Metabolic power response to added mass on the lower extremities during running

BACKGROUND

Wearable exoskeletal devices can enhance locomotor performance, but their mass results in a metabolic penalty. Previous studies have quantified the metabolic cost of running with added mass on the feet, but less is known about the effects of adding mass to the thigh and shank segments.

AIM

To quantify the metabolic cost of running with additional leg mass.

METHODS

15 participants (7 F, 8 M) completed treadmill running trials (3 m/s) normally and with lead mass (300-1350 g) attached to either the thigh, shank, or foot, bilaterally. We measured metabolic power using expired gas analysis.

RESULTS

Per 1000 g of added mass per leg, gross metabolic power increased by approximately 16% (foot) and 11% (shank) for females which was slightly greater than the 11% and 8% increases for males, respectively. For thigh loading, metabolic power increased by just 4% per 1000 g in both sexes.

CONCLUSION

Adding mass more distally on the leg increases the metabolic cost of running to a greater extent. For the same absolute added mass on the foot or shank, metabolic power increases more in females.

Joint kinematic and kinetic responses to added mass on the lower extremities during running

AIM

We analyzed the biomechanical response (joint angles, moments, and powers) to running with added leg mass. These data may help guide the design of wearable locomotor assistive devices (i.e., exoskeletons), which are becoming more prevalent.

METHODS

15 participants (7 females, 8 males) completed treadmill running trials (3m•s-1) normally and with lead mass (300-1350 g) attached to the thigh, shank, or foot, bilaterally. We quantified the lower limb biomechanics combining motion capture and ground reaction force data using standard inverse dynamics analysis.

RESULTS

Only moderate kinematic changes occurred in response to the distal added limb mass. Maximum hip flexion and maximum knee flexion angles during swing phase increased by approximately 9% and 6% respectively for each 1 kg added to each foot. However, adding even small masses made dramatic changes to the joint moments and powers, mostly during the swing phase. For example, adding 1 kg to each foot increased maximum joint moments by as much as 40% (knee extension in late swing) and maximum joint power by as much as 50% (hip generation in late swing).

CONCLUSION

Leg joint kinematics were largely conserved in response to adding mass to the legs. Adding mass to the leg distally increased joint power mainly at the knee and hip joints during the swing phase, whereas adding mass proximally mainly affected the ankle joint mechanics during the stance phase. These changes have implications for shoe designs, people who run with added mass on their legs for sport/strength training and for the design of wearable devices.

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 (Ṁ) 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 Ṁ. Our purpose was to generate a general equation for estimating Ṁ 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 Ṁ (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 Ṁ 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 Ṁ 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 (GRF) 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 while 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-hour 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.

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, CI_95% [0.9 to 1.9], d_z = 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.

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 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{F}}_{a}$\end{document}F¯a) of a runner is increased due to the need to exert centripetal force. The increased \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{F}}_{a}$\end{document}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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{F}}_{a}$\end{document}F¯a, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{F}}_{a}$\end{document}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.

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.

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 (CoT_vert). Using video analysis, we measured stride frequency (SF) and stride length (SL).

RESULTS

Compared to W, CoT_vert 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.

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.

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. 10 healthy men walked on both a straight, flat, smooth floor and on an outdoor trail strewn with rocks and boulders. In both locations, subjects performed 5-7 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/sec) was found to be statistically not different (p-value =0.09) than the speed that minimized COT (1.34 +/− 0.03 m/sec). On rough terrain, the preferred speed (1.07+/−0.05 m/sec) was slower than the COT minimum speed (1.13 +/− 0.07 m/sec) and was statistical significant (p-value=0.02). Since near the optimum speed the COT function is very shallow, these changes in speed result in 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.

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.

Calculating metabolic energy expenditure across a wide range of exercise intensities: the equation matters

We compared 10 published equations for calculating energy expenditure from oxygen consumption and carbon dioxide production using data for 10 high-caliber male distance runners over a wide range of running velocities. We found up to a 5.2% difference in calculated metabolic rate between 2 widely used equations. We urge our fellow researchers abandon out-of-date equations with published acknowledgments of errors or inappropriate biochemical/physical assumptions.

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, 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 hr−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 hr−1, metabolic rate increased 131% from 9.28 to 21.44 W kg−1. Contact time (tc) decreased from 0.280 sec to 0.190 sec, 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.

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.

Correction to: A Comparison of the Energetic Cost of Running in Marathon Racing Shoes

An Online First version of this article was made available online at https://link.springer.com/article/10.1007/s40279-017-0811-2 on 16 November 2017. An error was subsequently identified in the article, and the following correction should be noted.

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.

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.

A test of the metabolic cost of cushioning hypothesis during unshod and shod running

PURPOSE

This study aimed to investigate the effects of surface and shoe cushioning on the metabolic cost of running. In running, the leg muscles generate force to cushion the impact with the ground. External cushioning (surfaces or shoes) may reduce the muscular effort needed for cushioning and thus reduce metabolic cost. Our primary hypothesis was that the metabolic cost of unshod running would decrease with a more cushioned running surface. We also hypothesized that because of the counteracting effects of shoe cushioning and mass, unshod running on a hard surface would have approximately the same metabolic cost as running in lightweight, cushioned shoes.

METHODS

To test these hypotheses, we attached 10- and 20-mm-thick slats of the same foam cushioning used in running shoe midsoles to the belt of a treadmill that had a rigid deck. Twelve subjects who preferred a midfoot strike pattern and had substantial barefoot/minimalist running experience ran without shoes on the normal treadmill belt and on each thickness of foam. They also ran with lightweight, cushioned shoes on the normal belt. We collected V˙O2 and V˙CO2 to calculate the metabolic power demand and used a repeated-measures ANOVA to compare between conditions.

RESULTS

Compared to running unshod on the normal belt, running unshod on the 10-mm-thick foam required 1.63% ± 0.67% (mean ± SD) less metabolic power (P = 0.034) but running on the 20-mm-thick foam had no significant metabolic effect. Running with and without shoes on the normal belt had similar metabolic power demands, likely because the beneficial energetic effects of cushioning counterbalanced the detrimental effects of shoe mass.

CONCLUSIONS

On average, surface and shoe cushioning reduce the metabolic power required for submaximal running.

Older Runners Retain Youthful Running Economy despite Biomechanical Differences

PURPOSE

Sixty-five years of age typically marks the onset of impaired walking economy. However, running economy has not been assessed beyond the age of 65 yr. Furthermore, a critical determinant of running economy is the spring-like storage and return of elastic energy from the leg during stance, which is related to leg stiffness. Therefore, we investigated whether runners older than 65 yr retain youthful running economy and/or leg stiffness across running speeds.

METHODS

Fifteen young and 15 older runners ran on a force-instrumented treadmill at 2.01, 2.46, and 2.91 m·s(-1). We measured their rates of metabolic energy consumption (i.e., metabolic power), ground reaction forces, and stride kinematics.

RESULTS

There were only small differences in running economy between young and older runners across the range of speeds. Statistically, the older runners consumed 2% to 9% less metabolic energy than the young runners across speeds (P = 0.012). Also, the leg stiffness of older runners was 10% to 20% lower than that of young runners across the range of speeds (P = 0.002), and in contrast to the younger runners, the leg stiffness of older runners decreased with speed (P < 0.001).

CONCLUSIONS

Runners beyond 65 yr of age maintain youthful running economy despite biomechanical differences. It may be that vigorous exercise, such as running, prevents the age related deterioration of muscular efficiency and, therefore, may make everyday activities easier.

Ground reaction forces during steeplechase hurdling and waterjumps

Athletes in the 3,000 m steeplechase track and field event negotiate unmovable hurdles and waterjumps. Ground reaction forces (GRF) in the steeplechase were quantified to elucidate injury risks / mechanisms and to inform coaches. Five male and five female steeplechasers participated. GRF were measured during treadmill running, and using specially mounted force platforms, during hurdle and waterjump takeoffs and landings at 5.54 m/s (males) or 5.00 m/s (females). Results are presented as: male mean ± SD / female mean ± SD. Initial and active peaks of vertical GRF during treadmill running were 2.04 ± 0.72 / 2.25 ± 0.28 BW and 3.11 ± 0.27 / 2.98 ± 0.24 BW. Compared to treadmill running, peak vertical forces were greater (p < 0.001) for: hurdle takeoff (initial: 4.25 ± 0.86 / 3.78 ± 0.60 BW, active: 3.82 ± 0.20 / 3.74 ± 0.32 BW), hurdle landing (active: 4.41 ± 1.13 / 4.21 ± 0.21 BW), waterjump takeoff (initial: 4.32 ± 0.67 / 4.56 ± 0.54 BW, active: 4.00 ± 0.24 / 3.83 ± 0.31 BW), and waterjump landing (initial: 3.45 ± 0.34 / #3.78 ± 0.32 BW, active:5.40 ± 0.78 / #6.23 ± 0.74 BW); (#) indicates not statistically compared (n = 2). Based on horizontal impulse, athletes decelerated during takeoff steps and accelerated during landing steps of both hurdling and waterjumps. Vertical GRF peaks and video indicated rearfoot strikes on the treadmill but midfoot strikes during hurdle and waterjump landings. Potentially injurious GRF occur during the steeplechase, particularly during waterjump landings (up to 7.0 BW).

Motor-Driven (Passive) Cycling: A Potential Physical Inactivity Countermeasure?

We have previously shown that motor-driven (passive) stationary cycling elevates energy expenditure (EE).

PURPOSE

This study aimed to quantify how acute passive cycling affects glucose and insulin responses to an oral glucose tolerance test (OGTT) and basic cognition compared with sitting and moderate-intensity active cycling.

METHODS

Twenty-four physically inactive healthy males completed three trials in randomized order involving 30-min conditions of sitting, passive cycling, and moderate-intensity cycling. During each condition, EE was measured, and participants performed cognitive tests. After each condition, a 2-h OGTT was performed.

RESULTS

EE was significantly higher during the cycling conditions compared with sitting (1.36 ± 0.58 and 6.50 ± 1.73 kcal·min greater than sitting for passive and moderate-intensity, respectively). A significant correlation was found between body fat percentage and postsitting OGTT 2-h postplasma glucose (r = 0.30, P < 0.05); thus, participants were divided into lean (n = 11) and nonlean (n = 13) groups. In the nonlean group, compared with sitting, passive cycling lowered 2-h postplasma glucose (7.7 ± 1.3 vs 6.9 ± 1.6 mmol·L, respectively, P < 0.05), and the Matsuda whole-body insulin sensitivity index (WBISI) was higher (2.74 ± 0.86 vs 3.36 ± 1.08, P < 0.05). In addition, passive and moderate-intensity cycling had similar beneficial effects on 2-h postplasma glucose and WBISI. Cognitive performance did not significantly differ between the sitting and passive cycling conditions.

CONCLUSIONS

Two-hour postplasma glucose was lower and WBISI after acute passive cycling was higher in nonlean participants. Given that and the increase in EE without changes in cognitive performance, we propose passive cycling as a promising intervention to counteract some of the deleterious effects of prolonged sitting in the workplace.

Energetics of vertical kilometer foot races; is steeper cheaper?

Vertical kilometer foot races consist of a 1,000-m elevation gain in <5,000 m of overall distance, and the inclines of the fastest courses are ∼30°. Previous uphill locomotion studies have focused on much shallower angles. We aimed to quantify the metabolic costs of walking and running on very steep angles and to biomechanically distinguish walking from running. Fifteen runners (10 male, 5 female, 32.9 ± 7.5 yr, 1.75 ± 0.09 m, 64.3 ± 9.1 kg) walked and ran for 5 min at seven different angles (9.4, 15.8, 20.4, 24.8, 30.0, 35.0, and 39.2°) all at a fixed vertical velocity (0.35 m/s). We measured the metabolic rates and calculated the vertical costs of walking (Cwvert) and running (Crvert). Using video analysis, we determined stride frequency, stride length, and duty factor (fraction of stride that each foot is in ground contact). At all angles other than 9.4°, Cwvert was cheaper than Crvert (average −8.45 ± 1.05%; P < 0.001). Further, broad minima for both Cwvert and Crvert existed between 20.4 and 35.0° (average Cwvert 44.17 ± 0.41 J·kg−1·m−1 and average Crvert 48.46 ± 0.35 J·kg−1·m−1). At all angles and speeds tested, both walking and running involved having at least one foot on the ground at all times. However, in walking, stride frequency and stride length were ∼28% slower and longer, respectively, than in running. In conclusion, we found that there is a range of angles for which energy expenditure is minimized. At the vertical velocity tested, on inclines steeper than 15.8°, athletes can reduce their energy expenditure by walking rather than 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.

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.

Effect of running speed and leg prostheses on mediolateral foot placement and its variability

This study examined the effects of speed and leg prostheses on mediolateral (ML) foot placement and its variability in sprinters with and without transtibial amputations. We hypothesized that ML foot placement variability would: 1. increase with running speed up to maximum speed and 2. be symmetrical between the legs of non-amputee sprinters but asymmetrically greater for the affected leg of sprinters with a unilateral transtibial amputation. We measured the midline of the body (kinematic data) and center of pressure (kinetic data) in the ML direction while 12 non-amputee sprinters and 7 Paralympic sprinters with transtibial amputations (6 unilateral, 1 bilateral) ran across a range of speeds up to maximum speed on a high-speed force measuring treadmill. We quantified ML foot placement relative to the body’s midline and its variability. We interpret our results with respect to a hypothesized relation between ML foot placement variability and lateral balance. We infer that greater ML foot placement variability indicates greater challenges with maintaining lateral balance. In non-amputee sprinters, ML foot placement variability for each leg increased substantially and symmetrically across speed. In sprinters with a unilateral amputation, ML foot placement variability for the affected and unaffected leg also increased substantially, but was asymmetric across speeds. In general, ML foot placement variability for sprinters with a unilateral amputation was within the range observed in non-amputee sprinters. For the sprinter with bilateral amputations, both affected legs exhibited the greatest increase in ML foot placement variability with speed. Overall, we find that maintaining lateral balance becomes increasingly challenging at faster speeds up to maximum speed but was equally challenging for sprinters with and without a unilateral transtibial amputation. Finally, when compared to all other sprinters in our subject pool, maintaining lateral balance appears to be the most challenging for the Paralympic sprinter with bilateral transtibial amputations.

Running for exercise mitigates age-related deterioration of walking economy

INTRODUCTION

Impaired walking performance is a key predictor of morbidity among older adults. A distinctive characteristic of impaired walking performance among older adults is a greater metabolic cost (worse economy) compared to young adults. However, older adults who consistently run have been shown to retain a similar running economy as young runners. Unfortunately, those running studies did not measure the metabolic cost of walking. Thus, it is unclear if running exercise can prevent the deterioration of walking economy.

PURPOSE

To determine if and how regular walking vs. running exercise affects the economy of locomotion in older adults.

METHODS

15 older adults (69 ± 3 years) who walk ≥ 30 min, 3x/week for exercise, "walkers" and 15 older adults (69 ± 5 years) who run ≥ 30 min, 3x/week, "runners" walked on a force-instrumented treadmill at three speeds (0.75, 1.25, and 1.75 m/s). We determined walking economy using expired gas analysis and walking mechanics via ground reaction forces during the last 2 minutes of each 5 minute trial. We compared walking economy between the two groups and to non-aerobically trained young and older adults from a prior study.

RESULTS

Older runners had a 7-10% better walking economy than older walkers over the range of speeds tested (p = .016) and had walking economy similar to young sedentary adults over a similar range of speeds (p =  .237). We found no substantial biomechanical differences between older walkers and runners. In contrast to older runners, older walkers had similar walking economy as older sedentary adults (p =  .461) and ∼ 26% worse walking economy than young adults (p<.0001).

CONCLUSION

Running mitigates the age-related deterioration of walking economy whereas walking for exercise appears to have minimal effect on the age-related deterioration in walking economy.

Muscle contributions to propulsion and braking during walking and running: insight from external force perturbations

There remains substantial debate as to the specific contributions of individual muscles to center of mass accelerations during walking and running. To gain insight, we altered the demand for muscular propulsion and braking by applying external horizontal impeding and aiding forces near the center of mass as subjects walked and ran on a treadmill. We recorded electromyographic activity of the gluteus maximus (superior and inferior portions), the gluteus medius, biceps femoris, semitendinosus/membrinosus, vastus medialis, lateral and medial gastrocnemius and soleus. We reasoned that activity in a propulsive muscle would increase with external impeding force and decrease with external aiding force whereas activity in a braking muscle would show the opposite. We found that during walking the gastrocnemius and gluteus maximus provide propulsion while the vasti are central in providing braking. During running, we found that the gluteus maximus, vastus medialis, gastrocnemius and soleus all contribute to propulsion.

Applying the cost of generating force hypothesis to uphill running

Historically, several different approaches have been applied to explain the metabolic cost of uphill human running. Most of these approaches result in unrealistically high values for the efficiency of performing vertical work during running uphill, or are only valid for running up steep inclines. The purpose of this study was to reexamine the metabolic cost of uphill running, based upon our understanding of level running energetics and ground reaction forces during uphill running. In contrast to the vertical efficiency approach, we propose that during incline running at a certain velocity, the forces (and hence metabolic energy) required for braking and propelling the body mass parallel to the running surface are less than during level running. Based on this idea, we propose that the metabolic rate during uphill running can be predicted by a model, which posits that (1) the metabolic cost of perpendicular bouncing remains the same as during level running, (2) the metabolic cost of running parallel to the running surface decreases with incline, (3) the delta efficiency of producing mechanical power to lift the COM vertically is constant, independent of incline and running velocity, and (4) the costs of leg and arm swing do not change with incline. To test this approach, we collected ground reaction force (GRF) data for eight runners who ran thirty 30-second trials (velocity: 2.0–3.0 m/s; incline: 0–9°). We also measured the metabolic rates of eight different runners for 17, 7-minute trials (velocity: 2.0–3.0 m/s; incline: 0–8°). During uphill running, parallel braking GRF approached zero for the 9° incline trials. Thus, we modeled the metabolic cost of parallel running as exponentially decreasing with incline. With that assumption, best-fit parameters for the metabolic rate data indicate that the efficiency of producing mechanical power to lift the center of mass vertically was independent of incline and running velocity, with a value of ∼29%. The metabolic cost of uphill running is not simply equal to the sum of the cost of level running and the cost of performing work to lift the body mass against gravity. Rather, it reflects a constant cost of perpendicular bouncing, decreased costs of parallel braking and propulsion and of course the cost of lifting body mass against gravity.

Partitioning the metabolic cost of human running: a task-by-task approach

Compared with other species, humans can be very tractable and thus an ideal "model system" for investigating the metabolic cost of locomotion. Here, we review the biomechanical basis for the metabolic cost of running. Running has been historically modeled as a simple spring-mass system whereby the leg acts as a linear spring, storing, and returning elastic potential energy during stance. However, if running can be modeled as a simple spring-mass system with the underlying assumption of perfect elastic energy storage and return, why does running incur a metabolic cost at all? In 1980, Taylor et al. proposed the "cost of generating force" hypothesis, which was based on the idea that elastic structures allow the muscles to transform metabolic energy into force, and not necessarily mechanical work. In 1990, Kram and Taylor then provided a more explicit and quantitative explanation by demonstrating that the rate of metabolic energy consumption is proportional to body weight and inversely proportional to the time of foot-ground contact for a variety of animals ranging in size and running speed. With a focus on humans, Kram and his colleagues then adopted a task-by-task approach and initially found that the metabolic cost of running could be "individually" partitioned into body weight support (74%), propulsion (37%), and leg-swing (20%). Summing all these biomechanical tasks leads to a paradoxical overestimation of 131%. To further elucidate the possible interactions between these tasks, later studies quantified the reductions in metabolic cost in response to synergistic combinations of body weight support, aiding horizontal forces, and leg-swing-assist forces. This synergistic approach revealed that the interactive nature of body weight support and forward propulsion comprises ∼80% of the net metabolic cost of running. The task of leg-swing at most comprises ∼7% of the net metabolic cost of running and is independent of body weight support and forward propulsion. In our recent experiments, we have continued to refine this task-by-task approach, demonstrating that maintaining lateral balance comprises only 2% of the net metabolic cost of running. In contrast, arm-swing reduces the cost by ∼3%, indicating a net metabolic benefit. Thus, by considering the synergistic nature of body weight support and forward propulsion, as well as the tasks of leg-swing and lateral balance, we can account for 89% of the net metabolic cost of human running.