Trunk and limb loads caused similar increases in metabolic rate. During trunk loading, the net metabolic rate (gross metabolic rate - resting metabolic rate) increased by 17% above the unloaded value across all speeds. This percentage increase is less than has been found in most studies of humans and other mammals. The economical load carriage of guinea fowl is consistent with predictions based on the relative cost of the stance and swing phases of the stride in this species. However, the available comparative data and considerations of the factors that determine the cost of carrying extra mass lead us to the conclusion that the cost of load carrying is unlikely to be a reliable indicator of the distribution of energy use in stance and swing. Both loading regimes caused small changes in the swing and/or stance durations, but these changes were less than 10%.

Loading the tarsometatarsal segment increased its segmental energy by 4.1 times and the segmental mechanical power averaged over the stride by 3.8 times. The increases in metabolism associated with limb loading appear to be linked to the increases in mechanical power. The delta efficiency (change in mechanical power divided by the change in metabolic power) of producing this power increased from 11% in walking to approximately 25% in running. Although tarsometatarsal loading was designed to increase the mechanical energy during swing phase, 40% of the increase in segmental energy occurred during late stance. Thus, the increased energy demand of distal limb loading in guinea fowl is predicted to cause increases in energy use by both stance- and swing-phase muscles.

One approach to revealing the energetic costs of various mechanical functions during walking and running has been to infer these costs from the effects of changing external loads. For example, application of loads to the trunk has been used to increase the costs of supporting body weight and accelerating body mass (Taylor et al.,1980; Grabowski et al.,2005). In the absence of changes in duty factor or stride frequency, trunk loads should have negligible effects on the cost of swinging the limbs. Conversely, loads applied to the distal limbs have been used to affect the costs of swinging the limbs with presumably little effect on the energy expended while the foot is in contact with the ground(Martin, 1985; Steudel, 1990a; Steudel, 1990b).

### Animals

Guinea fowl (Numida meleagris L.) were obtained from The Guinea Farm (New Vienna, IA, USA) as hatchlings and cage-reared at the Northeastern University Division of Laboratory Medicine. All procedures with live guinea fowl were conducted under protocols approved by the Northeastern University Institutional Animal Care and Use Committee. At the time of the measurements the birds were between 10 and 14 months old. Birds had ad libitumaccess to food and water and were maintained on a 12 h:12 h light:dark cycle. Body mass was 1.46±0.04 kg (mean ± s.e.m., N=6, range 1.31-1.59 kg; three non-ovulating females and three males).

### Training

For 2 months prior to measurements of rates of oxygen consumption(O2) we trained the birds to run on a motorized treadmill (Trimline 2600, Hebb Industries,Tyler, TX, USA; 120 cm×44 cm tread area). During training and subsequent experiments the guinea fowl ran inside a three-sided box with a partial top. The box was open at the back and had a mirror mounted on the front facing the running bird. A duct brought cool air from an air conditioner to one side of the box. Airflow was controlled such that sufficient cool air was allowed to flow into the box to prevent the birds from continuously panting. The training regime consisted of running for approximately 30 min per day, 5 days a week,at speeds ranging from 1.5 to 3.28 m s-1. At the end of the training period all birds could sustain 30 min of exercise at 2.5 m s-1.

A canvas backpack was constructed so that weights could be added to the trunk of the running birds. The pack weighed 32 g and was held in place anteriorly with straps around each wing and posteriorly with a strap running circumferentially around the bird posterior to the sternum(Fig. 1). A lead weight could be attached to the pack with Velcro. The weight consisted of a 5.5 cm wide strip of lead that was shaped so that it conformed to the back of the bird and extended approximately 5 cm ventrally along each side of the birds' body. The weight was positioned such that it was approximately above the birds' center of mass. The mass of the load was adjusted for the body mass of the bird. The combined mass of the weight and backpack averaged 22.8±0.5% (mean± s.e.m.) of the individual's body mass.

Fig. 1.

Approximate position and method of attachment of distal limb and trunk loads. Birds were either limb- or trunk-loaded. Both loading conditions are shown here for illustrative purposes. The limb load was placed near the distal end of the tarsometatarsal segment.

Fig. 1.

Approximate position and method of attachment of distal limb and trunk loads. Birds were either limb- or trunk-loaded. Both loading conditions are shown here for illustrative purposes. The limb load was placed near the distal end of the tarsometatarsal segment.

Distal limb loading was accomplished by taping weights to the tarsometatarsal segment (Fig. 1). Limb weights were constructed from approximately 2 cm wide strips of lead that were wrapped around the distal portion of the tarsometatarsal segments and secured in place with duct tape. A mass of 37 g was attached to each limb. The total mass of the limb weights (74 g) was approximately 5% of the body mass.

### Respirometry

Details of the respirometry setup are the same as described previously(Ellerby et al., 2003). Respiratory gases were collected using a flow-through respirometry system with the birds wearing a loose-fitting mask. Excurrent air from the mask passed through a column of Drierite™ to absorb water and then through a rotameter-type flow meter (factory rated accuracy ±3%) with a needle valve on the outlet to control flow. The flow meter calibration was checked using a recording spirometer of known accuracy. The ambient flow rate was set at 83.3 ml s-1 and 167 ml s-1 for birds at rest and during exercise, respectively. These flow rates kept the excurrent fractional O2 contents above 0.2. Gas withdrawn for O2 measurements passed through a column of CO2 absorbent (Ascarite™) and additional Drierite™ before measurement of O2 content using an Amtek S-3A/II O2 analyzer (AEI Technologies Inc., Naperville,IL, USA). This dual-channel oxygen analyzer was operated in differential mode in which the expired air from the mask was compared with dry,CO2-free room air that was pumped continuously through the second cell of the analyzer. Oxygen consumption rates(O2) in ml s-1 was calculated as described previously(Ellerby et al., 2003) using the appropriate equation for downstream flow(Withers, 1977). Oxygen consumption was converted to rate of energy use in using a conversion factor of 20.1 W s ml-1 O2.

Resting metabolic rate was measured while the birds sat in a darkened box on the treadmill. Sufficient time, usually 10-20 min, was allowed for resting metabolic rate to stabilize.

The birds ran at a steady speed until O2 stabilized for at least 1 min. Typically, birds spent 2-4 min at a given speed to assure a stabile reading. The time to 100% response of our system was less than 30 s when the flow rate was 167 ml s-1. After the resting O2 measurements,the birds were typically given a 2 min conditioning run at 1.5 m s-1 before measuring O2 during walking at 0.5 m s-1. After running at 1.5 m s-1, the O2 at 0.5 m s-1 stabilized more quickly than if the walking trial was initiated immediately following the resting measurements. As long as a conditioning run was done, the metabolic rate after 1 min at a given speed accurately reflects the steady state value at that speed.

Initial O2measurements obtained during load-carrying were high, but decreased on subsequent runs. Approximately three running sessions were needed with either the trunk or limb loads for the birds to become accustomed to the loads and for O2 to stabilize at a given speed.

### Kinematics and dynamics of normal and loaded strides

For these measurements, we carried out a separate set of running experiments, using the same birds as for the O2 measurements. The guinea fowl ran inside a three-sided box, open at the back, with a mirror mounted on the side facing the running bird. The left side of the box was made of transparent acrylic to enable filming of the running birds. The feathers of the left leg were trimmed so that the distal limb segments could be clearly seen, and the proximal and distal ends of the tarsometatarsal segment were highlighted with white paint. High-speed video was obtained at a frame rate of 500 Hz using a NAC HSAV-1000 video camera (NAC, Tokyo, Japan) for the running speeds and loading conditions under which O2 measurements were made.

For measurement of swing and stance durations, 8-10 strides were selected for each weighting condition at each speed. Strides were selected during which the birds maintained a steady position on the treadmill. Toe-off and foot-down times were measured to the nearest 2 ms. The mean value of the 8-10 strides was used for further analysis.

To allow calculation of the mechanical energy of the tarsometatarsal segment during running, we measured the center of mass and moment of inertia of this segment both with and without the lead weight added. The birds were sacrificed after a separate series of experiments to determine muscle blood flow (Ellerby and Marsh,2006). The limbs were frozen and the intact tarsometatarsal segment removed by separating the bones at their articulations. We determined the location of the center of mass of the loaded and unloaded limb segment by using a suspension technique whereby the intersection point from two different suspension positions was taken as the location of the segment's center of mass.

To measure the moment of inertia about the center of mass, the frozen segment was suspended by one end from a stiff steel pin about which it could pivot. The segment was allowed to swing back and forth through an arc about the pivot. We obtained high-speed video at 500 Hz of the swinging segment to determine the period of the oscillation (t) in seconds. This period was used to calculate the moment of inertia (I) about the pivot point in kg m2 using the following equation derived from the basic mechanics of a pendulum:
$\ I=\frac{t^{2}mcg}{4{\pi}^{2}},$
(1)
where m is the mass of the tarsometatarsal segment (kg), c is the distance between the center of mass and the axis of suspension (m), and g is the acceleration due to gravity (m s-2). The moment of inertia about the center of mass was then calculated using the parallel axis theorem as:
$\ I_{0}=I-mc^{2}.$
(2)
For each individual and speed we selected one stride during steady speed running under the limb-loaded condition and one stride under unloaded conditions for analysis of tarsometatarsal segment mechanical energy. We captured frames of the video at sufficient resolution to obtain approximately 100 data points during the stride (0.004-0.008 ms intervals, depending on speed). Using NIH Image (version 1.63), we hand-digitized the positions of the proximal and distal ends of the tarsometatarsus. The position data were used to calculate the horizontal and vertical coordinates of the center of mass of the tarsometatarsal segment and the angle of this segment with reference to the horizontal (Φtm). The horizontal and vertical coordinates were smoothed using a smoothing interpolation routine in the application Igor Pro (Wavemetrics, Lake Oswego, OR, USA) and differentiated with respect to time to obtain the horizontal (vx) and vertical(vy) translational velocities of the segment in the fixed coordinate system of the video field. The values of vx were corrected for the speed of the treadmill to obtain the velocity with reference to a fixed point on the belt(vx). The values of øtm were smoothed and differentiated to determine the angular velocity of the tarsometatarsal segment (ω). The instantaneous horizontal (EK,x) and vertical (EK,y) translational and rotational(EK,rot) kinetic energies were calculated as:
$\ E_{\mathrm{K},\mathrm{x}}=\frac{1}{2}mv_{\mathrm{x}}^{2},$
(3)
$\ E_{\mathrm{K},\mathrm{y}}=\frac{1}{2}mv_{\mathrm{y}}^{2},$
(4)
$\ E_{\mathrm{K},\mathrm{rot}}=\frac{1}{2}I{\phi}_{\mathrm{tm}}^{2}=\frac{1}{2}I_{0}{\Omega}^{2}.$
(5)

We also calculated the gravitational potential energy(Eg=mgh) of the tarsometatarsal segment, where h is the height of the center of mass of the tarsometatarsal segment above the tread. The values of EK,x, EK,y, EK,rot and Eg were summed at each point in the stride to determine the total instantaneous segmental energy (Etm). The sum of the increases in segmental energy (Epos) over the stride was then determined by summing the positive increments in Etm. The positive mechanical power of the segment pos averaged over the whole stride was calculated by dividing Epos by the stride time.

The segmental mechanical energy term calculated here is not the same as that calculated by Fedak et al. (Fedak et al., 1982) and others, who have partitioned external' andinternal' work. They (Fedak et al.,1982) calculated the kinetic energy with respect to the center of mass, whereas we used a fixed reference point on the ground. Both techniques are useful, but in different contexts. The method of calculating internal work is useful when summing with external work calculated from the force-plate plate measures of the work done on the center of mass. In this case, the internal work represents work not appearing in the measurement of external work. Our calculation of segmental energy is more useful for examining the temporal distribution of the work used to produce changes in the velocity of the segment. The best example that illustrates this difference is the kinetic energy of the foot. The internal work method calculates a large peak in kinetic energy of the foot during stance when this segment is stationary on the ground, but this is due to changes in the velocity of the center of mass with the respect to the foot, and not due to energy changes in the foot segment.

### Statistical analyses

Values are means ± 1 s.e.m. We carried out statistical analyses using SPSS (Versions 10 and 11 for the Macintosh). ANOVA was used to test for significant differences in the measured values with loading condition and speed as the factors.

We found that both running speed (ANOVA, P<0.001) and loading condition (ANOVA, P<0.001) had significant effects on metabolic rate. The relationship between metabolic rate and speed was linear during both trunk-loaded and unloaded running (Fig. 2).

Fig. 2.

Metabolic rate (W; see Materials and methods) as a function of treadmill speed in guinea fowl with no load (closed circles), trunk load (open circles)or distal limb load (open triangles). The data were collected at 0.5 m s-1 intervals under all conditions, but the loaded data are offset slightly on the speed axis for clarity. Values are means ± 1 s.e.m.(N=6).

Fig. 2.

Metabolic rate (W; see Materials and methods) as a function of treadmill speed in guinea fowl with no load (closed circles), trunk load (open circles)or distal limb load (open triangles). The data were collected at 0.5 m s-1 intervals under all conditions, but the loaded data are offset slightly on the speed axis for clarity. Values are means ± 1 s.e.m.(N=6).

The net metabolic rates during unloaded and trunk-loaded running were calculated by subtracting the value for resting metabolic rate measured with the birds sitting quietly in a darkened box on the treadmill (the zero speed value in Fig. 2).

The fractional increase in net metabolic rate during trunk loading did not change significantly (ANOVA, P=0.26) with speed(Fig. 3). The overall fractional increase across all individuals and speeds was 0.17±0.01. The mean fractional increase in body mass was 0.23±0.005. Thus, the increment in net locomotor costs due to trunk loading was 74% of the increment in body mass (Fig. 3).

Distal limb loading, accomplished by loading the tarsometatarsal segments with a total mass equal to approximately 5% of body mass, increased the metabolic rate by a similar amount to the 23% load carried on the trunk(Fig. 2).

Loading caused small but significant (ANOVA; P<0.001) changes in the durations of the stance and swing phases of the stride(Fig. 4). Paired comparisons indicated that trunk loading had no significant effect on swing duration, but caused a significant 4% increase in stance duration (Scheffé test; P<0.001). Ankle loading resulted in an approximately 9% increase in both stance and swing durations (Scheffé test; P<0.03).

The load on the tarsometatarsal segment increased the segment's mass by 4.3-fold and shifted its center of mass from the approximate midpoint of the segment to 72% of the distance from the proximal to the distal end(Table 1). Despite the large increase in mass, the segment moved in a similar manner during a stride in the loaded and unloaded conditions (Fig. 5).

Table 1.

Tarsometatarsal segment mass (kg) 0.011±0.001 0.048±0.001
Center of mass location (% of segment length from proximal end) 49.0±0.4 71.8±2.0
Moment of inertia (kg m2) about center of mass 6.93±0.52×10−6 1.29±0.11×10−5
Tarsometatarsal segment mass (kg) 0.011±0.001 0.048±0.001
Center of mass location (% of segment length from proximal end) 49.0±0.4 71.8±2.0
Moment of inertia (kg m2) about center of mass 6.93±0.52×10−6 1.29±0.11×10−5

Values are means ± s.e.m. (N=6).

Fig. 3.

Fractional increases in net metabolic rate at different speeds in trunk-loaded guinea fowl. Net metabolic rates were calculated by subtracting the resting metabolic rate of the bird sitting quietly in a darkened box on the treadmill from the rates measured at the different speeds. Fractional increases were then calculated by subtracting the unloaded net metabolic rate from the loaded net metabolic rate and dividing by the unloaded net metabolic rate. The broken line indicates the fractional increase in total mass (body mass plus load) produced by loading the trunk. Values are means ± 1 s.e.m. (N=6).

Fig. 3.

Fractional increases in net metabolic rate at different speeds in trunk-loaded guinea fowl. Net metabolic rates were calculated by subtracting the resting metabolic rate of the bird sitting quietly in a darkened box on the treadmill from the rates measured at the different speeds. Fractional increases were then calculated by subtracting the unloaded net metabolic rate from the loaded net metabolic rate and dividing by the unloaded net metabolic rate. The broken line indicates the fractional increase in total mass (body mass plus load) produced by loading the trunk. Values are means ± 1 s.e.m. (N=6).

Fig. 4.

Mean stance (closed symbols) and swing (open symbols) durations during unloaded (circles), trunk-loaded (squares) and distal limb-loaded (triangles)locomotion. Values are means ± 1 s.e.m. (N=6). Closely spaced points are offset slightly on the speed axis for clarity.

Fig. 4.

Mean stance (closed symbols) and swing (open symbols) durations during unloaded (circles), trunk-loaded (squares) and distal limb-loaded (triangles)locomotion. Values are means ± 1 s.e.m. (N=6). Closely spaced points are offset slightly on the speed axis for clarity.

The energy of the tarsometatarsal segment rose in late stance as it began to accelerate forward due to translation caused by extension of the ankle and flexion of the toe joints. Segment energy continued to rise during the first part of swing as it was moved forward rapidly(Fig. 6). At higher speeds, the energy of the segment was dominated by the horizontal translational energy(EK,x). The gravitational potential energy(Eg) remained relatively constant with speed at approximately 6 mJ, and thus was a larger fraction of the total energy at lower speeds. The vertical translational kinetic energy(EK,y) and rotational kinetic energies(EK,rot) were small fractions of the total energy at all speeds.

Fig. 5.

Horizontal and vertical coordinates of the proximal end of the tarsometatarsal segment (A) and segment angle (B) in a representative bird unloaded (closed symbols) and limb-loaded (open symbols), running at 1.5 m s-1. Horizontal coordinates from the video were corrected for tread speed and thus the total distance moved in A indicates the stride length. Data were collected at 0.004 s intervals from foot-down to the next foot-down.

Fig. 5.

Horizontal and vertical coordinates of the proximal end of the tarsometatarsal segment (A) and segment angle (B) in a representative bird unloaded (closed symbols) and limb-loaded (open symbols), running at 1.5 m s-1. Horizontal coordinates from the video were corrected for tread speed and thus the total distance moved in A indicates the stride length. Data were collected at 0.004 s intervals from foot-down to the next foot-down.

The total instantaneous energy of the segment (Etm) was substantially increased by loading the segment(Fig. 7). Most of the increment in Etm was due to the continuous rise in segmental energy in late stance and early swing. We estimated the total positive work done on the segment (Epos) by summing the positive increments in Etm over the stride. The value of Eposfor the loaded segment was on average 4.1 times the value for the unloaded segment (Fig. 8A). Under most loaded and unloaded conditions, approximately 60% of the positive work was done during swing and the remaining 40% was due to the rise in segmental energy during late stance (Fig. 8A).

Dividing the total positive work by the stride time provides an estimate of the mechanical power used to move the segment averaged over the entire stride(pos). The value of posincreased by an average of 3.8 times by loading the segment(Fig. 8B). The increase in power is less than the increase in segment energy because the stride duration is slightly longer when the segment is loaded.

Fig. 6.

The components of the energy in a single unloaded tarsometatarsal segment as a function of time during one stride in a representative bird running at 1.5 m s-1. Horizontal (EK,x; solid black line),vertical (EK,y; broken red line) and rotational(Erot; dotted blue line) kinetic energies are indicated. The green broken and dotted line indicates the gravitational potential energy(Eg) referenced to zero at the lowest point in the stride. The vertical line indicates the stance swing transition. (Please note that the dip in EK,x shown during in swing phase is representative in that a decrease in this mechanical energy term occurred during mid-swing in 4 of the 5 birds measured. However, the magnitude of the mid-swing decrease in EK,x in the example shown was the largest found.)

Fig. 6.

The components of the energy in a single unloaded tarsometatarsal segment as a function of time during one stride in a representative bird running at 1.5 m s-1. Horizontal (EK,x; solid black line),vertical (EK,y; broken red line) and rotational(Erot; dotted blue line) kinetic energies are indicated. The green broken and dotted line indicates the gravitational potential energy(Eg) referenced to zero at the lowest point in the stride. The vertical line indicates the stance swing transition. (Please note that the dip in EK,x shown during in swing phase is representative in that a decrease in this mechanical energy term occurred during mid-swing in 4 of the 5 birds measured. However, the magnitude of the mid-swing decrease in EK,x in the example shown was the largest found.)

The curvilinear relationship of mechanical power as a function of speed was also seen in the increment in metabolic power(Fig. 9). The ratio of the increase in segment mechanical power and the increase in metabolic power due to loading provides an estimate of the efficiency of the overall locomotor system in performing the extra work required by the distal limb loading. This estimate of efficiency increased with speed, going from 0.11 at 0.5 m s-1 to 0.26 at 2.0 m s-1(Fig. 9).

Fig. 7.

Total mechanical energy of a single tarsometatarsal segment as a function of time during one stride in a representative bird running at 1.5 m s-1. Broken line, unloaded segmental energy; solid line, loaded segmental energy. The vertical line indicates the stance-swing transition.

Fig. 7.

Total mechanical energy of a single tarsometatarsal segment as a function of time during one stride in a representative bird running at 1.5 m s-1. Broken line, unloaded segmental energy; solid line, loaded segmental energy. The vertical line indicates the stance-swing transition.

### Economy of carrying loads on the trunk

The net metabolic rate of guinea fowl during walking and running increases by 17% for an increase in body mass of 23% across all speeds. In a study of human load-carrying (Griffin et al.,2003), it was suggested that subtracting resting metabolic rate from the total metabolic rate is the most appropriate way to compare differences in loaded and unloaded conditions, a practice also followed in reduced weight studies (Farley and McMahon, 1992) and in recent trunk-loading investigations(Griffin et al., 2003; Grabowski et al., 2005). Our own work on the distribution of blood flow in the guinea fowl suggests that this correction for resting metabolism is appropriate because oxygen delivery to the tissues that are not involved in exercise changes little from rest to exercise (Ellerby et al.,2005).

Fig. 8.

(A) Mean sum of the positive increments in mechanical energy(Epos) of both tarsometatarsal segments as a function of treadmill speed, in the unloaded (closed symbols) and loaded (open symbols)segments. Circles, total Epos over the whole stride;triangles, Epos for swing phase only. (B) Mean mechanical power of the unloaded (closed circles) and loaded (open circles)tarsometatarsal segments, calculated by dividing total Epos for the stride by the stride duration. Values are means ± 1 s.e.m. (N=6).

Fig. 8.

(A) Mean sum of the positive increments in mechanical energy(Epos) of both tarsometatarsal segments as a function of treadmill speed, in the unloaded (closed symbols) and loaded (open symbols)segments. Circles, total Epos over the whole stride;triangles, Epos for swing phase only. (B) Mean mechanical power of the unloaded (closed circles) and loaded (open circles)tarsometatarsal segments, calculated by dividing total Epos for the stride by the stride duration. Values are means ± 1 s.e.m. (N=6).

Fig. 9.

Mean increments in metabolic (circles) and mechanical (diamonds) power,calculated as loaded power minus unloaded power. Values are means ± 1 s.e.m. (N=6). Numbers between the metabolic and mechanical power indicate the mean delta efficiency (± 1 s.e.m.) calculated by dividing the mechanical power increment by the metabolic power increment.

Fig. 9.

Mean increments in metabolic (circles) and mechanical (diamonds) power,calculated as loaded power minus unloaded power. Values are means ± 1 s.e.m. (N=6). Numbers between the metabolic and mechanical power indicate the mean delta efficiency (± 1 s.e.m.) calculated by dividing the mechanical power increment by the metabolic power increment.

Fig. 10.

Fig. 10.

### Load-carrying economy and the relative costs of swing and stance

Following these inferences, the low metabolic ratios of trunk-loaded guinea fowl would be explained by the relatively high swing-phase costs found in this species. Guinea fowl use about a quarter of the total energy use in swing-phase muscles (Marsh et al.,2004). Accepting this line of reasoning would suggest that walking and running birds have much higher swing-phase costs than mammals, despite birds having similar transport costs to mammals(Fedak and Seeherman, 1979; Taylor et al., 1982), and similar increments in total mechanical energy over the stride(Heglund et al., 1982).

However, a number of indications suggest that this seemingly straightforward line of reasoning could lead to misleading conclusions. First,in contrast to the gross metabolic ratios presented in Taylor et al.(Taylor et al., 1980), the net metabolic ratios calculated from this earlier study are actually greater than the mass ratios, as they are for most of the data on both walking and running mammals (Figs 10 and 11). Thus, the cost of transporting a unit of added mass during trunk loading in mammals must be more than the unit cost of transporting the original body mass. This change in mass-specific cost violates the fundamental assumption required to use metabolic ratios to deduce the division of stance and swing costs.

Fig. 11.

Mean metabolic response to trunk loading in running mammals and guinea fowl. The solid line with a slope of 1.0 is included for reference. Net metabolic ratios were calculated as in Fig. 10. Solid circles, fit humans (Davies, 1980; Taylor et al., 1982; Epstein et al., 1987; Thorstensson, 1986; Bilzon et al., 2001). Open circles, very well trained adult humans, most of whom competed in distance or middle distance events (Cureton et al.,1978; Cooke et al.,1991; Bourdin et al.,1995). Open triangles, children(Thorstensson, 1986; Cooke et al., 1991; Davies, 1980). Plus signs,quadrupeds ranging in size from rats to horses(Taylor et al., 1980; Wickler et al., 2001) (S. J. Wickler, personal communication). Asterisks, values for guinea fowl running at 1.0 and 1.5 m s-1. For the human studies not providing resting metabolic rates, the resting values were calculated as for the data in Fig. 10. Resting rates for the quadrupeds (Taylor et al.,1980) were calculated as 1.2 times the basal value obtained by entering the body mass (Mb) in the allometric equation, BMR b=3.89Mb0.76(Peters, 1983).

Fig. 11.

Mean metabolic response to trunk loading in running mammals and guinea fowl. The solid line with a slope of 1.0 is included for reference. Net metabolic ratios were calculated as in Fig. 10. Solid circles, fit humans (Davies, 1980; Taylor et al., 1982; Epstein et al., 1987; Thorstensson, 1986; Bilzon et al., 2001). Open circles, very well trained adult humans, most of whom competed in distance or middle distance events (Cureton et al.,1978; Cooke et al.,1991; Bourdin et al.,1995). Open triangles, children(Thorstensson, 1986; Cooke et al., 1991; Davies, 1980). Plus signs,quadrupeds ranging in size from rats to horses(Taylor et al., 1980; Wickler et al., 2001) (S. J. Wickler, personal communication). Asterisks, values for guinea fowl running at 1.0 and 1.5 m s-1. For the human studies not providing resting metabolic rates, the resting values were calculated as for the data in Fig. 10. Resting rates for the quadrupeds (Taylor et al.,1980) were calculated as 1.2 times the basal value obtained by entering the body mass (Mb) in the allometric equation, BMR b=3.89Mb0.76(Peters, 1983).

Despite the apparent change in the mass-specific transport costs during loading in mammals, the difference between the mammalian metabolic ratios and those found in guinea fowl might still be taken to suggest that the costs of swing are higher in guinea fowl than in the quadrupedal mammals and humans studied. However, current evidence suggests that the cost of swinging the limbs is substantial in running humans. Modica and Kram provided a mechanical assist to early swing and found a reduction of up to 20% in net energy cost during running (Modica and Kram,2005). Electromyographic (EMG) evidence suggests substantial swing-phase muscle activity during human running(Andersson et al., 1997; Prilutsky et al., 1998), and Modica and Kram found a reduction of up to 74% in mean EMG amplitude in the swing active rectus femoris muscle with their swing assist device(Modica and Kram, 2005). Two other swing-phase muscles that they measured, the anterior tibialis and the biceps femoris, did not show reduced mean EMG amplitudes, suggesting that they eliminated only a portion of the swing-phase costs. Gottschall and Kram used a swing assist device during human walking in a more complicated experimental design (Gottschall and Kram,2005) than that used by Modica and Kram during running(Modica and Kram, 2005). They estimated that the cost of swinging the limb is at least 10% of the net cost of walking. However, the cost is likely higher than this because they found that their swing assist device reduced mean EMG amplitude in the iliopsoas and rectus femoris by at most 26% and 52%, respectively, and mean EMG amplitude remained unchanged or increased in two other swing-phase muscles. Substantial swing-phase costs also have been suggested for human walking by a recent study using a model based on pendulum mechanics combined with energetic measurements(Doke et al., 2005). We conclude that the available evidence suggests that swing-phase costs in humans are of similar magnitude to those in guinea fowl, at least during running, and likely during walking as well. Similar swing-phase costs in the presence of very different net metabolic ratios during trunk loading suggests that factors other than the relative cost of swing are important in determining the relation between the increase in load and the increase in metabolic rate.

Fulfilling all of these assumptions seems unlikely in many cases. Depending on the direction of the deviations from these assumptions, the fractional increase in energy use due to trunk loading could be more or less than the value predicted from the relative cost of stance and swing during normal locomotion. Considering just the assumption regarding fiber type usage will suffice to illustrate our point. This assumption seems unlikely to be fulfilled, given the typical mixture of fiber types found in the limb muscles of birds and mammals and the known recruitment order of these fiber types. In a task of increasing intensity, slow fibers are expected to be recruited first followed by faster fibers, both within a muscle(Henneman et al., 1965) and probably among muscles with similar function(Sokoloff et al., 1999). Thus,activating an increasing volume of muscle to support the increased body weight would be hypothesized to require increasing recruitment of faster, less economical fiber types. This hypothesis could help explain the increase in the net metabolic ratios with increasing load found in most human walking studies(Fig. 10). The heavier loads would be expected to recruit greater numbers of faster, less economical fibers.

These considerations lead us to the conclusion that the economical load carriage of guinea fowl compared with mammals cannot be attributed with any certainty to differences in the relative costs of stance and swing. This conclusion is supported by the observation that guinea fowl and humans differ substantially in the economy of load-carrying during running(Fig. 10), despite having a similar proportion of their total energy expenditure devoted to swinging the limbs (Marsh et al., 2004; Modica and Kram, 2005). We suspect that the diversity of load-carrying economies found in humans and other mammals is caused by underlying variation in both biomechanics and physiology, which could include variation in the relative cost of swing and stance. However, using trunk loading to partition the costs of swing and stance does not seem practical, given the large amount of variation due to other causes. Sorting out the underlying causes of differences in load-carrying economy will require knowledge of which muscles are responsible for the increased energy use (Ellerby and Marsh, 2006) and improving knowledge of the relations between biomechanics and energy use.

Fig. 12.

Increase in metabolic rate due to distal limb loading in walking and running mammals and guinea fowl. The data were calculated by subtracting the loaded metabolic rate from the unloaded metabolic rate and dividing by the mass of the added load. Closed circles, walking and running humans(Soule and Goldman, 1969; Jones et al., 1984; Martin, 1985; Miller and Stamford, 1987; Claremont and Hall, 1988; Bhambhani et al., 1989). Open squares, trotting dogs (Steudel,1990a). Open triangles, trotting horses(Wickler et al., 2004). Horse data were calculated from original data provided by S. J. Wickler (personal communication).

Fig. 12.

Increase in metabolic rate due to distal limb loading in walking and running mammals and guinea fowl. The data were calculated by subtracting the loaded metabolic rate from the unloaded metabolic rate and dividing by the mass of the added load. Closed circles, walking and running humans(Soule and Goldman, 1969; Jones et al., 1984; Martin, 1985; Miller and Stamford, 1987; Claremont and Hall, 1988; Bhambhani et al., 1989). Open squares, trotting dogs (Steudel,1990a). Open triangles, trotting horses(Wickler et al., 2004). Horse data were calculated from original data provided by S. J. Wickler (personal communication).

The comparative data in Fig. 12 and our data on guinea fowl reinforce the idea that the mass of the distal segments of the moving limbs could be an important component of the cost of legged locomotion (Steudel,1990b). We do not know the energy cost of moving the unloaded tarsometatarsal segment in guinea fowl. However, if the cost per mass for moving the combined mass of the two segments (22 g) were similar to the cost of moving the added mass, the metabolic cost of moving this segment would represent approximately 5% of the net metabolic rate during running. The possibility exists that moving the unloaded segment is much less expensive due to energy saving mechanisms that are disrupted by segment loading, but the high cost of swing phase in guinea fowl suggests that this is not the case(Marsh et al., 2004).

In guinea fowl, the tarsometatarsal segments moved similarly in the unloaded and loaded conditions (Fig. 5). Therefore, the mechanical energy of the segment should have increased by the same ratio as the increase in segment mass, which was about 4.3 times. The actual average increment across all speeds of 4.1 times is close to this predicted value. Because the stride duration was lengthened slightly by distal limb loading, the mechanical power increment averaged over the whole stride, 3.8 times, was less than the increment in mechanical energy.

Despite our conclusion that the increase in mechanical energy of the tarsometatarsus resulting from its loading is a reasonable estimate of total increase in limb mechanical energy, the approach adopted here cannot discriminate at which joints, or in which muscles, the additional mechanical energy is produced. Changes in the segmental energy of distal segments are due in part to muscles acting directly on the segment, but are also driven by joint reaction forces transferred from adjacent segments(Martin and Cavanagh, 1990; Winter, 1990). Thus, much of the additional mechanical energy of the tarsometatarsus is likely produced by muscles that are distributed throughout the entire limb. Therefore, we would predict that loading the tarsometatarsal segment would cause changes in energy use across many muscles in the leg(Ellerby and Marsh, 2006).

Improving our understanding of the relationship between mechanical and metabolic energetics in distal limb loading will likely benefit from an interaction between more sophisticated biomechanical analyses than those used in the current study and information currently avaliable on the energy use by individual muscles (Ellerby and Marsh,2006). For example, inverse dynamics analysis can better identify the joints at which mechanical power is produced because it accounts for transfer of energy between adjacent segments(Winter, 1990; Zatsiorsky, 2002). What inverse dynamics studies cannot account for fully is the transfer of energy by two-joint muscles and the effects of co-contraction at joints. However, the distribution of energy use by individual muscles along with EMG data could be used to supplement the inverse dynamics calculations, and aid the implementation of optimization models that are capable of partitioning the mechanical energetics of the limb segments among individual muscles(Anderson and Pandy, 2001; Neptune and Sasaki, 2005).

Given the assumption of equivalent limb trajectories during loaded and unloaded locomotion, the efficiency of performing the extra work due to distal limb loading can be calculated by dividing the increase in mechanical power of the tarsometatarsal segment by the increase in metabolic rate. The delta efficiencies calculated in this way are apparent mechanical efficiencies and do not necessarily represent muscle efficiencies. The estimated efficiency of producing the extra positive work due to distal limb loading increases with increasing running velocity. At the walking speed of 0.5 m s-1,this measure of efficiency is 11% (Fig. 9). At 1.0 m s-1, which is a transitional speed between gaits (Gatesy, 1999), the efficiency is 18%. When running at 1.5 or 2.0 m s-1, the efficiency is approximately 25%, which approximates the maximum value expected for aerobically functioning muscle. This trend of increasing efficiency with speed has also been found for total joint work during the unloaded swing phase, but the overall efficiencies are lower (J.R. and R.L.M., unpublished).

The high efficiencies of performing the extra positive work during running suggest that the metabolic cost of absorbing work to decelerate the segment at foot-down is not very large. If the cost of absorbing work were substantial,then this cost would have to be removed from the metabolic cost before calculating the efficiency of positive work, which would make the efficiency of the positive work greater than the known efficiency of skeletal muscle. Alternatively, the extra negative work required due to loading could require metabolic energy, but the absorbed work could be returned to do positive work in early stance, thus canceling the cost.

The only other data from which similar calculations of efficiency during distal limb loading can be made are those of Martin(Martin, 1985). Martin reports the segmental energy of the foot during running at 3.33 m s-1 in the unloaded condition and when human subjects wore shoes with an added mass of either 0.5 or 1.0 kg distributed equally between the shoes. Martin reports the sum of the absolute values of the positive and negative changes in energy during swing. In human running, almost all the increase and decrease in the segmental energy of the foot occurs during swing, with only a small increase before toe-off (Williams and Cavanagh,1983). Thus, the positive increments in energy of the foot should be half the values given by Martin, but the values in his table are for a single foot. With these assumptions, the efficiency of moving the extra mass on the foot is approximately 40% under either loading condition. This value clearly exceeds the maximum value expected from skeletal muscle, and some energy saving mechanisms must be used to decrease the metabolic burden of moving the load. One possible explanation of this high delta efficiency of foot loading in human running is the transfer of energy between the limbs(Williams and Cavanagh, 1983). During the flight phase, the decrease in energy of one limb in late swing corresponds in time to the increase in energy in the contralateral limb that has just left the ground. This correspondence suggests that such a transfer could occur but does not prove that it actually does occur. A similar mechanism is not available to guinea fowl running at the speeds used here,because the duty factor is approximately 0.5 at the highest speed, and the negative and positive powers of the contralateral limbs are not in phase.

### Conclusions

(1) Fractional increases in metabolic rate during trunk loading in guinea fowl were found to be lower than the values recorded for most mammals. Some data on select groups of humans are similar to the values for guinea fowl. We suggest that the diversity of load-carrying economies in various studies could be caused by a number of underlying biomechanical and physiological factors,and should not be viewed as indicative of the relative costs of swing and stance.

(2) The metabolic cost of distal limb loading in guinea fowl appears to be linked to the increases in the mechanical energy in the loaded segment. The large effect of distal limb loading is consistent with the idea that the mass of the distal segments is an important determinant of the cost of running.

(3) The efficiency of performing the extra positive power to move the loaded segment was approximately 25% at the two highest speeds tested. This value is approximately equal to the maximal value expected for efficiency of skeletal muscle and suggests that the either the extra negative work done to decelerate the loaded segment does not require much metabolic energy, or that energy saving mechanisms reduce the total cost of the positive and negative work.

(4) A substantial portion, approximately 40%, of the increase in mechanical energy in the loaded tarsometatarsal segment of guinea fowl occurs during late stance phase. Thus, depending on the location of the load, distal limb loading should not be viewed as influencing only swing-phase costs. Attaching the load to a long segment that undergoes substantial translational acceleration during late stance requires increased stance-phase work as well.

List of symbols

• c

distance between the center of mass and the axis of suspension

•
• Eg

instantaneous gravitational potential energy

•
• EK,rot

instantaneous rotational kinetic energy

•
• EK,x, EK,y

instantaneous vertical, horizontal translational kinetic energy

•
• pos

positive mechanical power of the segment averaged over the whole stride

•
• Epos

sum of the positive increments in segmental energy for the whole stride

•
• Etm

total instantaneous segmental energy

•
• g

acceleration due to gravity

•
• h

height

•
• I

moment of inertia

•
• m

mass

•
• øtm

angle with reference to the horizontal

•
• t

period of the oscillation

•
• O2

rate of oxygen consumption

•
• vx

horizontal velocity referenced to the video field

•
• vx

horizontal velocity referenced to a fixed point on the treadmill belt

•
• vy

vertical velocity

•
• ω

angular velocity

This work was supported by NIH grant AR47337 to R.L.M. We are grateful to Karen Bioski and Julia Vasic for their assistance in performing the respirometry experiments and collecting kinematic data. Dr Phillip E. Martin kindly clarified some issues regarding his data(Martin, 1985). Dr Steven J. Wickler provided access to original data, as did Drs Guillaume J. Bastien and Norman C. Heglund, for which we are very grateful.

Anderson, F. C. and Pandy, M. G. (
2001
). Static and dynamic optimization solutions for gait are practically equivalent.
J. Biomech.
34
,
153
-161.
Andersson, E. A., Nilsson, J. and Thorstensson, A.(
1997
). Intramuscular EMG from the hip flexor muscles during human locomotion.
Acta Physiol. Scand.
161
,
361
-370.
Bastien, G. J., Schepens, B., Willems, P. A. and Heglund, N. C. (
2005
). Energetics of load carrying in Nepalese porters.
Science
308
,
1755
.
Bhambhani, Y., Burnham, R., Singh, M. and Gomes, P.(
1989
). Ankle and wrist weights: their effect on physiologic responses during treadmill running.
Arch. Phys. Med. Rehabil.
70
,
291
-296.
Bilzon, J. L. J., Allsopp, A. J. and Tipton, M. J.(
2001
Occup. Med.
51
,
357
-361.
Bourdin, M., Belli, A., Arsac, L. M., Bosco, C. and Lacour, J. R. (
1995
). Effect of vertical loading on energy cost and kinematics of running in trained male subjects.
J. Appl. Physiol.
79
,
2078
-2085.
Cavagna, G. A., Willems, P. A. and Heglund, N. C.(
2000
). The role of gravity in human walking: pendular energy exchange, external work and optimal speed.
J. Physiol.
528
,
657
-668.
Chang, Y. H. and Kram, R. (
1999
). Metabolic cost of generating horizontal forces during human running.
J. Appl. Physiol.
86
,
1657
-1662.
Chang, Y. H., Huang, H. W., Hamerski, C. M. and Kram, R.(
2000
). The independent effects of gravity and inertia on running mechanics.
J. Exp. Biol.
203
,
229
-238.
Claremont, A. D. and Hall, S. J. (
1988
Med. Sci. Sports Exerc.
20
,
167
-171.
Cooke, C. B., McDonagh, M. J., Nevill, A. M. and Davies, C. T. (
1991
). Effects of load on oxygen intake in trained boys and men during treadmill running.
J. Appl. Physiol.
71
,
1237
-1244.
Cureton, K. J., Sparling, P. B., Evans, B. W., Johnson, S. M.,Kong, U. D. and Purvis, J. W. (
1978
). Effect of experimental alterations in excess weight on aerobic capacity and distance running performance.
Med. Sci. Sports
10
,
194
-199.
Datta, S. R., Chatterjee, B. B. and Roy, B. N.(
1975
). Maximum permissible weight to be carried on the head by a male worker from eastern India.
J. Appl. Physiol.
38
,
132
-135.
Davies, C. T. (
1980
). Metabolic cost of exercise and physical performance in children with some observations on external loading.
Eur. J. Appl. Physiol. Occup. Physiol.
45
,
95
-102.
Doke, J., Donelan, J. M. and Kuo, A. D. (
2005
). Mechanics and energetics of swinging the human leg.
J. Exp. Biol.
208
,
439
-445.
Donelan, J. M., Kram, R. and Kuo, A. D. (
2002
). Mechanical work for step-to-step transitions is a major determinant of the metabolic cost of human walking.
J. Exp. Biol.
205
,
3717
-3727.
Duggan, A. and Haisman, M. F. (
1992
). Prediction of the metabolic cost of walking with and without loads.
Ergonomics
35
,
417
-426.
Ellerby, D. J. and Marsh, R. L. (
2006
). The energetic costs of trunk and distal limb loading during walking and running in guinea fowl Numida meleagris. II. Muscle energy use as indicated by blood flow.
J. Exp. Biol.
209
,
2064
-2075.
Ellerby, D. J., Cleary, M., Marsh, R. L. and Buchanan, C. I.(
2003
). Measurement of maximum oxygen consumption in guinea fowl Numida meleagris indicates that birds and mammals display a similar diversity of aerobic scopes during running.
Physiol. Biochem. Zool.
76
,
695
-703.
Ellerby, D. J., Henry, H. T., Carr, J. A., Buchanan, C. I. and Marsh, R. L. (
2005
). Blood flow in guinea fowl Numida meleagris as an indicator of energy expenditure by individual muscles during walking and running.
J. Physiol.
564
,
631
-648.
Epstein, Y., Stroschein, L. A. and Pandolf, K. B.(
1987
). Predicting metabolic cost of running with and without backpack loads.
Eur. J. Appl. Physiol. Occup. Physiol.
56
,
495
-500.
Farley, C. T. and McMahon, T. A. (1992). Energetics of walking and running: insights from simulated reduced-gravity experiments.
J. Appl. Physiol.
73
,
2709
-2712.
Fedak, M. A. and Seeherman, H. J. (
1979
). Reappraisal of energetics of locomotion shows identical cost in bipeds and quadrupeds including ostrich and horse.
Nature
282
,
713
-716.
Fedak, M. A., Heglund, N. C. and Taylor, C. R.(
1982
). Energetics and mechanics of terrestrial locomotion. II. Kinetic energy changes of the limbs and body as a function of speed and body size in birds and mammals.
J. Exp. Biol.
79
,
23
-40.
Gatesy, S. M. (
1999
). Guineafowl hind limb function. I: Cineradiographic analysis and speed effects.
J. Morphol.
240
,
115
-125.
Gottschall, J. S. and Kram, R. (
2003
). Energy cost and muscular activity required for propulsion during walking.
J. Appl. Physiol.
94
,
1766
-1772.
Gottschall, J. S. and Kram, R. (
2005
). Energy cost and muscular activity required for leg swing during walking.
J. Appl. Physiol.
99
,
23
-30.
Grabowski, A., Farley, C. T. and Kram, R.(
2005
). Independent metabolic costs of supporting body weight and accelerating body mass during walking.
J. Appl. Physiol.
98
,
579
-583.
Griffin, T. M., Roberts, T. J. and Kram, R.(
2003
). Metabolic cost of generating muscular force in human walking: insights from load-carrying and speed experiments.
J. Appl. Physiol.
95
,
172
-183.
Heglund, N. C., Fedak, M. A., Taylor, C. R. and Cavagna, G. A. (
1982
). Energetics and mechanics of terrestrial locomotion. IV. Total mechanical energy changes as a function of speed and body size in birds and mammals.
J. Exp. Biol.
97
,
57
-66.
Heglund, N. C., Williams, P. A., Penta, M. and Cavagna, G. A. (
1995
Nature
375
,
52
-54.
Henneman, E., Somjen, G. and Carpenter, D. O.(
1965
). Functional significance of cell size in spinal motoneurons.
J. Neurophysiol.
28
,
560
-580.
Hong, Y., Li, J. X., Wong, A. S. and Robinson, P. D.(
2000
). Effects of load carriage on heart rate, blood pressure and energy expenditure in children.
Ergonomics
43
,
717
-727.
Jones, B. H., Toner, M. M., Daniels, W. L. and Knapik, J. J.(
1984
). The energy cost and heart-rate response of trained and untrained subjects walking and running in shoes and boots.
Ergonomics
27
,
895
-902.
Jones, C. D. (
1989
). Energy cost of carrying loads.
Eur. J. Clin. Nutr.
43
,
881
-883.
Kram, R. (
1996
). Inexpensive load carrying by rhinoceros beetles.
J. Exp. Biol.
199
,
609
-612.
Lawrence, P. R. and Stibbards, R. J. (
1990
). The energy costs of walking carrying and pulling loads on flat surfaces by Brahman cattle and Swamp buffalo.
Anim. Prod.
50
,
29
-40.
Lloyd, R. and Cooke, C. B. (
2000
). The oxygen consumption associated with unloaded walking and load carriage using two different backpack designs.
Eur. J. Appl. Physiol.
81
,
486
-492.
Maloiy, G. M., Heglund, N. C., Prager, L. M., Cavagna, G. A. and Taylor, C. R. (
1986
). Energetic cost of carrying loads: have African women discovered an economic way?
Nature
319
,
668
-669.
Marsh, R. L., Ellerby, D. J., Carr, J. A., Henry, H. T. and Buchanan, C. I. (
2004
). Partitioning the energetics of walking and running: swinging the limbs is expensive.
Science
303
,
80
-83.
Martin, P. E. (
1985
Med. Sci. Sports Exerc.
17
,
427
-433.
Martin, P. E. and Cavanagh, P. R. (
1990
). Segment interactions within the swing leg during unloaded and loaded running.
J. Biomech.
23
,
529
-536.
Merati, G., Negrini, S., Sarchi, P., Mauro, F. and Veicsteinas,A. (
2001
). Cardio-respiratory adjustments and cost of locomotion in school children during backpack walking (the Italian Backpack Study).
Eur. J. Appl. Physiol.
85
,
41
-48.
Miller, J. F. and Stamford, B. A. (
1987
). Intensity and energy cost of weighted walking vs. running for men and women.
J. Appl. Physiol.
62
,
1497
-1501.
Modica, J. R. and Kram, R. (
2005
). Metabolic energy and muscular activity required for leg swing in running.
J. Appl. Physiol.
98
,
2126
-2131.
Myers, M. J. and Steudel, K. (
1985
.). Effect of limb mass and its distribution on the energetic cost of running.
J. Exp. Biol.
116
,
363
-373.
Neptune, R. R. and Sasaki, K. (
2005
). Ankle plantar flexor force production is an important determinant of the preferred walk-to-run transition speed.
J. Exp. Biol.
208
,
799
-808.
Peters, R. H. (
1983
).
The Ecological Implications of Body Size
. Cambridge: Cambridge University Press.
Pierrynowski, M. R., Winter, D. A. and Norman, R. W.(
1981
). Metabolic measures to ascertain the optimal load to be carried by man.
Ergonomics
24
,
393
-399.
Prilutsky, B. I., Gregor, R. J. and Ryan, M. M.(
1998
). Coordination of two-joint rectus femoris and hamstrings during the swing phase of human walking and running.
Exp. Brain Res.
120
,
479
-486.
Quesada, P. M., Mengelkoch, L. J., Hale, R. C. and Simon, S. R. (
2000
). Biomechanical and metabolic effects of varying backpack loading on simulated marching.
Ergonomics
43
,
293
-309.
Royer, T. D. and Martin, P. E. (
2005
). Manipulations of leg mass and moment of inertia: effects on energy cost of walking.
Med. Sci. Sports Exerc.
37
,
649
-656.
Rubenson, J. (
2005
). Mechanical energetics as a determinant of the metabolic cost of bipedal locomotion.
PhD thesis
, School of Human Movement and Exercise Science, University of Western Australia, Perth, Australia.
Sokoloff, A. J., Siegel, S. G. and Cope, T. C.(
1999
). Recruitment order among motoneurons from different motor nuclei.
J. Neurophysiol.
81
,
2485
-2492.
Soule, R. G. and Goldman, R. F. (
1969
). Energy cost of loads carried on the head, hands, or feet.
J. Appl. Physiol.
27
,
687
-690.
Soule, R. G., Pandolf, K. B. and Goldman, R. F.(
1978
). Energy expenditure of heavy load carriage.
Ergonomics
21
,
373
-381.
Steudel, K. (
1990a
). The work and energetic cost of locomotion. I. The effects of limb mass distribution in quadrupeds.
J. Exp. Biol.
154
,
273
-285.
Steudel, K. (
1990b
). The work and energetic cost of locomotion. II. Partitioning the cost of internal and external work within a species.
J. Exp. Biol.
154
,
287
-303.
Taylor, C. R. (
1985
). Force development during sustained locomotion: a determinant of gait, speed and metabolic power.
J. Exp. Biol.
115
,
253
-262.
Taylor, C. R. (
1994
). Relating mechanics and energetics during exercise. In
Comparative Vertebrate Exercise Physiology: Unifying Physiological Principles
. Vol.
38A
(ed. J. H. Jones), pp.
181
Taylor, C. R., Heglund, N. C., McMahon, T. A. and Looney, T. R. (
1980
). Energetic cost of generating muscular force during running: comparison of small and large animals.
J. Exp. Biol.
86
,
9
-18.
Taylor, C. R., Heglund, N. C. and Maloiy, G. M.(
1982
). Energetics and mechanics of terrestrial locomotion. I. metabolic energy consumption as a function of speed and body size in birds and mammals.
J. Exp. Biol.
97
,
1
-21.
Thorstensson, A. (
1986
). Effects of moderate external loading on the aerobic demand of submaximal running in men and 10 year-old boys.
Eur. J. Appl. Physiol. Occup. Physiol
.
55
,
569
-574.
Wickler, S. J., Hoyt, D. F., Cogger, E. A. and Hall, K. M.(
2001
). Effect of load on preferred speed and cost of transport.
J. Appl. Physiol.
90
,
1548
-1551.
Wickler, S. J., Hoyt, D. F., Clayton, H. M., Mullineaux, D. R.,Cogger, E. A., Sandoval, E., McGuire, R. and Lopez, C.(
2004
). Energetic and kinematic consequences of weighting the distal limb.
Equine Vet. J.
36
,
772
-777.
Williams, K. R. and Cavanagh, P. R. (
1983
). A model for the calculation of mechanical power during distance running.
J. Biomech.
16
,
115
-128.
Winter, D. A. (
1990
).
Biomechanics and Motor Control of Human Movement
. New York: John Wiley.
Withers, P. C. (
1977
). Measurement of VO2, VCO2, and evaporative water loss with a flow-through mask.
J. Appl. Physiol.
42
,
120
-123.
Zatsiorsky, V. M. (
2002
).
Kinetics of Human Motion
. Champaign, IL: Human Kinetics.