Effect of Strength Training Programs in Middle- and Long-Distance Runners' Economy at Different Running Speeds: A Systematic Review with Meta-analysis

Abstract

Background: Running economy is defined as the energy demand at submaximal running speed, a key determinant of overall running performance. Strength training can improve running economy, although the magnitude of its effect may depend on factors such as the strength training method and the speed at which running economy is assessed.

Aim: To compare the effect of different strength training methods (e.g., high loads, plyometric, combined methods) on the running economy in middle- and long-distance runners, over different running speeds, through a systematic review with meta-analysis.

Methods: A systematic search was conducted across several electronic databases including Web of Science, PubMed, SPORTDiscus, and SCOPUS. Using different keywords and Boolean operators for the search, all articles indexed up to November 2022 were considered for inclusion. In addition, the PICOS criteria were applied: Population: middle- and long-distance runners, without restriction on sex or training/competitive level; Intervention: application of a strength training method for ≥ 3 weeks (i.e., high loads (≥ 80% of one repetition maximum); submaximal loads [40-79% of one repetition maximum); plyometric; isometric; combined methods (i.e., two or more methods); Comparator: control group that performed endurance running training but did not receive strength training or received it with low loads (< 40% of one repetition maximum); Outcome: running economy, measured before and after a strength training intervention programme; Study design: randomized and non-randomized controlled studies. Certainty of evidence was assessed with the GRADE approach. A three-level random-effects meta-analysis and moderator analysis were performed using R software (version 4.2.1).

Results: The certainty of the evidence was found to be moderate for high load training, submaximal load training, plyometric training and isometric training methods and low for combined methods. The studies included 195 moderately trained, 272 well trained, and 185 highly trained athletes. The strength training programmes were between 6 and 24 weeks' duration, with one to four sessions executed per week. The high load and combined methods induced small (ES = - 0.266, p = 0.039) and moderate (ES = - 0.426, p = 0.018) improvements in running economy at speeds from 8.64 to 17.85 km/h and 10.00 to 14.45 km/h, respectively. Plyometric training improved running economy at speeds ≤ 12.00 km/h (small effect, ES = - 0.307, p = 0.028, β₁ = 0.470, p = 0.017). Compared to control groups, no improvement in running economy (assessed speed: 10.00 to 15.28 and 9.75 to 16.00 km/h, respectively) was noted after either submaximal or isometric strength training (all, p > 0.131). The moderator analyses showed that running speed (β₁ = - 0.117, p = 0.027) and VO₂max (β₁ = - 0.040, p = 0.020) modulated the effect of high load strength training on running economy (i.e., greater improvements at higher speeds and higher VO₂max).

Conclusions: Compared to a control condition, strength training with high loads, plyometric training, and a combination of strength training methods may improve running economy in middle- and long-distance runners. Other methods such as submaximal load training and isometric strength training seem less effective to improve running economy in this population. Of note, the data derived from this systematic review suggest that although both high load training and plyometric training may improve running economy, plyometric training might be effective at lower speeds (i.e., ≤ 12.00 km/h) and high load strength training might be particularly effective in improving running economy (i) in athletes with a high VO₂max, and (ii) at high running speeds.

Protocol registration: The original protocol was registered ( https://osf.io/gyeku ) at the Open Science Framework.

Fig. 1

Flow diagram of the study selection process. *Studies found from notifications of new studies found in the search strategy in the different databases. **Studies found in the reference lists of articles, reviews, systematic reviews, and meta-analyses retrieved from our search strategy

Fig. 2

Forest plots of the included studies for high load training and its effect on running economy. The black squares represent the mean observed effect size of the study, the size of square represent the weight of the study and the black lines represent the 95% confidence interval. The grey line represents a 95% confidence interval based on the sampling variance of individual observed effect sizes in a study, and its thickness is proportional to the number of effect sizes reported within studies. J number of effect sizes within studies

Fig. 3

Forest plots of the included studies for combined methods training and its effect on running economy. The black squares represent the mean observed effect size of the study, the size of square represent the weight of the study and the black lines represent the 95% confidence interval. The grey line represents a 95% confidence interval based on the sampling variance of individual observed effect sizes in a study, and its thickness is proportional to the number of effect sizes reported within studies. J number of effect sizes within studies

Fig. 4

Forest plots of the included studies for submaximal load training and its effect on running economy. The black squares represent the mean observed effect size of the study, the size of square represent the weight of the study and the black lines represent the 95% confidence interval. The grey line represents a 95% confidence interval based on the sampling variance of individual observed effect sizes in a study, and its thickness is proportional to the number of effect sizes reported within studies. J number of effect sizes within studies

Fig. 5

Forest plots of the included studies for plyometric training and its effect on running economy. The black squares represent the mean observed effect size of the study, the size of square represent the weight of the study and the black lines represent the 95% confidence interval. The grey line represents a 95% confidence interval based on the sampling variance of individual observed effect sizes in a study, and its thickness is proportional to the number of effect sizes reported within studies. J number of effect sizes within studies

Fig. 6

Forest plots of the included studies for isometric training and its effect on running economy. The black squares represent the mean observed effect size of the study, the size of square represent the weight of the study and the black lines represent the 95% confidence interval. The grey line represents a 95% confidence interval based on the sampling variance of individual observed effect sizes in a study, and its thickness is proportional to the number of effect sizes reported within studies. J number of effect sizes within studies

Fig. 7

Meta-regression analysis for the effect of absolute speed (continuous) on running economy effect size in high load training. ES effect size, J number of effect sizes within studies

Fig. 8

Meta-regression analysis for the effect of initial VO₂max on running economy effect size in high load training. ES effect size, J number of effect sizes within studies

Fig. 9

Sub-group analysis for the effect of absolute speed (categorical) on running economy effect size in plyometric training. ES effect size, J number of effect sizes within studies