Oxygen consumption is a key to sustain the repeated muscle contractions required for endurance exercise. There are 3 major physiological factors that determine power output that can be transformed into performance pace in distance running: (a) gross energy cost of running, commonly referred to as work economy or running economy (RE), (b) maximal aerobic power (V[Combining Dot Above]O2max), and (c) the ability sustain a high percent of V[Combining Dot Above]O2max (fractional utilization of V[Combining Dot Above]O2max, % V[Combining Dot Above]O2max) for an extended period of time (14,20,23,46,54). These 3 factors explain more than 70% of interindividual variance in long-distance running performance (20). Among a homogenous group of endurance athletes, RE may be a better predictor of performance than V[Combining Dot Above]O2max (14,45,50). Indeed, variation in RE has been found to explain 65.4% of the variation observed in race performance in a 10-km race (14).
Research to elucidate the variables causing differences in RE is substantial (4,60,71). Nevertheless, it is not yet clear precisely which factors contribute to better RE. Running economy is believed to be influenced by gender (19), type of training (52,61), fitness level (51), peak stride forces (62), muscle stiffness (22), nitrate supplementation (44), and a number of other biomechanical and physiological factors (4,60,71). It seems that concurrent strength or (36,73) plyometrics training (61,67) and a distance running program may be common elements that improve RE. Researchers speculate that strength training increases RE in part by altering neuromuscular (52) and elastic components in the musculoskeletal system (8,61).
Turning to the relationship between RE and flexibility, stiff tendon structures have a favorable effect on stretch-shortening cycle exercise, such as running (3). Tendon stiffness may also enhance force and power production during dynamic muscle action (8). This may potentially increase muscle output and propulsion force for the same or less metabolic cost by augmenting effective force transmission from the contractile elements to the bone (8), thereby improving RE and consequently, performance (18,61). Studies of sit-and-reach flexibility among male (17,37) and female runners (66), as well as hamstring flexibility in untrained subjects (27), provide evidence of a negative relationship between musculoskeletal system flexibility and oxygen cost of locomotion. Moreover, knee extensor endurance (in the quadriceps muscles) has been shown to significantly correlate with RE in elite male middle-distance runners (70). Recent studies suggest that local eccentric and concentric muscular endurance in the knee flexors and hip flexors helps maintain stride mechanics by delaying kinematic changes correlated with fatigue during endurance running (29,30). Additionally, a study has found that hamstring strength is a singular correlate to performance in long-distance running (65).
Although hamstring and quadriceps strength seem to be critical components of RE, their relative importance is unknown. Currently, there is little research relating muscle ratios to functional tasks that are specific to a sport. Although research has shown that interventions of either strength exercises or plyometric exercises increase RE, it is still unclear by what mechanisms this occurs. Understanding the relationship between muscular strength distribution, flexibility, and aerobic capacity is important because such knowledge may advance training methods and thereby improve distance running performance.
The above findings showing a relationship between both flexibility and muscular strength with endurance performance may be due to both neuromuscular and elastic components in the muscles, or to how the muscles work together, that is, their agonist-antagonist relationship. To date, there has been little research examining the relative muscle strength ratios required to succeed in sports. To the best of our knowledge, this is the first report on f-H:Q ratios applied to functional tasks (RE and flexibility) that are specific to sport (distance running).
Therefore, the purpose of this study was twofold: First, to investigate the differences and the relationship between RE (gross oxygen cost of running) and functional hamstring to quadriceps torque ratios (f-H:Q) among highly trained (HT) and recreational (REC) female runners. Second, to examine RE and its relationship to differences in hamstring/trunk flexibility, and anthropometric variables commonly known to be associated with RE among the 2 trained states. In addition to these 2 main purposes, a secondary purpose of the study was to establish norms and provide recommendations for f-H:Q ratio values for healthy female runners. We hypothesized that (a) compared with REC runners, HT runners would possess higher f-H:Q ratios at low, medium, and high angular velocities; (b) that f-H:Q would correlate with better RE at submaximal velocities; and (c) that RE would be inversely correlated with flexibility scores on the sit-and-reach test.
Experimental Approach to the Problem
For testing our hypotheses, we used a cross-sectional design to investigate the impact of relative muscle strength, flexibility, and anthropometrics on gross oxygen cost of running (RE). To integrate the concept of studying the hamstring and quadriceps muscles in motion, we used a functional method of describing the hamstring to quadriceps relationship, referred to as functional hamstring to quadriceps ratios (f-H:Q) (2). The HT and REC female runners were divided according to their training status and race experience. HT runners in this study were defined as those who were (a) members of a running club or a university track and field running team at the time of the study, and who had, over the past 3 years, (b) run ≥40 miles per week, (c) run ≥4 times per week, and (d) competed in at least 3 races per year. The REC runners were recruited based on the following inclusion criteria: having run (a) 5–30 miles per week and (b) 2–4 times per week over the last 3 years.
Subjects reported to the laboratory on 2 separate occasions, separated by at least 48 hours. All test trials were completed within 2 weeks for each subject. The dependent variables in visit 1 included subjects' anthropometrics (stature, body mass, body composition, girth measures, and leg length), gross oxygen cost of running (RE), % of heart rate max (% HRmax), respiratory exchange ratio (RER), % of V[Combining Dot Above]O2peak (% V[Combining Dot Above]O2peak), and a maximal aerobic power test. On visit 2, sit-and-reach flexibility and peak muscular hamstring to quadriceps (H:Q) torque ratios at slow, medium, and fast velocities were measured. Slow, medium, and fast isokinetic velocities were defined as 60, 120, and 180°·s−1, respectively. Similar velocities have been used in previous investigations of f-H:Q ratios in healthy male Kenyan runners (40), and when examining c-H:Q ratios in healthy male and female intercollegiate varsity athletes (57). Girth and leg length variables were selected because of both findings from previous studies that examined factors influencing RE and because of their potential mechanical advantage.
The hypotheses were tested by determining the differences and correlations among the variables. The Student's t-test was the primary statistical tool used to determine differences in the dependent variables across the 2 trained groups. Pearson's correlation coefficients were used to identify possible relevant relationships. The independent variables were the velocity of the isokinetic dynamometer with 3 factors: 60, 120, and 180°·s−1, and the velocity of the treadmill with 2 factors: 160.9 and 201.2 m·min−1.
Twenty-one female runners, 10 highly trained, and 11 REC runners between the ages of 21–39 years (mean ± SD: 27.4 ± 5.0 years) from local running clubs and the local community were recruited on a voluntary basis to participate in the study. Exclusion criteria were any overt metabolic, endocrine, cardiovascular, neurological, or metabolic diseases; musculoskeletal injury related or unrelated to overtraining; consumption of medications or using any drugs that may influence the energy metabolism system; and pregnancy. In addition, exclusion criteria included any hamstring or quadriceps injury in the past 6 months. Before participation, all the subjects were informed about the experimental procedures, and the possible risks and discomforts associated with the study. The subjects then signed a written informed consent form before participating. This study was conducted according to the Declaration of Helsinki, and the protocol was fully approved by the San Francisco State University Institutional Review Board for Human Subjects (protocol number: H11-59).
The HT subjects consisted of competitive distance runners (n = 7) competing across a range of distances throughout the year (5k-marathon distances). Each subject in the HT group regularly undertook 1–2 interval and 1 continuous running sessions per week. Subject characteristics are listed in Table 1. The HT and REC runners reported their total training frequency the last year to be 9.1 (2.8) and 4.1 (1.1) times per week, respectively. Five and 6 subjects performed supplementary resistance training among the HT and REC runners, respectively. The REC runners reported that they performed most of their weekly running at low intensities.
Before testing on visit 1, the subjects completed a questionnaire on physical exercise background and present activity level, demographics, medical history, and previous injury history. Conventional H:Q ratios (Hcon:Qcon) have been reported to be much lower among injured individuals (57). Thus, the authors in the present study screened for recent injuries in the hamstring and quadriceps muscles. Past injuries could mask “true” values because the injury might have affected the athletes' ability to strength train or run hard workouts. Consequently, the results in the present study are deemed to be representative for healthy athletes.
Three of the HT runners reported hamstring injuries during the past 6 months and were excluded from the data. Table 1 presents the 18 remaining subjects' demographic variables. The HT and REC subjects (n = 18) had been running for 11.7 ± 4.3 years and 4.7 ± 2.3 years, respectively, before the study. All of the HT and all but one of the REC runners had run at least 3 or more times on a treadmill before participating in the study and were thus familiar with treadmill running already. None of the participants were familiar with the isokinetic dynamometer before participating in the study.
The study took place in the basic training phase for the HT runners, and all subjects were tested in the period between January 2012 and March 2012.
Before the tests, the subjects were given instructions on standardized behavior to follow. Subjects were instructed to arrive at the laboratory in a fully rested and hydrated state, at least 2 hours postprandial, and to avoid strenuous exercise for 24 hours before the test. Before visit 1, the subjects were instructed to bring their lightest shoes (racing shoes/lightweight trainers) and to refrain from any eating and drinking 2 hours before arrival. In addition, to avoid possible confounders related to ergogenic effects, the subjects were asked to refrain from consuming caffeine and alcohol 24 hours before testing on both visits. To participate in the study, each subject had to sign statements that they had followed these preparticipation procedures. All subjects were instructed to maintain and record their current training habits using a self-reported activity diary.
Before each test trial, the analyzers and flow meters were calibrated strictly according to the manufacturer's recommendations using certified commercial gas preparations of known oxygen and carbon dioxide concentration and a manual 3-L syringe (Hans-Rudolph, Kansas City, MO, USA). Temperature, barometric pressure, and humidity were calibrated and updated from an electronic device (Perception II; Davis Instruments, Hayward, CA, USA). During all V[Combining Dot Above]O2 testing, heart rate data were monitored continuously using a Polar FT1 heart rate monitor (Polar Electro OY, Kempele, Finland).
All V[Combining Dot Above]O2 determinations were performed on a motorized Quinton Q65 Series 90 treadmill (Quinton Instruments, Q65, Series 90; Seattle, WA, USA) following identical procedures as follows: The subjects were fitted with a mouthpiece for expired gas analysis. The researcher described the potential for discomfort from exercising with a mouthpiece and nose clip and explained the 15-grade scale for rating of perceived exertion (RPE) (9). Open circuit spirometry was used to calculate oxygen uptake (V[Combining Dot Above]O2) for steady state and maximal aerobic power (V[Combining Dot Above]O2peak) measures with a metabolic cart (Parvo Medics' True One 2400 [ParvoMedics Inc., Yorba Sandy, UT, USA]) by sampling breath-by-breath expired gases. Each subject had her nose clamped while breathing through a mouthpiece connected to a 2-way nonrebreathing valve. Ambient air was inspired through a dry hose, and expired air was directed to a mixing chamber.
To minimize diurnal biological deviations, the researchers planned to test each subject at the same time of day for both visits, however, the researchers were not able to do this for all subjects because of some of the participants' scheduling constraints. Each subject was advised to maintain a regular diet the day before testing. The procedures were administered to all subjects by the same investigator.
The laboratory conditions were relatively stable (temperature: 20.1 ± 0.5° C, humidity: 45.4 ± 7.9%) for the duration of the study.
Standing height was determined without shoes using a wall-mounted stadiometer (Doran Scales Inc., Batavia, IL, USA) to the nearest 0.1 cm, and measures were taken after a voluntary deep inspiration. Girth (circumference) measurements were taken to the nearest 0.1 cm with a measuring tape using a calibrated tension device (Creative Health Products Inc., Ann Arbor, MI, USA) as follows: leg length (anterior superior iliac spine to lateral malleolus), hip girth (maximum circumferences over the buttocks), thigh girth (midway between inguinal crease and the proximal border of patella), leg girth (maximal circumferences between knee and ankle while the leg measured was relaxed), and ankle girth (minimum circumferences just above lateral malleolus).
Body Composition Testing
Estimation of body composition (fat mass, fat-free mass [FFM], and percent body fat) was assessed through air displacement plethysmography using the BOD POD (BOD POD; Life Measurement Instruments, Concord, CA, USA). The BOD POD was calibrated before each test according to the manufacturer's instructions using a cylinder of known volume (50.094 L) and for body mass using two 10-kg weights. All examinations were performed by the same researcher. Body composition was measured using measured thoracic gas volumes (Vtgmeas) (48). Each subject wore only a swim cap and Speedo or Lycra spandex-type swimsuit or single-layer compression shorts and jog bra without padding or wires. Each subject's body mass (kg) was measured on a calibrated electronic scale connected to the BOD POD. Computer software calculated FFM and percent body fat using the Siri equation (60). After the BOD POD test, subjects were allowed to consume water ad libitum before and between the treadmill tests. Body mass index (BMI) was calculated as body mass in kilograms divided by height in meters squared (kg·m−2).
Running Economy Test
The RE test was preceded by a 10-minute warm-up at 4 mph (107.3 m·min−1) at 1% grade. Running economy was assessed by having the subjects run for 6-minute intervals at 6 and 7.5 mph, respectively (160.9 and 201.2 m·min−1). The submaximal running speed was set to no higher than 201.2 m·min−1 to make sure both groups ran at their steady-state oxygen consumption (25) and below their lactate threshold (60–90% of V[Combining Dot Above]O2max), where RE has been shown to be independent of running speed (32). A 1% incline was used to mimic outdoor conditions (38). Subjects had a 5-minute recovery period (standing fully connected to the metabolic cart) between the 2 intervals to make sure the intervals were performed at submaximal intensities. Serial, 30-second V[Combining Dot Above]O2 collections were made for each 6-minute interval. Running economy was defined as the steady-state oxygen consumption in ml·kg−1·min−1 obtained during each 6-minute workload and was measured as the average per minute V[Combining Dot Above]O2 during the last 3 minutes under steady-state conditions. V[Combining Dot Above]O2 was also normalized to mL·kg−0.75·min−1 (7,63). To allow for comparison between treadmill speeds, RE was normalized per distance covered by dividing the velocity (km·min−1) into the V[Combining Dot Above]O2 (ml·kg·min−1) consumed at the submaximal velocity (66). Metabolic rate during running was calculated as gross metabolic cost (resting oxygen uptake plus cost of movement) of steady-state treadmill running, divided by oxygen consumption (ml·kg·min−1).
V[Combining Dot Above]O2peak Test
All participants performed a graded exercise test to volitional exhaustion to determine V[Combining Dot Above]O2peak after the last 6-minute interval at 201.2 m·min−1. V[Combining Dot Above]O2peak was determined using a modified Åstrand incremental treadmill protocol, which required subjects to run at an increased grade increment every second minute. The subjects were instructed to run to maximal volitional fatigue. The first 2 minutes of each incremental test were run at a 0% treadmill grade. Treadmill velocity was set at a constant of either 215, 241, or 268 m·min−1 (subject's preference). The grade was increased by 2% every 2 minutes thereafter until the subject reached voluntary exhaustion. The running speed was individually chosen to limit the exercise period to 6–8 minutes. Strong verbal encouragement was given throughout the test. All runners in this study reached voluntary exhaustion within 6 minutes. V[Combining Dot Above]O2peak was considered to be the highest V[Combining Dot Above]O2 recorded during the test session. To confirm that maximal effort had been achieved, 2 of the following criteria had to be attained: a change in V[Combining Dot Above]O2 of < ±2.1 ml·kg·min−1 with concomitant increase in workload, an RER value ≥1.10, attainment of age predicted maximal heart rate (220-age ± 5%), volitional exhaustion, or a rating of perceived exertion ≥17. This measure was used for descriptive purposes only.
On visit 2, the sit-and-reach test was conducted after a 10-minute warm-up, running at a self-selected speed on the treadmill. The subjects were instructed to jog at a comfortable pace. Stretching or any kind of flexibility exercises were prohibited before initiation of the sit-and-reach test to minimize interindividual variability with the standardized protocol. The sit-and-reach test was conducted using a specially designed sit-and-reach box (Acuflex; Novel Products Inc., Rockton, IL, USA).
The subjects were seated on the floor, knees extended and legs together, with their bare feet against the sit-and-reach box (90° in the ankle joints), keeping the palm of their right hand on top of the left hand. The subjects were then instructed to slowly reach toward their toes (zero point on the sit-and-reach box), and beyond if possible, while pushing the slide as far forward on the sit-and-reach box as possible without flexing their knees. The subjects were asked to hold this stretch for a minimum of 2 seconds at the furthermost point reached. The most distant point reached with their fingertips was taken as the measure for the sit-and-reach test. The best score from 3 attempts was recorded and used for statistical analysis.
Isokinetic Strength Testing
Muscle test analysis was measured using a Biodex System 3 dynamometer (Biodex Medical System, Shirley, NY, USA) interfaced with a computer. After a full explanation of the procedures, subjects were seated upright on the dynamometer with their hip joint at approximately 90° flexion, and the rotation axis of the dynamometer was visually oriented with the participant's lateral condyle at the right knee. The input axis of the dynamometer was aligned with the axis of the knee. Each subject was fastened with restraining straps used to prevent unwanted movements in accordance with the Biodex User Guide (Biodex Pro Manual, Applications/Operations; Biodex Medical Systems).
The range of motion (ROM) of the knee was set to 0–90°. To restrict any undesirable (lateral) movements and to maintain body posture, a thigh strap was applied over the test leg, and the subjects were instructed to grip the handles on each side of the Dynamometer while performing the test. Only the right leg was tested.
Gravity correction was attained by measuring the gravity effect torque, that is, the flexor torque applied on the dynamometer arm with the knee in a relaxed state at full extension. The thigh strap was released between each trial to ensure adequate blood flow to the thigh muscles. The testing protocol for each subject consisted of both concentric and eccentric muscle actions, respectively. Before the testing intervention, subjects did a 5-repetition (Con/Con) warm-up at 60°·s−1 to ensure familiarity with the isokinetic mode of muscle action.
Each test trial involved 5 repetitions at the following velocities: 60, 120, and 180°·s−1, respectively. The Con/Con muscle actions were performed with extension followed by flexion. Sixty seconds of rest between sets has been shown to be adequate for recovery following a set of isokinetic exercise (53). A 90-second rest period was required between testing of each velocity within a set, and a 240 seconds recovery period between trials to make sure the subjects were fully recovered. Subjects were asked to perform the movement at their maximal effort. Every effort was made to ensure that the subjects made genuinely maximal contractions for each trial. Verbal encouragement was given throughout the testing session for all subjects to attain maximum performance (49). The repetition resulting in the greatest maximal peak torque for the hamstring and quadriceps was used for data analysis. Peak torque was expressed in absolute values (N·m) and relative to bodyweight (N·m·kg−1).
The strength data were used to calculate both the functional H:Q (f-H:Q) and conventional H:Q peak torque ratios (c-H:Q). The f-H:Q was calculated as maximal eccentric hamstring to concentric quadriceps moments (Hecc:Qcon, representative for knee extension), or as maximal concentric hamstring divided by maximal eccentric quadriceps moments (Hcon:Qecc, representative for knee flexion) (1,2). The f-H:Q ratio is thought to be more appropriate for strength evaluation than the c-H:Q ratio using concentric torques of both muscle groups, as it better reflects the true agonist-antagonist muscle interaction (1).
Isokinetic strength assessment has demonstrated high reliability and validity for measuring muscular peak torque at velocities below 300°·s−1 (21). The isokinetic dynamometer controls velocity of movement, as it provides accommodating resistance throughout a joint's ROM, and it is a tool that can easily be used to assess dynamic muscle actions in research and clinical settings (21).
Conventionally, a standard ratio of oxygen uptake has been denoted in milliliters per kilogram per minute to compare individuals with different body mass. However, as neither maximal aerobic capacity nor submaximal oxygen uptake increases at the same rate as body mass (7,69), and to be mass independent, allometric scaling is recommended when assessing both submaximal and maximal aerobic capacities (11). Body mass raised to the power of 1 (per kg body mass) underestimates maximal aerobic capacity (V[Combining Dot Above]O2max) and overestimates submaximal aerobic capacity (RE) in heavier individuals. For lighter individuals, measurements conducted without allometric scaling will overestimate maximal aerobic capacity and underestimate submaximal aerobic capacity. Thus, to reduce the influence of body mass on oxygen uptake during running, research suggests that V[Combining Dot Above]O2 should be scaled to body mass to the power of 0.75 (ml·kg−75·min−1) (7,31,58). Accordingly, oxygen uptake values are presented to the power of 0.75 in the present study.
Descriptive and inferential statistics were performed. Data are presented as mean and SD unless otherwise specified. The Shapiro-Wilk test of normality revealed that the data were normally distributed, and all had an observed power greater than 0.95. Once the assumption of normality was confirmed, parametric tests were performed. Conventional and functional H:Q ratios were calculated for both trained states. Student's unpaired t-tests were used to examine possible differences between groups (HT vs. REC) on physical and physiological variables. Pearson's product-moment correlations (r) were used to evaluate relationships between dependent variables within and between the 2 groups: (a) strength ratios and RE and (b) anthropometrics and RE. Further correlation analysis between demographic and other possible relevant physiological variables and RE were also investigated. In all cases, an a priori alpha level of p ≤ 0.05 was used for statistical significance in 2-tailed tests. The data analysis was performed using the software program SPSS (version 17.0, SPSS Inc., Chicago, IL, USA).
The results of the maximal cardiovascular treadmill tests are listed in Table 2. There were no significant differences between groups regarding age, BMI, or body mass (p > 0.05) (Table 1). No significant differences were found between the 2 groups in the amount of self-reported strength training, in the number of subjects that performed hamstring-emphasized strength exercises as part of their strength training, nor in the use of hormonal contraception (p > 0.05). Comparison of anthropometric variables, using whole group correlations, showed a positive relationship between calf girth and submaximal V[Combining Dot Above]O2 consumption (ml·kg−0.75·min−1) at 201.2 m·min−1 (R = 0.57, p ≤ 0.05). No correlations were found between the anthropometric variables body mass, stature, BMI, leg length, ankle circumference, and RE (ml·kg−0.75·min−1) (p > 0.05). A negative relationship was found between the total miles run per week and submaximal V[Combining Dot Above]O2 consumption (ml·kg−0.75·min−1) at 201.2 m·min−1 (R = −0.55, p ≤ 0.05). V[Combining Dot Above]O2peak was significantly higher in the HT runners than among the REC, when normalized per kg−1 (p ≤ 0.05) and when expressed per kg0.75 (p ≤ 0.05) (Table 2). The HR at 201.2 m·min−1 expressed as percentage of HR at V[Combining Dot Above]O2peak correlated inversely with RE (R = −0.51, p ≤ 0.05). No significant correlation was found between V[Combining Dot Above]O2peak and f-H:Q strength (p > 0.05).
Oxygen Cost of Running
Results of the steady-state measures are listed in Table 3. The HT had a significantly lower submaximal V[Combining Dot Above]O2 at 160.9 and 201.2 m·min−1 when expressed as ml·kg−0.75·min−1 (6.5 and 8.8% difference, respectively, p ≤ 0.05). The intensity levels for the 2 submaximal speeds such as 160.3 m·min−1 and 201.2 m·min−1 were on average lower for the HT, as confirmed by significantly lower % V[Combining Dot Above]O2peak, % HRmax, and rate of perceived exertion (RPE) (Table 3). Rating of perceived exertion was 11.8% lower among the HT at both submaximal treadmill velocities, confirming their higher fat utilization compared with the REC runners. Whole group correlations revealed a significant positive relationship between calf circumference and miles run per week (R = 0.57, p ≤ 0.05) and a significant negative relationship between miles run per week and RE at 201.2 m·min−1 ml·kg−0.75·min−1 (R = −0.55, p ≤ 0.05).
H:Q ratios between groups are shown in Table 4. The average percent difference between the 2 trained states for the 3 velocities at Hecc:Qcon, Hcon:Qecc, and Hcon:Qcon were 15.5, 11.8, and 15.7%, respectively. There were no significant differences in Hcon:Qcon ratios at 120 and 180°·s−1 among the HT vs. REC runners (p > 0.05). Hecc:Qcon was significantly higher among the HT than among the REC subjects at 120 (p ≤ 0.05) and at 180°·s−1 (p ≤ 0.05) (Figure 2).
Whole group correlations demonstrated a significant negative correlation between Hcon:Qecc 120°∣sec−1 and RER at 201.2 m·min−1 (R = −.66, p ≤ 0.05) (Figure 1). There was a negative but non-significant relationship between Hcon:Qecc at 60°∣sec−1 (R = −0.45, p > 0.05) and at 120°∣sec−1 (R = −0.33, p > 0.05) and V[Combining Dot Above]O2 consumption at 201.2 m∣min−1 (mL∣kg−0.75∣km1 ) (Figure 2). However, the relationship between Hcon:Qecc at 180°·sec−1 and V[Combining Dot Above]O2 consumption at 201.2 m∣min−1 (mL∣kg−0.75·km−1) was significant (R = −0.48, p ≤ 0.05). No significant correlation was found between Hcon:Qcon and RE at 201.2 m·min−1 (R = −0.07, −0.06, −0.16, p ≤ 0.05) at 60, 120, and 180°·sec−1, respectively.
There were also no significant whole group correlations found between Hecc:Qcon and submaximal V[Combining Dot Above]O2 (RE) at 160.9 m·min−1 and 201.2 m·min−1 (Figure 3; Table 5). Hecc:Qcon 60°·sec−1 was significantly negatively correlated with RER at 160.9 m·min−1 (R = 0.49, p ≤ 0.05), but not at 201.2 m·min−1 (R = −0.34, p > 0.05) (Table 5).
Whole group correlations demonstrated that RER at 201.2 m·min−1 was significantly negatively correlated with km per week of running (R = −0.65, p ≤ 0.05) and RE (ml·kg−0.75·km−1) (R = −0.55, p ≤ 0.05). Calf girth was positively correlated with RE (ml·kg−0.75·km−1) at 201.2 m·min−1 (R = −0.57, p ≤ 0.05).
When expressed in absolute values (N·m), we did not find any significant differences between the 2 groups regarding peak torque at any of the 3 velocities, in either knee flexion eccentric or concentric muscle actions (p > 0.05). The average knee flexion eccentric and concentric peak torque for the isokinetic velocities were 148.8 ± 24.6 vs. 134.6 ± 23.0 N·m and 71.7 ± 12.0 vs. 64.6 ± 13.7 N·m for HT and REC runners, respectively. The average knee extension eccentric and concentric peak torque values for HT and REC runners were 205.7 ± 42.8 vs. 210.1 ± 52.2 N·m and 111.7 ± 15.3 vs. 118.5 ± 27.5 N·m, respectively.
Peak torque normalized to body weight (N·m·kg−1) revealed no significant differences between groups at 60°·s−1 (p > 0.05). However, there were significant differences between the 2 groups (HT vs. REC) in knee flexion at 120°·s−1 (1.3 ± 0.2 vs. 1.0 ± 0.1 N·m·kg−1, p ≤ 0.05, respectively) and 180°·s−1 (1.2 ± 0.1 vs. 0.9 ± 0.1 N·m·kg−1, p ≤ 0.05, respectively). There were no significant differences in maximal isokinetic knee extension torque normalized to body weight among the 2 groups at any of the 3 velocities (p > 0.05). No significant relationships were found between hamstring peak torque expressed in absolute (N·m) or relative values (N·m·kg−1) and RE (p > 0.05).
Although the HT runners (n = 7) scored 13.2% (29.6 vs. 33.8 cm) lower on the sit-and-reach test than the REC female runners, there were no significant differences in sit-and-reach measures between groups (p > 0.05). There was no significant correlation between submaximal V[Combining Dot Above]O2 and sit-and-reach flexibility at any of the submaximal treadmill speeds (Table 5). When normalized per kilometer (ml·kg−0.75·km−1) among HT and REC runners (n = 17), there were weak negative correlations between submaximal V[Combining Dot Above]O2 at 201.2 m·min−1 and flexibility (R = −0.12, p > 0.05). Finally, when comparing RER vs. flexibility among all groups at the 2 submaximal velocities, there was a weak positive correlation (R = 0.40, p > 0.05) (Figure 5, Table 5).
The main findings of this study can be summarized as follows: (a) RE was significantly positively correlated with f-H:Q ratios (Hcon:Qecc) at both 120 and 180°·s−1, (b) Hecc:Qcon was significantly higher among the HT compared to the REC female runners at both 120 and 180°·s−1, (c) neither absolute knee extensor torque nor knee flexor torque was significantly correlated with RE, and (d) no statistically significant relationship was found between RE and flexibility. In addition, the HT female runners had a trend toward consistently higher f-H:Q at all 3 angular velocities (60, 120, and 180°·s−1).
As hypothesized, the runners with higher f-H:Q ratios tended to be more economical at a submaximal velocity. Our results indicate that relative muscle strength (higher f-H:Q ratios) may be an important factor regulating the metabolic cost of running, and therefore a determinant of success in distance running. The results may have functional implications for future directions in the assessment of strength profiles among runners, and in planning and developing adequate training programs to improve running performance.
Commonly, the conventional ratio (c-H:Q ratio) is reported to be 0.50–0.80 in healthy subjects (15), that is, the hamstrings have approximately 50–80% of the quadriceps strength depending on angular velocity, test position, population group, and use of gravity correction. Studies have consistently demonstrated that when tested as conventional isokinetic torque, hamstring to quadriceps torque ratios in women do not increase at velocities that approaches those of functional activities, whereas male hamstring to quadriceps torque ratios increase as velocity increases (33). Nevertheless, the results from c-H:Q ratios at different velocities in this study were not consistent with the analyses by Hewett et al. (33). In our study, all subjects' c-H:Q ratios increased from slow to fast velocities. In contrast to our study's healthy cohort, the results found in the meta-analysis by Hewett et al. (33) may reflect the lack of noninjured, well-trained subjects in previous investigations. Future studies should therefore investigate the potential impact of both injury and training status on c-H:Q and f-H:Q ratios among large cohorts to determine the relationship between these variables.
The precise mechanism whereby the results of this study were obtained can only be speculated on, that is, significant differences in f-H:Q ratios between the 2 trained states, and the positive correlation between increased f-Hcon:Qecc ratios with greater RE. Running involves muscle work, which consists of a combination of eccentric muscle contraction followed immediately by a concentric muscle contraction. This process is commonly referred to as the stretch-shorten cycle (SSC). Several studies link increased joint and musculotendinous stiffness to better RE (5,18,61,66). Because the elastic contributions are greater during eccentric muscle actions than concentric actions, f-H:Q (Hecc:Qcon) ratios are more forceful (1). The force generated during a prestretched muscle is added to the force in the subsequent concentric contraction, without a proportionate required metabolic cost. Thus, the SSC generates more force and is more efficient (more work per unit of metabolic energy input) than pure concentric contractions. Higher Hecc:Qcon among HT when performing eccentric muscle work can be explained by previous research finding that muscle output in high-force isometric and dynamic muscle actions is positively related to the stiffness of the tendinous structures (8). In addition, our data indicate a trend toward differences in musculoskeletal flexibility between the HT and REC runners. It is possible that the sit-and-reach test in our study was not sufficient to detect any real differences in musculoskeletal flexibility among the HT and REC female runners. It has been suggested that inflexibility in certain areas of the musculoskeletal system may lower the aerobic demand of running by increasing storage and return of elastic energy during the SSC, and reducing the need for muscle-stabilizing activity (17).
In addition to the possible contribution of strain energy among the HT runners, their significantly greater ratios compared with the REC runners may reflect natural selection, biomechanics, or training background. It is also possible that the difference in running routine alone between the 2 trained states in our study has resulted in greater f-H:Q ratios among the HT female runners. The HT had no differences in anatomical traits from the REC runners in the present study, except for longer legs and greater leg length relative to height (Table 1). This might be a biomechanical advantage and therefore a possible explanation for the greater torque production in the HT runners in our study.
We did not find any significant differences in absolute muscle strength in our study between the 2 groups. Although hamstring peak torque normalized to body weight (N·m·kg−1) was significantly different between the 2 groups at medium and high angular velocities, no significant relationship was found between hamstring absolute or normalized peak torque and RE. From our study, the low-energy expenditure among the HT may partly be related to the higher f-H:Q ratios of the subjects' hamstring muscles but not absolute strength. It is known that runners with a strong musculoskeletal apparatus tend to exhibit more stable running styles, which seem to increase muscular efficiency, thereby allowing the athletes to run long distances at maximal aerobic speed (26). At increasing speeds, the hamstring muscles' biceps femoris activity electromyography (EMG) is highly correlated with energy expenditure (42). Poor technique, in turn, may partially be caused by vertical mediolateral and braking forces, resulting from insufficient hamstring muscle actions (42). In the study by Kyrolainen et al., the treadmill velocities were higher than in our study. In addition, we did not measure EMG activity in our study. It is possible, however, that higher treadmill speeds would have revealed correlations between absolute knee flexor strength and RE.
As anticipated, functional Hecc:Qcon ratios were higher than the conventional ratios (c-H:Q) for both groups and for each individual. Eccentric muscle action, that is, muscle lengthening, is an important factor in stability and energy saving when running (59). Perhaps the higher f-H:Q ratios in HT runners in our study can be explained by their increased eccentric strength, because of more elasticity and stiffness, which affects eccentric hamstring torque. This can be explained by the force-velocity curve, which shows that eccentric contraction generates higher forces than concentric contractions. Also, increased angular velocity results in greater force when contracting eccentrically, while the opposite is true when contracting concentrically (1).
Previous investigations in nonathletes found that the functional Hecc:Qcon ratio produced a 1:1 hamstring to quadriceps strength relationship for fast knee extension (1). To the best of our knowledge, only 1 study has previously investigated f-H:Q ratios in runners (40). Interestingly, the study by Kong and de Heer (40) investigating anthropometric variables in 6 highly trained male, sub-elite, Kenyan runners (40), found Hecc:Qcon ratios corresponding to the Hecc:Qcon ratios for the HT female runners in the present study, with f-H:Q ratios greater than 1.0 at all angular velocities. Taken together, the results by Kong and de Heer and our study results indicate that f-H:Q ratios are similar between the sexes for distance runners who are at the same relative performance level, and that runners exhibit a higher f-H:Q ratios (Hecc:Qcon) than athletes in other sports (1,35). However, the study by Kong and de Heer (40) involved only 6 athletes, did not include a control group, and the authors did not test for V[Combining Dot Above]O2 consumption, blood profile or musculoskeletal stiffness in any of the muscle measurements, making it impossible to elucidate any relationship between f-H:Q and endurance performance. In fact, an intervention study (35) on 12 female soccer players after 6 weeks with hamstring-emphasized strength training reported average post-test Hecc:Qcon ratios for both legs combined to be lower than that found for both the HT and REC runners in the present study. However, the soccer players in the same study had mean c-H:Q ratios higher than the HT and REC runners in our study. It should be assumed that soccer players should display both lower c-H:Q and lower f-H:Q ratios than runners considering the soccer players' more dominant quadriceps muscles from the kicking motion. In sum, the results in our study suggest that subjects who run long distances have higher functional Hecc:Qcon strength ratios than subjects in other sports, which could possibly be due to weaker quadriceps muscles or stronger hamstring muscles in runners, or a combination of these two.
The lack of significant relationships between the isokinetic measures for Hecc:Qcon and RE were not unexpected, considering the limited specificity using this type of assessment, and the muscle mechanics during treadmill running vs. overground running. The main reasons for the lack of strong correlations for Hecc:Qcon and RE may be due to the low treadmill speed, the fact that the testing was performed on a treadmill, rather than overground running, or simply because the isokinetic strength tested may not reflect the mechanical requirements experienced while running.
Running economy has been shown to be independent of running speed, as expressed per distance covered, for well-trained distance runners within a range of 60–90% of V[Combining Dot Above]O2peak (32). The well-trained women in the study by Helgerud et al. (32) had a higher average V[Combining Dot Above]O2max (157.8 ± 17.8 ml·kg−0.75·min−1) than reported in the present study for the REC runners but lower than for the HT runners in our study. This makes it difficult to assess whether the V[Combining Dot Above]O2 intensity was valid at higher velocities for the REC runners in the present study. The HT reached the 60–90% V[Combining Dot Above]O2 intensity range at 201.2 m·min−1. Hence, the submaximal V[Combining Dot Above]O2 measures in the present study are deemed to be representative for RE measured at race speeds for distances from 10,000 m and above for the HT (32).
Respiratory exchange ratio at 201.2 m·min−1 was significantly negatively correlated with Hcon:Qecc at 120°·s−1 when analyzed for the whole group (n = 17), possibly indicating that other measures of the metabolic demand of running might have resulted in more accurate measurements of RE than those used in the present study. Functional H:Q, measured as Hcon:Qecc at 180°·s−1, was significantly correlated with submaximal V[Combining Dot Above]O2 (ml·kg−0.75·min−1) at 201.2 m·min−1, suggesting that strong knee flexion may be important for running at low oxygen cost.
Contrary to previous reports (17,27,37,66), and our initial hypothesis, there was no significant relationship between oxygen demand at submaximal running and flexibility in the present study, although HT runners had lower flexibility scores than less economical REC runners, as confirmed by the sit-and-reach test. This result is in accordance with a study by Craib et al. (17), which found no significant correlation between RE and sit-and-reach flexibility (r = 0.12), despite significant correlation between RE and inflexibility in the ankle and the hip. However, the results by Craib et al. are not in line with previous findings relating sit-and-reach flexibility to better RE in both male and female collegiate distance runners (66), and among internationally competitive male long-distance runners (37). These latter studies support the theory that both muscle strength and stiffness are related to improved performance. Perhaps running in and of itself causes stiffer and more economical locomotion, or the HT athlete's inflexibility and outstanding RE in the present study can be explained partly by the COL5A1 genotype, which recently has been associated with both a subject's ROM (13), and endurance performance (10,55) in separate studies.
The reason for this inverse relationship possibly relates to the stretch and recoil of tendon and muscle springs, which adds mechanical work, while active, energy-demanding muscle fibers produce the high forces necessary for momentum (56). Thus, muscles act as active struts rather than pure working machines (56), making this storage and return of elastic-strain energy an important energy-saving mechanism for running (3). Also, storage of elastic energy in the contracted muscle during eccentric muscle actions is an important mediator to increase the total work output during muscle contractions (6). As a result, athletes who are able to use more of this highly efficient energy source would clearly have an advantage as their metabolic cost of movement is lower. Lending support for this theory, optimal musculotendinous stiffness for maximum concentric and isometric activities has been shown to be toward the stiff end of the elasticity continuum (72), meaning that a stiffer muscle may enhance the force production capabilities of the contractile units. Thus, muscle stiffness, in addition to strength, may be an important factor explaining differences in endurance determinants such as RE (43).
The large differences in average distance run per week between the HT and REC (104.6 vs. 32 km·wk−1, p ≤ 0.05) may explain the lower oxygen demand of running among the HT runners. It is possible that higher treadmill velocities might have revealed even larger differences. The lack of correlation between RE and f-H:Q ratios at the lower angular velocities among the HT and REC in the present study can also be explained by the fact that the percentage of energy expenditure due to fat oxidation at any given absolute running speed is inversely related to V[Combining Dot Above]O2max (16). The percentage of energy expenditure due to fat oxidation at any given common running speed increases with endurance training (12), and a higher V[Combining Dot Above]O2max is related to higher fat oxidation (47). Moreover, at any given submaximal speed, runners with a higher V[Combining Dot Above]O2max may require higher V[Combining Dot Above]O2 due to their greater reliance on fat utilization (24). Thus, the HT females in this study may spend more V[Combining Dot Above]O2 relative to the REC female runners at the submaximal paces, thus not reflecting their “true” RE for the HT. Support for this view can be found in the lower RER and % V[Combining Dot Above]O2peak among the HT in this study compared with the REC runners. As a result, the HT runners' RE may be considered “poor” simply because of the additional oxygen required to metabolize fat as opposed to carbohydrate. Thus, the lack of a larger difference between the trained groups possibly indicates that submaximal V[Combining Dot Above]O2 was a poor indicator of RE in the present study.
The respiratory exchange ratio did not increase between the 2 speeds for the HT, and there was a significantly lower RER between HT and REC runners at the highest submaximal speed, indicating that it was a low intensity for trained runners. Respiratory exchange ratio increased only among REC runners from 160.9–201.2 m·min−1 (4.7% increase in RER), indicating that intensity affected substrates metabolized during the two 6-minute intervals among the REC runners but not in the HT runners. Respiratory exchange ratio at 160.9 m·min−1 was significantly inversely related to Hecc:Qcon at 60°·s−1, reflecting that RER might be a better indicator of RE than submaximal V[Combining Dot Above]O2 in the present study. The lack of larger differences in submaximal V[Combining Dot Above]O2 between groups may also explain why the correlation coefficients between H:Q strength ratios and RE were higher for the RER and f-H:Q strength relationship.
The lack of a significant correlation between Hecc:Qcon ratios and submaximal V[Combining Dot Above]O2 was expected, as both treadmill velocities might have been too slow to reach the threshold at which hamstring eccentric strength contributes significantly to the aerobic demand of running. Considering the greater active lengthening contraction (eccentric work) of the hamstring during the late swing phase at higher velocities (64) and the accompanying increased activation in the hamstring muscles with increased treadmill speed (34,41), it is possible that testing RE at higher velocities would reveal greater differences between an individual's hamstring muscle strength and the H:Q ratios. This would perhaps result in a higher correlation between H:Q ratios and RE, as well as other variables related to RE.
Another possible explanation for the lack of a significant correlation between H:Q ratio strength and submaximal V[Combining Dot Above]O2 at all the tested velocities in the present study could be the difference in technique for treadmill running vs. overground running. Overground running may place greater demands on the hamstring muscles to produce propulsive forces than treadmill running (39). Wank et al. (68) found greater electromyographic activity of the biceps femoris muscle during treadmill running compared with overground running. Thus, whether treadmill results are transferable to overground running remains unclear. In the present study, the relatively low treadmill speed may not have reached the threshold level at which hip extensors, which include the hamstring and glutes, become more important for propulsion forces. The significant differences between the 2 groups at the medium and fast velocities may reflect the HT runners' tendency to perform more race-specific workouts at higher intensities than REC runners; for example, high-intensity interval training on a track. This type of activity would target the hamstring muscles specifically at higher velocities. Moreover, Koller et al. (39) found a significant decrease in hamstring, but not quadriceps, eccentric strength after overground running (running a marathon), implying that the 2 muscle groups may be exposed to a different level of fatigue during distance running. These large muscle groups' association with RE may be explained by their high activation rate during running for propulsion and support (28), which makes them consume a substantial amount of oxygen during locomotion.
There are 2 main ways of changing the H:Q ratios: (1) by increasing hamstring torque and (2) by decreasing quadriceps torque. The reasons for the higher H:Q ratios among the HT runners in the present study were a combination of stronger hamstrings and weaker quadriceps muscles in the HT vs. REC runners. The hamstring muscles among the HT had higher peak torque at both concentric and eccentric hamstring muscle actions. The opposite was true for the quadriceps muscles; the REC runners had higher values than the HT runners at all angular velocities. There were no differences in the amount of self-reported strength training sessions between groups in terms of frequency or hours per week. Nevertheless, among those HT and REC runners who reported that they did strength training, the HT performed more specific hamstring exercises per week.
In conclusion, this is the first study to investigate the relationship between relative muscle strength in the thigh muscles and RE. Highly trained female runners had significantly higher f-H:Q ratios at velocities approaching those that occur during running. The f-H:Q ratios observed for all the HT female runners indicated a significant functional capacity of the hamstring muscles for providing muscular stability at the knee joint in fast knee extension compared with the REC runners. Taken together, the results from our study suggest that relative hamstring to quadriceps muscle strength may be a contributing factor for RE. As a final point, the HT leg strength was lower compared with the REC runners, despite higher f-H:Q ratios. Therefore, running performance in long-distance events, at least to a certain level, may be related to greater hamstring muscle strength relative to quadriceps muscle strength and not to absolute muscle strength per se.
From a practical viewpoint, our results indicate that thigh muscle strength distribution (relative concentric to eccentric hamstring muscle strength), and not muscle strength per se, may have a regulatory role in determining RE. In fact, no significant correlation was found between RE and isolated quadriceps or hamstring peak torque were found. Because of the significantly higher f-H:Q and the significant relationship between f-H:Q and RE, strength training to improve this ratio should be considered for middle-distance and long-distance running events to further lower the oxygen cost of running (i.e., improve RE) and thereby, improve performance. As running is basically a series of horizontal jumps requiring a strong and highly efficient extensor apparatus, and improved plyometrics has shown to be related to greater RE, we suggest that runners should aim to include hamstring muscle strengthening exercises that imply horizontal motions.
Our results suggest that coaches and athletes in middle-distance and long-distance running should focus on increasing the functional hamstring:quadriceps muscle strength ratio by implementing additional conditioning exercises or exercises specifically targeting the hamstring muscles. These exercises should primarily focus on eccentric hamstring muscle actions and may include fast downhill running, over-speed running, hill bounding, drills, as well as resistance or plyometric.
The authors are grateful to the 21 runners who took part in this study. For their assistance and cooperation with this research, the authors thank Gonzalo Villablanca, Morgan Tapper, Alicia B. Olsen, and Annie Peck. The authors thank Annie Bersagel and Håkon Brox for their valuable comments on drafts of this article. The authors have no funding or conflicts of interest to disclose.
1. Aagaard P, Simonsen EB, Magnusson SP, Larsson B, Dyhre-Poulsen P. A new concept for isokinetic hamstring: Quadriceps muscle strength
ratio. Am J Sports Med 26: 231–237, 1998.
2. Aagaard P, Simonsen EB, Trolle M, Bangsbo J, Klausen K. Isokinetic hamstring/quadriceps strength
ratio: Influence from joint angular velocity, gravity correction and contraction mode. Acta Physiol Scand 154: 421–427, 1995.
3. Alexander RM. Walking and running: Legs and leg movements are subtly adapted to minimize the energy costs of locomotion. Am Scientist 72: 348–354, 1984.
4. Anderson T. Biomechanics and running economy. Sports Med 22: 76–89, 1996.
5. 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 209: 3345–3357, 2006.
6. Asmussen E, Bonde-Petersen F. Storage of elastic energy in skeletal muscles in man. Acta Physiol Scand 91: 385–392, 1974.
7. Bergh U, Sjodin B, Forsberg A, Svedenhag J. The relationship between body mass and oxygen uptake during running in humans. Med Sci Sports Exerc 23: 205–211, 1991.
8. Bojsen-Møller J, Magnusson SP, Rasmussen LR, Kjaer M, Aagaard P. Muscle performance during maximal isometric and dynamic contractions is influenced by the stiffness of the tendinous structures. J Appl Physiol (1985) 99: 986–994, 2005.
9. Borg GA. Psychophysical bases of perceived exertion. Med Sci Sports Exerc 14: 377–381, 1982.
10. Brown JC, Miller CJ, Posthumus M, Schwellnus MP, Collins M. The COL5A1 gene, ultra-marathon running performance, and range of motion. Int J Sports Physiol Perform 6: 485–496, 2011.
11. Buresh R, Berg K. Scaling oxygen uptake to body size and several practical applications. J Strength
Cond Res 16: 461–465, 2002.
12. Carter SL, Rennie C, Tarnopolsky MA. Substrate utilization during endurance exercise in men and women after endurance training. Am J Physiol Endocrinol Metab 280: E898–E907, 2001.
13. Collins M, Mokone GG, September AV, van der Merwe L, Schwellnus MP. The COL5A1 genotype is associated with range of motion measurements. Scand J Med Sci Sports 19: 803–810, 2009.
14. Conley DL, Krahenbuhl GS. Running economy and distance running performance of highly trained athletes. Med Sci Sports Exerc 12: 357–360, 1980.
15. Coombs R, Garbutt G. Developments in the use of the hamstring/quadriceps ratio for the assessment of muscle balance. J Sports Sci Med 1: 56–62, 2002.
16. Costill DL, Fink WJ, Getchell LH, Ivy JL, Witzmann FA. Lipid metabolism in skeletal muscle of endurance-trained males and females
. J Appl Physiol (1985) 47: 787–791, 1979.
17. Craib MW, Mitchell VA, Fields KB, Cooper TR, Hopewell R, Morgan DW. The association between flexibility
and running economy in sub-elite male distance runners. Med Sci Sports Exerc 28: 737–743, 1996.
18. Dalleau G, Belli A, Bourdin M, Lacour JR. The spring-mass model and the energy cost of treadmill running. Eur J Appl Physiol Occup Physiol 77: 257–263, 1998.
19. Daniels J, Daniels N. Running economy of elite male and elite female runners. Med Sci Sports Exerc 24: 483–489, 1992.
20. di Prampero PE, Atchou G, Bruckner JC, Moia C. The energetics of endurance running. Eur J Appl Physiol Occup Physiol 55: 259–266, 1986.
21. Drouin JM, Valovich-mcLeod TC, Shultz SJ, Gansneder BM, Perrin DH. Reliability and validity of the Biodex system 3 pro isokinetic dynamometer velocity, torque and position measurements. Eur J Appl Physiol 91: 22–29, 2004.
22. Dumke CL, Pfaffenroth CM, McBride JM, McCauley GO. Relationship between muscle strength
, power and stiffness and running economy in trained male runners. Int J Sports Physiol Perform 5: 249–261, 2010.
23. Farrell PA, Wilmore JH, Coyle EF, Billing JE, Costill DL. Plasma lactate accumulation and distance running performance. Med Sci Sports 11: 338–344, 1979.
24. Fletcher JR, Esau SP, Macintosh BR. Economy of running: Beyond the measurement of oxygen uptake. J Appl Physiol (1985) 107: 1918–1922, 2009.
25. Gaesser GA, Poole DC. The slow component of oxygen uptake kinetics in humans. Exerc Sport Sci Rev 24: 35–71, 1996.
26. Gazeau F, Koralsztein JP, Billat V. Biomechanical events in the time to exhaustion at maximum aerobic speed. Arch Physiol Biochem 105: 583–590, 1997.
27. Gleim GW, Stachenfeld NS, Nicholas JA. The influence of flexibility
on the economy of walking and jogging. J Orthopaedic Res 8: 814–823, 1990.
28. Hamner SR, Seth A, Delp SL. Muscle contributions to propulsion and support during running. J Biomech 43: 2709–2716, 2010.
29. Hayes PR, Bowen SJ, Davies EJ. The relationships between local muscular endurance and kinematic changes during a run to exhaustion at vVO2max. J Strength
Cond Res 18: 898–903, 2004.
30. Hayes PR, French DN, Thomas K. The effect of muscular endurance on running economy. J Strength
Cond Res 25: 2464–2469, 2011.
31. Helgerud J. Maximal oxygen uptake, anaerobic threshold and running economy in women and men with similar performances level in marathons. Eur J Appl Physiol Occup Physiol 68: 155–161, 1994.
32. Helgerud J, Storen O, Hoff J. Are there differences in running economy at different velocities for well-trained distance runners? Eur J Appl Physiol 108: 1099–1105, 2010.
33. Hewett TE, Myer GD, Zazulak BT. Hamstrings to quadriceps peak torque ratios diverge between sexes with increasing isokinetic angular velocity. J Sci Med Sport 11: 452–459, 2008.
34. Higashihara A, Ono T, Kubota J, Okuwaki T, Fukubayashi T. Functional differences in the activity of the hamstring muscles with increasing running speed. J Sports Sci 28: 1085–1092, 2010.
35. Holcomb WR, Rubley MD, Lee HJ, Guadagnoli MA. Effect of hamstring-emphasized resistance training on hamstring: Quadriceps strength
ratios. The J Strength
Cond Res 21: 41–47, 2007.
36. Johnston RE, Quinn TJ, Kertzer R, Vroman NB. Strength
training in female distance runners: Impact on running economy. J Strength
Cond Res 11: 224–229, 1997.
37. Jones AM. Running economy is negatively related to sit-and-reach test performance in international-standard distance runners. Int J Sports Med 23: 40–43, 2002.
38. Jones AM, Doust JH. A 1% treadmill grade most accurately reflects the energetic cost of outdoor running. J Sports Sci 14: 321–327, 1996.
39. Koller A, Sumann G, Schobersberger W, Hoertnagl H, Haid C. Decrease in eccentric hamstring strength
in runners in the Tirol Speed Marathon. Br J Sports Med 40: 850–852; discussion 852, 2006.
40. Kong PW, De Heer H. Anthropometric, gait and strength
characteristics of kenyan distance runners. J Sports Sci Med 7: 499–504, 2008.
41. Kyrolainen H, Avela J, Komi PV. Changes in muscle activity with increasing running speed. J Sports Sci 23: 1101–1109, 2005.
42. Kyrolainen H, Belli A, Komi PV. Biomechanical factors affecting running economy. Med Sci Sports Exerc 33: 1330–1337, 2001.
43. Kyrolainen H, Kivela R, Koskinen S, McBride J, Andersen JL, Takala T, Sipila S, Komi PV. Interrelationships between muscle structure, muscle strength
, and running economy. Med Sci Sports Exerc 35: 45–49, 2003.
44. Lansley KE, Winyard PG, Fulford J, Vanhatalo A, Bailey SJ, Blackwell JR, DiMenna FJ, Gilchrist M, Benjamin N, Jones AM. Dietary nitrate supplementation reduces the O2 cost of walking and running: A placebo-controlled study. J Appl Physiol (1985) 110: 591–600, 2011.
45. Larsen HB. Kenyan dominance in distance running. Comp Biochem Physiol A Mol Integr Physiol 136: 161–170, 2003.
46. Lazzer S, Salvadego D, Rejc E, Buglione A, Antonutto G, di Prampero PE. The energetics of ultra-endurance running. Eur J Appl Physiol 112: 1709–1715, 2012.
47. Lima-Silva AE, Bertuzzi RC, Pires FO, Gagliardi JF, Barros RV, Hammond J, Kiss MA. Relationship between training status and maximal fat oxidation rate. J Sports Sci Med 9: 31–35, 2010.
48. McCrory MA, Mole PA, Gomez TD, Dewey KG, Bernauer EM. Body composition by air-displacement plethysmography by using predicted and measured thoracic gas volumes. J Appl Physiol (1985) 84: 1475–1479, 1998.
49. McNair PJ, Depledge J, Brettkelly M, Stanley SN. Verbal encouragement: Effects on maximum effort voluntary muscle action. Br J Sports Med 30: 243–245, 1996.
50. Morgan DW, Baldini FD, Martin PE, Kohrt WM. Ten kilometer performance and predicted velocity at VO2max among well-trained male runners. Med Sci Sports Exerc 21: 78–83, 1989.
51. Morgan DW, Bransford DR, Costill DL, Daniels JT, Howley ET, Krahenbuhl GS. Variation in the aerobic demand of running among trained and untrained subjects. Med Sci Sports Exerc 27: 404–409, 1995.
52. 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 (1985) 86: 1527–1533, 1999.
53. Parcell AC, Sawyer RD, Tricoli VA, Chinevere TD. Minimum rest period for strength
recovery during a common isokinetic testing protocol. Med Sci Sports Exerc 34: 1018–1022, 2002.
54. Pate RR, Kriska A. Physiological basis of the sex difference in cardiorespiratory endurance. Sports Med 1: 87–98, 1984.
55. Posthumus M, Schwellnus MP, Collins M. The COL5A1 gene: A novel marker of endurance running performance. Med Sci Sports Exerc 43: 584–589, 2011.
56. Roberts TJ, Marsh RL, Weyand PG, Taylor CR. Muscular force in running turkeys: The economy of minimizing work. Science 275: 1113–1115, 1997.
57. Rosene JM, Fogarty TD, Mahaffey BL. Isokinetic hamstrings: Quadriceps ratios in intercollegiate athletes. J Athl Train 36: 378, 2001.
58. Saltin B, Larsen H, Terrados N, Bangsbo J, Bak T, Kim CK, Svedenhag J, Rolf CJ. 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 5: 209–221, 1995.
59. Sasaki K, Neptune RR. Muscle mechanical work and elastic energy utilization during walking and running near the preferred gait transition speed. Gait Posture 23: 383–390, 2006.
60. Saunders PU, Pyne DB, Telford RD, Hawley JA. Factors affecting running economy in trained distance runners. Sports Med 34: 465–485, 2004.
61. Spurrs RW, Murphy AJ, Watsford ML. The effect of plyometric training on distance running performance. Eur J Appl Physiol 89: 1–7, 2003.
62. Storen O, Helgerud J, Stoa EM, Hoff J. Maximal strength
training improves running economy in distance runners. Med Sci Sports Exerc 40: 1087–1092, 2008.
63. Svedenhag J. Maximal and submaximal oxygen uptake during running: How should body mass be accounted for? Scand J Med Sci Sports 5: 175–180, 1995.
64. Thelen DG, Chumanov ES, Hoerth DM, Best TM, Swanson SC, Li L, Young M, Heiderscheit BC. Hamstring muscle kinematics during treadmill sprinting. Med Sci Sports Exerc 37: 108–114, 2005.
65. Thomas TR, Zebas CJ, Bahrke MS, Araujo J, Etheridge GL. Physiological and psychological correlates of success in track and field athletes. Br J Sports Med 17: 102–109, 1983.
66. Trehearn TL, Buresh RJ. Sit-and-reach flexibility
and running economy of men and women collegiate distance runners. J Strength
Cond Res 23: 158–162, 2009.
67. Turner AM, Owings M, Schwane JA. Improvement in running economy after 6 weeks of plyometric training. J Strength
Cond Res 17: 60–67, 2003.
68. Wank V, Frick U, Schmidtbleicher D. Kinematics and electromyography of lower limb muscles in overground and treadmill running. Int J Sports Med 19: 455–461, 1998.
69. Welsman JR, Armstrong N, Nevill AM, Winter EM, Kirby BJ. Scaling peak VO2 for differences in body size. Med Sci Sports Exerc 28: 259–265, 1996.
70. Westblad P, Svedenhag J, Rolf C. The validity of isokinetic knee extensor endurance measurements with reference to treadmill running capacities. Int J Sports Med 17: 134–139, 1996.
71. Williams KR, Cavanagh PR. Relationship between distance running mechanics, running economy, and performance. J Appl Physiol (1985) 63: 1236–1245, 1987.
72. Wilson GJ, Murphy AJ, Pryor JF. Musculotendinous stiffness: Its relationship to eccentric, isometric, and concentric performance. J Appl Physiol (1985) 76: 2714–2719, 1994.
73. Yamamoto LM, Lopez RM, Klau JF, Casa DJ, Kraemer WJ, Maresh CM. The effects of resistance training on endurance distance running performance among highly trained runners: A systematic review. J Strength
Cond Res 22: 2036–2044, 2008.