Estimating Metabolic Energy Expenditure During Level Running in Healthy, Military-Age Women and Men.

ABSTRACT: Looney, DP, Hoogkamer, W, Kram, R, Arellano, CJ, and Spiering, BA. Estimating metabolic energy expenditure during level running in healthy, military-age women and men. J Strength Cond Res 37(12): 2496-2503, 2023-Quantifying the rate of metabolic energy expenditure (M) of varied aerobic exercise modalities is important for optimizing fueling and performance and maintaining safety in military personnel operating in extreme conditions. However, although equations exist for estimating oxygen uptake during running, surprisingly, there are no general equations that estimate M. Our purpose was to generate a general equation for estimating M during level running in healthy, military-age (18-44 years) women and men. We compiled indirect calorimetry data collected during treadmill running from 3 types of sources: original individual subject data (n = 45), published individual subject data (30 studies; n = 421), and published group mean data (20 studies, n = 619). Linear and quadratic equations were fit on the aggregated data set using a mixed-effects modeling approach. A chi-squared (χ2) difference test was conducted to determine whether the more complex quadratic equation was justified (p < 0.05). Our primary indicator of model goodness-of-fit was the root-mean-square deviation (RMSD). We also examined whether individual characteristics (age, height, body mass, and maximal oxygen uptake [V̇O2max]) could minimize prediction errors. The compiled data set exhibited considerable variability in M (14.54 ± 3.52 W·kg-1), respiratory exchange ratios (0.89 ± 0.06), and running speeds (3.50 ± 0.86 m·s-1). The quadratic regression equation had reduced residual sum of squares compared with the linear fit (χ2, 3,484; p < 0.001), with higher combined accuracy and precision (RMSD, 1.31 vs. 1.33 W·kg-1). Age (p = 0.034), height (p = 0.026), and body mass (p = 0.019) were associated with the magnitude of under and overestimation, which was not the case for V̇O2max (p = 0.898). The newly derived running energy expenditure estimation (RE3) model accurately predicts level running M at speeds from 1.78 to 5.70 m·s-1 in healthy, military-age women and men. Users can rely on the following equations for improved predictions of running M as a function of running speed (S, m·s-1) in either watts (W·kg-1 = 4.43 + 1.51·S + 0.37·S2) or kilocalories per minute (kcal·kg-1·min-1 = 308.8 + 105.2·S + 25.58·S2).

It is time to abandon single-value oxygen uptake energy equivalents.

Physiologists commonly use single-value energy equivalents (e.g., 20.1 kJ/LO2 and 20.9 kJ/LO2) to convert oxygen uptake (V̇o2) to energy, but doing so ignores how the substrate oxidation ratio (carbohydrate:fat) changes across aerobic intensities. Using either 20.1 kJ/LO2 or 20.9 kJ/LO2 can incur systematic errors of up to 7%. In most circumstances, the best approach for estimating energy expenditure is to measure both V̇o2 and V̇co2 and use accurate, species-appropriate stoichiometry. However, there are circumstances when V̇co2 measurements may be unreliable. In those circumstances, we recommend that the research report or compare only V̇o2.NEW & NOTEWORTHY We quantify that the common practice of using single-value oxygen uptake energy equivalents for exercising subjects can incur systematic errors of up to 7%. We argue that such errors can be greatly reduced if researchers measure both V̇o2 and V̇co2 and adopt appropriate stoichiometry equations.

The metabolic cost of emulated aerodynamic drag forces in marathon running.

The benefits of drafting for elite marathon runners are intuitive, but the quantitative energetic and time savings are still unclear due to the different methods used for converting aerodynamic drag force reductions to gross metabolic power savings. Further, we lack a mechanistic understanding of the relationship between aerodynamic drag forces and ground reaction forces (GRFs) over a range of running velocities. Here, we quantified how small horizontal impeding forces affect gross metabolic power and GRF over a range of velocities in competitive runners. In three sessions, 12 runners completed six 5-min trials with 5 min of recovery in-between. We tested one velocity per session (12, 14, and 16 km/h), at three horizontal impeding force conditions (0, 4, and 8 N) applied at the waist of the runners. On average, gross metabolic power increased by 6.13% per 1% body weight of horizontal impeding force but the increases varied considerably between individuals (4.17%-8.14%). With greater horizontal impeding force, braking GRF impulses decreased, whereas propulsive GRF impulses increased, but the impulses were not related to individual changes in gross metabolic power. Combining our findings with those of previous aerodynamics studies, we estimate that for a solo runner (52 kg) at 2-h marathon pace, overcoming aerodynamic drag force (1.39% BW) comprises 7.8% of their gross metabolic power and drafting can save between 3 min 42 s and 5 min 29 s.NEW & NOTEWORTHY We measured the metabolic and biomechanical effects of small horizontal impeding forces (representing realistic aerodynamic drag forces) on high-caliber runners across a range of velocities. Combining our metabolic results with existing aerodynamic models indicates that at 2-h marathon pace, optimal drafting likely allows a marathoner to run between 3 min 42 s and 5 min 29 s faster. Our rule-of-thumb (∼6% increase in gross metabolic power per 1% body weight of horizontal impeding force) will allow others to estimate the performance enhancement of different drafting formations.

Metabolic cost of level, uphill, and downhill running in highly cushioned shoes with carbon-fiber plates.

BACKGROUND: Compared to conventional racing shoes, Nike Vaporfly 4% running shoes reduce the metabolic cost of level treadmill running by 4%. The reduction is attributed to their lightweight, highly compliant, and resilient midsole foam and a midsole-embedded curved carbon-fiber plate. We investigated whether these shoes also could reduce the metabolic cost of moderate uphill (+3°) and downhill (-3°) grades. We tested the null hypothesis that, compared to conventional racing shoes, highly cushioned shoes with carbon-fiber plates would impart the same ∼4% metabolic power (W/kg) savings during uphill and downhill running as they do during level running. METHODS: After familiarization, 16 competitive male runners performed six 5-min trials (2 shoes × 3 grades) in 2 Nike marathon racing-shoe models (Streak 6 and Vaporfly 4%) on a level, uphill (+3°), and downhill (-3°) treadmill at 13 km/h (3.61 m/s). We measured submaximal oxygen uptake and carbon dioxide production during Minutes 4-5 and calculated metabolic power (W/kg) for each shoe model and grade combination. RESULTS: Compared to the conventional shoes (Streak 6), the metabolic power in the Vaporfly 4% shoes was 3.83% (level), 2.82% (uphill), and 2.70% (downhill) less (all p < 0.001). The percent of change in metabolic power for uphill running was less compared to level running (p = 0.04; effect size (ES) = 0.561) but was not statistically different between downhill and level running (p = 0.17; ES = 0.356). CONCLUSION: On a running course with uphill and downhill sections, the metabolic savings and hence performance enhancement provided by Vaporfly 4% shoes would likely be slightly less overall, compared to the savings on a perfectly level race course.

Nose-down saddle tilt improves gross efficiency during seated-uphill cycling.

Riding uphill presents a challenge to competitive and recreational cyclists. Based on only limited evidence, some scientists have reported that tilting the saddle nose down improves uphill-cycling efficiency by as much as 6%. PURPOSE: here, we investigated if simply tilting the saddle nose down increases efficiency during uphill cycling, which would presumably improve performance. METHODS: nineteen healthy, recreational cyclists performed multiple 5 min trials of seated cycling at ~ 3 W kg-1 on a large, custom-built treadmill inclined to 8° under two saddle-tilt angle conditions: parallel to the riding surface and 8° nose down. We measured subjects' rates of oxygen consumption and carbon dioxide production using an expired-gas analysis system and then calculated their average metabolic power during the last two min of each 5 min trial. RESULTS: we found that, compared to the parallel-saddle condition, tilting the saddle nose down by 8° improved gross efficiency from 0.205 to 0.208-an average increase of 1.4% ± 0.2%, t = 5.9, p < 0.001, CI95% [0.9 to 1.9], dz = 1.3. CONCLUSION: our findings are relevant to competitive and recreational cyclists and present an opportunity for innovating new devices and saddle designs that enhance uphill-cycling efficiency. The effect of saddle tilt on other slopes and the mechanism behind the efficiency improvement remain to be investigated.

The influence of bicycle lean on maximal power output during sprint cycling.

Competitive cyclists typically sprint out of the saddle and alternately lean their bikes from side to side, away from the downstroke pedal. Yet, there is no direct evidence as to whether leaning the bicycle or conversely, attempting to minimize lean, affects maximal power output during sprint cycling. Here, we modified a cycling ergometer so that it can lean from side to side but can also be locked to prevent lean. This modified ergometer made it possible to compare maximal 1-s crank power during non-seated, sprint cycling under three different conditions: locked (no lean), ad libitum lean, and minimal lean. We found that leaning the ergometer ad libitum did not enhance maximal 1-s crank power compared to the locked condition. However, trying to minimize ergometer lean decreased maximal 1-s crank power by an average of 5% compared to leaning ad libitum. IMU-derived measures of ergometer lean provided evidence that subjects leaned the ergometer away from the downstroke pedal during the ad-lib condition, as in overground cycling. This finding suggests that our ergometer provides a suitable emulation of bicycle-lean dynamics. Overall, we find that leaning a cycle ergometer ad libitum does not enhance maximal power output, but conversely, trying to minimize lean impairs maximal power output.

Does the preferred walk-run transition speed on steep inclines minimize energetic cost, heart rate or neither?

As walking speed increases, humans choose to transition to a running gait at their preferred transition speed (PTS). Near that speed, it becomes metabolically cheaper to run rather than to walk and that defines the energetically optimal transition speed (EOTS). Our goals were to determine: (1) how PTS and EOTS compare across a wide range of inclines and (2) whether the EOTS can be predicted by the heart rate optimal transition speed (HROTS). Ten healthy, high-caliber, male trail/mountain runners participated. On day 1, subjects completed 0 and 15 deg trials and on day 2, they completed 5 and 10 deg trials. We calculated PTS as the average of the walk-to-run transition speed (WRTS) and the run-to-walk transition speed (RWTS) determined with an incremental protocol. We calculated EOTS and HROTS from energetic cost and heart rate data for walking and running near the expected EOTS for each incline. The intersection of the walking and running linear regression equations defined EOTS and HROTS. We found that PTS, EOTS and HROTS all were slower on steeper inclines. PTS was slower than EOTS at 0, 5 and 10 deg, but the two converged at 15 deg. Across all inclines, PTS and EOTS were only moderately correlated. Although EOTS correlated with HROTS, EOTS was not predicted accurately by heart rate on an individual basis.

Effects of course design (curves and elevation undulations) on marathon running performance: a comparison of Breaking 2 in Monza and the INEOS 1:59 Challenge in Vienna.

Eliud Kipchoge made two attempts to break the 2-hour marathon, in Monza and then Vienna. Here we analyse only the effects of course elevation profile and turn curvatures on his performances. We used publicly available data to determine the undulations in elevation and the radii of the curves on the course. With previously developed equations for the effects of velocity, slope, and curvature on oxygen uptake, we performed simulations to quantify how much the elevation changes and curves of the Vienna course affect a runner's oxygen uptake (at a fixed velocity) or velocity (at a fixed oxygen uptake). We estimate that, after the initial downhill benefit, the course led to an overall oxygen uptake penalty of only 0.03%. When compared to a perfectly level straight course, we estimate that the combined effects of the undulations and curves of the Vienna course incurred a penalty of just 1.37 seconds. Kipchoge ran 2:00:25 in Monza Italy. Comparison with the Monza course profile indicates a 46.2 second (1.09% oxygen uptake) advantage of Vienna's course while the fewer curves of Vienna contributed ~ 1 second. The Vienna course was very well-chosen because it minimized the negative effects of elevation changes and curves.Abbreviations: CoT: Oxygen cost of transport; CV˙O2: Curved rate of oxygen consumption; V˙O2: Rate of oxygen consumption; WA: World Athletics.

Steep (30°) uphill walking vs. running: COM movements, stride kinematics, and leg muscle excitations.

PURPOSE: We sought to biomechanically distinguish steep uphill running from steep uphill walking and explore why athletes alternate between walking and running on steep inclines. METHODS: We quantified vertical center of mass (COM) accelerations and basic stride parameters for both walking and running at a treadmill speed of 1.0 m/s on the level and up a 30° incline. We also investigated how electromyography (EMG) of the gluteus maximus (GMAX), vastus medialis (VM), medial gastrocnemius (MG), and soleus (SOL) muscles differ between gaits when ascending steep hills. RESULTS: The vertical COM accelerations for steep uphill walking exhibited two peaks per step of magnitude 1.47 ± 0.23 g and 0.79 ± 0.10 g. In contrast, steep running exhibited a single peak per step pattern with a magnitude of 1.81 ± 0.15 g. Steep uphill running exhibited no aerial phase, 40% faster stride frequency, and 40% shorter foot-ground contact time compared to steep uphill walking but similar leg swing times. SOL showed 36% less iEMG per stride during steep uphill running versus steep uphill walking, but all other EMG comparisons between steep running and walking were not significantly different. CONCLUSIONS: Multiple biomechanical variables clearly indicate that steep uphill running is a distinctly different gait from steep uphill walking and is more similar to level running. The competing desires to minimize the energetic cost of locomotion and to avoid exhaustion of the SOL may be a possible explanation for gait alternation on steep inclines.

Modelling the effect of curves on distance running performance.

BACKGROUND: Although straight ahead running appears to be faster, distance running races are predominately contested on tracks or roads that involve curves. How much faster could world records be run on straight courses? METHODS: Here,we propose a model to explain the slower times observed for races involving curves compared to straight running. For a given running velocity, on a curve, the average axial leg force ( F ¯ a ) of a runner is increased due to the need to exert centripetal force. The increased F ¯ a presumably requires a greater rate of metabolic energy expenditure than straight running at the same velocity. We assumed that distance runners maintain a constant metabolic rate and thus slow down on curves accordingly. We combined published equations to estimate the change in the rate of gross metabolic energy expenditure as a function of F ¯ a , where F ¯ a depends on curve radius and velocity, with an equation for the gross rate of oxygen uptake as a function of velocity. We compared performances between straight courses and courses with different curve radii and geometries. RESULTS: The differences between our model predictions and the actual indoor world records, are between 0.45% in 3,000 m and 1.78% in the 1,500 m for males, and 0.59% in the 5,000 m and 1.76% in the 3,000 m for females. We estimate that a 2:01:39 marathon on a 400 m track, corresponds to 2:01:32 on a straight path and to 2:02:00 on a 200 m track. CONCLUSION: Our model predicts that compared to straight racecourses, the increased time due to curves, is notable for smaller curve radii and for faster velocities. But, for larger radii and slower speeds, the time increase is negligible and the general perception of the magnitude of the effects of curves on road racing performance is not supported by our calculations.

Cardiometabolic Effects of a Workplace Cycling Intervention.

BACKGROUND: In laboratory settings, cycling workstations improve cardiometabolic risk factors. Our purpose was to quantify risk factors following a cycling intervention in the workplace. METHODS: Twenty-one office workers who sat at work ≥6 hours per day underwent baseline physiological measurements (resting blood pressure, blood lipid profile, maximum oxygen consumption [V˙O2max], body composition, and 2-h oral glucose tolerance test). Participants were randomly assigned to a 4-week intervention only group (n = 12) or a delayed intervention group (n = 9) that involved a 4-week control condition before beginning the intervention. During the intervention, participants were instructed to use the cycling device a minimum of 15 minutes per hour, which would result in a total use of ≥2 hours per day during the workday. Following the intervention, physiological measurements were repeated. RESULTS: Participants averaged 1.77 (0.48) hours per day of cycling during the intervention with no changes in actigraphy-monitored noncycling physical activity. Four weeks of the workplace intervention increased V˙O2max (2.07 [0.44] to 2.17 [0.44] L·min-1, P < .01); end of V˙O2max test power output (166.3 [42.2] to 176.6 [46.1] W, P < .01); and high-density lipoprotein cholesterol (1.09 [0.17] to 1.17 [0.24] mmol·L-1, P = .04). CONCLUSIONS: A stationary cycling device incorporated into a sedentary workplace for 4 weeks improves some cardiometabolic risk factors with no compensatory decrease in noncycling physical activity.

Do poles save energy during steep uphill walking?

PURPOSE: In trail running and in uphill races many athletes use poles. However, there are few data about pole walking on steep uphill. The aim of this study was to compare the energy expenditure during uphill walking with (PW) and without (W) poles at different slopes. METHODS: Fourteen mountain running athletes walked on a treadmill in two conditions (PW and W) for 5 min at seven different angles (10.1°, 15.5°, 19.8°, 25.4°, 29.8°, 35.5° and 38.9°). We measured cardiorespiratory parameters, blood lactate concentration (BLa) and rating of perceived exertion (RPE). Then, we calculated the vertical cost of transport (CoTvert). Using video analysis, we measured stride frequency (SF) and stride length (SL). RESULTS: Compared to W, CoTvert during PW was lower at 25.4°, 29.8° and 35.5° PW ([Formula: see text] 2.55 ± 3.97%; [Formula: see text] 2.79 ± 3.88% and [Formula: see text] 2.00 ± 3.41%, p < 0.05). RPE was significantly lower during PW at 15.5°, 19.8°, 29.8°, 35.5° and 38.9° ([Formula: see text] 14.4 ± 18.3%; [Formula: see text] 16.2 ± 15.2%; [Formula: see text] 16.6 ± 16.9%; [Formula: see text] 17.9 ± 18.7% and [Formula: see text] 18.5 ± 17.8%, p < 0.01). There was no effect of pole use on BLa. However, BLa was numerically lower with poles at every incline except for 10.1°. On average, SF for PW was lower than for W ([Formula: see text] 6.7 ± 5.8%, p = 0.006) and SL was longer in PW than in W (+ 8.6 ± 4.5%, p = 0.008). CONCLUSIONS: PW on steep inclines was only slightly more economical than W, but the substantially lower RPE during PW suggests that poles may delay fatigue effects during a prolonged effort. We advocate for the use of poles during steep uphill walking, although the energetic savings are small.

Preferred walking speed on rough terrain: is it all about energetics?

Humans have evolved the ability to walk very efficiently. Further, humans prefer to walk at speeds that approximately minimize their metabolic energy expenditure per unit distance (i.e. gross cost of transport, COT). This has been found in a variety of population groups and other species. However, these studies were mostly performed on smooth, level ground or on treadmills. We hypothesized that the objective function for walking is more complex than only minimizing the COT. To test this idea, we compared the preferred speeds and the relationships between COT and speed for people walking on both a smooth, level floor and a rough, natural terrain trail. Rough terrain presumably introduces other factors, such as stability, to the objective function. Ten healthy men walked on both a straight, flat, smooth floor and an outdoor trail strewn with rocks and boulders. In both locations, subjects performed five to seven trials at different speeds relative to their preferred speed. The COT-speed relationships were similarly U-shaped for both surfaces, but the COT values on rough terrain were approximately 115% greater. On the smooth surface, the preferred speed (1.24±0.17 m s-1) was not found to be statistically different (P=0.09) than the speed that minimized COT (1.34±0.03 m s-1). On rough terrain, the preferred speed (1.07±0.05 m s-1) was significantly slower than the COT minimum speed (1.13±0.07 m s-1; P=0.02). Because near the optimum speed the COT function is very shallow, these changes in speed result in a small change in COT (0.5%). It appears that the objective function for speed preference when walking on rough terrain includes COT and additional factors such as stability.

Extrapolating Metabolic Savings in Running: Implications for Performance Predictions.

Training, footwear, nutrition, and racing strategies (i.e., drafting) have all been shown to reduce the metabolic cost of distance running (i.e., improve running economy). However, how these improvements in running economy (RE) quantitatively translate into faster running performance is less established. Here, we quantify how metabolic savings translate into faster running performance, considering both the inherent rate of oxygen uptake-velocity relation and the additional cost of overcoming air resistance when running overground. We collate and compare five existing equations for oxygen uptake-velocity relations across wide velocity ranges. Because the oxygen uptake vs. velocity relation is non-linear, for velocities slower than ∼3 m/s, the predicted percent improvement in velocity is slightly greater than the percent improvement in RE. For velocities faster than ∼3 m/s, the predicted percent improvement in velocity is less than the percent improvements in RE. At 5.5 m/s, i.e., world-class marathon pace, the predicted percent improvement in velocity is ∼2/3rds of the percent improvement in RE. For example, at 2:04 marathon pace, a 3% improvement in RE translates to a 1.97% faster velocity or 2:01:36, almost exactly equal to the recently set world record.

Level, uphill and downhill running economy values are strongly inter-correlated.

PURPOSE: Exercise economy is not solely an intrinsic physiological trait because economy in one mode of exercise (e.g., running) does not strongly correlate with economy in another mode (e.g. cycling). Economy also reflects the skill of an individual in a particular mode of exercise. Arguably, level, uphill and downhill running constitute biomechanically different modes of exercise. Thus, we tested the hypothesis that level running economy (LRE), uphill running economy (URE) and downhill running economy (DRE) would not be strongly inter-correlated. METHODS: We measured the oxygen uptakes of 19 male trained runners during three different treadmill running speed and grade conditions: 238 m/min, 0%; 167 m/min, + 7.5%; 291 m/min, - 5%. Mean oxygen uptakes were 46.8 (SD 3.9), 48.0 (3.4) and 46.9 (3.7) ml/kg/min for level, uphill and downhill running, respectively, indicating that the three conditions were of similar aerobic intensity. RESULTS: We reject our hypothesis based on the strong correlations of r = 0.909, r = 0.901 and r = 0.830, respectively, between LRE vs. URE, LRE vs. DRE and URE vs. DRE. CONCLUSION: Economical runners on level surfaces are also economical on uphill and downhill grades. Inter-individual differences in running economy reflect differences in both intrinsic physiology and skill. Individuals who have experience with level, uphill and downhill running appear to be equally skilled in all three modes.

The Biomechanics of Competitive Male Runners in Three Marathon Racing Shoes: A Randomized Crossover Study.

BACKGROUND: We have shown that a prototype marathon racing shoe reduced the metabolic cost of running for all 18 participants in our sample by an average of 4%, compared to two well-established racing shoes. Gross measures of biomechanics showed minor differences and could not explain the metabolic savings. OBJECTIVE: To explain the metabolic savings by comparing the mechanics of the shoes, leg, and foot joints during the stance phase of running. METHODS: Ten male competitive runners, who habitually rearfoot strike ran three 5-min trials in prototype shoes (NP) and two established marathon shoes, the Nike Zoom Streak 6 (NS) and the adidas adizero Adios BOOST 2 (AB), at 16 km/h. We measured ground reaction forces and 3D kinematics of the lower limbs. RESULTS: Hip and knee joint mechanics were similar between the shoes, but peak ankle extensor moment was smaller in NP versus AB shoes. Negative and positive work rates at the ankle were lower in NP shoes versus the other shoes. Dorsiflexion and negative work at the metatarsophalangeal (MTP) joint were reduced in the NP shoes versus the other shoes. Substantial mechanical energy was stored/returned in compressing the NP midsole foam, but not in bending the carbon-fiber plate. CONCLUSION: The metabolic savings of the NP shoes appear to be due to: (1) superior energy storage in the midsole foam, (2) the clever lever effects of the carbon-fiber plate on the ankle joint mechanics, and (3) the stiffening effects of the plate on the MTP joint.

Does Metabolic Rate Increase Linearly with Running Speed in all Distance Runners?

Running economy (oxygen uptake or metabolic rate for running at a submaximal speed) is one of the key determinants of distance running performance. Previous studies reported linear relationships between oxygen uptake or metabolic rate and speed, and an invariant cost of transport across speed. We quantified oxygen uptake, metabolic rate, and cost of transport in 10 average and 10 sub-elite runners. We increased treadmill speed by 0.45 m · s -1 from 1.78 m · s -1 (day 1) and 2.01 m · s -1 (day 2) during each subsequent 4-min stage until reaching a speed that elicited a rating of perceived exertion of 15. Average runners' oxygen uptake and metabolic rate vs. speed relationships were best described by linear fits. In contrast, the sub-elite runners' relationships were best described by increasing curvilinear fits. For the sub-elites, oxygen cost of transport and energy cost of transport increased by 12.8% and 9.6%, respectively, from 3.58 to 5.14 m · s -1 . Our results indicate that it is not possible to accurately predict metabolic rates at race pace for sub-elite competitive runners from data collected at moderate submaximal running speeds (2.68-3.58 m · s -1 ). To do so, metabolic rate should be measured at speeds that approach competitive race pace and curvilinear fits should be used for extrapolation to race pace.

What determines the metabolic cost of human running across a wide range of velocities?

The 'cost of generating force' hypothesis proposes that the metabolic rate during running is determined by the rate of muscle force development (1/tc, where tc=contact time) and the volume of active leg muscle. A previous study assumed a constant recruited muscle volume and reported that the rate of force development alone explained ∼70% of the increase in metabolic rate for human runners across a moderate velocity range (2-4 m s-1). We hypothesized that over a wider range of velocities, the effective mechanical advantage (EMA) of the lower limb joints would overall decrease, necessitating a greater volume of active muscle recruitment. Ten high-caliber male human runners ran on a force-measuring treadmill at 8, 10, 12, 14, 16 and 18 km h-1 while we analyzed their expired air to determine metabolic rates. We measured ground reaction forces and joint kinematics to calculate contact time and estimate active muscle volume. From 8 to 18 km h-1, metabolic rate increased 131% from 9.28 to 21.44 W kg-1tc decreased from 0.280 s to 0.190 s, and thus the rate of force development (1/tc) increased by 48%. Ankle EMA decreased by 19.7±11%, knee EMA increased by 11.1±26.9% and hip EMA decreased by 60.8±11.8%. Estimated active muscle volume per leg increased 52.8% from 1663±152 cm3 to 2550±169 cm3 Overall, 98% of the increase in metabolic rate across the velocity range was explained by just two factors: the rate of generating force and the volume of active leg muscle.

Contributions of metabolic and temporal costs to human gait selection.

Humans naturally select several parameters within a gait that correspond with minimizing metabolic cost. Much less is understood about the role of metabolic cost in selecting between gaits. Here, we asked participants to decide between walking or running out and back to different gait specific markers. The distance of the walking marker was adjusted after each decision to identify relative distances where individuals switched gait preferences. We found that neither minimizing solely metabolic energy nor minimizing solely movement time could predict how the group decided between gaits. Of our twenty participants, six behaved in a way that tended towards minimizing metabolic energy, while eight favoured strategies that tended more towards minimizing movement time. The remaining six participants could not be explained by minimizing a single cost. We provide evidence that humans consider not just a single movement cost, but instead a weighted combination of these conflicting costs with their relative contributions varying across participants. Individuals who placed a higher relative value on time ran faster than individuals who placed a higher relative value on metabolic energy. Sensitivity to temporal costs also explained variability in an individual's preferred velocity as a function of increasing running distance. Interestingly, these differences in velocity both within and across participants were absent in walking, possibly due to a steeper metabolic cost of transport curve. We conclude that metabolic cost plays an essential, but not exclusive role in gait decisions.