Altered Running Economy Directly Translates to Altered Distance-Running Performance

Abstract

Purpose 

Our goal was to quantify if small (1%–3%) changes in running economy quantitatively affect distance-running performance. Based on the linear relationship between metabolic rate and running velocity and on earlier observations that added shoe mass increases metabolic rate by ~1% per 100 g per shoe, we hypothesized that adding 100 and 300 g per shoe would slow 3000-m time-trial performance by 1% and 3%, respectively.

Methods 

Eighteen male sub-20-min 5-km runners completed treadmill testing, and three 3000-m time trials wearing control shoes and identical shoes with 100 and 300 g of discreetly added mass. We measured rates of oxygen consumption and carbon dioxide production and calculated metabolic rates for the treadmill tests, and we recorded overall running time for the time trials.

Results 

Adding mass to the shoes significantly increased metabolic rate at 3.5 m·s⁻¹ by 1.11% per 100 g per shoe (95% confidence interval = 0.88%–1.35%). While wearing the control shoes, participants ran the 3000-m time trial in 626.1 ± 55.6 s. Times averaged 0.65% ± 1.36% and 2.37% ± 2.09% slower for the +100-g and +300-g shoes, respectively (P < 0.001). On the basis of a linear fit of all the data, 3000-m time increased 0.78% per added 100 g per shoe (95% confidence interval = 0.52%–1.04%).

Conclusion 

Adding shoe mass predictably degrades running economy and slows 3000-m time-trial performance proportionally. Our data demonstrate that laboratory-based running economy measurements can accurately predict changes in distance-running race performance due to shoe modifications.

Physiologists generally agree that distance-running performance can be predicted by three parameters: the maximal rate of oxygen consumption (V˙O_2max), the blood lactate threshold, and running economy (RE) (¹⁴). RE is traditionally defined as the mass-specific submaximal rate of oxygen consumption (mL O₂·kg⁻¹·min⁻¹) at a defined submaximal running velocity (⁴). Submaximal oxygen uptake (V˙O_2submax) and metabolic rate increase with running velocity (¹⁵). Thus, an improvement in RE (lower rate of oxygen consumption at a given velocity) theoretically would allow an athlete to run at a faster velocity for the same physiological effort and thus improve performance (⁴). However, no study to date has demonstrated a direct link between altered RE and altered distance-running performance (¹⁰). If a link can be established, then laboratory-based measures of RE could be used to predict improvements in performance, without actually measuring performance. The primary objective of the present study was to quantify how changes in RE affect distance-running performance.

One way to predictably induce a change in RE is to add mass to the shoes (¹⁰). Frederick et al. (⁹) demonstrated that adding 100 g of mass per shoe degrades RE (increases V˙O_2submax) by approximately 1% over a range of running speeds. More recently, Franz et al. (⁸) confirmed those classic findings using modern, very lightweight racing flats.

In the present study, we imposed small degradations in RE by adding mass to running shoes and evaluated the corresponding effects on 3000-m time-trial performance. On the basis of the linear relationship between V˙O_2submax and running velocity combined with Frederick’s 1% rule for added shoe mass, we hypothesized that adding 100 and 300 g per shoe would slow 3000-m time-trial performance by 1% and 3%, respectively.

METHODS

Participants

Eighteen males (age = 24.2 ± 3.3 yr, mass = 64.2 ± 6.3 kg, height = 176.1 ± 7.0 cm) who wore men’s shoe sizes 9–11 and had recently run a sub-20-min 5-km race participated in this study (V˙O_2max = 63.8 ± 6.7 mL O₂·kg⁻¹·min⁻¹, range = 53.5–72.0 mL O₂·kg⁻¹·min⁻¹). Participants gave written informed consent that followed the guidelines of the institutional review board of the University of Colorado Boulder.

Experimental setup

All participants wore Nike Zoom Streak 5 racing flats in men’s size 9, 10, or 11. For each of the sizes, we used three nearly identical looking pairs of shoes (Fig. 1). The control (no added mass) shoe masses ranged from 202 g (size 9) to 225 g (size 11) per shoe. For the second pair in each size, we discreetly added 100 g of lead beads per shoe, which were distributed within the tongue of the shoe. For the third pair, we added 100 g within each tongue and 200 g of lead beads in medial and lateral side pockets, which were inconspicuously sewn inside the shoe uppers in the midfoot area.

FIGURE 1: The control shoe (left) and +300 g shoe (right) were nearly identical in appearance.

The study comprised five visits. Participants kept a 24-h dietary, sleep, and training log before each visit. We strongly encouraged the participants to match their diet, sleep, and training pattern for all time trials and laboratory measurements. If compliance was not met, we postponed the testing. Upon arrival for the first visit, we deceived the participants as to the actual study purpose. We told the participants that our objective was to establish a predictive equation for performance based on V˙O_2max and RE, validated with a series of weekly time trials. This deception was needed to ensure participants were not biased into thinking that they should be running faster or slower based on the shoes they were wearing. During each of the five visits, an experimenter deliberately helped the participant put on the shoes, such that the participants never handled the shoes. Slade et al. (²⁰) found that participants are able to perceive small differences in mass when they handle different shoes manually, but not when they are only worn on their feet. To make the deception plausible, we created two small, lightweight (4.6 g) faux accelerometers and lightly wrapped them with thin, self-adhering elastic bandage material (VetWrap; 3M Inc., St. Paul, MN) atop of the participant’s fourth metatarsal of each foot. To ensure that participants did not manually handle the shoes, we told them that because of the fragile nature of the accelerometers, we needed to place their socks and shoes back on their feet. We then allowed the participants to tie their shoes.

Experimental protocol

During visit 1, participants completed a set of laboratory treadmill tests. This visit was in part to decoy the participants because we told them that our objective was to establish a predictive equation for performance based on V˙O_2max and RE. To establish their RE, participants wore the control shoes and ran at velocities of 2.5, 3.0, 3.5, and 4.0 m·s⁻¹ on a classic Quinton 18–60 treadmill, which has a rigid bed. We used a handheld digital tachometer (Shimpo DT-107A; Electromatic Equipment Inc., Cedarhurst, NY) to measure the treadmill speeds. Trials lasted 5 min, and participants took a 5-min break in between trials. We measured submaximal rates of oxygen consumption and carbon dioxide production during the trials using an open-circuit expired-gas analysis system (True One 2400; Parvo Medics, Salt Lake City, UT).

After a 10-min break, participants completed a V˙O_2max test. On the basis of each participant’s most recent 5-km time, we set the treadmill speed between 3.5 and 4.5 m·s⁻¹ and increased the incline by 1% each minute until exhaustion (⁵). During the V˙O_2max test, we measured the rates of oxygen consumption and carbon dioxide production as well as heart rate (Polar Wearlink Nike+ Transmitter; Polar Electro Inc., Lake Success, NY). Together, these measurements allowed us to determine that V˙O_2max was reached. We used the maximum heart rate data to determine whether participants reached 90% of maximum heart rate during the second half of their time trials. This was our criterion for determining if participants gave an “honest effort” during the time trials.

During visits 2, 3, and 4, participants completed solo 3000-m time trials on the University of Colorado’s unbanked 200-m indoor track facility. The time trials took place once per week, for three successive weeks, on the same day of the week and the same time of day. During the time trials, participants wore each of the three pairs of shoes (control, +100 g, or +300 g), in counterbalanced randomized order. We instructed the participants to run the time trial as fast as possible, and we awarded a $30 monetary incentive per time trial when they met the 90% of maximum heart rate honest effort criterion. We did not allow the participants to wear a watch during the time trials but they were able to see a lap counter. We recorded 200-m lap split times but did not provide them to the runners. One experimenter read from a standardized cheering script for all participants during the time trials, and another experimenter recorded lap split times. We recorded the total times for each 3000-m time trial as the primary outcome measures and revealed them to the participants only at the end of their fifth visit.

During visit 5, participants ran on the treadmill at 3.5 m·s⁻¹ in the control shoes, the +100-g and +300-g shoes (5 min each). After a 5-min standing trial and warming up, participants completed four trials. They ran with the control shoes during the first and fourth trials and wore the +100-g or +300-g shoes during the second and third trials, in counterbalanced randomized order. During the 5-min break between trials, an experimenter took off the participants’ shoes, took the shoes outside the room, returned about a minute later, and put the appropriate shoes on the participants’ feet.

On visits 1 and 5, during each trial, we measured submaximal rates of oxygen consumption and carbon dioxide production. Traditionally, RE is defined as V˙O_2submax at a specific running velocity (⁴), but as shown by Fletcher et al. (⁷), expressing RE in terms of metabolic rate is a more appropriate measure because it accounts for changes in substrate utilization. Therefore, we expressed RE in terms of metabolic rate (W·kg⁻¹). We calculated metabolic rate based on the rates of oxygen consumption and carbon dioxide production over the last 2 min of each trial, using the Brockway equation (¹). For visit 5, we averaged the metabolic data for the two baseline trials (control shoes at 3.5 m·s⁻¹). One participant’s metabolic data for the two baseline trials differed by more than 10%, likely because of an equipment malfunction. Thus, we excluded his data for visit 5 from the analyses.

Statistics

We present all results as mean ± SD values in the text and figures. We used repeated-measures ANOVA and linear least-squares regression analysis to evaluate and quantify the effects of shoe mass on metabolic rate and on 3000-m running time. To evaluate the effect of shoe mass on pacing strategy, we performed a two-way repeated-measures ANOVA (lap number × shoe mass) on lap split time. Furthermore, we used a repeated-measures ANOVA to evaluate the effect of time trial order on 3000-m running time. We performed linear regression analysis to assess potential correlations between changes in metabolic rate and in 3000-m running time versus factors such as body mass and 3000-m running time in the control shoes. To evaluate the effects of individual time trial order on 3000-m running time, we used Spearman regression analyses. In addition, we calculated reliability measures for metabolic rate, following Saunders et al. (¹⁷). We calculated the typical error as the standard deviation of relative change between the two baseline trials (control shoes at 3.5 m·s⁻¹), divided by √2. The so-called smallest worthwhile change for metabolic rate was calculated as 0.2 times the between-participant standard deviation. We used a traditional level of significance (α = 0.05) and performed all analyses with MATLAB (The MathWorks, Inc., Natick, MA).

RESULTS

As expected, both submaximal V˙O₂ and gross metabolic rate increased linearly with running velocity (V˙O₂ [mL O₂·kg⁻¹·min⁻¹] = 10.61 × velocity [m·s⁻¹] + 2.28; r² = 0.779; metabolic rate [W·kg⁻¹] = 3.69 × velocity [m·s⁻¹] + 0.47; r² = 0.774). On the basis of the regression, a 1% increase in running velocity increased metabolic rate by 0.97%, i.e., almost exactly in direct proportion.

The metabolic rate for running at 3.5 m·s⁻¹ wearing the control shoes was 13.19 ± 1.26 (W·kg⁻¹). Adding mass to the shoes significantly increased metabolic rate (P < 0.001; Fig. 2). For the +100-g shoes, the metabolic rate was 0.61% ± 1.74% higher (13.27 ± 1.15 W·kg⁻¹), and for the +300-g shoes, the metabolic rate was 3.51% ± 1.18% higher (13.66 ± 1.36 W·kg⁻¹). A linear fit through the data predicts a metabolic rate increase of 1.11% per added 100 g of shoe mass at 3.5 m·s⁻¹ (95% confidence interval [CI] = 0.88%–1.35%).

FIGURE 2: Adding mass to the shoes significantly increased metabolic rate (P < 0.001). Linear least-squares regression equation for all data points: % increase in metabolic rate = 0.0111 × added mass in grams; r ² = 0.47. Error bars indicate ±1 SEM.

While wearing the control shoes, participants ran the 3000-m time trial in 626.1 ± 55.6 s (10:26.1 ± 0:55.6, range = 9:02.4–12:04.5; Table 1). Adding mass to the shoes significantly increased total running time (P < 0.001; Fig. 3). For the +100-g shoes, times averaged 0.65% ± 1.36% slower (10:30.0 ± 0:55.0), and for the +300-g shoes, times were 2.37% ± 2.09% slower (10:40.9 ± 0:58.1). On the basis of a linear fit of all the data, time increased 0.78% per added 100 g of shoe mass (95% CI = 0.52%–1.04%). All participants exceeded the 90% of maximum heart rate “honest effort” criterion during all three time trials.

TABLE 1: Individual performance times for the 3000-m time trials in the control shoes and percent changes in performance time compared with the control shoe for both the +100-g shoes and the +300-g shoes.

FIGURE 3: Adding mass to the shoes significantly slowed 3000-m performance time (P < 0.001). Linear least-squares regression equation for all data points: % change in time = 0.0078 × added mass in grams; r ² = 0.19. Error bars indicate ±1 SEM.

Pacing strategy was not affected by shoe mass (lap number–shoe mass interaction effect: P = 0.10; Fig. 4). During all three time trials, the participants went out fast in the first lap, and then they gradually slowed down, before “kicking” during their last lap(s). Furthermore, we observed no significant effect of time-trial order on 3000-m time: the participants ran their first (630.6 ± 51.5 s), second (632.6 ± 56.7 s), and third time trials (633.6 ± 61.5 s) equally fast (P = 0.73).

FIGURE 4: Pacing strategy was not affected by shoe mass (lap number–shoe mass interaction effect: P = 0.10). Relative differences from the average lap time, per lap for the time trials in the control (dotted line), +100-g (dashed line), and +300-g (solid line) shoes. Error bars indicate ±1 SEM and are slightly offset horizontally for clarity.

DISCUSSION

Overall, we accept our hypothesis that time-trial performance would slow when 100 and 300 g were added per shoe. The main effect for mass was significant, and the overall slope indicated a time increase of 0.78% per 100 g. We confirmed that metabolic rate increases linearly and proportionally with running velocity over the range of 2.5 to 4.0 m·s⁻¹. This linear relation between metabolic rate and running velocity is consistent with many previous studies (^3,6,12,15). We also confirmed that metabolic rate increases when 100 and 300 g were added per shoe. The main effect for mass was significant and the overall slope indicated a 1.11% increase in metabolic rate per 100 g at 3.5 m·s⁻¹.

Our observation that metabolic rate increases linearly with added shoe mass is consistent with earlier findings (^8,9). Frederick et al. (⁹) observed an increase in V˙O_2submax of 1.2% per 100 g added mass per shoe at a running velocity of 3.83 m·s⁻¹. Similarly, Franz et al. (⁸) observed a metabolic rate increase of 1.16% per 100 g added mass per shoe at a running velocity of 3.35 m·s⁻¹.

Can we directly translate these changes in RE to distance-running performance? On the basis of the observed linear relationship between metabolic rate and running velocity and the observed 1.11% increase in metabolic rate per 100 g added shoe mass, one might predict that 3000-m time-trial performance would have slowed by 1.11% per 100 g added shoe mass. However, we observed that overall race time increased by only 0.78% per added 100 g of shoe mass. Expecting the degradation in performance at race pace to be directly proportional to the degradation in RE measured at a slower submaximal pace assumes that the effect of adding shoe mass on metabolic rate is independent of running velocity. We only evaluated the effect of adding shoe mass at one speed (3.5 m·s⁻¹), so our data cannot confirm nor reject this assumption. However, in their seminal paper, Frederick et al. (⁹) evaluated the effect of adding mass on V˙O_2submax for a range of speeds, and their data suggest that the effect of adding shoe mass on V˙O_2submax is dependent on running velocity. Because it was a maximal effort, we could not measure the effects of shoe mass on RE at the average 3000-m time-trial pace of our participants (4.79 m·s⁻¹). Fortunately, Frederick et al. (⁹) studied a higher caliber group of runners and were thus able to measure V˙O_2submax up to 4.88 m·s⁻¹, just slightly faster than the time-trial pace of our participants. Frederick et al. reported that at 4.88 m·s⁻¹, V˙O_2submax increased by 0.8% per 100 g mass added to each shoe, which is strikingly close to the 0.78% decrease in performance per 100 g mass added to each shoe that we observed in our participants running on average at 4.79 m·s⁻¹. Taken together, these observations indicate that altered RE directly translates to altered distance-running performance.

Overall, we observed substantial interindividual variability in the responses to added shoe mass. For the change in metabolic rate, the 95% CI ranged from 0.88% to 1.35% per 100 g added mass. For the change in time-trial performance, the 95% CI ranged from 0.52% to 1.04% per 100 g added mass. As discussed above, the data of Frederick et al. (⁹) suggest that the effect of adding shoe mass on V˙O_2submax is dependent on running velocity. As such, one could expect that part of the interindividual variability in running performance might be related to differences in the individual running speed of the participants. We did a regression analysis on the individual change in time-trial performance per 100 g added shoe mass versus the individual race time in the control shoes but did not find a significant correlation (P = 0.76). Theoretically, the interindividual variability in performance changes to added shoe mass could be related to the order in which the individual participants ran their time trials. However, regression analyses showed no significant correlations between changes in performance and time-trial order (P > 0.2). An alternative possible explanation for the interindividual variability is the fact that the absolute differences in shoe mass (+100 g and +300 g, respectively) were identical for all participants and not adjusted for their body mass. One can imagine that adding 300 g mass to the shoe of a 60-kg runner might have a larger relative effect than adding mass to the shoe of an 80-kg runner. However, we did not observe significant correlations between changes in metabolic rate or in performance and body mass (all P > 0.2). Future studies could further address the large interindividual variability in the responses to added shoe mass, using kinetic and kinematic analyses of running with shoes of different masses.

During the time trials, only 1 of our 18 participants noticed that some of the shoes had added mass. This occurred just before that participant’s third time trial, when he was wearing the +300-g shoes. That participant has wide feet, and because of the bulk of the shoes, he noticed that the shoelaces of the +300-g shoes were shorter than for previous time trials. He subsequently felt the added lead pockets with his hands. As such, 17 of the 18 participants did not perceive a difference between the shoe masses during the time trials that were separated by a week.

Intriguingly, the pacing data of the time trials (Fig. 4) suggest that, in general, the participants unconsciously perceived differences in running effort related to the mass differences between the shoes and adjusted their effort level and pacing accordingly. That is, they unconsciously ran slower when they wore the heavier shoes. The similar pacing patterns for all shoe masses suggest that the varied effort was unconsciously sensed very quickly, within the first 200-m lap (Fig. 4). These observations are in line with recent findings on the selection of energetically optimal step frequencies during walking and running (^18,19). Snyder et al. (¹⁹) evaluated how quickly runners refind their optimal step frequency after a period of running while matching a metronome set to nonoptimal frequencies. They observed that responses were dominated by a fast process with a response time of ~1.5 s followed by a slower, fine-tuning process with a response time of ~34 s. Using robotic exoskeletons, Selinger et al. (¹⁸) shifted people’s energetically optimal walking step frequency and showed that people rapidly sense the change and alter their gait to minimize metabolic rate.

Recently, Fuller et al. (¹¹) similarly addressed how changes in RE affect distance-running performance. They compared V˙O_2submax and performance during a self-paced 5-km treadmill running trial between conventional running shoes and very lightweight racing flats. At submaximal speeds, participants used 0.7%–2.5% less oxygen running in the racing flats as compared with the conventional shoes. With the racing flats, they ran ~1.7% faster during the self-paced 5-km treadmill running trial. Although that study provided new insight, an important limitation was that it was impossible to blind the participants as to the shoe conditions during the treadmill time trials. As Fuller et al. noted in their discussion, runners expect superior race performance when wearing racing flats. Mohr et al. (¹⁶) showed that there can be dramatic placebo effects for athletic shoes, although they measured vertical jump performance rather than distance-running performance.

Limitations

Although we made every effort to minimize differences in sleep, training, and dietary status between the different time trials, we could not absolutely control for everything. We studied good but not elite runners, and thus directly extrapolating our results to elite runners should be conducted with caution. Furthermore, as mentioned, we could not measure the effects of shoe mass on RE at the 3000-m time-trial pace of our participants because this distance is normally run at an intensity close to or above V˙O_2max (²), invalidating RE measurements at that pace. Another limitation is that although we are ultimately interested in the effects of improved RE on performance, we could only study the effect of adding mass and thus RE degradation.

Implications

The current men’s world best for a marathon (42.195 km) is 2:02:57 (5.72 m·s⁻¹) and was run wearing ~230-g shoes. Assuming that our results for running 3000 m at 4.79 m·s⁻¹ directly transfer to running 42 km at 5.72 m·s⁻¹, a reduction of shoe mass by 100 g per shoe would reduce the marathon record by ~0.78% or 57.5 s. Extending this line of reasoning, one might expect that running barefoot (as opposed to wearing 230-g racing flats) could facilitate a marathon time of ~2:00:45, just through the elimination of the shoe mass. However, barefoot and shod running are different in more aspects than mass, and previous studies in our laboratory (^8,21) have demonstrated the interactions and trade-offs between shoe mass and shoe cushioning properties in terms of RE. For example, Tung et al. (²¹) showed that running barefoot on a rigid surface is equally economical as running in lightweight, cushioned shoes on the same surface. However, when Tung et al. eliminated shoe mass but conserved cushioning by having participants run barefoot on a surface cushioned with 10 mm of common midsole foam, RE was enhanced. Thus, to optimize RE, shoe mass should not be minimized by eliminating cushioning. Further, significant muscle damage occurs over a marathon (¹³). A shoe that combines low mass with minimal cushioning might provide energy savings when evaluated in short-term treadmill testing, but not be optimal for marathon running because extra cushioning might mitigate muscle damage and thus be beneficial in the long run.

While our observations indicate that altered RE directly translates to altered distance-running performance, it is important to note that changes in RE observed for a specific individual need to be considered in relation to the reliability of RE measurements. The typical error in RE in our study was 0.84%, and the “smallest worthwhile change” was 1.91% for our sample. When evaluating changes in RE for an individual runner, for a single observation, only changes in RE of 2.0% or more can be considered to be “real” and “worthwhile” (¹⁷) and not simply related to measurement error and typical variation. However, real effects smaller than 2% can be identified with repeated measurements on an individual and/or with adequate sample sizes as we have shown in the present study.

CONCLUSION

We confirmed that adding shoe mass degrades RE, and we showed, for the first time, that adding shoe mass slows 3000-m time-trial performance proportionally (0.78% per 100 g per shoe). Our data demonstrate that laboratory-based RE measurements can accurately predict changes in distance-running race performance due to shoe modifications.

This study was supported by Nike Inc. We thank Maddie Alm for assistance during the data collections.

Barry Spiering is an employee of Nike Inc., and Rodger Kram is a paid consultant to Nike Inc. The results of the present study do not constitute endorsement by the American College of Sports Medicine.