing reporting biases. In: Higgins JPT, Green S, editors. Conchrane handbook for systematic reviews of interventions. Chichester: Wiley; 2008. p. 297–333.

    Google Scholar 

  62. Morales-Artacho AJ, García-Ramos A, Pérez-Castilla A, Padial P, Gomez AM, Peinado AM, et al. Muscle activation during power-oriented resistance training: continuous vs. cluster set configurations. J Strength Cond Res. 2018. https://doi.org/10.1519/JSC.0000000000002811.

    Article  PubMed  Google Scholar 

  63. Kurt C, Kafkas ME, Kurtdere A, Selalmaz O. Influence of traditional and cluster set plyometric warm-ups on reactive strength index and leg stiffness in male rugby players. Isokinet Exerc Sci. 2018;26(3):237–44.

    Google Scholar 

  64. Koefoed N, Lerche M, Jensen BK, Kjær PIA, Dam S, Horslev R, et al. Peak power output in loaded jump squat exercise is affected by set structure. J Exerc Sci. 2018;11(1):776–84.

    Google Scholar 

  65. Hansen KT, Cronin JB, Newton MJ. The effect of cluster loading on force, velocity, and power during ballistic jump squat training. Int J Sports Physiol Perform. 2011;6(4):455–68.

    PubMed  Google Scholar 

  66. Cormie P, McCaulley GO, McBride JM. Power versus strength-power jump squat training: influence on the load-power relationship. Med Sci Sports Exerc. 2007;39(6):996–1003.

    PubMed  Google Scholar 

  67. Hardee JP, Lawrence MM, Zwetsloot KA, Triplett NT, Utter AC, McBride JM. Effect of cluster set configurations on power clean technique. J Sports Sci. 2013;31(5):488–96.

    PubMed  Google Scholar 

  68. Wagle JP, Cunanan AJ, Carroll KM, Sams ML, Wetmore A, Bingham GE, et al. Accentuated eccentric loading and cluster set configurations in the back squat: a kinetic and kinematic analysis. J Strength Cond Res. 2018. https://doi.org/10.1519/jsc.0000000000002677.

    Article  Google Scholar 

  69. Wetmore A, Wagle JP, Sams ML, Taber C, DeWeese BH, Sato K, et al. Cluster set loading in the back squat: kinetic and kinematic implications. J Strength Cond Res. 2019. https://doi.org/10.1519/JSC.0000000000002972.

    Article  PubMed  Google Scholar 

  70. Oliver JM, Kreutzer A, Jenke S, Phillips MD, Mitchell JB, Jones MT. Acute response to cluster sets in trained and untrained men. Eur J Appl Physiol. 2015;115(11):2383–93.

    PubMed  Google Scholar 

  71. Davies TB, Halaki M, Orr R, Helms ER, Hackett DA. Changes in bench press velocity and power after 8 weeks of high-load cluster- or traditional-set structures. J Strength Cond Res. 2019. https://doi.org/10.1519/jsc.0000000000003166.

    Article  PubMed  Google Scholar 

  72. Ho IMK, Luk JTC, Ngo JK, Wong DP. Effects of different intraset rest durations on lifting performance and self-perceived exertion during bench press exercise. J Strength Cond Res. 2019. https://doi.org/10.1519/jsc.0000000000003101.

    Article  Google Scholar 

  73. Iglesias-Soler E, Carballeira E, Sanchez-Otero T, Mayo X, Fernandez-Del-Olmo M. Performance of maximum number of repetitions with cluster-set configuration. Int J Sports Physiol Perform. 2014;9(4):637–42.

    PubMed  Google Scholar 

  74. Wagle JP, Taber CB, Carroll KM, Cunanan AJ, Sams ML, Wetmore A, et al. Repetition-to-repetition differences using cluster and accentuated eccentric loading in the back squat. Sports (Basel). 2018;6(3):59.

    Google Scholar 

  75. Girman JC, Jones MT, Matthews TD, Wood RJ. Acute effects of a cluster-set protocol on hormonal, metabolic and performance measures in resistance-trained males. Eur J Sport Sci. 2014;14(2):151–9.

    PubMed  Google Scholar 

  76. Moreno SD, Brown LE, Coburn JW, Judelson DA. Effect of cluster sets on plyometric jump power. J Strength Cond Res. 2014;28(9):2424–8.

    PubMed  Google Scholar 

  77. Cuevas-Aburto J, Jukic I, Chirosa-Ríos LJ, González Hernández J, Janicijevic D, Barboza-González P, et al. Effect of traditional, cluster, and rest redistribution set configurations on neuromuscular and perceptual responses during strength-oriented resistance training. J Strength Cond Res. 2020. https://doi.org/10.1519/JSC.0000000000003658.

    Article  PubMed  Google Scholar 

  78. Cuevas-Aburto J, Jukic I, González Hernández J, Janicijevic D, Barboza-González P, Chirosa L, et al. Effect of resistance training programs differing in the set configuration on maximal strength and explosive actions performance. Int J Sports Physiol Perform. 2020. [published ahead of print].

  79. Sanchez-Medina L, González-Badillo JJ. Velocity loss as an indicator of neuromuscular fatigue during resistance training. Med Sci Sports Exerc. 2011;43(9):1725–34.

    PubMed  Google Scholar 

  80. Jimenez-Reyes P, Pareja-Blanco F, Cuadrado-Peñafiel V, Morcillo J, Párraga J, González-Badillo J. Mechanical, metabolic and perceptual response during sprint training. Int J Sports Med. 2016;37(10):807–12.

    CAS  PubMed  Google Scholar 

  81. Morcillo JA, Jiménez-Reyes P, Cuadrado-Peñafiel V, Lozano E, Ortega-Becerra M, Párraga J. Relationships between repeated sprint ability, mechanical parameters, and blood metabolites in professional soccer players. J Strength Cond Res. 2015;29(6):1673–82.

    PubMed  Google Scholar 

  82. Lagally KM, Robertson RJ, Gallagher KI, Goss FL, Jakicic JM, Lephart SM, et al. Perceived exertion, electromyography, and blood lactate during acute bouts of resistance exercise. Med Sci Sports Exerc. 2002;34(3):552–9.

    PubMed  Google Scholar 

  83. Oliver JM, Kreutzer A, Jenke SC, Phillips MD, Mitchell JB, Jones MT. Velocity drives greater power observed during back squat using cluster sets. J Strength Cond Res. 2016;30(1):235–43.

    PubMed  Google Scholar 

  84. Stone JD, King AC, Goto S, Mata JD, Hannon J, Garrison JC, et al. Joint-level analyses of the back squat with and without intraset rest. Int J Sports Physiol Perform. 2019;14(5):583–9.

    PubMed  Google Scholar 

  85. Singh F, Foster C, Tod D, McGuigan MR. Monitoring different types of resistance training using session rating of perceived exertion. Int J Sports Physiol Perform. 2007;2(1):34–45.

    PubMed  Google Scholar 

  86. Vernon A, Joyce C, Banyard HG. Readiness to train: Return to baseline strength and velocity following strength or power training. Int J Sports Sci Coach. 2020. https://doi.org/10.1177/1747954119900120.

    Article  Google Scholar 

  87. Weakley J, Ramirez-Lopez C, McLaren S, Dalton-Barron N, Weaving D, Jones B, et al. The effects of 10%, 20%, and 30% velocity loss thresholds on kinetic, kinematic, and repetition characteristics during the barbell back squat. Int J Sports Physiol Perform. 2019. https://doi.org/10.1123/ijspp.2018-1008.

    Article  PubMed  Google Scholar 

  88. González-Badillo JJ, Rodríguez-Rosell D, Sánchez-Medina L, Ribas J, López-López C, Mora-Custodio R, et al. Short-term recovery following resistance exercise leading or not to failure. Int J Sports Med. 2016;37(04):295–304.

    PubMed  Google Scholar 

  89. Pareja-Blanco F, Rodríguez-Rosell D, Aagaard P, Sánchez-Medina L, Ribas-Serna J, Mora-Custodio R, et al. Time course of recovery from resistance exercise with different set configurations. J Strength Cond Res. 2018. https://doi.org/10.1519/JSC.0000000000002756.

    Article  PubMed  Google Scholar 

  90. Pareja-Blanco F, Villalba-Fernández A, Cornejo-Daza PJ, Sánchez-Valdepeñas J, González-Badillo JJ. Time course of recovery following resistance exercise with different loading magnitudes and velocity loss in the set. Sports. 2019;7(3):59.

    PubMed Central  Google Scholar 

  91. González-Badillo JJ, Pareja-Blanco F, Rodríguez-Rosell D, Abad-Herencia JL, del Ojo-López JJ, Sánchez-Medina L. Effects of velocity-based resistance training on young soccer players of different ages. J Strength Cond Res. 2015;29(5):1329–38.

    PubMed  Google Scholar 

  92. Pareja-Blanco F, Sánchez-Medina L, Suárez-Arrones L, González-Badillo JJ. Effects of velocity loss during resistance training on performance in professional soccer players. Int J Sports Physiol. 2017;12(4):512–9.

    Google Scholar 

  93. Pareja-Blanco F, Rodríguez-Rosell D, Sánchez-Medina L, Sanchis-Moysi J, Dorado C, Mora-Custodio R, et al. Effects of velocity loss during resistance training on athletic performance, strength gains and muscle adaptations. Scand J Sci Med Sports. 2017;27(7):724–35.

    CAS  Google Scholar 

  94. Schoenfeld BJ, Grgic J, Ogborn D, Krieger JW. Strength and hypertrophy adaptations between low vs. high-load resistance training: a systematic review and meta-analysis. J Strength Cond Res. 2017;31(12):3508–23.

    PubMed  Google Scholar 

  95. Ralston GW, Kilgore L, Wyatt FB, Baker JS. The effect of weekly set volume on strength gain: a meta-analysis. Sports Med. 2017;47(12):2585–601.

    PubMed  PubMed Central  Google Scholar 

  96. Mangine GT, Hoffman JR, Gonzalez AM, Townsend JR, Wells AJ, Jajtner AR, et al. The effect of training volume and intensity on improvements in muscular strength and size in resistance-trained men. Physiol Rep. 2015;3(8):e12472.

    PubMed  PubMed Central  Google Scholar 

  97. Denton J, Cronin JB. Kinematic, kinetic, and blood lactate profiles of continuous and intraset rest loading schemes. J Strength Cond Res. 2006;20(3):528–34.

    PubMed  Google Scholar 

  98. Joy J, Oliver J, McCleary S, Lowery R, Wilson J. Power output and electromyography activity of the back squat exercise with cluster sets. J Sports Sci. 2013;1:37–45.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ivan Jukic.

Ethics declarations

Author contributions

IJ performed the meta-analysis and meta-regression and wrote the first draft of the manuscript. All authors edited and revised the manuscript and approved the final version of the manuscript.

Funding

No external sources of funding were used to assist in the preparation of this article.

Conflict of interest

Ivan Jukic, Amador García Ramos, Eric Helms, Michael McGuigan and James Tufano declare that they have no conflicts of interest relevant to the content of this review.

Availability of data and material

The datasets generated during and/or analysed during the current review are available from the corresponding author on reasonable request.

Ethics approval

Not applicable.

Consent

Not applicable.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jukic, I., Ramos, A.G., Helms, E.R. et al. Acute Effects of Cluster and Rest Redistribution Set Structures on Mechanical, Metabolic, and Perceptual Fatigue During and After Resistance Training: A Systematic Review and Meta-analysis. Sports Med 50, 2209–2236 (2020). https://doi.org/10.1007/s40279-020-01344-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40279-020-01344-2

Navigation