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First published online May 18, 2006
Journal of Experimental Biology 209, 2050-2063 (2006)
Published by The Company of Biologists 2006
doi: 10.1242/jeb.02226
The energetic costs of trunk and distal-limb loading during walking and running in guinea fowl Numida meleagris : I. Organismal metabolism and biomechanics
Department of Biology, Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA
* Author for correspondence (e-mail: r.marsh{at}neu.edu)
Accepted 21 March 2006
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Summary |
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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.
Key words: guinea fowl, Numida meleagris, backpack loading, legged locomotion, segmental energy, oxygen consumption, limb loading, swing phase, stance phase, efficiency
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Introduction |
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; 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).
Despite the seemingly simple effects of these external loading regimes on
locomotor biomechanics, making inferences about normal locomotor function from
the increases in metabolic rates due to loading may be problematical. The most
straightforward use of the results of loading studies is simply the prediction
of the exercise energy metabolism of humans or other animals that normally
carry external loads on their body or limbs
(Soule and Goldman, 1969;
Epstein et al., 1987;
Lawrence and Stibbards, 1990;
Wickler et al., 2001;
Wickler et al., 2004).
However, loading studies have also been used to infer the relative costs of
weight support and producing the fluctuations in kinetic energy associated
with swinging the limbs during normal locomotion, and the conclusions reached
are not consistent. For example, the energetic cost of running in humans and
several quadrupeds was found to increase by the same percentage as the
increase in body weight due to backpack loading
(Taylor et al., 1980). These
data were taken as evidence that during unloaded running the majority of
metabolic energy is expended by muscles exerting force to support body weight
during the stance-phase of the stride and that the metabolic cost of swinging
the limbs is minimal (see also Taylor,
1985; Taylor,
1994). On the other hand, other investigators have concluded,
based on distal limb loading studies, that the cost of swinging the limbs
during unloaded running is substantial
(Steudel, 1990a;
Steudel, 1990b). Also, a
considerable number of trunk loading studies, many in walking humans, have
shown that the percentage increase in metabolic rate is considerably greater
than the percentage increase in body weight (see Discussion) and these data
are difficult to incorporate into Taylor's inferences
(Taylor et al., 1980). The
apparent inconsistencies in the conclusions of these studies may stem from the
failure to consider all the underlying assumptions about muscle function that
are necessary to connect the change in loading with the increased metabolic
cost (see Discussion).
We chose to examine the cost of carrying loads on the trunk and distally on
the legs in guinea fowl Numida meleagris. Why perform load-carrying
studies with a bird? The data on load-carrying collected to date have all been
on quadrupeds, with the exception of the numerous studies of humans. Studying
a biped has advantages in uncovering the underlying mechanisms that determine
the metabolic cost of running because the complication of the differing
functions of the forelimbs and hindlimbs in quadrupeds is avoided. The economy
of load-carrying may be different in humans and birds. Comparisons of human
and ostrich locomotion indicate that ostriches are economical runners compared
to humans (Fedak and Seeherman,
1979; Rubenson,
2005). The potential load-carrying ability of terrestrial birds is
indicated by the ability of ostriches to run while carrying nearly their own
weight in the form of a human rider on their back. Perhaps the most important
reason for using guinea fowl is that this model system offers the opportunity
to overcome the limitations of past work in this area by comparing alterations
in organismal energy use with changes in energy use at the level of individual
muscles (Ellerby and Marsh,
2006). Recently, energy use by all the individual leg muscles of
guinea fowl during level unloaded running has been analyzed using muscle blood
flow as the indicator of muscle metabolic rate
(Marsh et al., 2004;
Ellerby et al., 2005).
Therefore, at the onset of this study we were in the unique position of
already knowing the distribution of energy use between swing and stance.
Approximately 25% of the net energy use during walking and running is consumed
by muscles active during swing phase
(Marsh et al., 2004). We used
a combination of metabolic and mechanical measurements in this study to assess
the economy of carrying trunk loads and the efficiency of moving loads
attached to the distal limbs. The trunk and distal limb loading regimes were
expected to differentially influence stance and swing-phase mechanics,
respectively, and we examined the extent to which the stride characteristics
and segmental energetics were consistent with these predicted differential
influences on stance and swing. A companion study
(Ellerby and Marsh, 2006)
examines the distribution of increased energy use among the individual stance
and swing-phase muscles during trunk and distal limb loading.
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Materials and methods |
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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 cmx44 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.
Trunk and distal limb loading
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.
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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 DrieriteTM 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 (AscariteTM) and
additional DrieriteTM 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 O2
measurements 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.
On a given day,
O2 measurements
were obtained during unloaded running, and subsequently under one or both of
the loading conditions over and range of speeds. The order of the weighted
measurements was varied arbitrarily on any given day and unloaded measurements
were obtained both before and after the loaded measurements. The order of the
measurements did not appear to have any influence on the values obtained.
Under all loading conditions, the birds locomoted at 0.5, 1.0 and 1.5 m
s-1. Additionally, unloaded birds and birds with limb loads ran at
2.0 m s-1. The speed range and load sizes were chosen to ensure
that O2 was
always less than the maximum
O2 of the birds
(Ellerby et al., 2003). For
simplicity, 1.0 m s-1 is classified as a running speed in most of
what follows, but the transition between walking and running has no marked
discontinuity in stride kinematics in birds and this speed may be a
transitional speed between gaits in guinea fowl
(Gatesy, 1999). Following the
initial runs to accustom the birds to the loading, 3-5 sets of data were
collected for each bird. The mean values for each bird from these sessions
were used in the subsequent analyses.
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:
 | (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:
 | (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 (
tm). 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 (v'x) and vertical
(vy) translational velocities of the segment in the fixed
coordinate system of the video field. The values of
v'x 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 (
). The instantaneous horizontal (EK,x) and
vertical (EK,y) translational and rotational
(EK,rot) kinetic energies were calculated as:
 | (3) |
 | (4) |
 | (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' and
`internal' 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.
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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).
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Loaded and unloaded kinematics and segment energies
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).
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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.
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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 Epos for 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).
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pos). The value of pos
increased 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.
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).
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), 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).
Our data indicate that walking guinea fowl are economical in carrying trunk
loads when compared with most of the data on mammals during walking. The only
other bipedal animals on which loading studies have been conducted are humans.
Most studies of trunk loading in humans during walking have found that the
ratio of the loaded to unloaded net metabolic rate (metabolic ratio) is
greater than the ratio of the loaded mass to the unloaded mass (mass ratio,
Fig. 10). The major exceptions
to the poor economy of carrying loads during walking include two studies of
African women carrying loads on their heads
(Maloiy et al., 1986;
Jones, 1989), two studies of
children carrying moderate backpack loads, and a study of Nepalese porters
(Bastien et al., 2005)
(Fig. 10). Better mechanical
energy recovery by the inverted pendulum mechanism has been suggested as a
factor underlying the good economy of load-carrying in African women
(Heglund et al., 1995). Jones
has also noted (Jones, 1989)
that the better-than-average economy of these women depends on their body fat
content, with heavier individuals having net ratios similar to the Europeans
studied. Why the net metabolic ratios of Nepalese porters carrying very heavy
loads are much lower than most of the other values measured during walking
(Fig. 10) remains to be
explained. With the exception of these data, the net metabolic ratios during
human walking increase with increases in the load carried, particularly when
loads exceed 50% of the unloaded body mass
(Fig. 10A,B). This same trend
is evident in the data from large quadrupedal mammals walking with loads on
their backs (Fig. 10A,B).
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). The underlying causes of this variation in the cost of
carrying loads are not clear.
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Load-carrying economy and the relative costs of swing and stance
Some previous investigators reasoned that the cost of carrying loads could
be used to infer the relative costs of swing and stance
(Taylor et al., 1980;
Griffin et al., 2003). Loads
placed on the trunk should not influence the cost of swinging the limbs when
they are off the ground as long as the duty factor does not change
appreciably, which appears to be the case for guinea fowl (see
Fig. 4) and other animals
(Taylor et al., 1980;
Griffin et al., 2003). If one
assumes that the transport cost per unit of added mass is the same as the per
unit cost of transporting the original body mass, then the increase in
metabolic rate can be used to infer the proportion of unloaded cost devoted to
stance. For example, if swing phase incurs a significant cost, the metabolic
ratio should be less than the mass ratio during trunk loading because the
trunk loading is only increasing a portion of the total cost. Conversely, if
the costs of swing are negligible, the fractional increase (or decrease) in
load should exactly match the fractional increase in mass. This line of
reasoning is not affected by the distribution of mechanical functions of the
muscles during stance, e.g. the cost of weight support versus the
cost of work (Chang and Kram,
1999; Donelan et al.,
2002; Gottschalk and Kram, 2003), as long as these functions
represent similar fractions of the total cost in loaded and unloaded
conditions and the mass-specific cost of transporting the extra mass (weight)
is the same as the cost of transporting the original body mass. Data showing a
match between gross metabolic ratios and mass ratios in various animals during
running were taken as evidence in favor of minimal swing costs
(Taylor et al., 1980), as were
data on net metabolic ratios for human running in simulated reduced gravity
(Farley and McMahon, 1992). We
will not consider simulated reduced gravity experiments in any detail here
because interpreting these experiments is complicated by the reduction in
weight, but not mass (Grabowski et al.,
2005), and the interaction between the gravitational load the
horizontal force during propulsion (Chang
et al., 2000; Cavagna et al.,
2000).
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.
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.
We suggest that inferring the relative costs of swing and stance from loading studies is not practical because of the complex determinates of the cost of transporting the additional load. To infer the relative stance-phase costs from the increase in metabolic cost during trunk loading, at least the following assumptions need to be made about the muscles that are responsible for the increase in energy use. (1) The muscles would have to operate with the same mechanical advantage in transporting the added load as they do in transporting the normal body mass. However, the diverse set of muscles active in stance act at different joints and have different mechanical advantages in producing force on the ground. Thus, fulfilling this assumption would likely mean that the distribution of energy use among the stance-phase muscles would have to be the same in the loaded and unloaded conditions. (2) The proportion of different fiber types in the additional volume of muscle activated would have to be the same as that found in the volume of muscles used to support the normal body weight. Recruiting faster fibers would presumably increase the cost of weight support. (3) The muscle volume used to perform any additional work due to the increased mass would have to operate with the same efficiency as the muscle volume performing work during normal locomotion. (4) During loaded walking, a similar proportion of the work done would have to be conserved via the inverted pendulum mechanism as is conserved during normal walking. (5) During loaded running, elastic elements in series with the muscles would have to store and release a similar proportion of the total work as they do during unloaded running.
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.
Increases in metabolic rate due to distal limb loading
The large effects of distal limb loading that we measured in guinea fowl
are consistent with data on limb loading in mammals, but the effects of speed
on the metabolic increment among these species are less clear. Loading the
tarsometatarsal segments in guinea fowl with a total mass equivalent to 5% of
body mass increased metabolic rate by an amount comparable to loading the
trunk with 23% of body mass. Similar large effects of distal limb loading on
metabolic rate have been recorded in humans and two quadrupeds
(Martin, 1985;
Myers and Steudel, 1985;
Miller and Stamford, 1987;
Bhambhani et al., 1989;
Steudel, 1990a;
Steudel, 1990b;
Wickler et al., 2004). In
guinea fowl, the increment in metabolic cost increases curvilinearly with
speed (Fig. 9). The effect of
speed on the change in metabolic rate in other animals is difficult to
summarize because of large differences among studies in the size of the load
and its location on the distal limb, as well as substantial differences in
body size among the species studied. One way to collapse the data onto a
single plot is to calculate the net added cost of transporting a unit mass of
additional limb load (Fig.
12). The data on walking and running humans indicate that the
costs of moving a unit mass increases approximately linearly with speed
(Fig. 12). However, these data
come from different studies using various loads and methods of attaching the
load, and thus the exact relation with speed is uncertain. Two quadrupeds,
dogs and horses, both show increases with speed in the cost of moving a distal
limb load (Fig. 12), but the
change with speed in Steudel's data on dogs
(Steudel, 1990a) is remarkably
small.
|
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).
Relation of mechanical and metabolic power in distal limb loading
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.
Loading the distal limb affects stance-phase mechanical energy as well as
swing-phase energy. Approximately 40% of the increase in energy occurred
during stance and 60% during swing. The significant increase in energy in
stance occurs because of the extension of the ankle joint and flexion of the
tarsometatarsal-phalangeal joint in late stance, which cause net translation
of the tarsometatarsal segment. These joint movements have the effect of
accelerating the center of mass of the tarsometatarsal segment forward. Thus,
loading the tarsometatarsal segment is predicted to increase energy use by the
stance-phase muscles involved in leg extension in late stance as well as the
swing-phase muscles (Ellerby and Marsh,
2006). The effect of distal limb loading on stance-phase
mechanical work will, of course, depend on the location of the load. The
fraction of the segmental acceleration occurring during stance is expected to
be higher for more proximal segments and less for more distal segments. In
guinea fowl, the distal tarsometatarsus
(Fig. 1) is the most distal
part of the limb that can be easily loaded and loads on this segment appear to
have important influences on stance-phase mechanical work. This location of
the load may be analogous to ankle or shank loading in humans
(Bhambhani et al., 1989;
Royer and Martin, 2005), or
loading the dog hindlimb at the level of the metatarsals
(Steudel, 1990a). The loading
of the forelimbs of dogs done by Steudel was more proximal
(Steudel, 1990a). Loads placed
on the most distal part of the limb, e.g. the human foot or the hoof of horses
(Martin, 1985;
Wickler et al., 2004), would
be expected to be accelerated almost entirely during swing phase and thus have
little effect on stance costs.
Although mechanical energy was measured only for the tarsometatarsal
segment, measuring the increase in mechanical energy of this segment due to
loading the segment will provide an accurate estimate of the increase in
mechanical energy of the entire body if the other limb segments and the trunk
have the same mechanical energy in the loaded and unloaded condition. Studies
in humans that have measured the energy of all the limb segments have found
that distal limb loading only increases the energy in the loaded segment
(Martin, 1985;
Royer and Martin, 2005). We
did not measure the segmental energy in the other limb segments in this study,
but it seems likely that in guinea fowl as well only the energy in the
tarsometatarsal segment changed substantially. The trajectories of the
proximal end of the tarsometatarsal segment, i.e. the ankle, were similar in
the loaded and unloaded conditions at all speeds
(Fig. 5). Given that the
position of the ankle joint is determined by the combination of hip and knee
angles, moving the ankle joint through a similar set of coordinates in loaded
and unloaded birds requires the other linked segments of the limb to move
similarly under these conditions. If these unloaded segments moved in a
similar manner in loaded and unloaded guinea fowl, by necessity their
mechanical energies were also similar in loaded and unloaded birds.
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.
pos
O2
|
Acknowledgments |
|---|
). 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. |
Footnotes |
|---|
Present address: Department of Biological Sciences, Wellesley College, 106
Central Street, Wellesley, MA 02481, USA 
|
References |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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J. Rubenson, D. B. Heliams, S. K. Maloney, P. C. Withers, D. G. Lloyd, and P. A. Fournier Reappraisal of the comparative cost of human locomotion using gait-specific allometric analyses J. Exp. Biol., October 15, 2007; 210(20): 3513 - 3524. [Abstract] [Full Text] [PDF]
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A. P. Kabat, R. A. Phillips, J. P. Croxall, and P. J. Butler Differences in metabolic costs of terrestrial mobility in two closely related species of albatross J. Exp. Biol., August 15, 2007; 210(16): 2851 - 2858. [Abstract] [Full Text] [PDF]
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H. Pontzer Effective limb length and the scaling of locomotor cost in terrestrial animals J. Exp. Biol., May 15, 2007; 210(10): 1752 - 1761. [Abstract] [Full Text] [PDF]
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T. M. Griffin Powering locomotion? It's a loaded question J Appl Physiol, November 1, 2006; 101(5): 1273 - 1274. [Full Text] [PDF]
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J. Iriarte-Diaz, F. Bozinovic, and R. A. Vasquez What explains the trot-gallop transition in small mammals? J. Exp. Biol., October 15, 2006; 209(20): 4061 - 4066. [Abstract] [Full Text] [PDF]
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C. P. McGowan, H. A. Duarte, J. B. Main, and A. A. Biewener Effects of load carrying on metabolic cost and hindlimb muscle dynamics in guinea fowl (Numida meleagris) J Appl Physiol, October 1, 2006; 101(4): 1060 - 1069. [Abstract] [Full Text] [PDF]
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K. Phillips RUNNING COSTS J. Exp. Biol., June 1, 2006; 209(11): i - ii. [Full Text] [PDF]
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D. J. Ellerby and R. L. Marsh 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., June 1, 2006; 209(11): 2064 - 2075. [Abstract] [Full Text] [PDF]
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