Sustained running performance is reliant on a complex interaction of factors that lead to efficient muscular work and should result in a fast and effective running gait (25). Among the factors that may predict middle- and long-distance running performance, running economy (RE), commonly defined as the steady-state V̇o2 required at a given submaximal speed, has garnered the most attention over the last decade, although it is often still referred to as “being relatively ignored in the scientific literature” (12).
Traditionally, biomechanical factors (30,50), muscle fiber distribution (7,38), age (28), sex, (8) and anthropometric factors (32) have been found to account for interindividual variability in RE. However, RE is also largely influenced by training strategies, including a wide range of forms of strength training such as low-resistance training, high-resistance training, explosive training, and plyometric training (3). These different strength training modalities have been reported to improve RE not only in recreational runners but also in moderately trained and highly trained runners (3,4,58).
Running economy improvements, a consequence of strength training interventions, have been attributed to improved lower-limb coordination and muscle coactivation, which would ultimately increase muscle stiffness and decrease ground contact times (37). Similarly, strength training interventions have been suggested to increase type I and type II fibers' strength (53), resulting in less motor unit activation to produce a given force (3). This increase in strength may also improve biomechanical efficiency and muscle recruitment patterns (43), thus allowing a runner to run more efficiently at a given running speed.
However, despite the body of evidence supporting the use of strength training to improve RE, it has been traditionally overlooked by long-distance runners and their coaches to the extent that runners competing in the 2008 US Olympic Marathon trials “included little strength training in their training programmes… and nearly half the runners did no strength training at all” (26). This may be a consequence of long-distance runners and their coaches being unaware of the potential benefits of strength training to improve RE and thus, performance.
Previous review articles on the effects of strength training programs on RE did not perform a meta-analysis because they only summarized the available data (3,4,58). Thus, the aim of this study was to systematically review the body of scientific literature for original research and perform a meta-analysis, addressing the effects of strength training on RE in highly trained runners.
Experimental Approach to the Problem
A literature search was conducted on September 25, 2015. The following databases were searched: PubMed, SPORTDiscus, MEDLINE, and CINAHL (Cumulative Index to Nursing and Allied Health Literature). Databases were searched from inception to September 2015, with no language limitation. Abstracts and citations from scientific conferences were excluded.
In each database, the title, abstract, and keywords search fields were searched. The following keywords, combined with Boolean operators (AND and OR), were used: “running economy,” “cost of running,” “strength training,” “resistance training,” “weight training,” “weight lifting,” “plyometric,” “sled training,” “resisted sprints,” and “jump.” No additional filters or search limitations were used.
Studies were eligible for further analysis if the following inclusion criteria were met: (a) participants were middle- or long-distance runners (studies with triathletes or any other kind of athletes were excluded); (b) participants had a V̇o2max value >60 ml·kg−1·min−1; (c) studies were controlled trials published in peer-reviewed journals; (d) studies analyzed strength training programs with a duration greater than 4 weeks; and (e) RE was measured before and after the strength training intervention.
Two independent observers reviewed the studies and then individually decided whether inclusion was appropriate. In the event of a disagreement, a third observer was consulted to determine the inclusion of the study. A flow chart of the search strategy and study selection is shown in Figure 1.
The Physiotherapy Evidence Database (PEDro) scale (34) and Oxford's levels of evidence (36) were used by 2 independent observers to assess the methodological quality of the articles included in the meta-analysis. Oxford's level of evidence ranges from 1a to 5, with 1a being systematic reviews of high-quality randomized controlled trials and 5 being expert opinions. The PEDro scale consists of 11 different items related to scientific rigor. Items 2–11 can be rated with 0 or 1, so the highest rate in the PEDro scale is 10 and the lowest is 0.
Standardized mean difference (SMD) with 95% confidence intervals (CIs) between strength training and control conditions were calculated with RevMan 5.3.5 for Mac using a random effects model. Mean and SDs for the outcome measures were present in each original article, and it was not necessary to contact the authors for further data. The significance for an overall effect was set at p ≤ 0.05. Heterogeneity of the analyzed studies was assessed using an I-squared test, setting the significance level at p < 0.01. If heterogeneity was significant, further analysis (removing studies to detect the potential source of heterogeneity) was performed. Also, the contribution (%) of each study to the overall combined effect of the intervention was computed as an inverse proportion of the within-study variance (20). Finally, effects of the interventions (strength training programs) were qualitatively assessed using the following threshold values for the SMD, which werespecifically designed for high-level athletes (40): <0.25, trivial; 0.25–0.50, small; 0.50–1.0, moderate; and >1.0, large.
The search strategy yielded 699 total citations as presented in Figure 1. After removing duplicates and reviewing the resultant 174 full-text articles, 5 studies met the inclusion criteria (33,37,51,52,54). Excluded studies had at least one of the following characteristics: (a) participants had V̇o2max values <60 ml·kg−1·min−1; (b) participants were not middle- or long-distance runners; (c) lack of a control group; (d) strength training interventions lasted less than 4 weeks; and (e) RE was not measured. Thus, the overall sample for the present meta-analysis resulted in 93 high-level middle- and long-distance runners with V̇o2max >60 ml·kg−1·min−1.
Level of Evidence and Quality of the Studies
Three of the 5 included studies had a level of evidence 1b (high-quality randomized controlled trials). The 2 remaining studies had a level of evidence 2b because participants were not randomly allocated into the intervention group or control group. Also, the mean score in the PEDro scale was 5.4, with values ranging from 5 to 6 (Table 1).
Characteristics of the Participants
A summary of participants' characteristics is presented in Table 2. The total number of participants was 93 (78 men and 15 women) with an age ranging from 17.3 to 29.8 years. Participants' V̇o2max ranged between 61.2 and 71.1 ml·kg−1·min−1. All participants competed in middle- and long-distance running events at national or international level.
Characteristics of the Training Programs
The characteristics of the training programs of each study are depicted in Table 3. Three studies (51,52,54) randomly allocated the participants into the intervention group or control group, whereas the other 2 studies (33,37) matched the groups for age and training level in a nonrandomized way. Training interventions ranged from 8 to 12 weeks: 2 studies used 8-week programs (33,54), 2 studies used 9-week programs (37,51), and 1 study used a 12-week program (52). The participants trained 3 times per week in 4 studies (33,37,51,54) and 2 times per week in the other study (52). Training duration ranged from 15 to 90 minutes, with 4 of the 5 studies having sessions longer than 30 minutes (33,37,51,52). One study (54) used high loads (85% of 1 repetition maximum [1RM]) and just the half-squat exercise for the intervention group. In contrast, 4 studies (33,37,51,52) used 4 resistance exercises with 1–3 sets of 4–10 repetitions at low and moderate intensities (40–70% 1RM) in combination with 2–6 unloaded plyometric exercises for a total of 30–200 jumps or 5–10 repetitions of 20–150 m sprints.
Effects of Strength Training on Running Economy
The average RE change was −2.32 ± 2.07 and 0.57 ± 2.48 ml·kg−1·min−1 for the intervention group and control group, respectively. The meta-analysis demonstrated an overall, significant, large beneficial effect of the strength training interventions on RE when compared with the control group (SMD [95% CI] = −1.43 [−2.23 to −0.64], Z = 3.53, p < 0.001). Four studies showed a large effect of the intervention (SMD >1.0) and another one showed a moderate effect (Figure 2).
The I-squared test showed a significant heterogeneity among the included studies (I2 = 61%, p = 0.03). However, further analysis showed that the removal of the study, by Paavolainen et al. (37), reduced the heterogeneity to 0 (I2 = 0%, p = 0.77), indicating that this study was the source of heterogeneity. In addition, the contribution of this particular study was the lowest of the 5 studies (13.5% vs. 20.3–24.1%). When removing the aforementioned study, the recalculated average RE change was −1.88 ± 2.31 and 0.51 ± 2.76 ml·kg−1·min−1 for the intervention group and control group, respectively. This resulted in an overall large, beneficial, and significant effect of the strength training interventions (SMD [95% CI] = −1.06 [−1.56 to −0.56], Z = 4.16, p < 0.001).
The present meta-analysis shows an overall large beneficial effect of the strength training interventions on RE in highly trained middle- and long-distance runners when compared with the control group. Four of the 5 included studies presented an absolute SMD greater than 1, which is considered a large effect when studying high-level athletes (40), and the fifth study showed a moderate to high effect. Moreover, the overall 95% CI ranged from −2.23 to −0.64 SMD, that is, it did not cross 0 or become positive values, which would have meant trivial or negative effects of the intervention. Thus, 100% of the studies showed a significant and meaningful beneficial effect of strength training interventions on RE in highly trained middle- and long-distance runners. Interestingly, one particular study (37) showed a very large SMD, which was greater than those observed in the other studies (i.e., −3.78 vs. −1.43 for the overall effect). However, we could not find any particular explanation for the superior benefits of the intervention used in the study by Paavolainen et al. (37) because the number of strength training sessions conducted (3 sessions), its contents (resistance, plyometrics, and sprint exercises), the range of loads used (0–40% 1RM), and the duration of the intervention (9 weeks) were very similar to those in the other studies.
One of the main concerns when training strength and endurance concurrently is the well-known interference phenomenon, by which the development of one of these capacities is impaired by training the other (15). Thus, finding the right balance between strength and endurance training sessions seems to be crucial (1,13,16). It has been previously reported that just 1 resistance training session per week is not enough to increase muscle strength or power in elite middle- and long-distance runners probably because of the high endurance:strength training ratio (2). In this regard, although every study in this meta-analysis used a different configuration of exercises and training intensities, all included at least 2 strength training sessions per week during the intervention, with most studies (4/5) having 3 sessions per week (Table 3). Taking into account that runners conducted also 6–9 endurance training sessions per week, it results in a 6:2 to 9:3 ratio between weekly endurance:training sessions. All analyzed studies found significant improvements in muscle strength, power output, jump height, and RE (33,37,51,52,54); therefore, strength training sessions being ∼30% of the total training sessions might be a valid strategy to improve RE and muscle strength concurrently in highly trained runners according to our analysis.
The most common strength training programs in the analyzed studies consisted of lower-body resistance exercises such as back squats or leg extensions combined with plyometrics (33,37,51,52,54). Both types of strength training modalities have been probed, in both an isolated and combined way (11,41,55), to improve several variables related to neuromuscular performance such as maximal strength, muscle power output, tendon stiffness, and rate of force development (9,11,17,29,42). Although these factors have been specially studied in strength or explosive athletes such as weightlifters, rugby players, or sprinters (5,18,44), there is a growing body of research that highlights the importance of neuromuscular performance in middle- and long-distance runners (4,10,38,39). For example, a significant correlation has been observed between jumping ability and the time to cover 800, 3,000, and 5,000 m in highly trained runners (21). Similarly, recent studies, describing the muscle-tendon properties of world-class Kenyan runners, found that these athletes have higher jumping ability, muscle power, and smaller stretch-shortening amplitudes and contact times than national-level Japanese athletes (47,48), variables likely related to their more efficient RE (31,35,42).
It has to be noted that most strength training programs (4 of the 5 included studies in the present meta-analysis) used low to moderate training intensities for the resistance exercises (40–70% 1RM), and all of them used low to moderate training volumes (2–4 resistance lower-body exercises plus up to 200 jumps and 5–10 short sprints, for a total session duration of 30–60 minutes). Just one study used heavy loads (85% 1RM), but each strength training session consisted of just 4 sets of 4 repetitions of back squats for about 15 minutes. Furthermore, none of the studies used repetitions to failure, a common practice in bodybuilding that seems to maximize muscle hypertrophy (11,45) but that may impair muscular performance and produce an excessive degree of fatigue (19,23,24,57).
Training to failure (reaching the maximal number of repetitions that could be performed within a set for a determined load) produces an enormous metabolic and neuromuscular fatigue (19,46) that could lead to a transition to slow-twitch fiber type (14) and reduce the muscle power output (19,23). Therefore, because variables related to muscle power are crucial for distance-running performance, a non-to-failure approach aiming for the improvement of the neuromuscular performance might be more appropriate for highly trained middle- and long-distance runners.
The main limitation of the present meta-analysis is the small number of included studies. Although the role of strength training in the improvement of running performance has received a lot of attention during the last decade (4,42,58), most of the studies recruited amateur recreational runners instead of highly trained athletes (27,35,38). Considering that highly trained runners have different biomechanical and physiological profiles than nonelite athletes (6,48,49,56), future research analyzing elite runners is thus warranted. This may provide valuable information for coaches and applied scientists for the ongoing management of elite runner training programs and may be especially relevant in the context of a multifactorial approach to reach historic milestones such as the sub–2-hour marathon (22).
The present meta-analysis shows an overall unanimous, large, beneficial effect of the strength training in the RE of highly trained middle- and long-distance runners when compared with the control group. It seems that a strength training program consisting of 2–4 resistance exercises at 40–70% 1RM without reaching failure, plus plyometric exercises performed 2–3 times per week for an overall 3:1 endurance:strength training ratio, and lasting 8–12 weeks is a safe strategy to improve RE. This may help highly trained middle- and long-distance runners to achieve an optimum performance.
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