Concurrent Aerobic and Strength Training for Performance in Soccer

Abstract

This chapter discusses the state of research on concurrent endurance and strength training in soccer. The first part of the chapter provides rationales for soccer as a concurrent modality and describes the physiological and performance adaptations to concurrent training in soccer. Thereafter, some considerations for training programs design will be provided, by addressing the role of the different concurrent training variables (within session order, between mode recovery length and intensity and volume) on performance outcomes and likely role on injury prevention. Because each team and athlete constitute a specific case, coaches should adopt an integrated approach when considering the concurrent training design taking into account as many related factors as possible (e.g. individual player profile, period of the season, player returning from injury or not).

Appendix: Analysis and Interpretation of Results

To evaluate the magnitude of the effects, percent change was calculated for each dependent variable for each study using the below equation:

$$ \overline{\mathrm{x}} $$

(Eq. 27.1)

where M_post was the post-training mean and M_pre the baseline mean. ESs (Effect size) were computed to present standardized training-related effect on the outcome variables [74]. The different ES within individual studies were calculated with Cohen’s d, by dividing the raw ES (difference in means) by the pooled standard deviations, as proposed by Bornstein et al. [75] as followed (Eq. 27.1):

$$ \overline{\mathrm{x}} $$

(Eq. 27.2)

SD_pooled is the pooled SD of the measurements and was calculated as follows (Eq. 27.2):

$$ \overline{\mathrm{x}} $$

(Eq. 27.3)

where $\overline{\mathrm{x}}$ is the standard deviation of the performance test completed before the training intervention and $\overline{\mathrm{x}}$ is the standard deviation of the performance test completed after the training intervention. To account for possible overestimation of the true population, ESs were corrected accounting for the magnitude of the sample size of each study [76]. Therefore, a correction factor (CF) was calculated as proposed by Hedges and Olkin [76].

$$ \overline{\mathrm{x}} $$

(Eq. 27.4)

where df = n − 1. The corrected ES was calculated as follows:

$$ \overline{\mathrm{x}} $$

(Eq. 27.5)

Threshold values for ES_c were defined as trivial (<0.2), small (0.2–0.6), moderate (0.6–1.2), large (1.2–2.0), and very large (>2.0) [77].

Training efficiency for each dependent variable was calculated as follows:

$$ \overline{\mathrm{x}} $$

( Eq. 27.6)