The countermovement jump to monitor neuromuscular status: A meta-analysis.
OBJECTIVE
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.


METHODS
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.
University of São Paulo, School of Physical Education and Sport-Laboratory of Biomechanics, Brazil; Auckland University of Technology, Sport Performance Research Institute, New Zealand. Electronic address: claudinojgo@usp.br.
João Gustavo
JG
Claudino
Auckland University of Technology, Sport Performance Research Institute, New Zealand; Edith Cowan University, School of Exercise and Health Sciences, Australia.
John
J
Cronin
University of São Paulo, School of Physical Education and Sport-Laboratory of Biomechanics, Brazil.
Bruno
B
Mezêncio
Auckland University of Technology, Sport Performance Research Institute, New Zealand.
Daniel Travis
DT
McMaster
Auckland University of Technology, Sport Performance Research Institute, New Zealand; Edith Cowan University, School of Exercise and Health Sciences, Australia.
Michael
M
McGuigan
University of São Paulo, School of Physical Education and Sport-Laboratory of Adaptations to Strength Training, Brazil.
Valmor
V
Tricoli
University of São Paulo, School of Physical Education and Sport-Laboratory of Biomechanics, Brazil.
Alberto Carlos
AC
Amadio
University of São Paulo, School of Physical Education and Sport-Laboratory of Biomechanics, Brazil.
Julio Cerca
JC
Serrão
D000222:Adaptation, Physiological / Q000502:physiology*; D054874:Athletic Performance / Q000502:physiology*; D001696:Biomechanical Phenomena; D005080:Exercise Test / Q000379:methods*; D006801:Humans; D018763:Muscle Fatigue; D053580:Muscle Strength; D061886:Neuromuscular Monitoring / Q000379:methods*; D012680:Sensitivity and Specificity
Athletes; Prevention and control; Sports performance