Sports Medicine

, Volume 45, Issue 8, pp 1191–1205 | Cite as

The Optimal Load for Maximal Power Production During Lower-Body Resistance Exercises: A Meta-Analysis

  • Marco A. Soriano
  • Pedro Jiménez-Reyes
  • Matthew R. Rhea
  • Pedro J. MarínEmail author
Systematic Review



The development of muscular power is often a key focus of sports performance enhancement programs.


The purpose of this meta-analysis was to examine the effect of load on peak power during the squat, jump squat, power clean, and hang power clean, thus integrating the findings of various studies to provide the strength and conditioning professional with more reliable evidence upon which to base their program design.


A search of electronic databases [MEDLINE (SPORTDiscus), PubMed, Google Scholar, and Web of Science] was conducted to identify all publications up to 30 June 2014. Hedges’ g (95 % confidence interval) was estimated using a weighted random-effect model. A total of 27 studies with 468 subjects and 5766 effect sizes met the inclusion criterion and were included in the statistical analyses. Load in each study was labeled as one of three intensity zones: Zone 1 represented an average intensity ranging from 0 to 30 % of one repetition maximum (1RM); Zone 2 between 30 and 70 % of 1RM; and Zone 3 ≥70 % of 1RM.


These results showed different optimal loads for each exercise examined. Moderate loads (from >30 to <70 % of 1RM) appear to provide the optimal load for power production in the squat exercise. Lighter loads (≤30 % of 1RM) showed the highest peak power production in the jump squat. Heavier loads (≥70 % of 1RM) resulted in greater peak power production in the power clean and hang power clean.


Our meta-analysis of results from the published literature provides evidence for exercise-specific optimal loads for power production.


Publication Bias Peak Power Power Production Training Load Peak Power Output 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The authors declare they have no competing interests.

Conflicts of interest



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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Marco A. Soriano
    • 1
  • Pedro Jiménez-Reyes
    • 1
  • Matthew R. Rhea
    • 2
  • Pedro J. Marín
    • 3
    • 4
    Email author
  1. 1.Department of Physical Activity and Sport Sciences, Kinesiology and Biomechanics LaboratorySan Antonio Catholic University of MurciaMurciaSpain
  2. 2.Human Movement ProgramA. T. Still UniversityMesaUSA
  3. 3.Laboratory of PhysiologyEuropean University Miguel de CervantesValladolidSpain
  4. 4.CyMO Research InstituteValladolidSpain

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