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).

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.

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.

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.

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.

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.

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.

Comparison of running and cycling economy in runners, cyclists, and triathletes.

PURPOSE: Exercise economy is one of the main physiological factors determining performance in endurance sports. Running economy (RE) can be improved with running-specific training, while the improvement of cycling economy (CE) with cycling-specific training is controversial. We investigated whether exercise economy reflects sport-specific skills/adaptations or is determined by overall physiological factors. METHODS: We compared RE and CE in 10 runners, 9 cyclists and 9 triathletes for running at 12 km/h and cycling at 200 W. Gross rates of oxygen consumption and carbon dioxide production were collected and used to calculate gross metabolic rate in watts for both running and cycling. RESULTS: Runners had better RE than cyclists (917 ± 107 W vs. 1111 ± 159 W) (p < 0.01). Triathletes had intermediate RE values (1004 ± 98 W) not different from runners or cyclists. CE was not different (p = 0.20) between the three groups (runners: 945 ± 60 W; cyclists: 982 ± 44 W; triathletes: 979 ± 54 W). CONCLUSION: RE can be enhanced with running-specific training, but CE is independent of cycling-specific training.

Changing relative crank angle increases the metabolic cost of leg cycling.

PURPOSE: Historically, the efficiency of leg cycling has been difficult to change. However, arm cycling research indicates that relative crank angle changes can improve efficiency. Therefore, we investigated if leg cycling with different relative crank angles affects efficiency. METHODS: Ten healthy, male, recreational bicycle riders (27.8 ± 8.2 years, mean ± SD, mass 69.8 ± 3.2 kg) pedaled a pan-loaded cycle ergometer at a fixed power output of 150 watts at a cadence of 90 RPM. Each subject completed six, 5-min trials in random order at relative crank angles of 180°, 135°, 90°, 45°, 0°, and 180°. We averaged rates of oxygen uptake ([Formula: see text]) and carbon dioxide production ([Formula: see text]), and respiratory exchange ratio (RER) for the last 2 min of each trial. RESULTS: Crank angles other than 180° required a greater metabolic cost. As relative crank angle decreased from 180°, metabolic power monotonically increased by 1.6% at 135° to 8.2% greater when the relative crank angle was 0° (p < 0.001). CONCLUSIONS: We find that, unlike arm cycling, radically changing the relative crank angle on a bicycle from an out-of-phase (180°) to in-phase (0°) position decreases leg cycling efficiency by ~8%. We attribute the increase to changes in cost of breathing, muscle co-activation, trunk stabilization, power fluctuations, and possibly lifting the legs during the upstroke. Our findings may have relevance in the rehabilitation of patients recovering from stroke or spinal cord injury.

The metabolic costs of walking and running up a 30-degree incline: implications for vertical kilometer foot races.

PURPOSE: Vertical kilometer (VK) races, in which runners gain 1000 m of elevation in <5000 m of distance, are becoming popular. However, few studies on steep uphill running (>25°) exist. Previously, we determined that ~30° is the optimal angle for uphill running, costing the least amount of metabolic energy for a specific vertical velocity. To inform the training and strategy of VK racers, we quantified the metabolic cost of walking and running at various velocities up a 30° incline. METHODS: At 30°, 11 experienced runners (7 M, 4 F, 30.8 ± 7.9 years, 1.71 ± 0.08 m, 66.7 ± 9.4 kg) walked and ran for 5-min trials with 5-min rest between. Starting at 0.3 ms-1, we increased treadmill velocity by 0.1 ms-1 for each trial until subjects could not maintain the set velocity. We measured oxygen uptake (ml O2 kg-1 min-1) and metabolic power (W kg-1 = metabolic energy per unit time per unit body mass) and calculated metabolic costs of walking (C w) and running (C r) per unit distance (J kg-1 m-1). RESULTS: Oxygen uptake and metabolic power increased linearly with velocity. Between 0.3 and 0.7 ms-1, C w < C r. At 0.8 ms-1 there was no difference and extrapolation suggests that at faster velocities, running likely costs less than walking. CONCLUSION: On a 30° incline, metabolic power increases linearly with velocity. At speeds slower than 0.7 ms-1, walking requires less metabolic power than running (W kg-1) suggesting most VK racers should walk rather than run.

Maximum-speed curve-running biomechanics of sprinters with and without unilateral leg amputations.

On curves, non-amputees' maximum running speed is slower on smaller radii and thought to be limited by the inside leg's mechanics. Similar speed decreases would be expected for non-amputees in both counterclockwise and clockwise directions because they have symmetric legs. However, sprinters with unilateral leg amputation have asymmetric legs, which may differentially affect curve-running performance and Paralympic competitions. To investigate this and understand the biomechanical basis of curve running, we compared maximum curve-running (radius 17.2 m) performance and stride kinematics of six non-amputee sprinters and 11 sprinters with a transtibial amputation. Subjects performed randomized, counterbalanced trials: two straight, two counterclockwise curves and two clockwise curves. Non-amputees and sprinters with an amputation all ran slower on curves compared with straight running, but with different kinematics. Non-amputees ran 1.9% slower clockwise compared with counterclockwise (P<0.05). Sprinters with an amputation ran 3.9% slower with their affected leg on the inside compared with the outside of the curve (P<0.05). Non-amputees reduced stride length and frequency in both curve directions compared with straight running. Sprinters with an amputation also reduced stride length in both curve-running directions, but reduced stride frequency only on curves with the affected leg on the inside. During curve running, non-amputees and athletes with an amputation had longer contact times with their inside compared with their outside leg, suggesting that the inside leg limits performance. For sprinters with an amputation, the prolonged contact times of the affected versus unaffected leg seem to limit maximum running speed during both straight running and running on curves with the affected leg on the inside.

Pedelecs as a physically active transportation mode.

INTRODUCTION: Pedelecs are bicycles that provide electric assistance only when a rider is pedaling and have become increasingly popular. PURPOSE: Our purpose was to quantify usage patterns over 4 weeks of real-world commuting with a pedelec and to determine if pedelec use would improve cardiometabolic risk factors. METHODS: Twenty sedentary commuters visited the laboratory for baseline physiological measurements [body composition, maximum oxygen consumption ([Formula: see text]), mean arterial blood pressure (MAP), blood lipid profile, and 2-h oral glucose tolerance test (OGTT)]. The following 4 weeks, participants were instructed to commute using a pedelec at least 3 days week(-1) for 40 min day(-1) while wearing a heart rate monitor and a GPS device. Metabolic equivalents (METS) were estimated from heart rate data. Following the intervention, we repeated the physiological measurements. RESULTS: Average total distance and time were 317.9 ± 113.8 km and 15.9 ± 3.4 h, respectively. Participants averaged 4.9 ± 1.2 METS when riding. Four weeks of pedelec commuting significantly improved 2-h post-OGTT glucose (5.53 ± 1.18-5.03 ± 0.91 mmol L(-1), p < 0.05), [Formula: see text] (2.21 ± 0.48-2.39 ± 0.52 L min(-1), p < 0.05), and end of [Formula: see text] test power output (165.1 ± 37.1-189.3 ± 38.2 W, p < 0.05). There were trends for improvements in MAP (84.6 ± 10.5-83.2 ± 9.4 mmHg, p = 0.15) and fat mass (28.6 ± 11.3-28.2 ± 11.4 kg, p = 0.07). CONCLUSION: Participants rode a pedelec in the real world at a self-selected moderate intensity, which helped them meet physical activity recommendations. Pedelec commuting also resulted in significant improvements in 2-h post-OGTT glucose, [Formula: see text], and power output. Pedelecs are an effective form of active transportation that can improve some cardiometabolic risk factors within only 4 weeks.

The metabolic cost of human running: is swinging the arms worth it?

Although the mechanical function is quite clear, there is no consensus regarding the metabolic benefit of arm swing during human running. We compared the metabolic cost of running using normal arm swing with the metabolic cost of running while restricting the arms in three different ways: (1) holding the hands with the arms behind the back in a relaxed position (BACK), (2) holding the arms across the chest (CHEST) and (3) holding the hands on top of the head (HEAD). We hypothesized that running without arm swing would demand a greater metabolic cost than running with arm swing. Indeed, when compared with running using normal arm swing, we found that net metabolic power demand was 3, 9 and 13% greater for the BACK, CHEST and HEAD conditions, respectively (all P<0.05). We also found that when running without arm swing, subjects significantly increased the peak-to-peak amplitudes of both shoulder and pelvis rotation about the vertical axis, most likely a compensatory strategy to counterbalance the rotational angular momentum of the swinging legs. In conclusion, our findings support our general hypothesis that swinging the arms reduces the metabolic cost of human running. Our findings also demonstrate that arm swing minimizes torso rotation. We infer that actively swinging the arms provides both metabolic and biomechanical benefits during human running.

Forces and mechanical energy fluctuations during diagonal stride roller skiing; running on wheels?

Mechanical energy can be conserved during terrestrial locomotion in two ways: the inverted pendulum mechanism for walking and the spring-mass mechanism for running. Here, we investigated whether diagonal stride cross-country roller skiing (DIA) utilizes similar mechanisms. Based on previous studies, we hypothesized that running and DIA would share similar phase relationships and magnitudes of kinetic energy (KE), and gravitational potential energy (GPE) fluctuations, indicating elastic energy storage and return, as if roller skiing is like 'running on wheels'. Experienced skiers (N=9) walked and ran at 1.25 and 3 m s(-1), respectively, and roller skied with DIA at both speeds on a level dual-belt treadmill that recorded perpendicular and parallel forces. We calculated the KE and GPE of the center of mass from the force recordings. As expected, the KE and GPE fluctuated with an out-of-phase pattern during walking and an in-phase pattern during running. Unlike walking, during DIA, the KE and GPE fluctuations were in phase, as they are in running. However, during the glide phase, KE was dissipated as frictional heat and could not be stored elastically in the tendons, as in running. Elastic energy storage and return epitomize running and thus we reject our hypothesis. Diagonal stride cross-country skiing is a biomechanically unique movement that only superficially resembles walking or running.

The kangaroo's tail propels and powers pentapedal locomotion.

When moving slowly, kangaroos plant their tail on the ground in sequence with their front and hind legs. To determine the tail's role in this 'pentapedal' gait, we measured the forces the tail exerts on the ground and calculated the mechanical power it generates. We found that the tail is responsible for as much propulsive force as the front and hind legs combined. It also generates almost exclusively positive mechanical power, performing as much mass-specific mechanical work as does a human leg during walking at the same speed. Kangaroos use their muscular tail to support, propel and power their pentapedal gait just like a leg.

Activity and functions of the human gluteal muscles in walking, running, sprinting, and climbing.

It has been suggested that the uniquely large gluteus maximus (GMAX) muscles were an important adaptation during hominin evolution based on numerous anatomical differences between humans and extant apes. GMAX electromyographic (EMG) signals have been quantified for numerous individual movements, but not across the range of locomotor gaits and speeds for the same subjects. Thus, comparing relative EMG amplitudes between these activities has not been possible. We assessed the EMG activity of the gluteal muscles during walking, running, sprinting, and climbing. To gain further insight into the function of the gluteal muscles during locomotion, we measured muscle activity during walking and running with external devices that increased or decreased the need to control either forward or backward trunk pitch. We hypothesized that 1) GMAX EMG activity would be greatest during sprinting and climbing and 2) GMAX EMG activity would be modulated in response to altered forward trunk pitch demands during running. We found that GMAX activity in running was greater than walking and similar to climbing. However, the activity during sprinting was much greater than during running. Further, only the inferior portion of the GMAX had a significant change with altered trunk pitch demands, suggesting that the hip extensors have a limited contribution to the control of trunk pitch movements during running. Overall, our data suggest that the large size of the GMAX reflects its multifaceted role during rapid and powerful movements rather than as a specific adaptation for a single submaximal task such as endurance running.

Optimal starting block configuration in sprint running; a comparison of biological and prosthetic legs.

In the 2012 Paralympic 100 m and 200 m finals, 86% of athletes with a unilateral amputation placed their unaffected leg on the front starting block. Can this preference be explained biomechanically? We measured the biomechanical effects of starting block configuration for seven nonamputee sprinters and nine athletes with a unilateral amputation. Each subject performed six starts, alternating between their usual and unusual starting block configurations. When sprinters with an amputation placed their unaffected leg on the front block, they developed 6% greater mean resultant combined force compared with the opposite configuration (1.38 ± 0.06 vs 1.30 ± 0.11 BW, P = .015). However, because of a more vertical push angle, horizontal acceleration performance was equivalent between starting block configurations. We then used force data from each sprinter with an amputation to calculate the hypothetical starting mechanics for a virtual nonamputee (two unaffected legs) and a virtual bilateral amputee (two affected legs). Accelerations of virtual bilateral amputees were 15% slower compared with athletes with a unilateral amputation, which in turn were 11% slower than virtual nonamputees. Our biomechanical data do not explain the starting block configuration preference but they do explain the starting performance differences observed between nonamputee athletes and those with leg amputations.

Reduction of metabolic cost during motor learning of arm reaching dynamics.

It is often assumed that the CNS controls movements in a manner that minimizes energetic cost. While empirical evidence for actual metabolic minimization exists in locomotion, actual metabolic cost has yet to be measured during motor learning and/or arm reaching. Here, we measured metabolic power consumption using expired gas analysis, as humans learned novel arm reaching dynamics. We hypothesized that (1) metabolic power would decrease with motor learning and (2) muscle activity and coactivation would parallel changes in metabolic power. Seated subjects made horizontal planar reaching movements toward a target using a robotic arm. The novel dynamics involved compensating for a viscous curl force field that perturbed reaching movements. Metabolic power was measured continuously throughout the protocol. Subjects decreased movement error and learned the novel dynamics. By the end of learning, net metabolic power decreased by ~20% (~0.1 W/kg) from initial learning. Muscle activity and coactivation also decreased with motor learning. Interestingly, distinct and significant reductions in metabolic power occurred even after muscle activity and coactivation had stabilized and movement changes were small. These results provide the first evidence of actual metabolic reduction during motor learning and for a reaching task. Further, they suggest that muscle activity may not explain changes in metabolic cost as completely as previously thought. Additional mechanisms such as more subtle features of arm muscle activity, changes in activity of other muscles, and/or more efficient neural processes may also underlie the reduction in metabolic cost during motor learning.