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Human, but not canine or equine running performance, is significantly stratified by sex. The degree of stratification has obvious implications for classification and regulation in athletics. However, whether the widely cited sex difference of 10%-12% applies equally to sprint and endurance running events is unknown. Here, different determining factors for sprint (ground force/body mass) versus endurance performance (energy supply and demand) and existing trends, led us to hypothesize that sex performance differences for sprint running would increase with distance and be relatively small. We quantified sex performance differences using: 1 ) the race times of the world's fastest males and females ( n = 40 each) over a 15-year period (2003-2018) at nine standard racing distances (60-10,000 m), and 2 ) the 10-m segment times of male ( n = 14) and female ( n = 12) athletes in World Championship 100-m finals. Between-sex performance time differences increased with sprint event distance (60 m-8.6%, 100 m-9.6%, 200 m-11.0%, 400 m-11.7%) and were smaller than the relatively constant mean (12.4 ± 0.3%) observed across the five longer events from 800 to 10,000 m. Between-sex time differences for the 10-m segments within the 100-m dash event increased throughout spanning 5.6%-14.2% from the first to last segment. We conclude that sex differences in sprint running performance increase with race and running distance. NEW & NOTEWORTHY Sex performance differences for sprint running bursts are small (<6%), but widen as the distance sprinted increases (range: 5.6%-14.2%). The distance dependency identified here for sprinting differs from the prevailing literature view of between-sex performance differences for the human running of 10%-12% regardless of distance. The variable sprint margins observed reflect the relative performance benefits shorter females have for brief, acceleration-dependent efforts versus those taller males have for longer steadier-speed sprint efforts.

We addressed a practical question that remains largely unanswered after more than a century of active investigation: can equations developed in the laboratory accurately predict the energy expended under free-walking conditions in the field? Seven subjects walked a field course of 6,415 m that varied in gradient (-3.0 to +5.0%) and terrain (asphalt, grass) under unloaded (body weight only, W b ) and balanced, torso-loaded (1.30 × W b ) conditions at self-selected speeds while wearing portable calorimeter and GPS units. Portable calorimeter measures were corrected for a consistent measurement-range offset (+13.8 ± 1.8%, means ± SD) versus a well-validated laboratory system (Parvomedics TrueOne). Predicted energy expenditure totals (mL O 2 /kg) from four literature equations: ACSM, Looney, Minimum Mechanics, and Pandolf, were generated using the speeds and gradients measured throughout each trial in conjunction with empirically determined terrain/treadmill factors (asphalt = 1.0, grass = 1.08). The mean energy expenditure total measured for the unloaded field trials (981 ± 91 mL O 2 /kg) was overpredicted by +4%, +13%, +17%, and +20% by the Minimum Mechanics, ACSM, Pandolf, and Looney equations, respectively (corresponding predicted totals: 1,018 ± 19, 1,108 ± 26, 1,145 ± 37, and 1,176 ± 24 mL O 2 /kg). The measured loaded-trial total (1,310 ± 153 mL O 2 /kg) was slightly underpredicted by the Minimum Mechanics equation (-2%, 1,289 ± 22 mL O 2 /kg) and overpredicted by the Pandolf equation (+13%, 1,463 ± 32 mL O 2 /kg). Computational comparisons for hypothetical trials at different constant speeds (range: 0.6-1.8 m/s) on variable-gradient loop courses revealed between-equation prediction differences from 0% to 37%. We conclude that treadmill-based predictions of free-walking field energy expenditure are equation-dependent but can be highly accurate with rigorous implementation. NEW & NOTEWORTHY Here, we investigated the accuracy with which four laboratory-based equations can predict field-walking energy expenditure at freely selected speeds across varying gradients and terrain. Empirical tests involving 6,415-m trials under two load conditions indicated that predictions are significantly equation dependent but can be highly accurate (i.e., ±4%). Computations inputting identical weight, speed, and gradient values for different theoretical constant-speed trials (0.6-1.8 m/s) identified between-equation prediction differences as large as 37%.

Although running shoes alter foot-ground reaction forces, particularly during impact, how they do so is incompletely understood. Here, we hypothesized that footwear effects on running ground reaction force-time patterns can be accurately predicted from the motion of two components of the body's mass (m b ): the contacting lower-limb (m 1  = 0.08m b ) and the remainder (m 2  = 0.92m b ). Simultaneous motion and vertical ground reaction force-time data were acquired at 1,000 Hz from eight uninstructed subjects running on a force-instrumented treadmill at 4.0 and 7.0 m / s under four footwear conditions: barefoot, minimal sole, thin sole, and thick sole. Vertical ground reaction force-time patterns were generated from the two-mass model using body mass and footfall-specific measures of contact time, aerial time, and lower-limb impact deceleration. Model force-time patterns generated using the empirical inputs acquired for each footfall matched the measured patterns closely across the four footwear conditions at both protocol speeds ( r 2  = 0.96 ± 0.004; root mean squared error  = 0.17 ± 0.01 body-weight units; n = 275 total footfalls). Foot landing angles (θ F ) were inversely related to footwear thickness; more positive or plantar-flexed landing angles coincided with longer-impact durations and force-time patterns lacking distinct rising-edge force peaks. Our results support three conclusions: 1 ) running ground reaction force-time patterns across footwear conditions can be accurately predicted using our two-mass, two-impulse model, 2 ) impact forces, regardless of foot strike mechanics, can be accurately quantified from lower-limb motion and a fixed anatomical mass (0.08m b ), and 3 ) runners maintain similar loading rates (ΔF vertical /Δtime) across footwear conditions by altering foot strike angle to regulate the duration of impact. NEW & NOTEWORTHY Here, we validate a two-mass, two-impulse model of running vertical ground reaction forces across four footwear thickness conditions (barefoot, minimal, thin, thick). Our model allows the impact portion of the impulse to be extracted from measured total ground reaction force-time patterns using motion data from the ankle. The gait adjustments observed across footwear conditions revealed that runners maintained similar loading rates across footwear conditions by altering foot strike angles to regulate the duration of impact.

The metabolic energy that human walking requires can vary by more than 10-fold, depending on the speed, surface gradient, and load carried. Although the mechanical factors determining economy are generally considered to be numerous and complex, we tested a minimum mechanics hypothesis that only three variables are needed for broad, accurate prediction: speed, surface grade, and total gravitational load. We first measured steady-state rates of oxygen uptake in 20 healthy adult subjects during unloaded treadmill trials from 0.4 to 1.6 m/s on six gradients: -6, -3, 0, 3, 6, and 9°. Next, we tested a second set of 20 subjects under three torso-loading conditions (no-load, +18, and +31% body weight) at speeds from 0.6 to 1.4 m/s on the same six gradients. Metabolic rates spanned a 14-fold range from supine rest to the greatest single-trial walking mean (3.1 ± 0.1 to 43.3 ± 0.5 ml O 2 ·kg -body -1 ·min -1 , respectively). As theorized, the walking portion (V̇o 2-walk  =  V̇o 2-gross - V̇o 2-supine-rest ) of the body's gross metabolic rate increased in direct proportion to load and largely in accordance with support force requirements across both speed and grade. Consequently, a single minimum-mechanics equation was derived from the data of 10 unloaded-condition subjects to predict the pooled mass-specific economy (V̇o 2-gross , ml O 2 ·kg -body + load -1 ·min -1 ) of all the remaining loaded and unloaded trials combined ( n = 1,412 trials from 90 speed/grade/load conditions). The accuracy of prediction achieved ( r 2  = 0.99, SEE = 1.06 ml O 2 ·kg -1 ·min -1 ) leads us to conclude that human walking economy is predictably determined by the minimum mechanical requirements present across a broad range of conditions. NEW & NOTEWORTHY Introduced is a "minimum mechanics" model that predicts human walking economy across a broad range of conditions from only three variables: speed, surface grade, and body-plus-load mass. The derivation/validation data set includes steady-state loaded and unloaded walking trials ( n = 3,414) that span a fourfold range of walking speeds on each of six different surface gradients (-6 to +9°). The accuracy of our minimum mechanics model ( r 2  = 0.99; SEE = 1.06 ml O 2 ·kg -1 ·min -1 ) appreciably exceeds that of currently used standards., (Copyright © 2017 the American Physiological Society.)

Accurate prediction of the metabolic energy that walking requires can inform numerous health, bodily status, and fitness outcomes. We adopted a two-step approach to identifying a concise, generalized equation for predicting level human walking metabolism. Using literature-aggregated values we compared 1) the predictive accuracy of three literature equations: American College of Sports Medicine (ACSM), Pandolf et al., and Height-Weight-Speed (HWS); and 2) the goodness-of-fit possible from one- vs. two-component descriptions of walking metabolism. Literature metabolic rate values (n = 127; speed range = 0.4 to 1.9 m/s) were aggregated from 25 subject populations (n = 5-42) whose means spanned a 1.8-fold range of heights and a 4.2-fold range of weights. Population-specific resting metabolic rates (V̇o2 rest) were determined using standardized equations. Our first finding was that the ACSM and Pandolf et al. equations underpredicted nearly all 127 literature-aggregated values. Consequently, their standard errors of estimate (SEE) were nearly four times greater than those of the HWS equation (4.51 and 4.39 vs. 1.13 ml O2·kg(-1)·min(-1), respectively). For our second comparison, empirical best-fit relationships for walking metabolism were derived from the data set in one- and two-component forms for three V̇o2-speed model types: linear (∝V(1.0)), exponential (∝V(2.0)), and exponential/height (∝V(2.0)/Ht). We found that the proportion of variance (R(2)) accounted for, when averaged across the three model types, was substantially lower for one- vs. two-component versions (0.63 ± 0.1 vs. 0.90 ± 0.03) and the predictive errors were nearly twice as great (SEE = 2.22 vs. 1.21 ml O2·kg(-1)·min(-1)). Our final analysis identified the following concise, generalized equation for predicting level human walking metabolism: V̇o2 total = V̇o2 rest + 3.85 + 5.97·V(2)/Ht (where V is measured in m/s, Ht in meters, and V̇o2 in ml O2·kg(-1)·min(-1))., (Copyright © 2016 the American Physiological Society.)

Are the fastest running speeds achieved using the simple-spring stance mechanics predicted by the classic spring-mass model? We hypothesized that a passive, linear-spring model would not account for the running mechanics that maximize ground force application and speed. We tested this hypothesis by comparing patterns of ground force application across athletic specialization (competitive sprinters vs. athlete nonsprinters, n = 7 each) and running speed (top speeds vs. slower ones). Vertical ground reaction forces at 5.0 and 7.0 m/s, and individual top speeds (n = 797 total footfalls) were acquired while subjects ran on a custom, high-speed force treadmill. The goodness of fit between measured vertical force vs. time waveform patterns and the patterns predicted by the spring-mass model were assessed using the R(2) statistic (where an R(2) of 1.00 = perfect fit). As hypothesized, the force application patterns of the competitive sprinters deviated significantly more from the simple-spring pattern than those of the athlete, nonsprinters across the three test speeds (R(2) <0.85 vs. R(2) ≥ 0.91, respectively), and deviated most at top speed (R(2) = 0.78 ± 0.02). Sprinters attained faster top speeds than nonsprinters (10.4 ± 0.3 vs. 8.7 ± 0.3 m/s) by applying greater vertical forces during the first half (2.65 ± 0.05 vs. 2.21 ± 0.05 body wt), but not the second half (1.71 ± 0.04 vs. 1.73 ± 0.04 body wt) of the stance phase. We conclude that a passive, simple-spring model has limited application to sprint running performance because the swiftest runners use an asymmetrical pattern of force application to maximize ground reaction forces and attain faster speeds., (Copyright © 2014 the American Physiological Society.)

We formulated a "one-size-fits-all" model that predicts the energy requirements of level human walking from height, weight, and walking speed. Our three-component model theorizes that the energy expended per kilogram per stride is independent of stature at mechanically equivalent walking speeds. We measured steady-state rates of oxygen uptake of 78 subjects who spanned a nearly twofold range of statures (1.07-2.11 m) and sevenfold range of body masses (16-112 kg) at treadmill speeds from 0.4 to 1.9 m/s. We tested the size independence of the model by deriving best-fit equations in the form of the model on four stature groups (n ≥ 15): short, moderately short, moderately tall, and tall. The mean walking metabolic rates predicted by these four independently derived equations for the same set of reference subjects (n = 16; stature range: 1.30-1.90 m) agreed with one another to within an average of 5.2 ± 3.7% at the four intermediate speeds in our protocol. We next evaluated the model's gross predictive accuracy by dividing our 78 subjects into 39 stature-matched pairs of experimental and validation group subjects. The model best-fit equation derived on the experimental group subjects predicted the walking metabolic rates of the validation group subjects to within an average of 8.1 ± 6.7% (R(2) = 0.90; standard error of estimate = 1.34 ml O2·kg(-1)·min(-1)). The predictive error of the American College of Sports Medicine equation (18.0 ± 13.1%), which does not include stature as a predictor, was more than twice as large for the same subject group. We conclude that the energy cost of level human walking can be accurately predicted from height, weight, and walking speed.

Running speed is limited by a mechanical interaction between the stance and swing phases of the stride. Here, we tested whether stance phase limitations are imposed by ground force maximums or foot-ground contact time minimums. We selected one-legged hopping and backward running as experimental contrasts to forward running and had seven athletic subjects complete progressive discontinuous treadmill tests to failure to determine their top speeds in each of the three gaits. Vertical ground reaction forces [in body weights (W(b))] and periods of ground force application (T(c); s) were measured using a custom, high-speed force treadmill. At top speed, we found that both the stance-averaged (F(avg)) and peak (F(peak)) vertical forces applied to the treadmill surface during one-legged hopping exceeded those applied during forward running by more than one-half of the body's weight (F(avg) = 2.71 +/- 0.15 vs. 2.08 +/- 0.07 W(b); F(peak) = 4.20 +/- 0.24 vs. 3.62 +/- 0.24 W(b); means +/- SE) and that hopping periods of force application were significantly longer (T(c) = 0.160 +/- 0.006 vs. 0.108 +/- 0.004 s). Next, we found that the periods of ground force application at top backward and forward running speeds were nearly identical, agreeing to within an average of 0.006 s (T(c) = 0.116 +/- 0.004 vs. 0.110 +/- 0.005 s). We conclude that the stance phase limit to running speed is imposed not by the maximum forces that the limbs can apply to the ground but rather by the minimum time needed to apply the large, mass-specific forces necessary.

The recent competitive successes of a bilateral, transtibial amputee sprint runner who races with modern running prostheses has triggered an international controversy regarding the relative function provided by his artificial limbs. Here, we conducted three tests of functional similarity between this amputee sprinter and competitive male runners with intact limbs: the metabolic cost of running, sprinting endurance, and running mechanics. Metabolic and mechanical data, respectively, were acquired via indirect calorimetry and ground reaction force measurements during constant-speed, level treadmill running. First, we found that the mean gross metabolic cost of transport of our amputee sprint subject (174.9 ml O(2)*kg(-1)*km(-1); speeds: 2.5-4.1 m/s) was only 3.8% lower than mean values for intact-limb elite distance runners and 6.7% lower than for subelite distance runners but 17% lower than for intact-limb 400-m specialists [210.6 (SD 13.2) ml O(2)*kg(-1)*km(-1)]. Second, the speeds that our amputee sprinter maintained for six all-out, constant-speed trials to failure (speeds: 6.6-10.8 m/s; durations: 2-90 s) were within 2.2 (SD 0.6)% of those predicted for intact-limb sprinters. Third, at sprinting speeds of 8.0, 9.0, and 10.0 m/s, our amputee subject had longer foot-ground contact times [+14.7 (SD 4.2)%], shorter aerial [-26.4 (SD 9.9)%] and swing times [-15.2 (SD 6.9)%], and lower stance-averaged vertical forces [-19.3 (SD 3.1)%] than intact-limb sprinters [top speeds = 10.8 vs. 10.8 (SD 0.6) m/s]. We conclude that running on modern, lower-limb sprinting prostheses appears to be physiologically similar but mechanically different from running with intact limbs.

For both different individuals and modes of locomotion, the external forces determining all-out sprinting performances fall predictably with effort duration from the burst maximums attained for 3 s to those that can be supported aerobically as trial durations extend to roughly 300 s. The common time course of this relationship suggests a metabolic basis for the decrements in the force applied to the environment. However, the mechanical and neuromuscular responses to impaired force production (i.e., muscle fatigue) are generally considered in relation to fractions of the maximum force available, or the maximum voluntary contraction (MVC). We hypothesized that these duration-dependent decrements in external force application result from a reliance on anaerobic metabolism for force production rather than the absolute force produced. We tested this idea by examining neuromuscular activity during two modes of sprint cycling with similar external force requirements but differing aerobic and anaerobic contributions to force production: one- and two-legged cycling. In agreement with previous studies, we found greater peak per leg aerobic metabolic rates [59% (+/-6 SD)] and pedal forces at VO2 peak [30% (+/-9)] during one- vs. two-legged cycling. We also determined downstroke pedal forces and neuromuscular activity by surface electromyography during 15 to 19 all-out constant load sprints lasting from 12 to 400 s for both modes of cycling. In support of our hypothesis, we found that the greater reliance on anaerobic metabolism for force production induced compensatory muscle recruitment at lower pedal forces during two- vs. one-legged sprint cycling. We conclude that impaired muscle force production and compensatory neuromuscular activity during sprinting are triggered by a reliance on anaerobic metabolism for force production.

We hypothesized that the maximum mechanical power outputs that can be maintained during all-out sprint cycling efforts lasting from a few seconds to several minutes can be accurately estimated from a single exponential time constant (k(cycle)) and two measurements on individual cyclists: the peak 3-s power output (P(mech max)) and the maximum mechanical power output that can be supported aerobically (P(aer)). Tests were conducted on seven subjects, four males and three females, on a stationary cycle ergometer at a pedal frequency of 100 rpm. Peak mechanical power output (P(mech max)) was the highest mean power output attained during a 3-s burst; the maximum power output supported aerobically (P(aer)) was determined from rates of oxygen uptake measured during a progressive, discontinuous cycling test to failure. Individual power output-duration relationships were determined from 13 to 16 all-out constant load sprints lasting from 5 to 350 s. In accordance with the above hypothesis, the power outputs measured during all-out sprinting efforts were estimated to within an average of 34 W or 6.6% from P(mech max), P(aer), and a single exponential constant (k(cycle) = 0.026 s(-1)) across a sixfold range of power outputs and a 70-fold range of sprint trial durations (R2 = 0.96 vs. identity, n = 105; range: 180 to 1,136 W). Duration-dependent decrements in sprint cycling power outputs were two times greater than those previously identified for sprint running speed (k(run) = 0.013 s(-1)). When related to the respective times of pedal and ground force application rather than total sprint time, decrements in sprint cycling and running performance followed the same time course (k = 0.054 s(-1)). We conclude that the duration-dependent decrements in sprinting performance are set by the fractional duration of the relevant muscular contractions.

We hypothesized that the anaerobic power and aerobic power outputs during all-out runs of any common duration between 10 and 150 s would be proportional to the maximum anaerobic (E(an-max)) and aerobic powers (E(aer-max)) available to the individual runner. Seventeen runners who differed in E(an-max) and E(aer-max) (5 sprinters, 5 middle-distance runners, and 7 long distance runners) were tested during treadmill running on a 4.6 degrees incline. E(an-max) was estimated from the fastest treadmill speed subjects could attain for eight steps. E(aer-max) was determined from a progressive, discontinuous, treadmill test to failure. Oxygen deficits and rates of uptake were measured to assess the respective anaerobic and aerobic power outputs during 11-16 all-out treadmill runs that elicited failure between 10 and 220 s. We found that, during all-out runs of any common duration, the relative anaerobic and aerobic powers utilized were largely the same for sprint, middle-distance, and long-distance subjects. The similar fractional utilization of the E(an-max) and E(aer-max) available during high-speed running 1) provides empirical values that modify and advance classic theory, 2) allows rates of anaerobic and aerobic energy release to be quantified from individual maxima and run durations, and 3) explains why the high-speed running performances of different event specialists can be accurately predicted (R(2) = 0.97; n = 254) from two direct measurements and the same exponential time constant.

We hypothesized that all-out running speeds for efforts lasting from a few seconds to several minutes could be accurately predicted from two measurements: the maximum respective speeds supported by the anaerobic and aerobic powers of the runner. To evaluate our hypothesis, we recruited seven competitive runners of different event specialties and tested them during treadmill and overground running on level surfaces. The maximum speed supported by anaerobic power was determined from the fastest speed that subjects could attain for a burst of eight steps (approximately 3 s or less). The maximum speed supported by aerobic power, or the velocity at maximal oxygen uptake, was determined from a progressive, discontinuous treadmill test to failure. All-out running speeds for trials of 3-240 s were measured during 10-13 constant-speed treadmill runs to failure and 4 track runs at specified distances. Measured values of the maximum speeds supported by anaerobic and aerobic power, in conjunction with an exponential constant, allowed us to predict the speeds of all-out treadmill trials to within an average of 2.5% (R2 = 0.94; n = 84) and track trials to within 3.4% (R2 = 0.86; n = 28). An algorithm using this exponent and only two of the all-out treadmill runs to predict the remaining treadmill trials was nearly as accurate (average = 3.7%; R2 = 0.93; n = 77). We conclude that our technique 1) provides accurate predictions of high-speed running performance in trained runners and 2) offers a performance assessment alternative to existing tests of anaerobic power and capacity.

Mammals use the elastic components in their legs (principally tendons, ligaments, and muscles) to run economically, while maintaining consistent support mechanics across various surfaces. To examine how leg stiffness and metabolic cost are affected by changes in substrate stiffness, we built experimental platforms with adjustable stiffness to fit on a force-plate-fitted treadmill. Eight male subjects [mean body mass: 74.4 +/- 7.1 (SD) kg; leg length: 0.96 +/- 0.05 m] ran at 3.7 m/s over five different surface stiffnesses (75.4, 97.5, 216.8, 454.2, and 945.7 kN/m). Metabolic, ground-reaction force, and kinematic data were collected. The 12.5-fold decrease in surface stiffness resulted in a 12% decrease in the runner's metabolic rate and a 29% increase in their leg stiffness. The runner's support mechanics remained essentially unchanged. These results indicate that surface stiffness affects running economy without affecting running support mechanics. We postulate that an increased energy rebound from the compliant surfaces studied contributes to the enhanced running economy.

Seeking to develop a simple ambulatory test of maximal aerobic power (VO(2 max)), we hypothesized that the ratio of inverse foot-ground contact time (1/t(c)) to heart rate (HR) during steady-speed running would accurately predict VO(2 max). Given the direct relationship between 1/t(c) and mass-specific O(2) uptake during running, the ratio 1/t(c). HR should reflect mass-specific O(2) pulse and, in turn, aerobic power. We divided 36 volunteers into matched experimental and validation groups. VO(2 max) was determined by a treadmill test to volitional fatigue. Ambulatory monitors on the shoe and chest recorded foot-ground contact time (t(c)) and steady-state HR, respectively, at a series of submaximal running speeds. In the experimental group, aerobic fitness index (1/t(c). HR) was nearly constant across running speed and correlated with VO(2 max) (r = 0.90). The regression equation derived from data from the experimental group predicted VO(2 max) from the 1/t(c). HR values in the validation group within 8.3% and 4.7 ml O(2) x kg(-1) x min(-1) (r = 0.84) of measured values. We conclude that simultaneous measurements of foot-ground constant times and heart rates during level running at a freely chosen constant speed can provide accurate estimates of maximal aerobic power.

We twice tested the hypothesis that top running speeds are determined by the amount of force applied to the ground rather than how rapidly limbs are repositioned in the air. First, we compared the mechanics of 33 subjects of different sprinting abilities running at their top speeds on a level treadmill. Second, we compared the mechanics of declined (-6 degrees ) and inclined (+9 degrees ) top-speed treadmill running in five subjects. For both tests, we used a treadmill-mounted force plate to measure the time between stance periods of the same foot (swing time, t(sw)) and the force applied to the running surface at top speed. To obtain the force relevant for speed, the force applied normal to the ground was divided by the weight of the body (W(b)) and averaged over the period of foot-ground contact (F(avge)/W(b)). The top speeds of the 33 subjects who completed the level treadmill protocol spanned a 1.8-fold range from 6.2 to 11.1 m/s. Among these subjects, the regression of F(avge)/W(b) on top speed indicated that this force was 1.26 times greater for a runner with a top speed of 11.1 vs. 6.2 m/s. In contrast, the time taken to swing the limb into position for the next step (t(sw)) did not vary (P = 0.18). Declined and inclined top speeds differed by 1.4-fold (9.96+/-0.3 vs. 7.10+/-0.3 m/s, respectively), with the faster declined top speeds being achieved with mass-specific support forces that were 1.3 times greater (2.30+/- 0.06 vs. 1.76+/-0.04 F(avge)/ W(b)) and minimum t(sw) that were similar (+8%). We conclude that human runners reach faster top speeds not by repositioning their limbs more rapidly in the air, but by applying greater support forces to the ground.

We tested the importance of aerobic metabolism to human running speed directly by altering inspired oxygen concentrations and comparing the maximal speeds attained at different rates of oxygen uptake. Under both normoxic (20.93% O2) and hypoxic (13.00% O2) conditions, four fit adult men completed 15 all-out sprints lasting from 15 to 180 s as well as progressive, discontinuous treadmill tests to determine maximal oxygen uptake and the metabolic cost of steady-state running. Maximal aerobic power was lower by 30% (1.00 +/- 0.15 vs. 0.77 +/- 0.12 ml O2. kg-1. s-1) and sprinting rates of oxygen uptake by 12-25% under hypoxic vs. normoxic conditions while the metabolic cost of submaximal running was the same. Despite reductions in the aerobic energy available for sprinting under hypoxic conditions, our subjects were able to run just as fast for sprints of up to 60 s and nearly as fast for sprints of up to 120 s. This was possible because rates of anaerobic energy release, estimated from oxygen deficits, increased by as much as 18%, and thus compensated for the reductions in aerobic power. We conclude that maximal metabolic power outputs during sprinting are not limited by rates of anaerobic metabolism and that human speed is largely independent of aerobic power during all-out runs of 60 s or less.

We tested whether the rate at which force is applied to the ground sets metabolic rates during classical-style roller skiing in four ways: 1) by increasing speed (from 2.5 to 4.5 m/s) during skiing with arms only, 2) by increasing speed (from 2.5 to 4.5 m/s) during skiing with legs only, 3) by changing stride rate (from 25 to 75 strides/min) at each of three speeds (3.0, 3.5, and 4.0 m/s) during skiing with legs only, and 4) by skiing with arms and legs together at three speeds (2.0-3.2 m/s, 1.5 degrees incline). We determined net metabolic rates from rates of O2 consumption (gross O2 consumption - standing O2 consumption) and rates of force application from the inverse period of pole-ground contact [1/tp(arms)] for the arms and the inverse period of propulsion [1/tp(legs)] for the legs. During arm-and-leg skiing at different speeds, metabolic rates changed in direct proportion to rates of force application, while the net ground force to counteract friction and gravity (F) was constant. Consequently, metabolic rates were described by a simple equation (metab = F . 1/tp . C, where metab is metabolic rates) with cost coefficients (C) of 8.2 and 0.16 J/N for arms and legs, respectively. Metabolic rates predicted from net ground forces and rates of force application during combined arm-and-leg skiing agreed with measured metabolic rates within +/-3. 5%. We conclude that rates of ground force application to support the weight of the body and overcome friction set the energetic cost of skiing and that the rate at which muscles expend metabolic energy during weight-bearing locomotion depends on the time course of their activation.