Physical Performance Sports Research Center (PPSRC), Universidad Pablo de Olavide, 41704, Seville, Spain.
Exercise and Rehabilitation Sciences Institute, School of Physical Therapy, Faculty of Rehabilitation Sciences, Universidad Andres Bello, 7591538, Santiago, Chile.
Sports Med. 2024 Apr;54(4):895-932. doi: 10.1007/s40279-023-01978-y. Epub 2024 Jan 2.
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.
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.
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).
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, β = 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 (β = - 0.117, p = 0.027) and VOmax (β = - 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 VOmax).
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 VOmax, and (ii) at high running speeds.
The original protocol was registered ( https://osf.io/gyeku ) at the Open Science Framework.
跑动经济性是指在次最大跑步速度下的能量需求,是整体跑步表现的关键决定因素。力量训练可以提高跑动经济性,尽管其效果的大小可能取决于力量训练方法和评估跑动经济性的速度等因素。
通过系统评价和荟萃分析,比较不同力量训练方法(例如,高负荷、增强式、综合方法)对中长跑运动员在不同跑步速度下的跑动经济性的影响。
通过 Web of Science、PubMed、SPORTDiscus 和 SCOPUS 等多个电子数据库进行系统搜索。使用不同的关键词和布尔运算符进行搜索,将截至 2022 年 11 月索引的所有文章都考虑纳入。此外,还应用了 PICOS 标准:人群:中长跑运动员,不限制性别或训练/竞技水平;干预:应用力量训练方法≥3 周(即高负荷(≥80%的一次重复最大值);亚最大负荷[40-79%的一次重复最大值];增强式;等长;综合方法(即两种或更多种方法);对照:进行耐力跑步训练但未接受力量训练或接受低负荷(<40%一次重复最大值)的对照组;结局:在力量训练干预计划前后测量跑动经济性;研究设计:随机和非随机对照研究。使用 GRADE 方法评估证据的确定性。使用 R 软件(版本 4.2.1)进行三级随机效应荟萃分析和调节分析。
高负荷训练、亚最大负荷训练、增强式训练和等长训练方法的证据确定性被认为是中度,而综合方法的证据确定性则较低。研究纳入了 195 名中度训练、272 名良好训练和 185 名高度训练的运动员。力量训练计划持续 6 至 24 周,每周进行 1 至 4 次训练。高负荷和综合方法在 8.64 至 17.85km/h 和 10.00 至 14.45km/h 的速度下分别产生较小(ES=-0.266,p=0.039)和中等(ES=-0.426,p=0.018)的跑动经济性改善。增强式训练在速度≤12.00km/h 时改善了跑动经济性(小效应,ES=-0.307,p=0.028,β=0.470,p=0.017)。与对照组相比,亚最大负荷或等长力量训练后,在 10.00 至 15.28 和 9.75 至 16.00km/h 的速度下,均未观察到跑动经济性的改善(均,p>0.131)。调节分析表明,跑步速度(β=-0.117,p=0.027)和最大摄氧量(β=-0.040,p=0.020)调节了高负荷力量训练对跑动经济性的影响(即,在更高的速度和更高的最大摄氧量下,改善更大)。
与对照组相比,高负荷、增强式和力量训练方法的综合应用可能会提高中长跑运动员的跑动经济性。其他方法,如亚最大负荷训练和等长力量训练,似乎对改善该人群的跑动经济性效果较差。值得注意的是,本系统评价中的数据表明,虽然高负荷训练和增强式训练都可能提高跑动经济性,但增强式训练可能在较低速度(即≤12.00km/h)下有效,而高负荷力量训练可能在提高跑动经济性方面特别有效:(i)在最大摄氧量较高的运动员中,以及(ii)在较高的跑步速度下。
原始方案已在开放科学框架(https://osf.io/gyeku)注册。