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Applied Sciences: Biodynamics

Six weeks of training does not change running mechanics or improve running economy


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Medicine & Science in Sports & Exercise: July 1996 - Volume 28 - Issue 7 - p 860-869
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It is reasonable to predict that, during a training program, runners use a self-optimization process to develop movement patterns that minimize energy cost and stresses to the body. Daniels(11) stated that training generally has the effect of lowering, and thereby improving, the rate of oxygen consumption at a given submaximal speed (referred to as running economy or ˙VO2submax). This contention is reinforced by the longitudinal data from Svedenhag and Sjodin (35) who observed that highly trained athletes exhibited a slow but steady improvement in running economy over the years of training. The observations that several running style variables influence running economy (e.g., 26,43) suggest that adjustments in running mechanics may improve economy and possibly performance. The extent and time course of changes in running mechanics with training are not well documented. Nelson and Gregor (28) have shown reductions in stride length at a given speed of running over the 4 yr of a collegiate running career, and it is likely that there are adjustments in running style over a much shorter time span, particularly in individuals who are initially less experienced. There are probably subtle differences in individual adaptations, but general trends with training (as seen in physiological measures) may be present. Cavanagh and Williams(7) found the most economical stride length of a group of runners was close to that which was freely chosen. However, it is not yet known whether or not a given stride length can become more economical over a period of training or if real changes in stride length occur.

Williams and Cavanagh (42,43) and Williams et al. (44) have provided support for the relationship between mechanical and physiological variables, showing that more economical runners tend to have identifiable patterns in their running mechanics. However, as pointed out by Martin and Morgan (23), the relationships that have been observed have generally been weak and inconsistent from study to study. In addition, there is a scarcity of experimental evidence indicating that biomechanical factors influence running economy (16). Such experimental evidence could be obtained by examining associations between running economy and mechanics before and after a program of running training. The approaches used to relate running mechanics and economy have generally been cross-sectional in nature, and it is possible that a longitudinal study that examines mechanics and economy over a period of training has greater potential to relate aspects of running technique to metabolic energy demands during running.

The purpose of this investigation was to identify adjustments in running style variables and running economy that may result from a period of running training in relatively untrained subjects. It was expected that there would be physiological adaptations to the running training, but this study was designed to explore the extent to which these changes reflect alterations in running style. By relating changes in running economy with adjustments in running kinematics after the training period, it was hoped to obtain further information concerning “economical” movement patterns.


Subjects and Experimental Design

From a group of 22 screened volunteers, 17 untrained male university students (aged 18-28) were selected to participate in the study based on their ability to run comfortably for 30 min at a 3.36 m·s-1 test treadmill speed. All subjects were physically active in various recreational sports, but had a minimal amount of running training in the 3 months before participation in the study (<6 miles·wk-1). Before any preliminary testing in the laboratory, subjects were required to complete an informed consent form and a medical questionnaire. The questionnaire ensured that subjects had no current injury or previous major lower extremity injury, and served to screen individuals for cardiovascular problems, because testing involved maximal treadmill runs to exhaustion. The experimental protocol was in four stages and is displayed in Table 1. Initially there was a treadmill familiarization phase where all subjects performed two treadmill accommodation runs (30 and 20 min). Then, pre-training biomechanical and physiological measurements were performed on the subjects. These measurements were repeated after 2-3 d to determine day-to-day variability. Subsequently, subjects were randomly assigned to a training (N = 9) or a nontraining control group (N = 8) for the training period of the study. During this stage, one subject from the training group dropped out of the program and one control subject became injured. Their results were excluded from the study. For the 15 subjects who completed the study,Table 2 presents the mean values of age, body mass, height, leg length, and hours of physical activity per week before entry into the study.

The training group underwent 6 wk of overground running training, while the control group maintained normal weekly activity but did not participate in any running program. The training involved a gradual increase in frequency, intensity, and distance of organized early morning runs supervised by the principal investigator (MJL). Weekly running mileage began at 15 miles and was increased to 20 miles. The average distance run during the 6-wk period was over 100 miles (103.4 ± 6.7 miles). The range was 98.7-118.1 miles for the group because two subjects were not restricted from running slightly longer runs. Flexibility and conditioning exercises were also incorporated into the running program. After the 6-wk period, both groups repeated the pretraining biomechanical and physiological measurements in the laboratory. A comparison of the mean biomechanical and physiological values pre- and post-training allowed the training effects to be determined.

Data Collection Procedures

The laboratory testing involved the use of high-speed film and expired air analysis while subjects ran on a motorized treadmill (Quinton Model 18-60) at a 3.36 m·s-1 test speed. After a 5-min warm-up at 3.08 m·s-1, subjects ran at the test speed for 2 min to establish a consistent gait pattern, and then were filmed for 12 cycles of running in the sagittal plane at a rate of 50 frames·s-1 using a Locam 16-mm camera. Cine film was digitized using a Microgrid II digitizing tablet(Summagraphics, Inc.) online to a SMS(PDP 11/73) computer. Six points captured the sagittal plane motion of approximate joint centers and other body landmarks. These locations were the neck, greater trochanter, lateral femoral epicondyle, lateral malleolus, and two locations on an athletic shoe to represent the posterior margin of the calcaneus and head of the fifth metatarsal. Skin marker placement was standardized by palpation of the respective hip, knee, and ankle bony prominences and also by bisection of the knee joint and neck. Subjects were also filmed while standing upright on the treadmill with knees fully extended so that segment correction angles could be determined. Each subject wore the same model Puma Stellar running shoes (in his size) supplied by the investigators throughout all treadmill testing, and the shoe markers remained glued in position for the pre- and post-training measurements. Women's sizes for the testing footwear were not available, consequently the study was restricted to male subjects. Subjects wore their own athletic shoes for the 6-wk running training period.

Each subject was then asked to run at the same test speed for 10 min, during which expired air analysis and heart rate monitoring was performed(refer to Table 1). These were monitored after filming so that there was no possibility of the breathing apparatus having an influence on running mechanics. Average stride length (distance between two footstrikes of the same foot) was determined every 30 s throughout the run by monitoring treadmill velocity fluctuations with each footfall. If treadmill belt speed is known and the velocity fluctuations are used as a timer, stride length can then be determined (6). A Parkinson Cowan flowmeter measured the volume of air inspired. Oxygen and carbon dioxide concentrations in the expired air were measured using Applied Electrochemistry S-3A and CD-3A O2 and CO2 analyzers. A Vantage Performance heart rate monitor(Polar Electro, Inc.) recorded heart rate (HR) every 5 s, and these data were downloaded to a microcomputer after each run. Submaximal physiological measures and stride length were averaged over the last 6 min of the 10-min run in order to minimize errors inherent in the measurement system.

Following 15 min of rest, each subject performed an incremental treadmill test to exhaustion. This test, using both increasing treadmill speed and grade, was designed to elicit maximal rates of oxygen consumption(˙VO2max) after approximately 10 min. Run time until exhaustion was recorded as a measure of running performance. The criteria published by Taylor et al. (36) for the attainment of ˙VO2max were used. Maximum heart rate (HRmax) was recorded near the finish of the ˙VO2max test. All subjects performed two maximal tests pre-training to allow for any “training effect” that could have occurred. The main goal of the pre-training data collection was to obtain reliable values for all of the biomechanical and physiological variables to be measured, taking into account stride-to-stride variation in biomechanical measures and day-to-day variation in all measures. Stride-to-stride variation was allowed for by using 12 running cycles to obtain mean values for kinematic measurements. After the training period, both groups performed the submaximal and maximal treadmill tests on one occasion in an identical manner as before, and pre- and post-training mean values for physiological and biomechanical measures were compared to determine training effects. Whenever possible, the time of the day for pre- and post-training testing was kept the same for a given subject. This was facilitated by subjects having the same class schedules and free time for testing during the study. An additional 10-min running economy test was also performed on subjects post-training(Table 1). The sub-maximal physiological variables(including running economy) and the stride lengths for the two post-training economy runs were averaged.

Biomechanical Variables

Specific biomechanical variables that had previously been related to running economy (43) and could easily be determined from sagittal plane film analysis were chosen for the study. All the selected variables were measured on the left side of the body, and are: 1) vertical oscillation throughout the cycle; 2) shank angle at footstrike; 3) trunk angle averaged throughout the cycle; 4) range of trunk lean throughout the cycle; 5) maximal ankle plantarflexion at or near toe-off; and 6) maximal knee flexion during the support phase.

The angle of trunk lean with respect to the vertical was calculated and averaged for each frame throughout the cycle (footstrike to ipsilateral footstrike) and an average was calculated for five complete cycles of running. The vertical path of the neck marker was digitized for five cycles of running, and an average value for vertical oscillation obtained. A Butterworth low-pass digital filter at a specified cutoff frequency of 6 Hz was applied to the trunk angle and vertical oscillation data in forward and backward passes to eliminate a phase lag. The measurement of the remaining kinematic variables is illustrated in Figure 1, and was associated with discrete instants of the running pattern (footstrike, midsupport, and toe-off), and therefore required only the digitization of specific frames of film surrounding the particular events. A mean value for each of these discrete variables was obtained from averaging measurements from 12 complete running cycles in each subject. Shank angle with respect to the vertical was determined at each ipsilateral footstrike, and maximum knee flexion during support was determined for each support phase. Maximum plantarflexion angle was determined at the frame closest to toe-off (see Fig. 1). Segment correction angles (calculated from upright standing position) were subtracted from each mean angle calculation taken during running.

Physiological Variables

The physiological variables recorded were those obtained from the respiratory analysis during the 10-min economy run and the maximal run to exhaustion. Rates of oxygen consumption were expressed in ml O2·kg·body mass·min-1.˙VO2submax and HR were expressed as a percentage of their maximal values and termed%˙VO2max and%HRmax, respectively. Submaximal pulmonary ventilation rate (˙VE) and respiratory exchange ratio (R) were also determined. R, ˙VE, HR,%˙VO2max, and%HRmax variables were chosen to provide an indication of the physiological response at the test speed relative to each individual. Thirty-s mean values for ˙VO2submax, R, and˙VE were recorded throughout all economy runs.˙VO2submax, R, ˙VE, and HR were averaged over the last 6 min of the 10-min economy run. All subjects were required to reach an approximate steady-state heart rate during this period to ensure that the exercise was predominantly aerobic. The steady-state was defined as a heart rate increase of less than 10 beats·min-1 during the 6 min.

Statistical Procedures

The individual mean biomechanical and physiological values for the two sets of treadmill runs obtained in the two pre-training visits were compared using a paired t-test. If there proved to be statistically significant differences between run 1 and run 2, then run 2 was used for pre-training measurements; otherwise, the results of run 1 and run 2 were averaged. The rationale behind this was that if run 2 was different from run 1, then adjustments in both running mechanics and economy may have occurred due to further treadmill running accommodation, and the last data collection was assumed to be the most representative. Determination of the effect of the training period on all measured variables was conducted by analyzing the differences between the pre- and post-training group means in a two-way analysis of variance (ANOVA) mixed design (two levels of time and two levels of group).


Pre-Training Data

The values of the kinematic variables that were measured in this study(Table 3) were similar to those reported in the literature at a comparable running speed (25,43). The pre-training biomechanical results shown in Table 3 represent the mean values from 13 of the 15 subjects, because film analysis could not be completed on run 2 for two subjects. Although there was a tendency for trunk lean to decrease on run 2, there proved to be no significant difference in results from run 1 to run 2, and the variables were significantly correlated between runs. Consequently, for comparison with post-training measures, all the results of the two pre-training runs were averaged. The mean day-to-day coefficient of variability in stride length was 0.98%. This corresponds to a stride length variation of 2.4 cm, which is slightly less than the average day-to-day variation in stride length (2.6-2.9 cm) found at a range of submaximal treadmill running speeds by Kram et al.(19). Although the shank and knee angle variables were significantly correlated between run 1 and run 2, their correlation coefficient values (r = 0.61 and 0.58, respectively) proved to be lower than those for the other biomechanical variables.

The comparison of mean physiological results between the two pre-training visits is summarized in Table 4. It can be seen that the oxygen consumption and heart rate measures were reproducible from day to day(r = 0.93-0.95). The day-to-day variability in ˙VO2submax is in close agreement with the results of Morgan et al. (27) who obtained a correlation of 0.95 between two economy runs in a group of 17 subjects. The pre-training ˙VO2max values and run times until exhaustion on the incremental test (used as a measure of running performance) were also found to be reproducible from run 1 to run 2. This indicates that there was little or no “learning effect” among subjects for the maximal treadmill test. Generally, the physiological measures showed very little intraindividual variation and the two pre-training evaluations were highly correlated. Consequently, for comparison with post-training measures, all the physiological results of the two pre-training visits were averaged.

Comparison of Biomechanical Measures Pre- and Post-Training

The pre- and post-training mean values for the biomechanical variables in both the control and training groups can be seen in Table 5. Statistical analysis revealed that there were no significant changes in any of the biomechanical variables for the training or control group after the 6 wk of training. For both groups, the mean difference between pre- and post-training kinematic measures was only 2%.

Comparison of Physiological Measures Pre- and Post-Training

The mean values for all physiological variables in the control group were very similar when pre- and post-tests were compared. The mean difference between tests in this group was only 0.6% (Table 6). Body weight for both the training and control group remained the same after the 6-wk period (Table 6). Seven of the eight subjects in the training group showed a mean difference of -0.04 kg between pre- and post-training weight. The remaining subject in the training group did lose 2.8 kg in body weight over the 6-wk period. In contrast to the control group, the training group demonstrated substantial and statistically significant changes in many of the physiological variables. As expected, running performance (run time for maximal test) and ˙VO2max were significantly increased with training. HR and measures of relative exercise intensity (%HRmax and%˙VO2max) for the training group were significantly reduced. Despite these physiological improvements, running economy became significantly worse with training (elevated ˙VO2submax). None of the subjects in the training group displayed a lower ˙VO2submax post-training, and the increases in ˙VO2submax did not appear to be influenced by the initial level of individual running economy (Fig. 2).


This study was designed to explore the extent to which changes in running economy with training reflect alterations in running style in previously untrained subjects. The training program showed that there were no changes in the mechanical variables measured that signaled changes in economy. Individualized optimization should ideally lead to the minimization of energy costs; however, none of the subjects demonstrated an improved economy of running after training. In fact, the change in training status generally had the effect of worsening economy, while running mechanics remained the same.

Running Mechanics

The minimal and nonsignificant differences in the group mean values for the kinematic variables pre- and post-training demonstrated that no general changes in running mechanics were present. Since the change in the group mean values after the 6-wk training period were so small, a post-hoc power analysis was not indicated. Similar results have been found by other researchers that have examined isolated aspects of running style after a short-term period of training (3,15,17). Girardin and Roy (17) found no changes in stride length or stride rate at a similar speed after a 6-wk period of nondirected running training in a group of relatively untrained subjects. Also, Elliott et al.(15) observed no significant changes in the position of the shank, thigh, or trunk at footstrike following two different types of running training over an 8-wk period. Stride length was monitored in novice runners over a 7-wk treadmill running training program in a study by Bailey and Messier (3). These authors discovered that, despite significant changes after 4 wk, there were no significant changes after the 7 wk of training. These group mean results tend to suggest that, during the initial stages of training, running style is generally resistant to change even in relatively untrained subjects. It remains to be determined whether general changes in mechanics can be identified over a much longer duration of training. This seems likely because substantial changes in stride length over a 4-yr period in a group of collegiate runners have been demonstrated(28), although these findings have never been replicated.

The biomechanical assessment was limited to a single moderate speed of running that was close to the average training pace for the subjects. It was postulated that the subjects would have the best opportunity to finely tune their mechanical movement patterns at their training pace. However, it is feasible that changes in running economy and running mechanics with training are speed-dependent. With this in mind, it would be beneficial to examine subjects over a range of speeds pre- and post-training, although the biomechanical analysis becomes labor intensive without the use of opto-electronic motion analysis systems. The evidence from Williams and Cavanagh (43) indicates that the selected variables were the most appropriate for monitoring economy-related adjustments in running mechanics due to training. Nevertheless, it is possible that some other mechanical aspects of running technique, which were not measured, could have changed. Kinematic measurements were made in the sagittal plane on the left side of the body only. It is plausible that kinematic changes could have occurred with training in other planes. Yet, the greatest proportion of segmental motion during running occurs in the sagittal plane and, therefore, it is likely that adjustments would be most readily detected in this plane. A complete three-dimensional analysis with repeated measures day-to-day was beyond the scope of the present training study.

To obtain reliable pre-training biomechanical data for comparative purposes, subjects were filmed on two occasions and discrete variables were averaged over 12 cycles of running. Several procedures were taken to minimize day-to-day variability due to methodology. These included precise and continuous monitoring of treadmill velocity, standardized marker placement, control of foot-wear with fixed markers, and subtracting a standing calibration from lower extremity measures. After these precautions, the kinematic data for a given day were found to be representative of group mean results, although data for individuals did show day-to-day differences. It is likely that the day-to-day variability in measures resulted primarily from differences in actual gait pattern rather than measurement discrepancies. The day-to-day correlations between kinematic variables ranged from 0.58 to 0.96, which is a similar range to that found by Martin (22). He found correlations of 0.62-0.94 in similar variables to those measured in the present study. Morgan et al. (27) also found similar correlations between kinematic variables from 2 d of testing. Two kinematic measures, namely maximal knee flexion during support and shank angle at footstrike, did demonstrate more intrasubject variability on a day-to-day basis. Similarly, Morgan et al. (27) observed that maximal knee flexion angle displayed greater day-to-day variability than a range of other kinematic variables. This suggests that subtle differences in mechanics due to training would likely be more difficult to detect for those variables.

Martin and Morgan (23) recently speculated that a global descriptor of the need for muscular force, such as total body mechanical power, would be more closely associated with oxygen demand than descriptors of discrete events of the gait pattern. This global mechanical variable could prove more useful in identifying the subtle modifications that may occur with training, but the number of necessary assumptions incorporated into the computations may reduce its sensitivity. Although no study has yet attempted to monitor mechanical power or energy variables over a training regime, the difference in mechanical power output between two groups of runners with markedly different levels of running economy was found to be nonsignificant (43). There is evidence from data collected on several species of animals that the metabolic demand of running is related to the time the foot applies force to the ground during each stride(20). Kram and Taylor (20) state that the cost of running is primarily determined by the cost of supporting the weight and by the time course of force application. The use of this general principle to examine the potential association between human running economy and running mechanics remains to be explored in the context of training effects.

Physiological Variables

In contrast to the lack of information on adaptations in biomechanics with training, there is an abundance of literature associated with the physiological adaptations to training. General physiological trends with training have been established (e.g., (14,32)) and significant changes in the physiological variables with training were found in this study. The training group experienced a large improvement in˙VO2max (6.2%), which is consistent with the results of Ekblom et al. (14) who also monitored subjects who were relatively untrained at the onset of training. The training group in the present study improved their running performance (run time on the incremental treadmill test), and the magnitude of the physiological response to the submaximal run at the same speed was less following training. This was confirmed by significant decreases in heart rate and significant decreases in specific physiological variables that provided an indication of relative exercise intensity, namely%˙VO2max,%HRmax, and R value. Despite these changes, the ˙VO2submax was increased significantly post-training. The improvement of running performance after training has previously been associated with a lower%˙VO2max rather than by an absolute reduction in ˙VO2submax by Wilcox and Bulbulian(37). These authors refer to the measurement of percentage utilization of ˙VO2max as the “relative economy” of each individual. Ramsbottom et al.(31) also found that ˙VO2submax was significantly increased at a range of speeds following 5 wk of running training that significantly increased ˙VO2max by 8%, and improved running performance by 2.5%. In the study by Ramsbottom et al.(31) a lower relative exercise intensity post-training was reflected in a reduction in%˙VO2max, heart rate, R value, and lower blood lactate levels over the range of submaximal speeds tested. This apparent reduction in metabolic demand at a given running speed, despite increases in ˙VO2submax, conforms with the pre- and post-training comparisons of the present study. The steady-state aerobic demand (or˙VO2submax) has recently been referred to as a “global indicator of metabolic demand” (24) and as a measure of “physiological efficiency” (41). The physiological findings of this study and those of Ramsbottom et al.(31) suggest that both these terms are probably inappropriate for ˙VO2submax measurements in relatively untrained subjects.

Decreases in R and blood lactate levels at the same absolute exercise intensity following training have been well documented(18). Ramsbottom et al. (31) contend that this reflects an increased aerobic capacity of human skeletal muscle and suggest that more of the energy needs of the muscle cell are met by aerobic metabolism causing a concomitant increase in oxygen consumption during submaximal exercise. The decreases in R found in the present study and in the study by Ramsbottom et al. (31) are associated with increases in fat utilization and can contribute to the increased oxygen consumption post-training. There would be an 8% energy difference if 1 liter of oxygen were to completely oxidize 1 gram of fat and 1 gram of carbohydrate, respectively. The magnitude of the decrease found in R for the Ramsbottom et al. study was sufficient to explain almost 50% of the increased˙VO2submax post-training. However, only 15% of the increased˙VO2submax found in this study could be potentially attributed to the decrease in R.

The mean increase in ˙VO2submax was not large (1.4 ml·kg-1·min-1, or 3.4%) but this magnitude can still be considered important. A similar change in economy (1.6 ml·kg-1·min-1) was found when the effects of drastic degradations in running technique on running economy were examined in a group of female runners (13). According to Frederick(16), if precautions are taken with the design of the experiment and with the collection of data, it is practical to use oxygen consumption measurements to find significant differences between treatments as small as 1-2%. The present study included design factors such as repeated measures and treadmill accommodation, together with a control of footwear during data collection. The time of the day for testing was also similar pre- and post-training. This protocol reduced extraneous variation in both physiological and biomechanical measurements. Wilmore et al.(45) also found an increase in ˙VO2submax with training that he explained by suggesting that the exercise time at a given workload was not long enough to obtain a steady-state level pre-training. Then, with improved ˙VO2 kinetics post-training, individuals were able to achieve a steady-state in the time interval and consequently had slightly higher oxygen consumption values. This explanation for increased ˙VO2submax cannot apply to subjects in the present study, because they exercised over a 10-min period with ample time to reach a steady-state condition. Alternatively, oxygen consumption expressed per kilogram body weight can also increase if body weight is reduced. Body weight remained constant for both the control and training groups in this study, and consequently cannot explain the increase in ˙VO2submax. It is also plausible that elevations in resting oxygen consumption with training may lead to an increased oxygen consumption during submaximal exercise. However, resting ˙VO2 is generally resistant to change, with only small decreases observed with increasing age in children (21). Physical training typically does not change basal metabolism or resting˙VO2(2). Overtraining could perhaps cause slightly elevated resting rates of oxygen consumption as the body struggles to adapt. This is unlikely in the present study because the training load was increased gradually. The validity of resting baseline subtractions has been questioned by Stainsby et al. (34), who point out that the energy use represented by such baselines change when the exercising conditions are altered.

The worsened running economy conflicts with the findings of most training studies where either no change(12,32,33,37) or a decrease in˙VO2submax was found with training(1,5,8-10,29,35). The relatively short duration of the present training study may partly explain the increased ˙VO2submax. Training studies that have demonstrated no change or an increase in ˙VO2submax have generally lasted 10 wk or less (e.g.,(3,12,30-32,45)) while those studies that demonstrate improvements in running economy tend to range from 13 wk to 2 yr in duration(1,5,8-10,29,35). The increase in ˙VO2submax may therefore be a time course phenomenon where the initial large increases in ˙VO2max may be associated with a corresponding increase in ˙VO2submax. During the early stages of training adaptation, the substantial increase in˙VO2max is probably an important factor in improved running performance. It can be speculated that after ˙VO2max has reached a near-plateau level, in order to further improve running performance, gradual reductions in ˙VO2submax may occur.

Relationship between Running Mechanics and Running Economy

Other researchers who have attempted to alter running mechanics through biomechanical feedback training over a similar time period(3,30,40) have also found little change in running mechanics. These researchers generally found little or no improvement in running economy throughout the training period. However, Williams(39) suggests that some individuals can modify their mechanics to result in substantial improvements in economy. This was shown to be the case for a single subject whose freely chosen stride length was already determined to be uneconomical from testing at various stride lengths. This result is probably atypical because the freely chosen stride length is generally close to the stride length at which oxygen cost is minimal(8).

If present, individual kinematic changes with training were generally small and characteristic to the particular individual. Unlike the physiological findings, the group mean biomechanical results indicated that there were no general changes associated with training. This lack of change suggests that the kinematic variables measured were not tightly linked to running economy. One of the training group subjects in the present study did show some changes in running mechanics with training. Maximum knee flexion during support for this subject was decreased (39.1 to 32.1) following training. This is in a direction that has been associated with poorer economy(43), and this subject had increased his oxygen cost by 1.8 ml·kg-1·min-1 after the 6 wk of training. However, other changes in running kinematics in this subjects were in the direction associated with good economy (43). His vertical oscillation decreased by 1.2 cm, and both shank angle at footstrike and plantarflexion at toe-off increased. Therefore, it is difficult to judge whether the changes in mechanics influenced the running economy. Williams and coworkers (38,43) indicate that individuality in the optimization process associated with training may be the reason that the number of significant relationships between mechanics and economy found for groups of subjects are fewer than expected. They contend that individuals adopt running techniques that are best suited to their own anatomical and physiological constraints, and running economy associated with an individual depends on the influence of a large number of mechanical variables, of which some would be economical and others would be uneconomical. Certainly the number of variables measured in the present study was small, and other unmeasured variables may have changed over the training period. However, in the present investigation, it seems more likely that the changes in running economy were influenced by physiological rather than biomechanical factors, because the physiological variables generally demonstrated much larger changes with training. For example, the subject who demonstrated some changes in running kinematics (mentioned above) also exhibited the largest improvement in˙VO2max.

Bailey and Pate (4) postulated that alterations in training status may precipitate changes in variables that both positively and negatively affect running economy. They stated that an enhanced training regimen may either favorably change running economy or degrade running economy. They contend that an improved running style and intracellular oxidative capacity with training can lead to a better running economy, whereas training-induced increases in mass distribution in the limbs and˙VO2max can lead to a worsened running economy. Their argument for the greater ˙VO2max potentially degrading running economy was that there would be increases in the percentage of fat oxidized for energy production at a given running speed. The results of the present study and those of Ramsbottom et al. (31) indicated that these changes in substrate utilization with training can only partly contribute to the observed elevations in ˙VO2submax. Ramsbottom et al.(31) suggest that a greater proportion of the energy demand of skeletal muscle could be met by aerobic metabolism, and this may cause the elevated ˙VO2submax. It remains to be established why running training that leads to a general reduction in the physiological response to a given running speed can cause an increase in the oxygen cost of running.

In the early stages of training, running style was shown to be resistant to change, and improvements in running performance were predominantly due to physiological adaptation. It is conceivable that factors leading to improved running economy, such as adjustments in movement patterns and ground reaction forces, may show their influence after a much longer period of training when physiological attributes such as ˙VO2max have attained a near-ceiling level of adaptation. With this in mind, an examination of running mechanics and economy over a much longer period of running training may uncover some of the clues to the effect of training on the development of an“economical” running gait.

Figure 1-The support phase of the running cycle is shown from footstrike (:
FS ) to toe-off ( TO ) to illustrate three kinematic variables measured.
Figure 2-Comparison of individual economy values pre- and post-training for the training group. Two subjects displayed no change in economy (:
diagonal line ) while five subjects demonstrated a clear increase in ˙VO2submax (or worsened economy).


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