Success in distance running can be attributed to a variety of physiological and biomechanical factors (1). From a physiological perspective, energy acquired via aerobic means contributes a significant proportion to performance outcomes of middle- and long-distance events (2). Indeed, several studies have demonstrated that aerobic qualities such as maximal oxygen uptake (V˙O2max), the speed associated with V˙O2max (sV˙O2max), running economy (RE), and submaximal lactate values have a strong relationship with distance running performance (3–5). These variables have also been shown to be important predictors of performance in adolescent distance runners (6,7).
In addition to an obvious need to develop aerobic qualities, it is apparent that the neuromuscular system plays an important role in optimizing distance running performance (8,9). RE, the metabolic cost of running a given distance, is underpinned by physiological attributes, anthropometrics, and biomechanics (10); however, there is also emerging evidence demonstrating that strength training enhances RE in trained distance runners (11–14). The proposed mechanism for this improvement relates to enhancements in neuromuscular characteristics such as lower limb stiffness and force-producing ability (15).
There is also convincing evidence that strength training is safe and effective for adolescent athletes (16). Current guidelines suggest that adolescents should participate in 2–3 supervised resistance training sessions per week (17). Studies that have investigated the effects of resistance training in youth populations have tended to focus on the development of strength-related qualities in prepubertal and peripubertal participants, which underpin a variety of different sports skills. Resistance training can also positively influence sprint performance (5–40 m), beyond that which would be expected with maturation alone (18). Mikkola and coauthors (19) provide the only study to investigate the effect of a strength training intervention on markers of performance in postpubertal runners (16–18 yr). Replacing 19% of total running volume with explosive strength training exercises for 8 wk improved neuromuscular and anaerobic characteristics, but without any significant effect on aerobic performance markers. The strength training activities (sprints, jumps, and low-load resistance training) were performed in low frequency (each on average once per week), and resistance training primarily targeted single-joint actions. It is recommended that distance runners incorporate 2–3 strength training sessions per week (20) and use multijoint closed-chain exercises, which provide a high level of mechanical specificity to the running action (21). Therefore, the effect of a strength training program, involving multijoint resistance exercises performed more than once per week by adolescent runners, on determinants of distance running performance remains unknown.
Accordingly, the purpose of this study was to examine the effect of supplementing postpubertal adolescent distance runners with strength training on the physiological and strength-related indicators of performance. It was hypothesized that the addition of strength training would result in superior improvements in RE, sV˙O2max, maximal speed, and strength measures compared with the control group (CG).
A sample size estimation of n = 20 was calculated a priori on the basis of a statistical power of 80%, at a 5% probability threshold, and an effect size (ES) of 0.67 for the primary outcome variable, RE. Typical error (TE) and minimal detectable change at the 95% confidence level (MDC95) for RE were derived from a previous reliability study in this population (22). On the basis of an anticipated 20% dropout, 25 participants (13 female; mean ± SD age, 17.2 ± 1.2 yr; range, 15.2–18.8 yr) initially volunteered to take part. The study received institutional-level ethical approval and was conducted in accordance with the Declaration of Helsinki. Participants were required to meet the following inclusion criteria: age of 15–18 yr, no formal strength training experience, free from injury in the month preceding the study, and competed regularly at county, regional, national, or international level in middle- (800–3000 m) or long-distance (5–10 km and cross-country) running. A parent/guardian provided a signature of consent before participation, and in the case of those 18 yr old, consent was provided by the participant themselves.
After baseline testing, participants were assigned to a strength training group (STG) or a CG using a pretest matched-pairs approach. Participants were ranked according to their baseline RE, paired, and randomly allocated to either the STG (n = 13) or CG (n = 12). This approach reduces the bias associated with randomization, because it decreases the likelihood of differences between study groups at baseline.
Testing took place for 2 d before and after the intervention period, at the same time of day for each participant, and under similar laboratory conditions (temperature, 16°C–20°C; relative humidity, 36%–54%; barometric pressure, 746–773 mm Hg). The first testing session involved measurements of anthropometrics, a submaximal running assessment, and a maximal running test. After 30 min of passive recovery, participants were familiarized with the strength tests. The second testing session took place 48–72 h later and was used to test participant’s maximal speed and force-producing capabilities under dynamic and isometric conditions. Every effort was made to schedule testing sessions on the same days before and after intervention to maximize the likelihood that participants would adhere to requests to adopt a similar pattern of exercise and diet in the 48 h prior.
Before each running trial, participants’ body mass was measured digitally to the nearest 0.1 kg (MPMS-230; Marsden Weighing Group, Oxfordshire, UK). Stature and sitting height were measured using a stadiometer to the nearest 1 cm (SECA GmbH & Co, Hamburg, Germany). Maturity offset was calculated for each participant from age, stature, and sitting height values using published formulae (23). The sum of skinfolds at four sites (biceps, triceps, subscapula, suprailiac) was assessed using calipers (Harpenden; Baty International, West Sussex, UK) according to International Society for the Advancement of Kinanthropometry guidelines.
Submaximal and maximal running tests
All running tests took place in the same physiology laboratory on a motorized treadmill (HP Cosmos Pulsar 4.0; Cosmos Sports & Medical GmbH, Munich, Germany). Expired air was collected via a low dead-space mask and monitored continuously via an automated open circuit metabolic cart (Oxycon Pro; Enrich Jaeger GmbH, Hoechberg, Germany) to quantify pulmonary ventilation, oxygen uptake (V˙O2), carbon dioxide production, and RER. Heart rate (HR) was also recorded continuously throughout the test (Polar RS400; Polar Electro Oy, Kempele, Finland). After a 5-min warm-up, participants completed a discontinuous incremental test at a 1% gradient (24) to determine RE, HR, and lactate response. Participants’ most recent race performances and their HR response during warm-up were used to determine the start speed and provide at least four speeds before lactate turn point. The test consisted of five to seven 3-min running stages with speed increases of 1 km·h−1 each stage, separated by 30-s rest to allow for a 20-μL sample of capillary blood to be taken from the earlobe. Each sample was hemolyzed and subsequently analyzed for blood lactate concentration (Biosen C-Line; EKF Diagnostic, Barleben, Germany). The test was discontinued when the rise in lactate exceeded 1 mmol·L−1 compared with the previous stage, which defined their speed at lactate turn point (sLTP).
The data analysis process used to obtain values for RE and V˙O2max has been described previously (22). Breath-by-breath data were initially filtered to remove any errant breath, which did not represent the underlying physiological response (25). The mean values for V˙O2, carbon dioxide production, RER, and HR from the final 60 s of the stage corresponding to sLTP and the two speeds prior (sLTP −1 km·h−1, sLTP −2 km·h−1) were used in subsequent analysis. The V˙O2 value was used with the RER value to quantify the energy cost of running using nonprotein quotient equations (26), which is likely to provide a more valid (27) and reliable (22) measure of RE compared with oxygen cost. Because sLTP varied across participants, RE was expressed as the energy cost of running per kilometer. Speed at fixed concentrations of blood lactate (sFBLC) was estimated from the speed–lactate curve for 2, 3, and 4 mmol·L−1 (s2, s3, and s4 mmol·L−1) using published software (28).
After the submaximal running test, participants rested for 5 min before completing a continuous incremental treadmill test to volitional exhaustion to determine V˙O2max. The treadmill belt was set to sLTP, and the gradient was initially set at 1%. Thereafter, the gradient was increased by 1% every minute until volitional exhaustion, which typically took 6–8 min. V˙O2max. was taken as the highest V˙O2 achieved in a 30-s period (after filtering). Speed at V˙O2max. (sV˙O2max) was predicted for each participant by using the equation for the linear regression line for the relationship between V˙O2 and speed extrapolated to the V˙O2max value. The linearity of regression lines for participants across both trials was R2 = 0.981 ± 0.02. Prior test–retest reliability work, using a cohort with similar characteristics, demonstrated high intersession reliability for physiology variables (22).
Speed and strength tests
After a self-paced 3 min warm-up run, participants performed two submaximal 20-m sprints from a rolling start, followed by three maximal timed sprints (Brower Timing Systems, Draper, UT) in an indoor sports hall. Each sprint was interspersed by a 2-min walk recovery. Participants were instructed to initiate their sprint with a sufficiently long approach to enable maximal speed to be reached by the first set of timing gates. To assess dynamic strength capabilities, participants performed three squat jumps for maximum height on a fixed-force plate sampling at 1000 Hz (Kistler 9287BA; Kistler Instruments Ltd, Hampshire, UK). Each attempt was separated by a 90-s passive recovery. Participants were instructed to place their hands on their hips and squat down to a comfortable position, hold this position for 3 s, and on a signal provided by the tester, jump as high as possible. If there was an indication on the force trace that a counter-movement had been used before initiation of the jump, the attempt was repeated. Peak displacement of the centre of mass was estimated using the velocity at take-off method (29). Peak vertical ground reaction force (vGRFjump) was recorded as the highest force produced during the concentric phase of the jump.
Maximal voluntary contraction (MVC) was assessed in a custom-built adjustable back-squat rig. Participants gripped a fixed bar, positioned across their upper back, and adopted a quarter-squat position with knees flexed at 140°. This position was determined during the familiarization session; thus, an identical set-up was used in subsequent trials. Participants stood on a force plate (PASPORT PS2141; PASCO, Roseville, CA) measuring at 1000 Hz and were instructed to push against the bar as hard as possible for 3–4 s. Two warm-up repetitions preceded three recorded attempts in which strong verbal encouragement was provided. Attempts were each separated by 90 s of rest. MVC was defined as the highest force value produced during the contraction. The best score over the three attempts was used in subsequent analysis for each test. The intersession reliability values (TE, intraclass correlation coefficient, MDC95) for speed (0.34%, 0.99, 1.0%), peak displacement (4.89%, 0.94, 13.5%), vGRFjump (5.71%, 0.50, 15.8%), and MVC (5.10%, 0.65, 14.1%) were considered acceptable in a group of adolescent distance runners (six girls, six boys, 17.8 ± 1.4 yr).
To account for differences in body mass between individuals, a ratiometric index has tended to be favored in similar studies for scaling parameters relating to V˙O2 (12,13,19). This scaling approach is only valid if the relationship between body mass and a physiological variable is directly proportional, which is rarely the case (30). To calculate appropriate scaling exponents for variables used in the present study, data from a larger cohort of adolescent distance runners (n = 42) were log transformed, and after an ANCOVA comparison for male and female participants, a common power function was calculated via linear regression. An exponent of two-thirds (95% confidence interval (CI), 0.34–0.98 for V˙O2max and 0.41–0.90 for V˙O2) was previously established for V˙O2 parameters (22), and applying the same mathematical process in a similar cohort of participants (n = 36), values of 0.76 (95% CI, 0.33–1.20) and 0.61 (95% CI, 0.03–1.22) were established for vGRFjump and MVC, respectively.
Both groups were instructed to continue their normal running training throughout the study period. The study took place during early off-season training period (September–December); therefore, participants were predominantly performing high-volume, low-intensity running. Participants maintained training logs, which detailed their daily running volume and the pace associated with each training session.
The STG supplemented their program with two sessions (60–70 min in duration) of strength training per week, each separated by 2–4 d. After a week of familiarization with exercise technique and equipment, participants completed a 10-wk program of progressive strength training, as shown in Table 1. Recent work has indicated that 6- to 8-wk programs elicit relatively small changes in RE, whereas programs of 10 wk or longer provides moderate-large effects (20). Each session commenced with a warm-up designed to enhance movement skill and mobility. The second part of the session involved plyometric- and sprinting-based exercises designed to improve explosive and reactive strength. The final part of each session was dedicated to resistance training primarily using free weights (barbells and dumbbells). Exercises that possessed similar kinematic characteristics to the running action were selected. Every session was supervised by professionally accredited strength and conditioning coaches. Intensity of each exercise was moderated based on each participant’s technical ability and perceived effort, with load on resistance training exercises typically progressing by 5%–10% per week within a mesocycle.
An ANCOVA was performed (SPSS v22; IBM, Armonk, NY) on each dependent variable using baseline scores as the covariate, which adjusts for any chance imbalance between the STG and CG. The assumptions associated with ANCOVA were verified for all variables via Levene’s test for homogeneity of variance, Shapiro–Wilk test for the assumption of normality, and a customized ANCOVA model to assess homogeneity of regression. A multivariate analysis of variance with a Bonferroni post hoc correction was used to compare the data from training logs between groups. Significance was accepted at the P < 0.05 level with a 95% CI.
To facilitate more widespread use of our findings in applied settings, ES and magnitude-based inferences were identified to provide a more qualitative interpretation of the extent to which changes observed were meaningful. ES values were calculated (Microsoft Excel 2013) as a ratio of the difference between the mean change value for each group and the pooled SD at baseline for all participants, and were interpreted as trivial, <0.2; small, 0.2–0.6; moderate, 0.6–1.2; and large, >1.2 (31). For each variable, the MDC95, calculated using the TE of measurement for this group of participants (22), was entered along with the P value and ES into a published spreadsheet (32) to obtain the likelihood that the intervention was beneficial (or indeed harmful) to the population. The MDC95 represents the magnitude required for a change in score to be considered clinically meaningful, and therefore provided a robust threshold to judge the efficacy of the intervention. The resulting values were translated into descriptors using the modified thresholds proposed by Batterham and Hopkins (31): 0%–0.5%, most unlikely; 0.5%–5%, very unlikely; 5%–25%, unlikely; 25%–75%, possibly; 75%–95%, likely; 95%–99.5%, very likely; and >99.5%, most likely.
Interindividual responses to the intervention were considered by calculating the true individual difference in response using the following formula:
where SDSTG and SDCG represent the SD of the change score for the STG and CG groups, respectively. In this instance, it is more appropriate to use the SD of the CG change value as the comparator variable, rather than the TE derived from a short-term reliability study in this population (22), as within-subject biological variation is likely to increase over time (33). Descriptive statistics are presented as mean ± SD.
On the basis of maturity offset values, all participants were considered postpubertal (≥1.0 yr), even when the SE associated with the predictive equation was accounted for (23). Seven participants withdrew during the course of the study for the following reasons: injury (STG, n = 3; CG, n = 1), illness (STG, n = 1), time commitment (CG, n = 1), and voluntary dropout (CG, n = 1). The injuries that occurred in the STG were diagnosed as overuse type injuries that could not be directly attributed to the intervention. No other adverse effects were reported during the intervention period. The final sample consisted of nine participants in the STG (five girls, four boys) and nine in the CG (five girls, four boys). Group characteristics are shown in Table 2, with V˙O2max shown as a ratio to body mass for comparative purposes.
Table 3 displays a summary of the training undertaken by participants during the intervention period. Participants typically undertook 2–3 extensive interval training sessions per week at sLTP or faster. These were performed on the same days across the cohort. The remaining volume of running was undertaken at speeds below sLTP; however, interindividual variation was high (135 ± 74 min·wk−1). No significant differences (P > 0.05) between groups were noted in total training time, total running duration, running at low (<sLTP) and high (>sLTP) intensities (ES, 0.17) and aerobic cross-training (ES, 0.01). However, moderate ES values (0.6–0.7) were observed for the difference in total running duration in favor of the CG. Strength training time differed significantly between groups (F(1,16) = 44.96, P < 0.001; ES, 1.67). Engagement with strength training was high in the STG, with all participants completing ≥85% of sessions over the 10-wk intervention.
Body composition and running measures
ANCOVA revealed no significant differences between groups after training for body mass (F(1,16) = 0.98, P = 0.338), skinfolds (F(1,16) = 4.15, P = 0.060), V˙O2max (F(1,16) = 0.48, P = 0.499), sV˙O2max (F(1,16) = 1.11, P = 0.308), RE at LTP (F(1,16) = 0.57, P = 0.463), RE at LTP −1 km·h−1 (F(1,16) = 1.39, P = 0.256), RE at LTP −2 km·h−1 (F(1,16) = 2.34, P = 0.147), s2 mmol·L−1 (F(1,16) = 0.54, P = 0.474), s3 mmol·L−1 (F(1,16) < 0.01, P = 0.980), and s4 mmol·L−1 (F(1,16) = 0.01, P = 0.917). Table 4 shows changes in body composition and physiological parameters for each group and between-group comparisons. Body mass displayed a mean increase of (95% CI) 0 to 2.4% in the STG group, which was most likely trivial compared with the CG (ES, 0.08). Skinfold measures also exhibited minimal changes in both groups (ES, 0.24). V˙O2max displayed trivial changes (ES, 0.07) in both groups, and sV˙O2max improved in the STG by only a small margin (95% CI, −2.0% to 8.9%), which compared with the CG was likely trivial (ES, 0.34). RE improved between 3.2% and 3.7% and by a magnitude that approximated the MDC95 values at all three speeds in the STG group; however, increases were relatively small (ES, 0.31–0.51) and only considered “possibly beneficial.” Figure 1A shows the change in average RE for three speeds, which was also considered “possibly beneficial” (ES, 0.44; small) compared with the CG. sFBLC improved to a small extent (3.4%–5.8%) in both groups, but between-group effects were trivial (ES, 0.09–0.10). Within-group differences were considered “likely beneficial” or “very likely beneficial” for both groups.
Speed and strength measures
As shown in Figure 1B, 20-m sprint time improved by −0.10 s (95% CI, 1.8%–5.4%; ES, 0.32; small) in the STG, which generated a significantly faster time compared with the CG after training (F(1,16) = 7.86, P = 0.013) and was considered “very likely beneficial.” The STG also displayed significantly greater MVC at follow-up (F(1,16) = 5.07, P = 0.040; ES, 0.86; moderate) compared with the CG, a change which was deemed “possibly beneficial” (95% CI, 6.3%–24.5%; Table 5). The magnitude of between-group change in peak displacement was “most likely trivial” (ES, 0.10) and the difference was nonsignificant (F(1,16) = 0.18, P = 0.682). vGRFjump improved to a moderate extent (95% CI, −1.9% to 14.1%) in the STG compared with the CG (ES, 0.93), but this change was considered “most likely trivial” in the context of the MDC95 threshold (Table 5).
Interindividual differences in response could mainly be explained by the within-participant variability in change scores, because for all but one variable (RE at sLTP), the SD for pre-to-post differences was larger in the CG group compared with the STG group (see Tables 4 and 5). In standardized units, the individual response for RE at sLTP was 0.18, which indicates that individual responses were trivial between groups.
The primary aim of this study was to investigate the physiological effects of 10 wk of strength training in a group of competitive postpubertal distance runners. It was anticipated that the STG would demonstrate superior improvements in RE, sV˙O2max, sprint speed, and neuromuscular parameters compared with a CG. The main finding was that strength training provides a small benefit (3.2%–3.7%) to RE across a range of submaximal speeds, which can be considered “possibly beneficial.” Strength training is also likely to provide significant benefits to maximal sprint speed and isometric strength in runners of this age.
The findings of this study are in agreement with those of a recent meta-analysis in mainly adult runners, which showed that concurrent strength and endurance training can provide a small beneficial effect (3.9% ± 1.2%) to RE over a 6- to 14-wk period (20). Our results are also similar to the only other study that has investigated the efficacy of strength training in adolescent distance runners, which demonstrated small improvements (2.0%–2.7%; ES, 0.26–0.40) in RE at 12 and 14 km·h−1, and trivial changes at 10 and 13 km·h−1 (19). The superior effects we observed at all three speeds assessed (3.2%–3.7%; ES, 0.31–0.51) may be due to the longer intervention period (10 vs 8 wk), higher frequency of exposure to each type of strength training activity (2 vs 1 d·wk−1), and the choice of resistance training exercises (multijoint vs single joint). It is noteworthy that the intervention group in the study by Mikkola et al. (19) performed almost double the volume of training compared with the STG in the present study (273 ± 88 vs 528 ± 126 min·wk−1). Moreover, the CG in the present study spent 41% more time running than did the STG (ES, 0.69). This suggests that for the adolescent distance runner, strength training may be more effective than increasing endurance training volume at improving RE, at least in the short term. It is also possible that the moderate disparity in low-intensity running volume between the groups was advantageous to the STG group because less running may have facilitated the recovery process (34). Despite the apparent trend toward an improvement in RE, it is important to note that the change scores did not exceed the MDC95 for any speed or an average of measurements (Fig. 1A), indicating that only a possible benefit exists at specific speeds when TE of measurement is taken into account. A longer intervention period may therefore be required to provide higher certainty that strength training provides a practically significant benefit.
Neuromuscular factors, such as muscle activation and musculotendinous stiffness, play an important role in distance running (9,35); therefore, strategies to enhance these qualities are likely to lead to an improvement in physiological efficiency. A significant improvement in maximal force–producing capability was observed in the STG (95% CI, 6.3%–24.5%; ES, 0.86), which is in line with findings from previous studies in adult distance runners over a similar time frame (14,36). The strength training program, which included plyometrics, sprinting, and resistance training, was also shown to provide a small but very likely benefit to maximal sprint speed (95% CI, 1.8%–5.4%; ES, 0.32), an improvement that was more than three times higher than the MDC95 value. Maximal speed is an important anaerobic quality required for middle-distance running (37) and is also related to long-distance running performance (8,9). Maximal sprinting requires higher-ground reaction forces compared with submaximal running (38); therefore, this finding provides evidence that strength training can improve neuromuscular characteristics during a highly functional assessment of explosive strength in runners. Peak displacement and vGRFjump displayed changes that fell well within MDC95 limits; thus, the effect of strength training was at best trivial. The specificity of the exercises used in the strength training program (Table 1) may provide an explanation for this finding, because very little maximal concentric-dominant jumping was included. A relatively higher volume of near-maximal sprinting and loaded exercises that mimic a quarter-squat position were included, which seems to have provided a sufficiently high transfer of training effect to enhance 20-m sprint and MVC. The possibility that the bodyweight movement skill exercises included in the warm-up routine also contributed toward the improvements observed cannot be discounted. Dynamic postural control exercises reduce coactivation of muscles in the lower limb, which may have enhanced efficiency during running via improvements in stabilization strategy (39).
Despite our prediction that sV˙O2max would improve to a greater extent in the STG, this was not the case (95% CI, −2.0% to 8.9%; ES, 0.34; likely trivial benefit). sV˙O2max provides a composite measure of physiological performance that seems to differentiate adolescent runners with greater accuracy than traditional determinants (6). Our findings are in agreement with other works that used a similar intervention duration (12,19), but differ from studies that lasted ≥14 wk (11,13), suggesting that longer time frames may be required to realize a positive effect. It is also likely that large improvements in constituent qualities (V˙O2max, RE) are required to elicit a meaningful change in sV˙O2max. Although RE displayed small improvements, V˙O2max showed little alteration, implying that a greater stimulus may be required to influence these variables.
After an 11-wk period of running training, it was expected that aerobic variables would exhibit improvements in a group of adolescent athletes. The intervention period provided a small (3.4%–5.8%) but very likely or likely benefit to sFBLC in both groups, suggesting that the running training caused metabolic adaptations (40), which were not augmented by strength training (ES, 0.09–0.10; trivial). The lack of change in V˙O2max in both groups corroborates findings from previous investigations (11–14,36). Improvements in aerobic capacity are influenced by a variety of factors including initial training status, and the duration and nature of training conducted (41). Both groups spent 25%–28% of their running training above sLTP, an intensity that is likely to have provided a strong stimulus for improving V˙O2max (42). Therefore, it seems that the study duration and the initial fitness level of participants provide the most likely explanation for the unaltered values observed. Despite the absence of change in several parameters, it is notable that strength training caused no deleterious effects in physiological predictors of performance despite the STG spending of ~40% less time running compared with the CG.
Increases in body mass are potentially disadvantageous to distance runners; therefore, gains in muscle mass, which is often an inevitable consequence of strength training, are unfavorable. Although the CI for the change in body mass in the STG did not overlap zero (95% CI, 0%–2.4%), the differences between groups were most likely trivial (ES, 0.08). Furthermore, any slight increase in body mass in the STG did not adversely affect the physiological variables that were allometrically scaled for body mass. Despite the association between resistance training and a hypertrophy response (43), there is consensus that strength training has little effect on body mass in distance runners, at least in the short to medium term (20). The interference phenomenon, which is often observed when endurance and strength training are performed concurrently within the same program, has been offered as one explanation (44). The impairment of muscle fiber hypertrophy is likely to occur under conditions of energy depletion (45), or when strength training is performed alongside a high-frequency and high-intensity endurance exercise (46). Given the relatively low volume of endurance training undertaken by the STG (Table 2), the interference effect was perhaps less likely. Therefore, practitioners should be cognizant that gains in muscle mass may occur over longer periods if a low-volume of running is performed.
This study is subject to a number of limitations. First, with the exception of sprint time, the measures taken in this study were laboratory based; thus, it is not known what effect the training intervention had on middle- or long-distance performance. Second, the participants in the cohort were of both sexes and mixed event specialisms and abilities; therefore, had a more homogenous group been targeted, firmer conclusions would have been possible. Third, the scaling exponents used for normalization of body mass were derived from relatively small samples (n ≤ 42), which may have generated small errors during the calculation of values. Although we do not believe that these errors are sufficiently large to alter the findings of this study, the changes observed in RE were equal to or slightly less than the MDC95 at each speed (Table 4); therefore, a more accurate scaling factor may have provided greater confidence that the changes observed were meaningful. Finally, the study was conducted during the early off-season, which was characterized by training of a more extensive nature, known to cause interference with strength adaptation (44). It is not known what effect a strength training program would have on physiological parameters during a different training phase, particularly one that had a larger emphasis on intensive training.
In conclusion, the addition of low-frequency (2 d.wk−1) strength training to the program of an adolescent distance runner is possibly beneficial for RE at specific speeds and very likely to benefit maximal sprint speed, which are both important factors for middle- and long-distance running performance. It was speculated that changes in neuromuscular characteristics, such as maximal force–producing capability, underpin the small improvements in RE observed. A 10-wk period of strength training was insufficient to alter sV˙O2max; therefore, further studies are required to investigate the time course of change in this and other determinants. There seems to be little risk that strength training increases body mass; any change over a period of 2–3 months is likely to be trivial.
This article is dedicated to the memory of Lucy Pygott, a participant in this study who sadly passed away shortly after its completion.
The authors thank the British Milers Club for providing funding for this research. The technical support provided by Jack Lineham and Ian Grant during this project is also greatly appreciated. The authors would also like to thank the participants and their parents/guardians for the time they committed to this study.
The authors report no conflict of interests. The results of the present study do not constitute endorsement by the American College of Sports Medicine. The results of this study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.
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