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Effects of Resistance Training on Running Economy and Cross-country Performance


Medicine & Science in Sports & Exercise: December 2013 - Volume 45 - Issue 12 - p 2322–2331
doi: 10.1249/MSS.0b013e31829af603
Applied Sciences

Purpose: Heavy-resistance training and plyometric training offer distinct physiological and neuromuscular adaptations that could enhance running economy and, consequently, distance-running performance. To date, no studies have examined the effect of combining the two modes of training on running economy or performance.

Methods: Fifty collegiate male and female cross-country runners performed a 5-km time trial and a series of laboratory-based tests to determine aerobic, anthropometric, biomechanical, and neuromuscular characteristics. Thereafter, each athlete participated in a season of six to eight collegiate cross-country races for 13 wk. After the first 4 wk, athletes were randomly assigned to either heavy-resistance or plyometric plus heavy-resistance training. Five days after completing their final competition, runners repeated the same set of laboratory tests. We also estimated the effects of the intervention on competition performance throughout the season using athletes of other teams as controls.

Results: Heavy-resistance training produced small-moderate improvements in peak speed, running economy, and neuromuscular characteristics relative to plyometric resistance training, whereas changes in biomechanical measures favored plyometric resistance training. Men made less gains than women in most tests. Both treatments had possibly harmful effects on competition times in men (mean = 0.5%; 90% confidence interval = ±1.2%), but there may have been benefit for some individuals. Both treatments were likely beneficial for all women (−1.2%; ±1.3%), but heavy-resistance training was possibly better than plyometric resistance training.

Conclusions: The changes in laboratory-based parameters related to distance-running performance were consistent with the changes in competition times for women but only partly for men. Our data indicate that women should include heavy-resistance training in their programs, but men should be cautious about using it in season until more research establishes whether certain men are positive or negative responders.

1Sports Performance Research Institute New Zealand, Auckland University of Technology, Auckland, NEW ZEALAND; and 2Department of Kinesiology, Hope College, Holland, MI

Address for correspondence: Kyle R. Barnes, M.S., Sports Performance Research Institute New Zealand, Auckland University of Technology, Level 2, AUT-Millennium Campus, 17 Antares Place, Mairangi Bay, Auckland, New Zealand; E-mail:

Submitted for publication March 2013.

Accepted for publication May 2013.

Trained runners have superior running economy compared with lesser-trained or untrained runners (13,14,26,31,32,40), indicating positive adaptations in response to training programs. Recent research has shown running economy to improve in runners using traditional strength training or explosive, plyometric training (25,36,42). It is well documented that initial performance gains after traditional heavy-resistance training (HRT) are a result of predominantly neuromuscular rather than within muscle adaptations (i.e., hypertrophy) (28). These adaptations may include increases in strength, increased motor unit recruitment, and improved mechanical efficiency and muscle coordination (28,29,39). A key component to running economy is the ability to store and recover elastic energy from the eccentric contraction (8). Plyometric training is a form of strength training that aims to enhance the ability of the muscles to generate power through the stretch shortening cycle by use of explosive activities such as jumping, hopping, and bounding (48). Several studies have indicated improvement in running economy from concomitant plyometric and endurance training (36,43,48). Proposed explanations for the improvement include increased lower body muscle–tendon stiffness, degree of neural input to the muscle, enhanced muscle power development and elastic return, and improved motor unit synchronization (36,37,43). Conversely or in concert, improvements from either form of resistance training may enhance running mechanics. Improved biomechanical efficiency and improved leg muscle coactivation and coordination may allow for a reduction in relative workload (18,25,29). The combination of improved running mechanics, neuromuscular efficiency, and strength may result in a decrease in oxygen consumption, thereby improving running economy and ultimately performance. Indeed, the combination of HRT and plyometric training may facilitate additional improvements in running economy via accumulation of adaptations previously observed when either type of training is performed alone.

There is a strong association between running economy and distance-running performance (3,9,12,14). Accordingly, it is likely that any improvement in running economy because of training will be associated with improved distance-running performance. A review of the literature, however, produced no studies examining the effects of a resistance training intervention on running economy or performance during the competition phase of a running season, likely because coaches are often unwilling to do time trials or other performance tests that would interfere with preparations for actual competitions. Fittingly, Vandenbogaerde et al. (49) recently reported a novel design for investigating the effects of an intervention on competition performance, in which changes in performance between competitions before and after an intervention with a squad of athletes were compared with changes in performance in other squads over the same time frame. To enhance the ecological validity of the present study and as the primary purpose of the investigation, we adopted this research design in an attempt to compare the effects of HRT versus the combination of heavy-resistance and plyometric training on performance during the competitive phase of a men’s and women’s collegiate cross-country running season.

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Before the competitive season, an entire collegiate cross-country team performed a 5-km time trial and a series of laboratory tests including an incremental treadmill test to determine aerobic and biomechanical characteristics and a series of maximal jumps to determine muscle power characteristics. Thereafter, each athlete participated in a series of competitive collegiate cross-country races over a 13-wk period (Fig. 1). Approximately one-third of the way through the competitive season, each athlete was prescribed one of two resistance training programs: group 1, traditional HRT; group 2, plyometric and heavy-resistance training (PRT). We then estimated the effects of the intervention on performance in a design equivalent to a parallel group–controlled trial with athletes of other teams being the control group. Five days after completing the final competition of the season, each runner repeated the same set of laboratory tests. The study was approved by the Auckland University of Technology Ethics Committee, Auckland, New Zealand, and the Hope College Human Subjects Review Board, Holland, Michigan. All participants provided informed written consent to participate.

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Fifty collegiate cross-country runners (men = 28, women = 22) participated in the study (Table 1). Subjects all competed at the Division 3 National Collegiate Athletic Association level with both teams being ranked nationally. Eight runners failed to complete the prescribed training program and were eliminated from the study. The main reasons were as follows: inability to complete intervention or testing procedures (n = 3), dropout (n = 1), and injury (n = 4). The final sample size for analysis was 42 (men: n = 23, HRT = 13, PRT = 10; women: n = 19, HRT = 9, PRT = 10). All athletes trained and competed together 6 d·wk−1 under the guidance of the same coach and performed similar workouts to their teammates over the duration of the season. Training logs for all subjects were collected before and after the competitive season, and the primary author observed each training session and competition. During week 1, each subject completed a 5-km time trial on a flat 1250-m grass loop (Fig. 1). All subjects were instructed to run the distance “as fast as possible” to get a baseline measure of fitness and to prescribe subsequent training intensities under the guidance of their coach (Table 2). Gender and 5-km time was used to sequentially allocate subjects to either HRT or PRT (19). Participants had not previously undertaken any structured resistance or plyometric training in the previous 10 wk before the competitive season.

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Testing Procedures

Body composition

On arrival to the laboratory, subjects were weighed (BOD POD; CosMed USA, Inc., Chicago, IL) in their running shorts to the nearest 0.1 kg, and their body composition was determined using air-displacement plethysmography (BOD POD GS).

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Treadmill testing

All running tests were performed in a temperature-controlled laboratory (19°C–21°C; 65% RH) on a motorized treadmill (TrackMaster TMX425 Full Vision Inc., Newton, KS) set at a 1.0% gradient (11). Before each test, subjects warmed up at a self-selected exercise intensity for 5 min. The amount of work performed during the warm-up was recorded and repeated during subsequent exercise tests. After the warm-up, the subjects completed an incremental treadmill test to determine running economy involving repeated, progressively faster (increments of 1.0 km·h−1) 4-min stages at fixed running speeds (12–18 km·h−1 for men and 11–17 km·h−1 for women) until subjects were clearly no longer able to sustain a steady-state V˙O2 (i.e., a slow component was evident), as determined visually from real-time plots of V˙O2. From further posttest inspections of V˙O2 data, the maximum velocity at which steady-state oxygen consumption was achieved across the range of subjects was determined (14 km·h−1) and used thereafter as our primary measure of running economy. A 90-s recovery period occurred between each stage. Expired gases were measured continuously using a metabolic cart (ParvoMedics TrueOne 2400, Salt Lake City, UT) for the determination of V˙O2, carbon dioxide production, minute ventilation, and respiratory exchange ratio. Running economy was defined as the mean V˙O2 determined during the last minute of each running stage. In our laboratory, the typical error of measurement (20) of submaximal V˙O2 was 1.8%. Approximately 90 s after completion of the final submaximal running stage, V˙O2max was determined during an incremental test to volitional exhaustion. Subjects commenced running at 1.0 km·h−1 (1.0% gradient) below the final submaximal speed for 1 min. Thereafter, treadmill gradient was increased by 1% each minute until volitional exhaustion. The highest V˙O2 over a 30-s period during the test was considered V˙O2max. Changes in endurance performance were indicated by the peak running speed reached at the end of the incremental treadmill test. Because we used increases in gradient (rather than speed) in the latter part of the treadmill test, we calculated equivalent speed on the flat as S = ST + (0.045ST)i, where S is the peak speed in kilometers per hour, ST is the treadmill speed in kilometers per hour, and i is the treadmill inclination in percent (5). Heart rate was determined every 1 s throughout the incremental test using short-range telemetry (Polar RS800sd, Polar Electro, Finland).

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Force plate measures

After the incremental test, after a 30-min passive recovery period, subjects performed a five-jump plyometric test involving five continuous straight-leg jumps on an AccuPower force plate (Advanced Mechanical Technology Inc., Watertown, MA) to determine neuromuscular characteristics. Subjects were instructed to aim for maximal height with contact times as fast as possible, keeping legs straight throughout the jumping sequence. All tests were performed twice and care was taken to ensure subjects maintained erect posture and landed toes first, in the same spot as takeoff. The following parameters were determined: peak force, time to peak force, peak power, maximum rate of force development (RFD), and displacement. Leg stiffness was estimated by dividing the peak force by the vertical displacement measured during the five-jump test (6).

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Resistance training interventions

The resistance training interventions were implemented during week 4 of the competitive season. While maintaining their normal endurance running training, each athlete performed two resistance training sessions per week over a 7- to 10-wk period, with the exception of weeks 10, 12, and 13 before championship competitions where athletes performed only one session (Table 2, session 1). Specific details of each resistance training session are presented in Table 2. Briefly, a familiarization session occurred during week 3 and involved a measure of each athletes, three- to six-repetition maximum (RM) for the leg press exercise followed by a familiarization with each of the prescribed exercises. The 3–6RM of each subject was converted to 1RM using the 1RM prediction equation of Lander (30). Both HRT and PRT programs were periodized throughout the competitive season and matched for volume load throughout the study based on the methods of Stone et al. (44,45). Volume load for HRT and PRT was estimated for each training session using the number of sets, reps, load, and body mass of subjects (44,45). Each resistance training session included four lower body lifts or four complex set lifts, which included the identical lifts of the HRT group immediately followed by a plyometric exercise of a similar movement pattern. In addition, all athletes performed the same upper body lifts during each session. Resistance training sessions occurred approximately 30 min after endurance training sessions, and athletes were provided with details of the session (sets, repetitions, and weight) upon arrival to the gym. Weights for each athlete were uncontrolled, but recommendations were given based on the previous sessions performance, and subjects were encouraged to improve each week. All sessions were monitored, and careful attention was given to each athlete to ensure good technique. Athletes were required to record details of all training sessions (resistance and endurance) undertaken during the study. For each resistance training session, the weight (kg) and completed repetitions for each set was recorded, and for each endurance-training session, the training distance (km) and duration (min) were recorded.

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Performance during the competitive season

The competitive season occurred over a 10- to 13-wk duration (Fig. 1). Season length was dependent on both the individual and team achievement at championship competitions (weeks 10, 12, and 13). Only the top (fastest) seven athletes from a team competed in the regional (week 12) and national (week 13) competitions. Athletes competed in various cross-country competitions throughout the competitive season ranging from 5- to 8-km for men and 5- to 6-km for women. National Collegiate Athletic Association cross-country competition data were downloaded from selected team Web sites for the entire cross-country season. Each performance time was rounded to the nearest 0.1 s. To focus on the training team (DXC) where the resistance training interventions were implemented, we selected data only from teams that directly competed against our intervention squad at least one time throughout the competitive season. Individuals that did not compete in at least four competitions during the season including their teams’ inaugural and championship events were not included in the analysis. This selection process resulted in a total of 1741 individual performances in 37 competitions on 16 dates by 325 male athletes from 23 teams and 1652 individual performances in 37 competitions on 16 dates by 285 female athletes from 22 teams.

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Spreadsheets (22) were used to analyze the effects of training on laboratory test measures. Analyses of changes within each group were made using the post-only crossover spreadsheet. Comparisons of the changes between groups were made with the before and after parallel-group spreadsheet. The pretest value of the dependent variable was included as a covariate to improve precision of the estimate of the effects. The parallel-group spreadsheet also allowed assessment of the magnitude of the differences between the two training groups arising from randomization at baseline.

Several analyses of the competition data were performed, all with mixed linear models similar to that of Vandenbogaerde et al. (49) using Proc Mixed in the Statistical Analysis System (Version 9.2; SAS Institute, Cary, NC). The mean performances of each of the three training groups (PRT, HRT, and control) at each competition were estimated by inclusion of the identity of each competition as a fixed effect interacted with the group effect. Random effects in the model included the identity of the athlete (to account for differences in their ability), the interaction of the identity of the team with the identity of the competition (to account for the interdependence of athletes clustered within each team), and the residual error (representing within-athlete variability in performance between competitions). Effects for female and male runners were estimated in separate analyses. From these analyses, it was apparent that the mean performance times of the control athletes were substantially slower than those of the training team (DXC). The solution for the random effect for athlete was therefore used to filter out slower control runners. Mean performances in the three groups across all competitions were similar when control female athletes with values of their random effect >3 (i.e., more than 3% slower than the average athlete) were excluded; for men, the exclusion criterion was a value >5. The analyses with the filtered athletes provided the means for the competitions shown in Figure 2.

The effects of the treatment on competition time were then estimated via dummy variables having values of 1 for the intervention team (DXC) and 0 for the other (control) teams. Each competition in the intervention period was assigned a different dummy variable. The fixed effect for the interaction of training group and competition in the previous model was replaced with a fixed effect for competition only. The mean effects of each of the two types of resistance training at each competition in the intervention period were estimated with additional fixed effects consisting of the interaction of each dummy variable with the identity of the training group (PRT, HRT, and control). The overall means for each treatment and for both treatments combined were obtained by averaging the effects at the three competitions during weeks 8–12. (The effects at the national championship in week 13 for the seven top women were not included in the women’s overall mean.) Random effects for the athlete and for the interaction of team and competition were the same as in the previous model. Individual responses to the training at the first competition during the intervention period (week 6) and averaged over the subsequent competitions (weeks 8–12 for men; weeks 8–13 for women) were estimated by including random effects consisting of the interaction of appropriate dummy variables with the identity of the athlete. To allow for the possibility that the runners became more consistent in their performance later in the season, a novel approach was taken by interacting a term representing within-athlete variability between competitions (the interaction of athlete and competition identities) with a dummy variable declining linearly from 1 to 0 between the first and last competitions of the season. One value for this random effect was estimated for the training team and one for the control teams; similarly, a different residual error was specified for the training and control teams to allow for any difference in consistency of performance of these two groups of athletes.

Effects on dependent variables were estimated in percent units via log transformation. Uncertainty in the estimates of effects on performance (peak speed and competition time) was expressed as 90% confidence interval (CI) and as probabilities that the true value of the effect was beneficial, trivial, or harmful in relation to threshold values for benefit and harm. These probabilities are not presented quantitatively but were used to make a qualitative probabilistic clinical inference about the effect (24). Briefly, the effect was deemed unclear when the chance of benefit was sufficiently high to warrant use of the treatment but the risk of harm was unacceptable. Such unclear effects were identified as those with an odds ratio of benefit to harm of <66. All other effects were deemed clinically clear and assessed by estimating the probability that the true magnitude of the effect was at least as large as the threshold values for beneficial or harmful effects on performance. The threshold values for assessing the magnitude for small, moderate, and large beneficial or harmful effects on performance in the current study were ±0.5%, ±1.5%, ±2.7%, and ±4.2%, which are approximately 0.3, 0.9, 1.6, and 2.5 of the within-subject SD a top athlete would show between competitions (24). For top cross-country runners, this SD was 1.5%–1.7% in a previous study (23) and 1.3%–1.5% by the end of the season in the current study (see Results). The probabilities were reported qualitatively using the following scale: 25%–75%, possibly; 75%–95%, likely; 95%–99.5%, very likely; >99.5%, most likely (21). For the comparison of the effects in the two training groups, the probabilities of benefit and harm of plyometric resistance training were assessed relative to HRT, which was regarded as the reference or best-practice approach. Magnitudes of effects on measures other than performance were evaluated nonclinically (mechanistically) (24): if the CI overlapped thresholds for substantial positive and negative values (±0.20 standardized units, i.e., 0.20 of the between-subject SD of the dependent in the pretest), the effect was deemed unclear; all other effects were reported as the magnitude of the observed value and were evaluated probabilistically as described earlier, except that threshold values for assessing magnitudes of standardized effects were 0.20, 0.60, and 1.2 for small, moderate, and large, respectively (24).

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The proportion of training session’s athletes attended during the competition season was 97% ± 3% (mean ± SD). Before the competition season, PRT and HRT groups were similar for men and for women in 5-km time-trial performance, training volume, and body fat, but there were small to moderate differences between groups in body mass, age, and training history (Table 1). During the competition season, men performed, on average, 15.7 km·wk−1 of training higher than 80% of V˙O2max, and the women performed 14.6 km·wk−1, which was equivalent to 17.2% ± 2.5% of men’s and 20.7% ± 4.0% of women’s weekly training volume (Table 1). There was no substantial change in body mass from pre- to posttesting in men or women, and differences between groups were unclear. Small to moderate reductions in percent body fat were found within both male PRT (mean change score ± SD = −9.7% ± 23.0%; ±CI = ±10.8%) and HRT (−18.5% ± 20.5%; ±11.4%) and both female PRT (−6.9% ± 9.4%; ±6.6%) and HRT (−11.8% ± 12.6%; ±7.9%) groups, but PRT had a possibly small negative effect relative to HRT. Baseline values of other outcome measures, statistics for effects, and inferences about the interventions within and between groups for men and women are presented in Tables 3 and 4, respectively.

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Performance and aerobic measures

There were only small differences at baseline between groups for peak speed and running economy (mL·kg−1·km−1) in men and women, for vV˙O2max and %V˙O2max in men, and for V˙O2max and running economy (mL·kg−1·min−1) in women. Mean improvements in peak speed of small to very large magnitude were observed in both groups for men and women, but PRT was clearly harmful relative to HRT (Tables 3 and 4). After the intervention period, male HRT showed small or moderate improvements in aerobic measures, whereas the effects from PRT on aerobic measures were trivial (Table 3). Both female groups showed small to moderate improvements in all aerobic measures (Table 4). Male and female HRT showed greater improvements in running economy compared with PRT. Differences between groups on all other aerobic measures were unclear.

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Biomechanical measures

In both training groups and in both sexes, changes in contact time were opposite to those of flight time. The direction of the changes were opposite in the two training groups, and overall, the changes with PRT were clearly positive and small-moderate in magnitude relative to those with HRT (Tables 3 and 4).

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Neuromuscular measures

1RM improved in all groups, with male athletes improving by 20%–40% (Table 3) and female athletes improving by 30%–50% (Table 4). Improvements were greater with HRT. Changes in neuromuscular related measures from the five-jump test were small to moderate improvements with HRT and trivial or negative with PRT (Tables 3 and 4). Overall, PRT was associated with either unclear or negative effects on all neuromuscular measures in men and women. There was a moderate improvement in leg stiffness after HRT in men and women and unclear decrease (male) or possibly small improvement (female) after PRT (Tables 3 and 4, respectively).

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Competition measures

The residual error in competition times calculated at the beginning of the season was ∼2.0% for the training and control groups, and at the end of the season, it was 1.3%–1.4% in the training groups and 1.5% in the control group. Figure 2 shows the least-squares mean performance times for men and women in the competitions that the training groups entered. The mean effects of the training interventions on performance at each competition were generally consistent from week 8 through the end of the season for male and female athletes. Overall, PRT resulted in possible harm to competition times (slower run times) by 0.8% (90% CI = ±1.5%) compared with control male athletes. HRT was also possibly harmful to competition performance (0.1%; ±1.3%). The men’s overall mean performance was worse (slower) than that of the control teams by 0.5% (±1.2%) after implementation of the two resistance training interventions. There was an unclear difference between PRT and HRT (−0.7% ±1.5%). There was a likely beneficial effect of PRT training for women, resulting in −1.1% (±1.3%) faster run times (compared with control female athletes). HRT was also likely beneficial to competition performance, −1.4% (±1.4%). The women’s overall mean performance was better (faster) than that of control teams by −1.2% (±1.3%). When compared with HRT, PRT was possibly harmful (0.3%; ±1.0%). Individual responses expressed as an SD for both treatments combined was 0.3% (90% CI = −1.2% to 1.3%) for men and −0.6% (−1.0% to 0.5%) for women.

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Previous studies (17,36,43) have reported that various forms of resistance training may lead to improved endurance performance in trained subjects. However, the optimal prescription of resistance training to improve endurance running performance has yet to be firmly established. Accordingly, we investigated whether the combination of plyometric training and heavy-resistance training (PRT) may facilitate additional improvements in neuromuscular efficiency, strength, and running mechanics compared with HRT alone during the competition phase of a men’s and women’s collegiate cross-country season. Interestingly, our data revealed distinct differences between the prescribed training regimes in terms of performance gains and physiological adaptations and an apparent gender-specific response to resistance training.

To determine the effects of HRT and PRT on performance from competition data, the coefficient of variation (CV) representing typical variation in performance time for the faster male and female runners across the competition season was determined. The CV sets the benchmark for the smallest worthwhile change in an athlete’s performance and for the typical (standard) error of measurement of tests used to assess the smallest important or worthwhile change (24). Our CV of ∼2.0% at the start of the competitions and ∼1.5% at the end are in line with the 1.5%–1.7% reported by Hopkins and Hewson (23) and were the basis of using a ±0.5% threshold value for beneficial and harmful effects on performance (approximately 0.3 of the within-subject SD top athletes show between competitions) (23,24). Accordingly, there were substantial beneficial mean effects on competition performance for the female training groups compared with controls (−1.2% ± 1.3%), whereas resistance training for men proved to be possibly harmful (0.5% ±1.2%). This observation could be an indication that endurance-trained female athletes may have a greater requirement in terms of resistance training maintenance (38), whereas this type of training for men might be beneficial in general only during the preseason or build-up phase of training when there is less emphasis on competition and gains can be made in physiological measures without the risk of harm to competition performance. The differences in effects between men in women could also be due in part to differences in training intensity and competition distance. The proportion of training that occurred at ≥80% V˙O2max for women was moderately higher than that for men (Table 1), which might have translated into performance enhancement over the women’s shorter race distance (5–6 vs 8–10 km for the men). Although we observed an overall benefit in competition performance from either form of resistance training in women and harm in men, HRT was substantially better for women (0.3%; ±1.0%) whereas PRT was worse (−0.7% ± 1.5%).

In addition to actual competition data, we also observed a substantial increase in laboratory-derived peak running speed after HRT (4.6% and 4.4% in men and women, respectively) compared with PRT (1.0% and 2.2% in men and women, respectively). Peak running speed has been shown to be a good indicator of endurance performance in middle- and long-distance running events (4,34,35,41,47), and Noakes et al. (34,35) has suggested that peak running speed could be used as a measure of the “muscle power” factor in endurance runners. Muscle power is defined as an ability of the neuromuscular system to produce power during maximal exercise when glycolytic and/or oxidative energy production are high and muscle contractility may be limited (34). Indeed, in addition to the aerobic processes related to distance-running performance, the neuromuscular and anaerobic characteristics related to peak running speed are also strongly involved in distance-running performance.

In the present study, changes in physiological measures related to distance-running performance were consistent with performance data, indicating greater improvements after HRT than matched volume-load PRT (Tables 3 and 4). Specifically, the addition of HRT improved running economy by 1.7% and 3.4% in men and women, respectively, whereas PRT only improved running economy by 0.2% and 1.0% (Table 3 [men] and Table 4 [women]). Although both HRT and PRT results are in accordance with growing literature demonstrating that HRT or plyometric training improved the running economy of well-trained athletes (15,2533,36,42,43,46,48), the magnitude of enhancements was lower in our study compared with previous studies reporting effects after heavy-resistance (15,25,33,46) or plyometric training (36,42,43,48). This could be due to different phases of season that the studies were performed. Regardless, in both HRT and PRT, improvements in running economy occurred in the absence of any substantial change in V˙O2max, suggesting that improved running economy was a result of neuromuscular characteristics rather than improved cardiorespiratory fitness. This is a reasonable assertion because both HRT and PRT groups performed the same endurance training outside their respective resistance training programs. In further support, running economy improved in accord with many of the neuromuscular measures (Tables 3 and 4), which also coheres well with previous studies (10,33,36,42,43,46), reporting the importance of the neuromuscular characteristics in determining running economy and running performance after combined resistance and endurance training in runners.

With regard to changes in strength and neuromuscular measures that could be responsible for the greater improvements in running economy and peak running speed after HRT, it has been purported (3,26) that the nervous system plays an important role in regulating muscle stiffness and utilization of muscle elasticity during stretch-shortening cycle exercises, such as running, in which high contraction velocities are used. In the present study, small to moderate increases in leg stiffness occurred in the male and female HRT groups, and PRT training was associated with moderate negative effects on leg stiffness compared with HRT (Tables 3 and 4). Interestingly, the group with the smallest improvement in 1RM (male PRT) was the only group not to elicit a concomitant increase in stiffness. One of the most important roles of the muscle during running is to modulate the leg stiffness and the storage recoil of energy. The conversion of energy to motion involves recoil of some elastic energy in muscle and tendon; thus, a “stiffer” muscle or tendon would be better at transferring energy economically or without the need for additional oxygen consumption (7,10,43). Indeed, previous evidence has shown a negative correlation between leg stiffness and cost of running (1,2). Kerdok et al. (27) have shown changes in both muscle-tendon stiffness and running economy when manipulating the running surface, indicating that runners adjust the level of leg stiffness toward the most optimal degree to maintain consistent running mechanics on different surfaces. This could be important, particularly in cross-country runners like those in the present study where competitions often take place on a variety of undulating surfaces in a single competition. Conversely, the training-induced alterations in biomechanical measures support PRT training and therefore are not likely related to the changes in running economy, peak speed, or competition performance. Other studies have indicated that these biomechanical adaptations also occurred in response to plyometric training (36,42). Collectively, these findings suggest that HRT had a positive influence on cross-country running performance because of the improved running economy, peak speed, and neuromuscular characteristics.

Finally, it was not surprising to observe the magnitude of improvement in maximal strength (20%–40% in the leg press for most athletes) in our sample of distance runners with limited resistance training experience. The enhancements in 1RM strength from HRT were 30% and 50% greater than PRT in men and women, respectively, indicating a positive effect to HRT on strength parameters. The increased muscular strength due to resistance and/or plyometric training might primarily come from neural adaptations without observable muscle hypertrophy (16,39). The finding that no substantial change in body weight and small to moderate reductions in percent fat in both PRT and HRT groups, suggesting that little, if no hypertrophy occurred because of the resistance training interventions supports this suggestion. Increases in body mass are an undesirable side effect to resistance training that could be counterproductive to distance-running performance.

In conclusion, both HRT and PRT had a likely beneficial effect on competition times in women, whereas both treatments had possibly harmful effects in men. However, when comparing the two treatments, the addition of plyometric training to HRT was harmful to cross-country competition performance and most laboratory-based measures when compared with a matched volume-load HRT program. The greater improvements in competition performance and an enhancement in running economy and peak speed after HRT, compared with PRT, was probably a result of improvements in lower limb strength, leg stiffness, and utilization of stored elastic energy. Overall, our data indicate that women should include HRT in their programs, but men may want to implement such training in season with caution until more research establishes characteristics of positive or negative responders.

The authors thank Hope College for their generosity and the use of laboratory space and equipment, the Men’s and Women’s Cross Country teams at Hope College for participating in this research study, and the Salome Emmanuel and Kate Nelson-Nix for their assistance during data collection.

No funding was received for this study, and the authors have no professional relationship with a for-profit organization that would benefit from this study.

Publication does not constitute endorsement by the American College of Sports Medicine.

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1. Albracht K, Arampatzis A. Influence of the mechanical properties of the muscle-tendon unit on force generation in runners with different running economy. Biol Cybern. 2006; 95: 87–96.
2. Arampatzis A, De Monte G, Karamanidis K, Morey-Klapsing G, Stafilidis S, Bruggemann GP. Influence of the muscle-tendon unit’s mechanical and morphological properties on running economy. J Exp Biol. 2006; 209: 3345–57.
3. Barnes KR, Hopkins WG, McGuigan MR, Kilding AE. Effects of different uphill interval-training programs on running economy and performance. Int J Sports Physiol Perform. In press.
4. Billat LV, Koralsztein JP. Significance of the velocity at V˙O2max and time to exhaustion at this velocity. Sports Med. 1996; 22: 90–108.
5. Brooks GA, Fahey TD, White TP. Exercise Physiology : Human Bioenergetics and Its Applications. 2nd ed. Mountain View (CA): Mayfield Pub. Co.; 1996. p. xviii, 750.
6. Cavagna GA, Franzetti P, Heglund NC, Willems P. The determinants of the step frequency in running, trotting and hopping in man and other vertebrates. J Physiol. 1988; 399: 81–92.
7. Cavagna GA, Kaneko M. Mechanical work and efficiency in level walking and running. J Physiol. 1977; 268: 467–81.
8. Cavanagh PR, Kram R. Mechanical and muscular factors affecting the efficiency of human movement. Med Sci Sports Exerc. 1985; 17 (3): 326–31.
9. Conley DL, Krahenbuhl GS. Running economy and distance running performance of highly trained athletes. Med Sci Sports Exerc. 1980; 12 (5): 357–60.
10. Dalleau G, Belli A, Bourdin M, Lacour JR. The spring-mass model and the energy cost of treadmill running. Eur J Appl Physiol. 1998; 77: 257–63.
11. Daniels JT. Daniels’ Running Formula. Champaign (IL): Human Kinetics; 1998.
12. Daniels JT. A physiologist’s view of running economy. Med Sci Sports Exerc. 1985; 17 (3): 332–8.
13. Daniels JT. Running with Jim Ryun: a five-year study. Phys Sportsmed. 1974; 2: 62–7.
14. Daniels JT, Daniels N. Running economy of elite male and elite female runners. Med Sci Sports Exerc. 1992; 24 (4): 483–9.
15. Guglielmo LG, Greco CC, Denadai BS. Effects of strength training on running economy. Int J Sports Med. 2009; 30: 27–32.
16. Hakkinen K. Neuromuscular adaptation during strength-training, aging, detraining, and immobilization. Crit Rev Phys Rehab Med. 1994; 6: 161–98.
17. Hickson RC, Dvorak BA, Gorostiaga EM, Kurowski TT, Foster C. Potential for strength and endurance training to amplify endurance performance. J Appl Physiol. 1988; 65: 2285–90.
18. Hoff J, Helgerud J, Wisloff U. Maximal strength training improves work economy in trained female cross-country skiers. Med Sci Sports Exerc. 1999; 31 (6): 870–7.
19. Hopkins WG. Assigning aubjects to groups in a controlled trial. Sportscience. 2010; 14: 7–12.
20. Hopkins WG. Measures of reliability in sports medicine and science. Sports Med. 2000; 30: 1–15.
21. Hopkins WG. A spreadsheet for deriving a confidence interval, mechanistic inference and clinical inference from a p value. Sportscience. 2007; 11: 16–20.
22. Hopkins WG. A spreadsheet to compare means of two groups. Sportscience. 2007; 11: 22–3.
23. Hopkins WG, Hewson DJ. Variability of competitive performance of distance runners. Med Sci Sports Exerc. 2001; 33 (9): 1588–92.
24. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc. 2009; 41 (1): 3–13.
25. Johnston R, Quinn T, Kertzer R, Vroman N. Strength training in female distance runners: impact on running economy. J Strength Cond Res. 1997; 11: 224–9.
26. Jones AM. A five year physiological case study of an Olympic runner. Br J Sports Med. 1998; 32: 39–43.
27. Kerdok AE, Biewener AA, McMahon TA, Weyand PG, Herr HM. Energetics and mechanics of human running on surfaces of different stiffnesses. J Appl Physiol. 2002; 92: 469–78.
28. Kraemer WJ, Fleck SJ, Evans WJ. Strength and power training: physiological mechanisms of adaptation. Exerc Sport Sci Rev. 1996; 24: 363–97.
29. Kyrolainen H, Belli A, Komi PV. Biomechanical factors affecting running economy. Med Sci Sports Exerc. 2001; 33 (8): 1330–7.
30. Lander J. Maximums based on reps. Natl Strength Cond Assoc J. 1985; 6: 60–1.
31. Lucia A, Esteve-Lanao J, Olivan J, et al. Physiological characteristics of the best Eritrean runners-exceptional running economy. Appl Physiol Nutr Metab. 2006; 31: 530–40.
32. Lucia A, Olivan J, Bravo J, Gonzalez-Freire M, Foster C. The key to top-level endurance running performance: a unique example. Br J Sports Med. 2008; 42: 172–4; discussion 4.
33. Millet GP, Jaouen B, Borrani F, Candau R. Effects of concurrent endurance and strength training on running economy and V˙O2 kinetics. Med Sci Sports Exerc. 2002; 34 (8): 1351–9.
34. Noakes TD. Implications of exercise testing for prediction of athletic performance: a contemporary perspective. Med Sci Sports Exerc. 1988; 20: 319–30.
35. Noakes TD, Myburgh KH, Schall R. Peak treadmill running velocity during the V˙O2 max test predicts running performance. J Sports Sci. 1990; 8: 35–45.
36. Paavolainen L, Hakkinen K, Hamalainen I, Nummela A, Rusko H. Explosive-strength training improves 5-km running time by improving running economy and muscle power. J Appl Physiol. 1999; 86: 1527–33.
37. Paavolainen L, Nummela A, Rusko H, Hakkinen K. Neuromuscular characteristics and fatigue during 10 km running. Int J Sports Med. 1999; 20: 516–21.
38. Peterson MD, Pistilli E, Haff GG, Hoffman EP, Gordon PM. Progression of volume load and muscular adaptation during resistance exercise. Eur J Appl Physiol. 2011; 111: 1063–71.
39. Sale DG. Neural adaptation to resistance training. Med Sci Sports Exerc. 1988; 20 (8 Suppl): S135–45.
40. Saltin B, Larsen H, Terrados N, et al. Aerobic exercise capacity at sea level and at altitude in Kenyan boys, junior and senior runners compared with Scandinavian runners. Scand J Med Sci Sports. 1995; 5: 209–21.
41. Saunders PU, Cox AJ, Hopkins WG, Pyne DB. Physiological measures tracking seasonal changes in peak running speed. Int J Sports Physiol Perform. 2010; 5: 230–8.
42. Saunders PU, Telford RD, Pyne DB, et al. Short-term plyometric training improves running economy in highly trained middle and long distance runners. J Strength Cond Res. 2006; 20: 947–54.
43. Spurrs RW, Murphy AJ, Watsford ML. The effect of plyometric training on distance running performance. Eur J Appl Physiol. 2003; 89: 1–7.
44. Stone M, O’Bryant H, Pierce K, et al. Periodization: effects of manipulating volume and intensity. Part 1. Strength Cond. 1999; 21: 56–62.
45. Stone MH, Stone M, Sands B. Principles and Practice of Resistance Training. Champaign (IL): Human Kinetics; 2007. p. vii, 376.
46. Storen O, Helgerud J, Stoa EM, Hoff J. Maximal strength training improves running economy in distance runners. Med Sci Sports Exerc. 2008; 40 (6): 1087–92.
47. Stratton E, O’Brien BJ, Harvey J, et al. Treadmill velocity best predicts 5000-m run performance. Int J Sports Med. 2009; 30: 40–5.
48. Turner AM, Owings M, Schwane JA. Improvement in running economy after 6 weeks of plyometric training. J Strength Cond Res. 2003; 17: 60–7.
49. Vandenbogaerde TJ, Hopkins WG, Pyne DB. A competition-based design to assess performance of a squad of elite athletes. Med Sci Sports Exerc. 2012; 44 (12): 2423–27.


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