The countermovement jump to monitor neuromuscular status: A meta-analysis
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Published on: June 10, 2025
João Gustavo Claudino1, John Cronin2, Bruno Mezêncio3
1University of São Paulo, School of Physical Education and Sport-Laboratory of Biomechanics, Brazil; Auckland University of Technology, Sport Performance Research Institute, New Zealand.
Objectives:
The primary objective of this meta-analysis was to compare countermovement jump (CMJ) performance in studies that reported the highest value as opposed to average value for the purposes of monitoring neuromuscular status (i.e., fatigue and supercompensation). The secondary aim was to determine the sensitivity of the dependent variables.
Design:
Systematic review with meta-analysis.
Methods:
The meta-analysis was conducted on the highest or average of a number of CMJ variables. Multiple literature searches were undertaken in Pubmed, Scopus, and Web of Science to identify articles utilizing CMJ to monitor training status. Effect sizes (ES) with 95% confidence interval (95% CI) were calculated using the mean and standard deviation of the pre- and post-testing data. The coefficient of variation (CV) with 95% CI was also calculated to assess the level of instability of each variable. Heterogeneity was assessed using a random-effects model.
Results:
151 articles were included providing a total of 531 ESs for the meta-analyses; 85.4% of articles used highest CMJ height, 13.2% used average and 1.3% used both when reporting changes in CMJ performance. Based on the meta-analysis, average CMJ height was more sensitive than highest CMJ height in detecting CMJ fatigue and supercompensation. Furthermore, other CMJ variables such as peak power, mean power, peak velocity, peak force, mean impulse, and power were sensitive in tracking the supercompensation effects of training.
Conclusions:
The average CMJ height was more sensitive than highest CMJ height in monitoring neuromuscular status; however, further investigation is needed to determine the sensitivity of other CMJ performance variables.
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