Effects of complex training versus heavy resistance training on neuromuscular adaptation, running economy and 5-km performance in well-trained distance runners

Experimental approach to the problem

The current study used a between-group repeated-measure design to test the hypothesis that CT improves neuromuscular adaptations, running economy, and endurance running performance to a greater extent as compared to that with HRT. This study began in the early stage of the winter training block (November, December, and January), lasted for 10 weeks, and was comprised of four parts: 2 weeks of preparatory strength training, a pre-test, 8 weeks of training intervention, and a post-test (Fig. 1). After the preparatory strength training, all participants underwent a two-day test protocol to assess body composition, 1RM strength, counter movement jump (CMJ), and drop jump (DJ), running economy, VO_2max, and 50-m and 5-km running performance as the pre-test. They were then divided into three groups matched by age, body composition, VO_2max, and 5-km running time. Groups consisted of: a complex training group, CT (n = 10) who performed complex training and endurance training; a heavy resistance training group, HRT (n = 9) who performed heavy strength training and endurance training; and a control group, CON (n = 9) who performed strength-endurance training and endurance training. After the 8-week training intervention, all participants completed a post-test, which was identical to the pre-test.

Participants

Twenty-eight male runners on a collegiate long-distance running team volunteered to participate in this study and their basic physical characteristics, related body composition, and physiological indicators are provided in Table 1. All the participants competed at the collegiate level, they had a minimum of four years of training experience for long-distance running and were free from injury. They stayed and trained together at the training center affiliated with the Shanghai University of Sport, China, thus having similar training, recovery, and nutritional environments. The participants and their coaches were extensively informed about the research process and potential risks associated with the study and subsequently signed an informed consent document before participation. All participants were free from any health problems that would affect physical performance or put them at risk. The study was approved by the Ethics Committee of Shanghai University of Sport, China (ID number:2017047).

Procedures

Training intervention

During the training intervention, the participants were asked to perform 9 training sessions per week (six endurance and three strength sessions) with the schedule provided in Table 2. Participants performed different strength training programs according to their group allocation (Table 3). Participants were asked to avoided any other strength training besides the training intervention. All strength sessions were supervised by certified strength and conditioning specialists.

Statistical analyses

Statistical analyses were performed using SPSS 22.0 (IBM Corp. Armonk, NY, USA). Means  ± standard deviations (SD) are shown in Table 4–6. Normality and homogeneity of variances were tested via Shapiro–Wilk and Levene’s tests, respectively. All variables were tested using a one-way analysis of variance (ANOVA) to determine differences among the three training groups. Two-way repeat measures were performed using ANOVA with time (pre- vs. post-test) and group (CT vs. HRT vs. CON) as factors to assess training-related effects. When a statistically significant difference was found for time by group interaction effects or main effects (p ≤ 0.05), pairwise comparisons were performed using a post hoc t test with Bonferroni correction. For effect size, partial eta-squared was calculated (η2), and 0.2, 0.5, and 0.8 were interpreted as small, medium and large effect sizes, respectively (Cohen, 1988). An alpha level of p < 0.0167 was set for establishing statistical significance.

Strength and power test

The two-way repeated measures ANOVA showed a significant time by group interaction or time main effect on 1RM (F_(2,25) = 17.414, p < 0.001, η2 = 0.578), CMJ height (F_(2,25) = 7.404, p = 0.03, η2 = 0.372), peak power (F_(1,25)11.589, p = 0.002, η2 = 0.317), DJ height (F_(2,25) = 25.355, p < 0.001, η2 = 0.670), and RSI (F_(2,25) = 4.914, p = 0.016, η2 = 0.282). The Bonferroni post hoc test showed that there were significant increases between the pre- and post-tests in: 1RM strength (CT: 16.88%, p < 0.001; HRT: 18.80%, p < 0.001) and CMJ height (CT: 11.28%, p < 0.001; HRT:8.96%, p < 0.001), but not for peak power (CT: 8.20%, p = 0.02; HRT: 5.05%, p = 0.036) in the CT and HRT groups. Only the CT group showed a significant increase between the pre- and post-test for DJ height (12.94%, p < 0.001) and RSI (19.99%, p < 0.001). No significant changes were observed in the CON group for these indicators (Fig. 2).

Running economy and related physiological test

Table 6 demonstrated that there were no significant differences in VO_2max in the time by group interaction or main effects after the 8-week intervention. For the RE test, there was a significant interaction between time and group on 16 km.h⁻¹RE (F_(2,25) = 5.646, p = 0.009, η2 = 0.311). The Bonferroni post hoc test found that there was a significant improvement between the pre- and post-test only in the CT group (−7.38%, p < 0.001). There was a significant time main effects for 12 km.h⁻¹ RE (F_(1,25) = 23.554, p < 0.001, η2 = 0.485) and each group had a significant improvement between the pre- and post-test (CT: −4.47%, p = 0.005; HRT: −4.11%, p = 0.016; CON: −4.38%, p = 0.01). For the 14 km.h⁻¹ RE, a significant time main effect (F_(1,25) = 28.9244, p < 0.001, η2 = 0.536) was observed: the CT (−7.68%, p < 0.001) and HRT groups (−4.89%, p = 0.009) showed a significant improvement between the pre- and post-test (Fig. 3). In addition, there was a significant time by group interaction in BLa at 16 km.h⁻¹ (F_(2,25) = 4.2182, p = 0.026, η2 = 0.252). The Bonferroni post hoc test identified that the BLa at 16 km.h⁻¹ was significantly decreased in the CT group (−40.80%, p < 0.001) (Fig. 4). There were no significant time by group interactions or main effects on HR at any speed after the intervention.

5-km running and 50-m sprint performance test

There was a statistically significant time by group interaction in 5-km running performance (F_(2,25) = 9.627, p = 0.001, η2 = 0.435). The Bonferroni post hoc test showed that there were significant improvements between the pre- and post-tests in the CT (−2.80%, p < 0.001) and HRT (−2.09%, p < 0.001) groups, and the CT group was significantly lower when compared to the CON group at the post-test (p = 0.001). There was a significant time main effects for the 50-m sprint performance (F_(1,25) = 14.903, p = 0.001, η2 = 0.373), and the CT (−2.26%, p = 0.003) and HRT groups (−2.14%, p = 0.007) showed a significant improvement between the pre- and post-tests (Fig. 5). There were no significant changes in the CON group in terms of 5-km running or 50-m sprint performance after the intervention.

Neuromuscular adaptation

Neuromuscular adaptations, including 1 RM strength and jumping ability, were especially important indicators of increasing athletic capacity in strength and power athletes, and recent studies showed that they also have a positive impact on modifying distance running performance such as increasing force production during running and increasing RE (Berryman et al., 2017; Stone, Stone & Sands, 2006). After the training intervention, there was a significant increase in 1RM strength for participants in the CT (16.88%) and HRT groups (18.80%), but not in the CON group. In accordance with our results, previous studies have demonstrated similar improvements (12–33.2%) in 1 RM strength after 8 weeks of HRT among distance runners (Damasceno et al., 2015; Storen et al., 2008). These results were consistent because the similar heavy load strength programs (i.e., 3–5 RM) were adopted in previous studies. Heavy strength training exerts high mechanical stimulus on the neuromuscular system, thus producing good effects on maximal strength development. It has been reported that high load strength training (3–6 RM) is associated with maximal force generation without muscle hypertrophy, and is attributed to neural adaptation (Docherty & Sporer, 2000). This is consistent with our results. Considering that no changes in body weight, fat-free mass, and BMI were observed, we hypothesized that the improved maximum strength likely resulted from neural adaptation such as motor unit recruitment and increase in firing frequency (Cormie, Mcguigan & Newton, 2011). This was a concern for distance runners because increased body mass negatively impacts physiological parameters, such as VO_2max or RE, and inevitably impairs endurance development (Beattie et al., 2017).

With respect to power performance, the CT and HRT groups showed a significant increase in CMJ height (11.28% and 8.96%, respectively) after the intervention. The CMJ height increase in the CT group was higher than the improvements (4.5–8.9%) noted in previous studies which used HRT or PLY training interventions in distance runners with similar intervention timeframes (Ramírezcampillo et al., 2014; Vikmoen et al., 2016). This may be attributed to the different strength training protocols in the training arrangement. Previous research has confirmed the positive effects of CT on increasing power performance and may be more efficient than using one strength training method (Macdonald, Lamont & Garner, 2012). The mechanism underpinning this phenomenon may be attributable to HRT causing increases in motor neuron excitability, thus creating optimal conditions for subsequent plyometric exercises (Esformes & Bampouras, 2013). The cumulative effect of CT would then lead to greater power output (Jones & Lees, 2003).

Reactive strength is defined as the ability of the muscle–tendon complex to produce greater force during the concentric contraction immediately following a rapid eccentric contraction (Beattie et al., 2016). These indicators, such as DJ and RSI, are very important for distance running performance (Beattie et al., 2017). In the present study, the CT group showed a significant improvement in DJ performance (12.94%) and RSI (19.99%) between the pre- and post-tests, whereas no changes were noted in the HRT and CON groups. The advantageous CT effect on reactive strength can be explained by the inclusion of plyometric exercises such as drop jump, single leg hops, and hurdle hops in CT. These exercises exerted maximal external force on the neuromuscular system in a very short time, greatly enhancing power by developing SSC utilization and leg musculotendinous stiffness (Saunders et al., 2006; Spurrs, Murphy & Watsford, 2003). Interestingly, our results are also consistent with those of the study by Ramírezcampillo et al., (2014), who reported improvement in DJ performance by 16.7% after 6 weeks of plyometric training in competitive distance runners. Although the plyometric exercise volume in both this study and ours was relatively small (60 vs.72 contacts per session), the training appears sufficient in distance runners, who may not be accustomed to such exercises. Recent research supported that a low-volume plyometric program can produce similar performance improvements in terms of reactive strength compared with that in a high-volume plyometric program (Jeffreys et al., 2017). Taken together, our results support the hypothesis that CT increases neuromuscular facilitation and is appropriate for long-distance runners and may be more efficient than HRT and PLY training methods.

Running economy

Running economy is described as the steady-state oxygen consumption at a given running speed and is a critical physiological measurement to determine distance running success (Saunders et al., 2004). After training intervention, participants in the CT and HRT groups showed a significant improvement in 12 km.h⁻¹ RE (4.47 vs. 4.11%) and 14 km.h⁻¹ RE (7.68 vs. 4.89%). Our findings are consistent with those of previous research that reported similar increases in RE after heavy strength interventions for the same level distance runners (Storen et al., 2008). The RE improvement in the CT and HRT groups could be attributed to neuromuscular adaptations after heavy strength training. The multi-joint and closed-chain high load exercises, such as back squat, increases maximum force and peak power, theoretically allowing the athletes to maintain constant speed or to perform each running action at a relatively lower force ratio (Aagaard & Andersen, 2010; Ronnestad & Mujika, 2014). Meanwhile, strength training may potentiate the neural recruitment and function of type I muscle fibers as well as postponing the activation of the less efficient type II fibers (Schumann et al., 2016), therefore reducing overall energy consumption. In terms of changes in 16 km.h⁻¹ RE, only the CT group showed a significant improvement (−7.38%) after training intervention. This may indicate that integrating CT or HRT training into endurance training programs can elicit similar improvements of RE at low speed; as speed increases, the role of CT becomes more substantial. In line with our results, Sedano et al. (Sedano et al., 2013) combined three jumping exercises into a strength training program (70%1RM) and showed significantly improvements in well-trained runners’ RE at 16 km.h⁻¹. Therefore, it can be hypothesized that reactive strength adaptation plays an important role in RE improvement at higher speeds. The elastic energy stored during the eccentric contractions of running and recoiled following concentric contractions makes an extensive contribution to propulsion (Anderson, 1996), and may even exceed the efficiency of conversion from chemical energy to kinetic energy by the muscle (Williams, 1985). The complex pair exercise in CT may enhance the runners’ reactive strength and the ability to store and utilize elastic energy by facilitating the SSC, it theoretically improving the leg stiffness and reducing the time the athletes’ foot spends in contact with ground, thereby decreasing the energy cost during running. As running speed increases, elastic mechanisms prevail over the muscle contractile capabilities and may even account for a bigger proportion of the total work (Saunders et al., 2006).

In addition, the CT group showed a decrease in BLa concentrations at 16 km.h⁻¹, which means that athletes could run at the same speed with smaller anaerobic energy consumption. The potential explanation may be that CT combined with endurance training increases the strength and function of type I muscle fibers while delaying the recruitment of type II muscle fibers (Marcinik et al., 1991; Midgley, McNaughton & Jones, 2007); thus, reducing lactate production and improving the capability for dispelling lactate at higher running velocities (Jones & Carter, 2000). Therefore, CT can achieve the HRT and PLY training effects at the same time leading to significantly increased RE and decreased BLa concentrations at high speeds.

Running performance

The goal of athletic training is to create meaningful improvements in performance. This study revealed that both the CT and HRT groups showed a significant improvement (−2.8% vs. −2.09%) in 5-km running performance, and the CT group was significantly lower than that in the CON group. The magnitude of the improvement was in line with that in previous studies, which have shown that the increase in running performance was accompanied by augmentation of neuromuscular adaptations and running economy (Berryman et al., 2017; Damasceno et al., 2015; Ramírezcampillo et al., 2014). For example, Ramírezcampillo et al. (Ramírezcampillo et al., 2014), demonstrated that jumping and 2.4 km running performance in highly-trained athletes improved simultaneously after performing 8 weeks of plyometric and endurance training. Damasceno et al. (2015), found that the addition of 8 weeks of heavy strength training to endurance programs resulted in a significant enhancement in 10-km time trials. Since there was no significant change for VO_2max, we speculated that the 5-km performance improvements in the HRT and CT groups were due to the comprehensive effects of neuromuscular adaptations, RE increase, and the decrease in BLa at constant speeds. In addition, strength and plyometric exercises (back squat, Bulgarian squat and single leg hops, etc.) in the CT group, which involved similar kinematic traits to a running gait, likely provided the greatest improvement in endurance performance (Bazyler et al., 2015). On the other hand, the participants in the CT and HRT groups achieved a significant improvement in their 50-m sprint performance (−2.26% and −2.14% respectively) whereas no significant improvements were found in the CON group. This is in line with that in previous studies, which showed improvements of 1.1–3.4% following various strength training interventions in distance runners (Mikkola et al., 2007; Paavolainen, Nummela & Rusko, 1999; Ramírezcampillo et al., 2014). The enhancement of sprint performance plays an important role in distance races and allows runners to hold a favorable position at the start and to accelerate at the end of the race. These findings highlighted the necessity of utilizing a multicomponent conditioning program, such as CT, for developing endurance performance.

It is worth noting that the present study only enrolled young male distance runners; therefore, the results may not necessarily be broadly applicable to all types of runners. Since CT was at a relatively high intensity, coaches should be prudent when using this type of training. The age, gender and strength level must be considered before the onset of training. In addition, as HRT has similar training effects on RE and neuromuscular adaptations, coaches could use this type of training to replace the CT when the training subjects are female or older runners. We suggest that a training block of CT could follow a block of HRT so that basic strength levels could be developed. In future studies, we need to investigate the effects of CT on different types of runners (e.g., female or older runners) for longer periods of time (>16 weeks) to optimize endurance performance.