1 THE MODULATION OF OUTDOOR RUNNING SPEED: THE INFLUENCE OF GRADIENT A thesis submitted for the degree Doctor of Philosophy 2010 Andrew D Townshend B. App Sc (QUT) School of Human Movement Studies Queensland University of Technology Brisbane, Australia
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THE MODULATION OF OUTDOOR RUNNING SPEED:
THE INFLUENCE OF GRADIENT
A thesis submitted for the degree
Doctor of Philosophy
2010
Andrew D Townshend
B. App Sc (QUT)
School of Human Movement Studies
Queensland University of Technology
Brisbane, Australia
2
KEY WORDS
Downhill
Field study
Gait
Global Positioning System
Gradient
Locomotion
Overground
Pacing strategy
Performance
Running
Speed regulation
Speed measurement
Uphill
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ABSTRACT
This thesis aimed to investigate the way in which distance runners modulate their
speed in an effort to understand the key processes and determinants of speed selection
when encountering hills in natural outdoor environments. One factor which has limited
the expansion of knowledge in this area has been a reliance on the motorized treadmill
which constrains runners to constant speeds and gradients and only linear paths.
Conversely, limits in the portability or storage capacity of available technology have
restricted field research to brief durations and level courses. Therefore another aim of
this thesis was to evaluate the capacity of lightweight, portable technology to measure
running speed in outdoor undulating terrain.
The first study of this thesis assessed the validity of a non-differential GPS to measure
speed, displacement and position during human locomotion. Three healthy participants
walked and ran over straight and curved courses for 59 and 34 trials respectively. A
non-differential GPS receiver provided speed data by Doppler Shift and change in GPS
position over time, which were compared with actual speeds determined by
chronometry. Displacement data from the GPS were compared with a surveyed 100m
section, while static positions were collected for 1 hour and compared with the known
geodetic point. GPS speed values on the straight course were found to be closely
correlated with actual speeds (Doppler shift: r = 0.9994, p < 0.001, Δ GPS position/time:
r = 0.9984, p < 0.001). Actual speed errors were lowest using the Doppler shift method
(90.8% of values within ± 0.1 m.sec -1). Speed was slightly underestimated on a curved
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path, though still highly correlated with actual speed (Doppler shift: r = 0.9985, p <
0.001, Δ GPS distance/time: r = 0.9973, p < 0.001). Distance measured by GPS was
100.46 ± 0.49m, while 86.5% of static points were within 1.5m of the actual geodetic
point (mean error: 1.08 ± 0.34m, range 0.69-2.10m). Non-differential GPS
demonstrated a highly accurate estimation of speed across a wide range of human
locomotion velocities using only the raw signal data with a minimal decrease in
accuracy around bends. This high level of resolution was matched by accurate
displacement and position data. Coupled with reduced size, cost and ease of use, the
use of a non-differential receiver offers a valid alternative to differential GPS in the
study of overground locomotion.
The second study of this dissertation examined speed regulation during overground
running on a hilly course. Following an initial laboratory session to calculate
physiological thresholds (VO2 max and ventilatory thresholds), eight experienced long
distance runners completed a self- paced time trial over three laps of an outdoor
course involving uphill, downhill and level sections. A portable gas analyser, GPS
receiver and activity monitor were used to collect physiological, speed and stride
frequency data. Participants ran 23% slower on uphills and 13.8% faster on downhills
compared with level sections. Speeds on level sections were significantly different for
78.4 ± 7.0 seconds following an uphill and 23.6 ± 2.2 seconds following a downhill.
Speed changes were primarily regulated by stride length which was 20.5% shorter
uphill and 16.2% longer downhill, while stride frequency was relatively stable. Oxygen
consumption averaged 100.4% of runner’s individual ventilatory thresholds on uphills,
78.9% on downhills and 89.3% on level sections. Group level speed was highly
predicted using a modified gradient factor (r2 = 0.89). Individuals adopted distinct
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pacing strategies, both across laps and as a function of gradient. Speed was best
predicted using a weighted factor to account for prior and current gradients. Oxygen
consumption (VO2) limited runner’s speeds only on uphill sections, and was maintained
in line with individual ventilatory thresholds. Running speed showed larger individual
variation on downhill sections, while speed on the level was systematically influenced
by the preceding gradient. Runners who varied their pace more as a function of
gradient showed a more consistent level of oxygen consumption. These results suggest
that optimising time on the level sections after hills offers the greatest potential to
minimise overall time when running over undulating terrain.
The third study of this thesis investigated the effect of implementing an individualised
pacing strategy on running performance over an undulating course. Six trained distance
runners completed three trials involving four laps (9968m) of an outdoor course
involving uphill, downhill and level sections. The initial trial was self-paced in the
absence of any temporal feedback. For the second and third field trials, runners were
paced for the first three laps (7476m) according to two different regimes (Intervention
or Control) by matching desired goal times for subsections within each gradient. The
fourth lap (2492m) was completed without pacing. Goals for the Intervention trial were
based on findings from study two using a modified gradient factor and elapsed distance
to predict the time for each section. To maintain the same overall time across all paced
conditions, times were proportionately adjusted according to split times from the self-
paced trial. The alternative pacing strategy (Control) used the original split times from
this initial trial. Five of the six runners increased their range of uphill to downhill speeds
on the Intervention trial by more than 30%, but this was unsuccessful in achieving a
more consistent level of oxygen consumption with only one runner showing a change
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of more than 10%. Group level adherence to the Intervention strategy was lowest on
downhill sections. Three runners successfully adhered to the Intervention pacing
strategy which was gauged by a low Root Mean Square error across subsections and
gradients. Of these three, the two who had the largest change in uphill-downhill speeds
ran their fastest overall time. This suggests that for some runners the strategy of
varying speeds systematically to account for gradients and transitions may benefit race
performances on courses involving hills.
In summary, a non – differential receiver was found to offer highly accurate measures
of speed, distance and position across the range of human locomotion speeds. Self-
selected speed was found to be best predicted using a weighted factor to account for
prior and current gradients. Oxygen consumption limited runner’s speeds only on
uphills, speed on the level was systematically influenced by preceding gradients, while
there was a much larger individual variation on downhill sections. Individuals were
found to adopt distinct but unrelated pacing strategies as a function of durations and
gradients, while runners who varied pace more as a function of gradient showed a
more consistent level of oxygen consumption. Finally, the implementation of an
individualised pacing strategy to account for gradients and transitions greatly increased
runners’ range of uphill-downhill speeds and was able to improve performance in some
runners. The efficiency of various gradient-speed trade- offs and the factors limiting
faster downhill speeds will however require further investigation to further improve the
3 ASSESSMENT OF SPEED AND POSITION DURING HUMAN LOCOMOTION USING NON-DIFFERENTIAL GPS ................................................................................................................. 44
5 THE EFFECT OF AN INDIVIDUALISED PACING STRATEGY ON RUNNING PERFORMANCE OVER AN UNDULATING COURSE ........................................................................................... 94
APPENDIX ONE- Adherence to an imposed pacing strategy................................................ 144
APPENDIX TWO - Differences in displacement of the GPS receiver at three different locomotion speeds. .............................................................................................................. 159
APPENDIX THREE - Spatial distribution of GPS positions relative to known geodetic point 160
APPENDIX FOUR –Validation studies of GPS and DGPS for speed (A) and distance/position (B) ......................................................................................................................................... 161
APPENDIX FIVE - Summary of regression weightings for group and individual subjects .... 163
APPENDIX SIX - Circle Earth Formula ................................................................................... 164
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LIST OF FIGURES
Figure Page
2.1 Theoretical representation of time v energy cost 19
3.1 Plot of errors in speed determination using GPS (Doppler shift-top figure) or GPS (∆ distance/time-bottom figure) over a straight course 57
3.2 Plot of errors in speed determination using GPS (Doppler shift-top figure) or GPS (∆ distance/time-bottom figure) over a curved path 58
4.1 Overhead picture and schematic showing section length, average gradients and subsection divisions for one lap of course 73
4.2 Changes in speed, kinematics and physiological variables across three laps of an undulating course 81
4.3 Speed changes on level sections following uphill or downhill running 82
4.4 Individual pacing strategies showing relative differences in speeds across (top) gradients and (bottom) laps 83
5.1 Experimental Design (A) and Schematic (B) of self-paced and researcher-paced field trials 102
5.2 Overhead picture and schematic showing section length, average gradients and subsection divisions for one lap of course 103
5.3 Speed on uphill/downhill sections expressed as the difference from the mean level speed 112
5.4 Oxygen consumption (VO2) on uphill/downhill sections expressed as the difference from the mean VO2 on the level 112
5.5 Total time to complete course across different conditions 113
5.6 Time to complete lap four following the paced conditions expressed as the difference from the self-paced trial 113
A2 Differences in displacement of the GPS receiver at three different locomotion speeds 159
A3 Spatial distribution of GPS positions relative to known geodetic point 160
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LIST OF TABLES
Table Page
2.1 Studies of self-paced strategies in distance running 35
2.2 Experimental pacing interventions in distance running 36
3.1 Comparison of two different GPS methods of speed determination with actual speeds using the mean of all one second values across the entire 20-60m straight section 56
3.2 Comparison of GPS speed determination with actual speeds before and after corrections for reductions in GPS displacement due to leaning 56
4.1 Demographic and physiological data for participants 79
4.2 Kinematic and physiological variables across sections and laps 80
5.1 Demographic and physiological data for participants 109
5.2 Comparison of speed on laps/gradients between conditions 110
5.3 Comparison of VO2 on laps/gradients between conditions 112
A1.1 Pacing adherence on intervention trial using different criteria 149
A1.2 Pacing adherence on control trial using different criteria 150
A1.3 Individual pacing adherence across different gradients 151
A1.4 Group pacing adherence as a function of gradients 152
A1.5 Wet Bulb Globe Temperature for each trial 153
A1.6 Assessment of adherence to pacing by different criteria: INT trial 154
A1.7 Assessment of adherence to pacing by different criteria: CON trial 154
A4 Validation studies of GPS and DGPS during human locomotion 161
A5 Summary of regression weightings for group and individual subjects 163
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ABBREVIATIONS
GPS Global Positioning System
HR Heart rate (beats per minute, bpm)
VO2 Volume of oxygen consumed (L/min)
VO2 max Maximal Oxygen Consumption (mls.kg.min -1)
VT Ventilatory Threshold (L/min, % of VO2 max)
vVO2 max Speed at point of Maximal Oxygen Consumption (m.s -1, km/hr)
vVT Speed at Ventilatory Threshold (m.s -1, km/hr)
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STATEMENT OF ORIGINAL AUTHORSHIP
The work contained in this thesis has not been previously submitted to meet the
requirements for an award at this or any other higher education institution. To the best
of my knowledge and belief, the thesis contains no material previously published or
written by another person except where due reference is made.
Andrew D Townshend Date
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ACKNOWLEDGEMENTS First and foremost I thank my principal supervisor Charles Worringham for providing the
initial encouragement to embark upon this journey. Throughout this process I have
benefitted from your diversity of knowledge, sense of humour and calm manner. You
always knew when to assist and when to encourage independence to aid me in my learning
process as a researcher.
I also wish to thank my associate supervisor Ian Stewart, for your honest appraisals and
pragmatic approach which always kept me on track as well as the patience and
understanding you displayed when I needed it the most and the self-belief you always tried
to engender in me.
QUT (APA) and the Australian Research Council (APAI) are gratefully acknowledged for
providing much needed financial support. Sincere appreciation is also extended to Alive
Technologies for financial and technical support provided in the early stages of my PhD.
I am deeply indebted to all my participants. Your enthusiasm and good humour when asked
to run up hills early in the morning made the trials possible and enjoyable.
Thanks also to all the postgraduate students for their empathy, assistance, encouragement
and welcome distractions, especially Mandy, Corey, Emily and Sandi.
Sincere thanks to my parents for their support and encouragement. Thank you Dad for
enabling me to have the types of opportunities you never had and Mum for continually
inspiring me to do my best and realise my potential in every way.
And last but not least, thankyou to Adam and Brandon for providing an unwavering source
of motivation and inspiration. You always provided me with a sense of perspective and a
reason to smile at the end of the most demanding or tiring of days. I couldn’t have
completed this without you.
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1 GENERAL INTRODUCTION
A long-term goal of research in locomotion is to understand the physiology and
biomechanics of the organism when it is moving freely in a natural environment. A
particular challenge in this scenario is to understand the determinants and constraints
which affect the self selection of locomotion speeds. In early man, the need to select these
speeds effectively may have been essential for survival, either to hunt or scavenge
successfully (21) or to escape from prey or changing weather conditions. In modern day
humans, the need to optimize speed selection is particularly important in endurance sports
such as distance running, where it is crucial in order to minimize the time taken to complete
the given distance.
While the duration of the event will play a major role in the selection of the overall average
speed, running outdoors also requires the runner to vary speeds continually in response to
changing conditions. This may include alterations in temperature, head or tailwinds, varying
surfaces, and positive and negative gradients of varying degree and length. Of these factors,
gradients pose a particular challenge as they may lead to large changes in speed which have
a significant effect on energy expenditure.
While many aspects of distance running have been extensively researched (12, 13, 22, 89,
90, 101, 116), there are few studies on the self selection of speed (93, 141). A particular
problem is that speeds selected in the majority of studies are determined by the researcher
and paced by the use of the motorized treadmill. Conversely, outdoor studies which allow
spontaneous speed selection have generally been restricted to level courses, thus excluding
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analysis of speed changes as a function of gradient (14, 52, 141). Therefore the available
literature on self-selected speed is very limited.
Only two studies of distance running have investigated self-selected speeds over hills and
both had methodological limitations considered later in the review of literature (Chapter
Two). Staab et al (123) measured the energy cost of preferred speeds over positive and
negative gradients during trials on the motorized treadmill. Although runners adjusted their
speeds inversely with gradient, this was insufficient to achieve a consistent level of energy
expenditure. In contrast, Mastroianni et al (84) examined natural speed changes on an
overground course but found a surprisingly small proportion of speed could be explained by
gradient.
This paucity of studies leaves many questions unanswered regarding the way in which
runners manage trade-offs to spontaneously modulate their speed in hilly terrain. For
example, one key trade-off is the way in which runners balance the minimization of time
with the need to select an optimal level of energy expenditure, while another is the
selection of an appropriate combination of stride length and stride frequency to produce
these speeds. Accordingly, this thesis aimed to investigate the way in which runners
modulate their speeds in an effort to understand the key processes and determinants of
speed selection when encountering hills in natural outdoor environments.
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Overall Research Aims
The overall aims of this research were twofold:
1. Characterize the way in which runners spontaneously change speeds as a function
of gradient on an undulating course while simultaneously investigating the
concomitant changes in oxygen consumption and aspects of the gait cycle.
2. Determine whether an individually prescribed pacing strategy which varied speeds
at frequent intervals to account for hills and transitions between gradients could
improve performance compared with a self- paced run.
More specific aims for each study are presented in the relevant chapters.
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2 LITERATURE REVIEW
2.1 Introduction This review of the literature will examine current knowledge on the way in which runners’
self-select speed. The initial section will examine the role that potential regulatory
mechanisms play in the continuous self-selection of speeds. Next, the characteristics and
determinants of gait parameters which produce these speeds will be examined. Finally
empirical evidence will be presented on spontaneous speed selection from treadmill and
field studies as well as studies which have manipulated runners’ speeds and examined the
consequent effects on physiological responses and performance.
2.2 Regulation of speed It is generally acknowledged that no single factor governs the regulation of sub-maximal
endurance running speed (134). While a range of factors have been shown to be involved in
the process of modulating one’s effort (and therefore speed), the influence of some are
only prominent under certain conditions. For example, humans have been shown to
routinely decrease exercise intensity in order to prevent core temperature reaching
excessive levels, with a proposed critical ceiling of approximately 40 degrees Celsius (99,
103). A similar decrease in intensity is shown when the availability of energy substrates,
such as glycogen, is limited (110). These factors, however, may play a limited role in speed
regulation for brief exercise durations or in cool environments. Related to these internal
factors are external variables such as the terrain or the presence of hills which may also
play a role in regulating speed. A decreased perception of stability (32) as may be
experienced on uneven terrain (84) or increased eccentric loading (9) experienced when
running downhill (87, 88) have both been shown to decrease running speeds in these
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specific conditions. While factors such as these may be more influential under specific
conditions, one regulator that is prominent under all conditions is the selection and
appropriate management of energy expenditure (measured indirectly by oxygen
consumption). The role of energy expenditure in speed selection will thus be the focus of
the following section.
Energy Cost
Optimal performance requires a continuous trade off between speed and the resultant
energy cost, which in turn involves appropriate contributions of aerobic and anaerobic
metabolism. Thus optimal performance is constrained by a range of variables. While
external factors such as gradient and task duration will be considered later in the review,
the primary internal factor which governs selection of a suitable speed is the individual’s
current physiological capacity. When attempting to minimise time, runners thus need to
select a running speed that corresponds to the highest level of oxygen consumption they
can sustain for the required duration (35). This relationship between performance time and
energy cost can be represented by a parabolic shaped function (Figure 2.1). When speed is
too low (shown at ‘A’ on the descending portion of the curve), the time cost exceeds any
time saving due to the lower energy expenditure. Conversely, if the speed selected is too
fast (‘B’ on the ascending part of the curve), the rate of energy expenditure will exceed the
individual’s current aerobic capacity, ultimately causing them to slow excessively, thus
incurring a time cost that more than offsets the gains of the preceding period at a higher
speed. To minimise time, an energetically optimal speed (EOS) must be selected.
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Time
Energy Cost
Figure 2.1: Theoretical representation of time v energy cost
Optimal energy cost - running v walking
Though energy expenditure during walking has been extensively studied , the dynamics of
walking and running differ in several important respects which make it difficult to apply
optimization principles to running that have been identified for walking. Mechanically,
walking can be likened to the motion of an inverted pendulum where the work to move the
body segments in sequence is produced by the exchange of potential and kinetic energy
(114). As a result, the relationship between metabolic cost (as measured by VO2) and
walking speed has been found to fit a quadratic expression when VO2 is expressed as an
energetic cost per unit of distance walked (68). Accordingly, walking has an optimal speed
of approximately 1.1- 1.2 m.s-1 (corresponding to 2j.kg-1.m-1), which is close to the speed
that is self-selected by humans (114). Conversely, running has been described as a bouncing
spring where work is produced by the exchange of elastic energy (114). Although
mechanical power increases monotonically with increasing running speed, (28) the energy
cost of running a unit distance relative to mass is approximately the same across a wide
range of sub-maximal speeds (about 4 j.kg-1.m-1) (27, 74, 114) Thus the relationship
A B
EOS
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between energy cost and speed is linear with almost zero slope, although this linkage may
cease to be linear at extremely low and high running speeds (24).
It has been proposed that humans have evolved to select gait patterns (and thus speeds)
that minimize energy cost and that individually people learn energy saving behaviours
through trial and error and adapt their patterns of movement accordingly (86). It is likely
that the speed-energy cost trade-off is regulated continuously throughout exercise in
response to a range of feedback signals (132). Analysis of world record performance in
distance running events shows that runners routinely increase speeds in the final stages
(133). This suggests that runners must be conserving energy resources sufficiently to allow
this brief acceleration towards the end of their event (acknowledging that part of this
increase is obviously met by an unsustainable use of anaerobic energy stores). This implies
that runners are modulating efforts based on a perceived end-point. It is has been
suggested that when exercise duration is known, humans often subconsciously pre-set their
exercise intensity (termed teleo-anticipation) based on prior experience of what is required
to complete the exercise duration within the biomechanical and metabolic limitations of
the body (60). Thus optimal speed control likely commences with a ‘feed-forward’
selection of pace based on event duration, current fitness levels and prior experience, and
is subsequently regulated in response to afferent feedback from internal and external
sources.
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Effect of gradient on VO2
While knowledge of the exercise duration and experience may contribute to the selection
of an energy efficient pace on level ground, running overground frequently entails changes
of gradient and non-linear paths, which may play an increased role in the trade-off between
energy cost and speed. When free to vary their speed in treadmill studies, runners have
been shown to vary speeds inversely with gradient as expected but are unable to balance
speed changes sufficiently to achieve a consistent level of energy expenditure (123). Staab
(123) reported that although runners decreased speeds on uphills this was not enough to
affect increased anaerobic metabolic demands as evidenced by higher levels of blood
lactate compared with level sections. Conversely, though they increased speeds on
downhills this did not prevent a fall in oxygen consumption. This confirmed findings from
other treadmill studies that downhill speed is not limited by energy cost (83, 94). This study,
however, was not without limitation as speeds were adjusted manually by verbal direction
to a tester which does not accurately represent the spontaneous fluctuations experienced
during normal outdoor running. Conversely, Mastroianni (84) reported that runners’
relative effort (measured as % of VO2 max) was not related to gradient, suggesting that
runners attempted to achieve a constant level of energy expenditure. This conclusion was
weakened, however, by its method of calculation which used heart rate data and a heart
rate to oxygen consumption regression developed from earlier laboratory trials. In addition,
the short length of hills and sudden transitions between changing gradients limited
conclusions drawn on the speed-gradient relationship.
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With energy cost thus affected by a range of factors, variations in the relative contribution
of aerobic and anaerobic energy systems to meet the demands of the exercise task can be
expected. Accordingly, various physiological measures can be used as indicators of the
potential running speed which may be achieved or maintained for varying durations. One of
these is the anaerobic threshold.
Anaerobic threshold
When running continuously for longer than three to five minutes, aerobic metabolism
contributes the largest proportion of an individual’s energy supply (66). If an individual
attempts to run too fast in events of this duration, the rate of energy supply will be unable
to be met purely through these means and there will be an increased reliance on anaerobic
metabolism. This can be maintained only briefly, as the body’s mechanisms for lactate
removal will be inadequate to accommodate the rate of lactate produced. As a result, the
accompanying accumulation of lactate will cause the runner to slow down in order to
continue. The point at which this accumulation commences is generally referred to as the
“anaerobic threshold”(124). Among the multitude of studies of anaerobic threshold, many
provide indirect evidence that the selection of energetically optimal speeds (EOS) for
distance running are related to this marker (126). Runners who exceed this speed are
represented by the ascending portion of the curve in Figure 2.1.
The concept of anaerobic threshold and its determination is the subject of considerable
debate (23). The two most commonly used determinants of this proposed threshold are
derived from changes in either levels of blood lactate or respiratory variables.
Unfortunately, efforts to relate an individual’s anaerobic threshold with self-selected
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running speed have been problematic. For example, during a road relay, Zamparo et al
(141) found that variation in overall running speed was lower than variation in speed at the
lactate threshold, concluding that factors other than avoiding lactate accumulation must
dictate the speeds selected. The lack of a clear finding may also be a reflection of the
validity of the marker chosen for comparison. In this study, the anaerobic threshold was
defined by the onset of blood lactate accumulation (OBLA) at a measure of 4mM. This use
of an absolute lactate marker to represent an the anaerobic threshold can result in
inaccurate conclusions however, as it is insensitive to individual differences (124).
A major obstacle to the assessment of anaerobic contribution to energy expenditure
includes the practical difficulties of measuring all the relevant variables (23). For example, it
is dependent upon knowledge of the concentrations of ATP, CP, muscle glycogen and
lactate, the total water pool in the body available for lactate uptake, the distribution
between extra and intracellular water and the amount of exercising muscle mass (7). There
is also a lack of consensus surrounding the definition of an exact speed-energy cost
relationship at higher running speeds, although it is likely that this may be non-linear as
individuals are not in a “steady state” (107).
Fractional utilization of VO2 max
A less problematic approach to characterizing the relationship between running speed and
energy cost is by assessing the relative quantity of VO2 max used at sub-maximal speeds.
The highest proportion that is sustainable for a given distance was coined “fractional
utilization of VO2 max” by Costill et al (35) and is expressed as a percentage of a VO2 max
calculated for an individual during a progressive incremental test.
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Fractional utilization of VO2 max varies with the duration of the event and the capacity of
the individual and has been proposed as a primary determinant of running speed, especially
between runners with a similar VO2 max. This relative VO2 cost can be used as a predictor
of running speed in two ways. Firstly, faster runners have been found capable of
maintaining a higher percentage of VO2 max than slower runners for the same distance
(138) . Secondly, the economy or efficiency of runners can be compared by analyzing their
relative oxygen uptake per unit of mass and distance at relevant sub-maximal speeds.
Running economy has been shown to play a key role in the variance in speeds between
runners with similar VO2 max values. For example Scrimgeour et al (120) found that
variation in running economy between runners readily explained differences in their speeds
at each of several distances between 10 and 90 kms. Other research has detailed a
continuum of fractional proportions sustainable for different durations. This ranges from
85% of VO2 max during a 10km race (37), 75% during a marathon (34) , and approximately
65-75% for continuous runs of up to 4 hours (43). It is acknowledged, however, that a
comparison of absolute maintainable oxygen consumption requires that athletes have a
similar VO2 max as a significantly lower value will influence the relative intensity that can be
achieved. While providing a broad description of the relationship between relative oxygen
consumption and exercise duration, there is no information presently available as to how
this varies as a function of gradient when speeds are self-selected. The self reports of
competitive runners, however, suggest that even relatively modest gradients encountered
during long-distance events, (e.g. “heartbreak hill” in the Boston Marathon), can greatly
perturb attempts to maintain a constant energy expenditure.
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Heart rate
As an indirect measure of oxygen consumption (and thus energy cost), heart rate is often
used as a measure of physiological effort. Zamparo (141) has reported that runners self-
select speeds during level road running which minimize heart rate variation. This was
further supported by Mastroianni (84) on a hilly course who reported no relationship
between relative effort (estimated by heart rates) and hill grade. Both studies have
proposed that this reflects an attempt to maintain a constant level of energy cost. Esteve-
Lanao et al (48) has further shown that the relative heart rate (% maximum HR) profile was
similar between faster and slower runners, varied systematically with race distance and was
regulated by variations in running pace. There are, however, a number of factors to
consider when examining the heart rate-running speed relationship. It is widely known that
various physiological, environmental and psychological factors can affect heart rates. For
example, in constant exercise where intensities exceed the lactate threshold, a slow
component is evident and heart rates gradually increase (cardiac drift). Proposed causes
include an increase in catecholamines via stimulation of the sympathetic nervous system
resulting from increases in body temperature or dehydration (19). As a result, the heart
rate – running speed relationship changes with the duration of effort during high intensity
continuous exercise; if running speed is constant, heart rate increases over time, if heart
rate remains constant, running speed decreases over time (19). Variation has also been
noted between heart rates in competition and training at the same speeds which cannot be
explained by differences in terrain or psychological stress (121), while endurance training
can result in a decrease in sub-maximal heart rates at similar speeds due to increases in
stroke volume. As heart rate is subject to variation due to these and other factors, it is clear
that running speed and heart rate are not perfectly related (19). Despite these limitations,
heart rate may also play a role in effort regulation regardless of its association with energy
26
expenditure. Billat et al (15) reported maintenance in the similarity of heart rate variability
between trials and suggested that this may be indicative of its role as a feedback signal
which is used to minimize cardiovascular strain during exercise.
Summary
Despite the lack of agreement about the exact nature of the speed-energy cost
relationship, and the continued debate about the best measures, it is widely accepted that
runners select speeds for distance events in a way that reflects this relationship in a
predictable and individual manner. Whatever principles of energy expenditure are finally
determined as appropriate predictors of running speed, there are other aspects of speed
regulation that are not well understood. One of these is the question of how, at any given
speed, a runner will select the key biomechanical determinants of running speed, i.e. stride
length and stride frequency. In the following section of the review a series of factors will be
outlined which influence the selection of these fundamental gait parameters.
2.3 Regulation of gait parameters In the biomechanical analysis of gait, speed is commonly expressed as the simple product of
the number of gait cycles and the distance covered in each cycle, i.e. stride frequency
multiplied by stride length. Accordingly, running speed can be altered by changing either
one or both of these parameters. The factors that regulate which combination is selected is
however, not completely known (107). The following section reviews findings on the
characteristics of these gait parameters and their suggested regulators before discussing
some of the limitations in this field of research.
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Stride frequency
Stride frequency can be increased in two ways: either (i) decreasing ground contact time
and/or (ii) decreasing the time to reposition the limb for the next step (139) . Of these two,
the primary component is swing time as it represents the majority of total stride time (139).
Stride frequency has been found to be a stable and relatively invariant property with less
than 5% difference within individual distance runners across different days, speeds,
gradients or due to aging (detailed below). Brisswalter et al (23) reported stride frequency
to be the most stable of various physiological and kinematic parameters assessed during
sub-maximal treadmill running with a day to day variance of only 0.2-2.6 strides/min in
trained middle distance runners. Conoboy et al (33) also found that although running
speeds decrease with age, there is minimal variation in stride frequency, with less than 2%
difference between the stride frequency of older (60+) and younger (< 40) runners during a
marathon. The range of stride frequencies used by runners across different speeds has also
been shown to be narrow, with a study by Cavanagh and Kram (29) reporting only a 4%
increase as speeds increased from 3.15 – 4.12 m .s -1. Minetti et al (93) extended these
findings to gradient locomotion using a novel feedback-controlled treadmill, and reported
less than 5% variation in self-selected stride frequency from 0-10% gradients while speed
and stride length steadily decreased. It is suggested that this near independence of stride
frequency observed with speed and gradient is a reflection of the specific biomechanical
characteristics which differentiate running from walking (92).
Despite the finding of these low levels of individual variance across a range of conditions,
the determinants of self-selected stride frequencies are less clear and a range of factors has
28
been suggested, including various physical characteristics as well as the minimization of
energy cost and mechanical power.
Determinants of stride frequencies
Higher stride frequencies have been found in runners with a higher proportion of fast
twitch muscle fibres (6, 36) . Though this may suggest a genetic influence on the ability to
achieve a higher cadence, repositioning the limb during running is mainly achieved through
passive means by elastic recoil and inter-segment energy transfers (74), rather than power
generated actively by the muscles (135). Accordingly, muscle fibre types are unlikely to
greatly affect the minimum swing time (139). Cavanagh and Kram (29) compared the
relationship between various anthropometric characteristics and gait parameters of male
recreational distance runners during treadmill running at 3.15-4.12 m.s-1. No significant
interaction was found between stride frequency or stride length and leg length, height or
leg segment mass. These results suggest that anthropometric characteristics cannot be
used to predict stride frequency or stride length on an individual basis (29).
In contrast, analysis of constant speed running between 9-16 km.hr-1 has found that oxygen
consumption is minimized near the freely chosen step frequency (28). Cavagna (28) has also
showed that at speeds of less than 13km.hr-1, energy is saved by selecting a stride
frequency in line with the apparent natural frequency of the body’s ‘bouncing system’ (2.6-
2.8 Hz) even if this requires a mechanical power larger than necessary. Thus, for constant
speeds, it is suggested that people choose the stride frequency that minimizes energy
consumption.
29
Stride length
As stride frequencies have been found to be relatively invariant across a range of speeds
and gradients (64, 93), it is unsurprising that most studies point to regulation of stride
length as the main determinant of running speed (33). The primary determinants of the
specific stride lengths selected have been attributed to different factors, which are outlined
below.
Determinants of stride lengths
Links between physical characteristics and stride lengths are conflicting. As noted earlier,
Cavanagh and Kram (29) found no link between stride length and either leg length or leg
mass. Conversely, longer limbs have been shown to increase stride length by resulting in a
greater forward propulsion (71, 135), suggesting that physical characteristics may play
some role. Despite this sprinters have been found to take longer strides than non-sprinters
without having longer legs (6) so anthropometric characteristics are unlikely to be the only
determinant of variations in runner’s stride lengths.
As with stride frequency, it is suggested that runners freely select the stride length which
minimizes energy cost at any given speed (86). This claim has been supported by studies
which have shown that the aerobic demand of running increases when stride lengths are
shorter or longer than preferred (30), Kaneko, 1987. Conversely, Morgan (96) showed that
a number of runners exhibit uneconomical stride lengths. Consequently, their study
successfully used audiovisual feedback to adjust these runners’ strides to more economical
lengths thus reducing the aerobic demand of their running at any given speed. It is been
30
suggested that this deviation from optimal stride lengths may be an individual characteristic
reflecting differential responses to other factors, such as the attenuation of shock.
Research by Mercer (87) has shown that shock attenuation was only altered with changes
in stride length rather than frequency. Hamill et al (59) has suggested that this would be
most relevant to individuals with injuries or other pathologies as they may choose to
forsake maximising oxygen consumption and choose gait parameters which maximise shock
attenuation and protect injured structures. This may also apply to healthy individuals when
running on downhill gradients. In support of this, research by Minetti (94) showed that on
extreme downhill slopes, runners choose speeds approximately 30% lower than
energetically optimal. As stride length is known to provide the largest contribution to
alterations in running speed, this suggests that during sufficiently steep downhill gradients,
shock attenuation may be a stronger determinant of preferred stride lengths than energy
cost even within healthy individuals.
These variations in stride length due to gradients and possible shock attenuation contrast
sharply with the relatively invariant reports for stride frequencies across a range of
conditions. Fatigue and aging have also been reported to contribute to short and long-term
variation in stride lengths respectively within individual distance runners, though reports on
the latter are conflicting. Conoboy (33) noted that decreases in speed between older (60+)
and younger (40-49) runners during a marathon race could only be attributed to changes in
stride length rather than frequency. Differences in reported changes of stride length
because of fatigue may be due to variations in the duration, intensity and protocol used in
the analyses. Elliott (46) found stride length to decrease due to fatigue during track
31
running. Conversely, Gazeau et al (54) found increases in stride length over time during a
run to exhaustion at VO2 max pace on the treadmill. A recent study by Hayes (63) however,
further highlights the extent of individual factors as their results showed considerable intra-
individual variability with some runners increasing stride length due to fatigue, others
decreasing and others remaining the same.
Summary
In summary it appears that stride frequency is relatively invariant across a range of
conditions and its selection may be determined by both physiological and biomechanical
factors. Research suggests that frequencies selected may be based on minimizing both the
external mechanical power per step as well as the metabolic energy cost (28). In contrast,
changes in speed have been shown to be regulated primarily through alterations to stride
lengths. The most likely candidate for the selection of stride lengths during running appears
to be the minimization of energy cost, as preferred stride lengths are usually the most
economical, however the determinants of this parameter may change based on conditions
such as gradient or vary between or within individuals due to fatigue, aging or the need to
attenuate shock.
The literature on stride length and stride frequency is still incomplete. In particular, the
effects of gradient rely on treadmill studies using imposed speeds. Conversely, studies
allowing speeds to be self-selected generally occur on flat courses. It remains unclear
whether these principles apply in the same way when runners are free to self-select speed
and encounter changing gradients.
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In addition to understanding the regulators that determine the selection of gait parameters
(and speed) a thorough understanding of speed selection also requires knowledge of the
way in which people distribute their speed across an exercise bout. This distribution is
termed a “pacing strategy” (50) and has been studied in a range of time based events such
as cycling (51, 65), running (14, 55) , swimming (128) and rowing (53) Findings from studies
such as these are reviewed in the following section.
2.4 Pacing strategies Pacing strategies can be broadly categorized into either positive (speed declines throughout
the event), negative (speed increases towards the end of the event) or even pacing. When
technology allows analysis of smaller time segments, pace variations can be identified
which may reflect a range of variable strategies; i.e. starting and finishing faster while
slowing in the middle stages of the event (a parabolic shaped speed curve) or more subtle
variations from an even strategy (2). Such pacing strategies may reflect both a conscious
and unconscious regulation of speed in response to internal (physiological, biomechanical)
and external (distance, gradient, competition) factors. Investigations of pacing have
generally used one of two approaches. Firstly, observations of self-paced events have been
conducted to understand the systematic variations self-selected during races or simulated
time trials. Alternatively, other researchers have conducted experimental trials where
athletes are constrained to different strategies to compare the effects on performance
and/or the accompanying physiological responses. A summary of these two types of pacing
studies are presented in Table 2.1 and Table 2.2 respectively. Findings from both of these
models will be explored to illustrate current knowledge in pacing.
33
Self pacing in short duration events (approximately < 10 minutes)
Mathematical modeling has provided evidence that athletes may benefit from a positive
pacing strategy in short duration events (135). Observations from swimming (127) cycling
(50) and speed skating (50) have further confirmed that elite athletes naturally adopt these
strategies in competition. It has been suggested that the prime reason that athletes adopt
a fast start strategy in events of this duration is to minimise the time spent in the
acceleration phase (2). While the role of aerodynamics or the effects of frictional or drag
forces play a decreased role in running when compared with these other sports, analysis of
elite runners in an event of similar duration (800m) has also shown the dominance of a
positive pacing strategy in 24 of the last 26 world records (133). While the need to minimise
acceleration time may influence the selection of a fast start in these events, it has been
shown that such strategies result in an increased oxygen consumption (115) and
accumulation of fatigue related metabolites (128) which may result in the latter stage
decrease in speed and a positive split race profile.
Conversely, in events which take longer than approximately four minutes to complete,
there appears to be a transition in the adopted strategies. Analysis of running events from
1500m to 2413m has shown that speed changes fitted a parabolic shaped curve, with a fast
start, a slower middle section before increasing speed again towards the end. This pattern
has been consistently shown in solo track trials (41, 61, 69) or in the presence of
competition (100). This pattern is also seen in other events, as shown, for example by
analysis of race profiles in the 2000m rowing event at the 2000 Olympics although the
increase in speed in the latter stages was not as large (53). Such ‘parabolic’ pacing
strategies are likely to combine elements of positive and negative pacing. While the former
34
is in keeping with a minimisation of acceleration time as for shorter events, the increase in
speed at the finish is likely to reflect a conscious harbouring of resources that if judged
optimally, enables the athlete to exhaust their anaerobic capacity upon (but not before) the
termination of the event (16).
Experimental manipulations of pacing-short duration events
In an effort to gauge whether the strategies adopted by athletes are optimal, a number of
studies have manipulated starting speed with mixed results on overall performance. The
most frequently cited study focused on performance of a brief duration (2-3 minutes) and
was conducted with cyclists during a 2000m ergometer time trial (51). Foster et al (51)
reported that an even paced strategy in which the first half of exercise was completed in
51% of the total time was more effective than a fast, very fast or very slow start. In
contrast, other studies have reported that a faster start is more effective. Ariyoshi et al (5)
compared trials in which runners covered 1400m in four minutes under fast-slow, even
pacing, and slow-fast conditions, followed by a time-trial to exhaustion (TTE) at a constant
speed. Six of the eight runners were found to run further during the TTE following the fast-
slow pacing condition, although this outcome measure has been shown to exhibit a much
higher level of within-individual variability compared with time trials in distance runners
(80). Support for a faster start has also been shown by Bowles et al (20) in a field study over
one mile where runners who ran the first quarter five seconds faster had better
performances compared with a comparatively slower start or even pacing. Though these
two studies offer persuasive evidence of the benefit of a faster start, both findings are
limited by the fact that mean speeds for the even paced trials were based on arbitrarily
Table 2.1: Studies of self-paced strategies in distance running
Author (year) Distance (m) or
Duration (mins)
Surface/terrain Subjects Observed strategy
Tucker et al (2006) 800m Track (level) 26 world record holders Positive
Hanon et al (2008) 1500m Track (level) 11 elite middle distance Fast start & finish
Noakes et al (2009) 1609m Track (level) 32 world record holders Fast start & finish
Crouter et al (2001) 1609m Track (level) 15 trained cross country Fast start & finish
Jackson et al (1981) 2413m Track (level) 67 college aged males Fast start & finish
Nummela et al (2008) 5000m Track (level) 18 trained distance Fast start & finish
Tucker et al (2006) 5000m Track (level) 32 world record holders Even with “endspurt”
Staab et al (1992) 30 minutes Treadmill (hills) 11 trained N/A-times constrained
Mastroianni et al (2000) 8250m Trail (hills) 10 recreational Positive
Tucker et al (2006) 10000m Track (level) 34 world record holders Even with “endspurt”
Ely et al (2008) 42200m Road (level) 219 elite marathoners Even: winners
Positive: others
Lambert et al (2004) 100000m Road (level) 67 elite ultra marathoners Positive
Table 2.2: Experimental pacing interventions in distance running
Figure 3.1- Plot of errors in speed determination using GPS (Doppler shift- top figure) or GPS (∆ distance/time -bottom figure) over a straight course. Mean error of the measurement and the 95% confidence limits are indicated by the central and outer broken lines respectively.
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Figure 3.2- Plot of errors in speed determination using GPS (Doppler shift- top figure) or GPS (∆ distance/time-bottom figure) over a curved path. Mean error of the measurement and the 95% confidence limits are indicated by the central and outer broken lines respectively.
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3.4 Discussion
The current study showed that non-differential GPS offers an accurate estimation of speed
and displacement in addition to static position during human overground locomotion.
Speed measured by Doppler Shift was found to be more accurate than differentiating the
unit’s distance output as a function of time, while errors were slightly increased around
bends.
Non exercise science fields such as engineering and studies of vehicular motion require
higher levels of static and dynamic accuracy from GPS receivers. Using high precision
geodetic receivers, sub-centimetre static positional accuracy has been reported in research
to detect deflections in long bridges (111), while dynamic measurements in the study of
vehicle states have reported velocity measurements with errors as low as 0.05m/s (11).
Locomotion research does not usually require this high level of accuracy; however a
comparison of the measurement precision achieved requires consideration of a range of
factors that can vary between human validation studies. This can include variations in the
type of receiver employed (differential, non-differential, WAAS enabled), the sampling
frequency utilised and the measurements assessed (speed by Doppler change, speed by
positional change, displacement, static position etc). Accordingly, a summary of the various
characteristics of previous GPS validation studies is included in Appendix 4.
When assessing speed using GPS, an important consideration is the time interval over
which to average measurements as this will often vary based on the requirements of the
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investigator. While measuring variation of speed within the gait cycle requires high
frequency receivers such as those used in Geomatics (50-60 Hz), comparison of speed
variations across long periods of data collection such as endurance activities (78, 79), can
involve averaging over intervals of seconds or even minutes. Similarly, comparison of speed
changes with relatively slowly changing physiological processes does not require data to be
collected at especially high frequencies. The current study was wholly concerned with
validation of the unit’s determination of speed. Accordingly, the raw, individual GPS values
were compared as this offered the most challenging test of the system’s performance. The
sampling frequency of 1Hz meant that the number of actual values collected within each
10m section ranged from a single value during the highest speeds to as many as nine data
samples during slow walking.
As the determination of actual speeds (using timing gates) still relied on average speed, a
number of steps were taken to minimise comparison errors. Firstly, the gates were placed
10m apart as this was the smallest distance which would ensure at least one sample would
be recorded within each interval at the highest speeds. To be confident in the validity of the
reference value, it was also imperative that there was minimal variation in speed, thus
sections were only compared where speeds varied by less than 2% from the preceding
section. Using the median speed values of 5 m.s -1, this represents a difference of less than
0.1 m.s -1 which is comparable to the proposed error of the system. Using these methods,
the highest level of precision was found using speed determined by Doppler shift with over
90% of values within 0.1 m.s -1 of actual speed. This represents an improved performance
relative to the study of Witte and Wilson (140) who reported errors in excess of 0.2 m.s -1
for 43% of values during straight trials, despite their study using reference values obtained
over shorter intervals
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Changes in satellite geometry are related to the accuracy of the position fix in terms of
latitude and longitude. Accordingly, it has been suggested that that this may also be
reflected in the accuracy of speed measurements(140) . These changes in satellite
availability are expressed by the Horizontal Dilution of Precision (HDOP) which is dependent
on the number of satellites used and their position, with a spread of satellites about the
horizon producing higher positional accuracy than many at the zenith (78). Higher
positional accuracy is reflected by a lower HDOP value, with values approaching 1 most
accurate, while a value of 50 would be considered unreliable). Despite this, HDOP values
were extremely low throughout this study (range 0.8-1.3) and showed no relationship with
speed errors. This finding agrees with Witte & Wilson (140) who also found no significant
relationship between HDOP and the accuracy of speed measurements.
Real human locomotion often involves walking and running around winding paths, hence it
was necessary to examine the systems performance over a course involving bends. As
found in previous studies (140), this study found GPS to slightly underestimate speed on a
curved path, with error increasing at higher velocities. Correcting data to account for lean
angles reduced the magnitude of these errors (see Table 3.2). Adjustments due to lean
were based on observations at only one location (the first timing gate). As this may over or
underestimate the average lean throughout the trial, the raw data is also presented (Table
3.2). Errors increased marginally when calculating speed by changes in GPS position over
time when compared with Doppler shift (Figure 3.2-bottom figure). This can be attributed
to the determination of the route as a series of chords inside the curves which would tend
to underestimate speeds (especially at higher velocities) as has been previously noted
(140). The bends involved in the curvilinear course used in the current study (radius 10m)
are in excess of those that are likely to be consistently experienced during outdoor running,
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yet the performance by this method still offered greater than 90% of adjusted values with
errors less than 0.2 m.s -1.
This study extends the only other validation study of GPS speed measurements using a non-
differential GPS since the removal of Selective Availability (140) in two ways. Firstly, by
assessing performance during human locomotion, where the braking and propulsive
characteristics within the gait cycle differ from the more continuous motion of cycling, and
additionally, by characterising specific performances at velocities more representative of
locomotion. Future validation studies using locomotion should look at further
improvements in the precision of the reference method, as more comprehensive
biomechanical studies may be able to employ non-differential GPS. As GPS chips are now
becoming commercially available with higher sampling frequencies, further validation may
also be needed to assess their impact upon speed determination with non-differential
receivers.
This study confined its analysis to the performance of only one model of GPS receiver.
Potential users of GPS for locomotion research would, in theory, have to repeat validation
procedures similar to those used here if alternative receivers are used. Clearly this is not
always practical. The following steps may, however, give the user some assurance of valid
data. A) Depending on the user’s accuracy requirements, the manufacturer’s specifications
for position and velocity error should meet or exceed those for the unit studied here if
comparable accuracy is required. B) Measurement of a stationary receiver over a period of
hours gives important basic information about error variance and drift, even if no geodetic
reference point is available. C) In many locations, accurately surveyed geodetic reference
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points are available and marked in public locations. This allows absolute position error to
be assessed. D) Velocity error is harder to assess, but a starting point would be to compute
average velocity over an accurate straight line course, such as the 100m straight of a
running track. This provides a straight reference line, a known distance, the possibility of
electronic timing accurate to 0.01 s, and in general, very good satellite availability.
Systematic average velocity errors should be apparent, even if assessing instantaneous
velocity errors is not feasible. This procedure has the additional advantage of providing
high precision evaluation of displacement.
The high level of measurement accuracy and portability of GPS offers the potential for a
broad range of applications across many scientific disciplines. The accurate measurement
of speed and displacement in the field enables an opportunity to conduct sports-specific
testing in the natural environment of the athlete, rather than the controlled environment
of the laboratory (77). Within the field of exercise science, the use of GPS in conjunction
with technology such as heart rate monitors, gas analysers and accelerometers can assist
field research into exercise physiology, metabolism and biomechanics (119). In addition to
the many exercise science and sports applications, this technique has many other potential
applications across clinical, rehabilitative or even occupational settings.
The positional validity found in this study would allow the researcher to relate changes in
position within a specific route to other variables of interest which can be simultaneously
measured. This could allow comparison of changes which take place when a person was
locomoting on different surfaces or within different “micro-climates”, while the accurate
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displacement data would enable examination of any aspect of data per unit distance, for
example, changes in kinematics in conjunction with step detection.
The high level of resolution in the raw speed measurements reported here would enable
even relatively subtle and short-term velocity differences to be detected. This could be
within an individual as a result of factors such as fatigue, weather conditions, gradients and
medications; or between groups, such as age cohorts, clinical intervention and control
subjects, or other groups defined by the research. For example, a change in gait speed of
the magnitude of 0.15-0.25 m/s has been established as representative of a clinical
difference in patients following traumatic brain injury (136). Similarly, a difference of
0.1m.s- 1 has been reported as significant in people with chronic obstructive pulmonary
disease or older patients with heart failure (1) while as little as 0.2 m.s - 1 differentiates
normal gait speed between healthy men in their forties and healthy women in their
seventies (18). A further advantage is the availability of continuous velocity data, which
could be of value even when average speeds over longer distances may not be reliable,
such as oscillations in speed due to environmental conditions or from different pacing
strategies in athletic events
In summary, non differential GPS receivers can provide highly accurate speed, displacement
and position data for human locomotion at varying speeds and on bends as well as
straights, while offering researchers advantages in size, weight and cost over differential
variation. It is noteworthy that distinct strategies have been observed in downhill running
kinematics (32), attributed to the conflict between the need to attenuate shock and the
requirements of controlling the stability of the head, arms and trunk. Resolving this conflict
in different ways may in part determine why some runners are capable of much faster
downhill running than others.
A final note concerning pacing strategies is that there was little if any relationship between
pacing over the three laps and pacing over the varying gradients, that is, those who
adopted a conservative strategy with respect to laps (minimising lap-to-lap energy
expenditure fluctuations by keeping average speed consistent) did not necessarily do so
over hills (minimising uphill vs. downhill energy expenditure fluctuations by increasing
speed differences on these sections) (Figure 4.4 bottom and top panels). If confirmed in
larger studies this would suggest that different factors can influence pacing at the macro
(whole distance) and micro (component section) levels.
Optimal pacing over a hilly course may thus require a more detailed analysis with strategies
varying throughout to take account of the length, type and gradient of any hills. This study
has shown that runners tended to limit uphill running to a speed which resulted in oxygen
consumption values in line with their ventilatory threshold. Conversely, there was a large
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potential to improve time on downhill sections as runners were not limited by physiological
cost. Despite this, runners may be unable or unwilling to greatly increase speeds on these
sections due to biomechanical or psychological factors already discussed. As reported
earlier, speeds on level sections have been shown to be affected by a preceding uphill or
downhill. In this study speeds on level sections following an uphill were lower than mean
level speeds for almost 80 seconds.
Conversely, while speeds were elevated for a short time on levels after a downhill, the VO2
on these sections was still well below their ventilatory threshold. One possible suggestion
for minimising time on hilly courses may be to balance the time cost of running slightly
slower uphills, with the potential time saving if runners can return to a faster speed on the
level in a shorter time frame. Similarly, runners should take full advantage of running faster
on level sections following a downhill but limit increases to keep VO2 just below their
ventilatory threshold.
Summary
This study is the first to characterise how runners regulate their speeds during a time trial
on a hilly course through the recording of continuous metabolic, kinematic and speed data.
Speed was shown to be strongly predicted using a weighted gradient factor which
accounted for the influence of prior and current gradients. This was supported by findings
on the effect of hills on subsequent level sections where a lag effect on speed persisted for
almost 80 seconds. This research has suggested that these level sections following hills
represent the most likely source of potential improvements for runners wishing to minimise
their overall time in distance races on hilly courses. Future studies should test the feasibility
93
of athletes adopting these strategies. The limits on downhill running speed and the
efficiency of various gradient-speed trade-offs on hills also warrant further investigation,
not only to enhance performance, but, more broadly, to understand the optimisation
principles that account for the self-selected choice of running speed in humans.
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5 THE EFFECT OF AN INDIVIDUALISED PACING STRATEGY ON RUNNING PERFORMANCE OVER AN UNDULATING COURSE
5.1 Introduction
As athletes approach the limits of human endurance, scientists and coaches alike seek out
new ways to improve performance. One recent focus of attention has been the selection of
an appropriate pacing strategy (2, 134). As this is only relevant when performance
outcomes are time-based, research has primarily centred on a small group of sports,
including cycling (4, 51, 65), swimming (128, 129), rowing (53, 73) and running (14, 55).
Studies of pacing during running have generally taken one of two different methodological
approaches. The first has utilised a retrospective analysis of pacing from historical data of
noteworthy athletic events (47) or during successful world record attempts (100, 133). The
alternative approach, using experimental interventions to modify pacing, has been scarcer
and generally limited to events of short durations (< 5 minutes) (5, 20, 115). Of the few
studies which have investigated the application of different pacing regimes on events of
longer durations, all have been restricted to level courses such as athletic tracks (15) or
treadmills (55). Although positive and negative gradients are a key feature of courses used
for cycling and road running, the influence of this variable on pacing has only rarely been
investigated in cycling (8, 125) and not at all in distance running.
Manipulations of pacing in running have been further limited by the use of strategies which
only alter speeds at infrequent intervals. These have generally been confined to comparing
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faster (55) and/or slower starts (5) with even paced runs. The runner is then generally
allowed to run freely towards the end of the trial to assess the effectiveness of the prior
strategies, with a successful outcome defined by a faster overall time. Alternatively,
runners have been constrained to a constant pace throughout the trial and the associated
physiological responses compared with a freely paced run (14, 15, 38, 52).
Accordingly, to more closely align pacing to the demands frequently encountered in
outdoor running, a strategy must not only account for the presence of hills, but also use a
micro-level approach, where speeds are adjusted more frequently to account for the length
and grade of hills and transitions between gradients. Accordingly, this study had the
following aims:
1. To test the feasibility of athletes adhering to an imposed strategy such as this.
2. To assess whether this imposed strategy could improve running performance
compared with a self-paced run.
3. To examine the effects of the pacing strategy on the speed-VO2 trade off over hills.
4. To investigate whether the equation developed in Chapter Four could predict speed
as effectively using a different course and group of runners.
5.2 Methods
Participants.
Six healthy, well trained, male distance runners (age 31.2 ± 8.6 years, height 182.5 ± 7.7 cm,
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weight 71.4 ± 8.4 kg) were recruited for this study from local running clubs. All runners had
completed a 10 km race in less than 40 minutes in the previous 12 months (best time: 34.6
± 2.5 minutes). Individual participant data is listed in Table 5.1. Written informed consent
was obtained from all participants and the study was approved by the Human Research
Ethics Committee of the Queensland University of Technology.
Laboratory Trial.
All participants completed one laboratory and three field trials (Figure 5.1-A). The
laboratory session involved an incremental test on a motorised treadmill (Nautilus T718,
Nautilus, U.S.A) to determine the participants VO2 max and ventilatory threshold. Following
a brief warm up at a speed of their choice, participants commenced the test at a speed
between 13.5 and 15.5 km/hr. The treadmill speed was increased by 0.3 km/hr each
minute, while the grade was held constant at 1% as this has been shown to more accurately
reflect the energy cost of outdoor running (70). Pulmonary gas-exchange data was
collected using a breath by breath portable gas analyser (Cosmed K4b2, Cosmed, Rome,
Italy) which was calibrated before each test according to the manufacturer’s instructions.
Heart rate data from the accompanying chest strap was logged into the analyser’s memory
via an attached sensor. Achievement of at least two of the following variables was taken to
indicate that a participant had performed a maximal test: heart rate ± 10 beats per minute
of age-predicted maximum, respiratory exchange ratio > 1.10, and an increase in oxygen
consumption of less than 150 mls.min-1 with an increase in workload. Maximum oxygen
consumption (VO2 max) was determined by averaging the four highest successive 15
second values and was defined as the highest value achieved in either the laboratory or
field test, while ventilatory threshold was determined using the ventilatory equivalent
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method (10).
Field tests.
Each participant completed three field trials within a 3-6 week period. All trials were held in
the early morning hours (0600-0800) to attempt to minimise variations in environmental
conditions. Participants were asked to adhere to their normal training and dietary
schedules between sessions but to abstain from vigorous exercise, caffeine and alcohol in
the preceding 24 hours. Throughout each test, respiratory data was collected using the
same analyser worn in the laboratory, while continuous speed, position and displacement
data was provided by a lightweight, non-differential receiver (GPS-BT55, Wonde Proud,
Taiwan) which was worn within a specially designed pouch fitted to the rear of a cap.
Information from the GPS was wirelessly streamed (Bluetooth TM) to a smart phone (i-
mate SP3, i-mate, Dubai) which was attached to the arm with a Velcro strap.
Participants were driven over the course by car before their initial trial to familiarize them
with the nature and length of the course. The course consisted of four laps of a 2492m m
circuit which was conducted on bitumen roads. Each circuit was divided into four sections
completed in the following order: level section (650 m), uphill (557 m), level (750 m),
downhill (535 m). (NB: The uphill/downhill portion of the course used the same section of
road completed in opposite directions but the downhill section was slightly shorter due to
an earlier entry point following completion of the level section). These four sections were
further subdivided to allow a more frequent delivery of pacing information. The initial level
section consisted of two out and back stretches along a flat, level residential street. In order
to provide convenient locations for delivering pacing feedback, this was divided into four
equal parts with each turnaround point marking the end of a section. For each of the other
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sections (uphill, level after uphill and downhill), the roads were divided into six equal parts
by distance and marked with chalk to enable visual assessment of each sections completion
and the subsequent collection of split times. Gradients for each section for the uphill (in
order) were as follows: 5.1, 7.4, 7.8, 8.0, 11.0 and 9.3 %. Gradients were calculated using
trigonometry based on elevation changes measured with a surveyor’s level and staff and
distances measured by tape and measuring wheel following the route whose overall length
was measured using the GPS receiver.
Pacing Conditions
During the initial trial, runners were given the explicit goal of trying to minimise overall time
but were free to select their own pacing strategy. Trials were run as individual time trials,
no watches were worn by participants and no feedback was given so as to prevent any form
of external pacing. While it is acknowledged that pacing under these conditions is not
purely spontaneous as some degree of regulating intensity must be pre-selected even
before exercise has begun, the term ‘spontaneously paced’ is used to describe this
condition throughout this chapter.
For the second and third field trials, runners were paced for the first three laps according to
two different pacing regimes (Intervention and Control) while maintaining the same overall
time as that for the first three laps in the initial spontaneously paced trial. Runners
completed the fourth lap with no pacing (Figure 5.1-B).
The experimental pacing strategy (Intervention, INT) was based on an earlier study by the
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authors (131) (Chapter Four), which used a modified gradient factor to account for the
effect of the current and prior gradients. This predicted 89% of the variation in speed on an
undulating overground course. As the current study utilised both a different course and a
different group of runners, the authors adjusted the equation in two ways in an effort to
improve its predictive power. Firstly, as a second smaller predictor variable in the original
equation was the number of laps completed, this was recalculated and expressed as
elapsed distance in metres, a measure applicable to any course. Secondly, to better
determine what speeds would be optimal, the authors also recalculated the initial equation
using only those runners who had the lowest variation in energy expenditure as measured
by VO2. The rationale for this adjustment was that an optimal pacing regime would
minimise variations in energy expenditure. By selecting those runners from the preceding
study who had the lowest uphill-downhill oxygen consumption differences (and greatest
uphill-downhill speed differences), it was theorized that a pacing regime would be
instigated that more closely approached an optimal formula. For the two paced conditions,
section times predicted by this model were then proportionately adjusted according to the
split times from the initial spontaneously paced trial. This ensured that compliance with
the pacing strategies would result in the same overall time as in the spontaneous condition.
The alternative pacing strategy (Control, CON) used the original split times recorded by the
runner during the initial spontaneous trial. This condition allowed the effects of providing
pacing feedback to be determined when no actual change from spontaneous pacing for
each runner was required.
To deliver this pacing feedback at regular intervals, runners were provided with their split
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time when each subsection for the paced laps was completed (90-165 m) and instructed to
vary their pace upon receiving this feedback (speed up, slow down or maintain pace) in
order to achieve the desired goal time for the next subsection. Collection of splits and
provision of feedback was managed by a researcher who rode a moped ahead of the
runner. The order of trials was counterbalanced to rule out any learning effects.
Post trial questioning
Following each trial, each runner was questioned as to how easily they found it to adhere to
the pacing strategy. This question was asked specifically for each of the four gradients in
the order in which they were completed. Where runners expressed difficulty in adhering
during a particular gradient, they were further questioned as to their perceptions of
possible contributing factors. Although this information was subjective and anecdotal, all
runners were highly trained and experienced so their comments add potentially useful
insights about adherence to the imposed pacing strategy.
Data reduction and analysis
Data from the GPS and metabolic analyser were synchronised and converted to a common
file format using spreadsheets (Excel 2003, Microsoft, U. S.A). Mean speed and VO2 values
were calculated for each of the 16 gradients for each runner and these values were then
used for subsequent statistical analyses. Breath by breath VO2 data were removed from the
analysis if deemed to be higher or lower than physiologically possible according to the
following criteria: data were deemed too high if more than 10% above the highest 15
second average obtained during the laboratory trial, too low if equivalent to the VO2 of
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running at 7km/hr according to the ACSM metabolic equations (this was 2km slower than
the lowest average speed on any section). In two runners there was also some erroneous
ventilation data (1.2% of total data) due to secreted saliva temporarily impeding the
oscillation of the turbine. This led to a characteristic step-change in readings followed by a
steady return to expected values over a few seconds. These values were identified and
removed with remaining data averaged for the respective sections.
Statistical analysis
The effects of the independent variables of condition (spontaneous (SPON), intervention
(INT) or control (CON)), lap and gradient on speed and VO2 was assessed using a three way
repeated measures analysis of variance. Although the two level sections did not differ in
grade, they were treated as separate gradients for the purpose of analysis as this reflected
the differing effects of the preceding downhill or uphill sections. Tukey post hoc tests and
planned comparisons were further used to examine dependent variables where relevant.
Descriptive statistics were used to report performance differences across conditions and to
explore the effect that pacing regimes had on altering changes in speed and VO2 with
respect to gradient. To categorize and rank overall individual adherence to the pacing
regime, the root mean square error was calculated using the percentage deviation from the
intended goal time at both the gradient and subsection level (refer to Appendix 1 for full
details on assessment of adherence). Finally, multiple regression was used to determine
whether the prediction equation developed in Chapter Four remained valid when applied
to a different course. For all analyses, Statistica Software (Version 7, Statsoft, U.S.A.) was
used and the level of significance was set at p < 0.05.
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Figure 5.1 Experimental Design
A.
LAB SPONT PACED PACED
At least 7 days
B. Schematic of spontaneous and paced field trials
LAP ONE LAP TWO LAP THREE LAP FOUR
UNPACED (SPONTANEOUS)
PACED (CONTROL)
PACED (INTERVENTION)
Paced lap
Unpaced lap
NB. Control pacing matched split times from spontaneous, intervention used times based on prediction equation.
Three lap goal time was equal across trials. Order of paced trials was randomised and counterbalanced
103
Figure 5.2: Overhead picture and schematic showing section length, average gradients and subsection divisions for one lap of course
Colours in picture refer to similarly coloured sections in diagram with uphill/downhill sharing same path completed in opposite directions. The downhill is slightly shorter than the uphill due to an earlier exit point following the level section circuit marked in blue.
NB: Each of the four gradients was subdivided into eight equal sections. Only one is shown here for illustrative purposes.
104
5.3 Results
Laboratory test
These tests resulted in the following measures of physiological capacity: VO2 max, 69 ± 8.3
* significantly different compared with level after downhill, p < 0.05.
NB: VO2 not significantly different compared with Lap 1 on laps 2, 3 or 4 across all conditions.
112
Figure 5.3- Speed on uphill/downhill sections expressed as the difference from the mean level speed. Labels refer to individuals ordered from largest to smallest difference between Spontaneous and Intervention
between trials may thus need to be defined by a return to pre-trial levels of these markers
to ensure inadequate recovery does not overly contribute to differences in performance.
Assessment of psychological factors
Post trial questioning in the current study revealed that conscious regulation may limit
some runners from unduly increasing downhill speeds. Mastroianni et al (84) has noted
that differences in downhill speeds for cyclists may reflect individual differences in risk
tolerance. Thus, it is possible that psychological assessments and pre trial surveys which
detail runners injury histories, degree and amount of training and racing over hills and
normal approaches to running on downhills may provide further understanding of reasons
for inter-individual differences.
6.4 Recommended areas of further research
Explore reasons for differences in individual performance over hills
Staab et al (123) has previously found that the inclusion of an uphill and downhill of equal
gradient and duration resulted in approx 2-3% decrease in overall time compared with a
level course even if net elevation changes were equal. Though a comparison trial was not
conducted over a level course, recent performance times over level courses of equal
distance were recorded for each of the runners in Chapters Four and Five. It was noticeable
that each runner ran between four and seven minutes slower than recent race times
130
recorded for the equivalent distance on level courses. This larger difference due to the
presence of hills (approx 12-15%) may be a consequence of the increased grades on the hill
used in the current courses; 8-12% compared with a constant 5% for Staab et al (123) as
well as the physical and psychological effect of carrying the extra monitoring equipment.
Accordingly, future research should attempt to elucidate the reasons for individual
variation in performance on courses involving hills. While ventilatory threshold (108) and
peak speed achieved during incremental treadmill tests (104) have been found to strongly
predict distance running performance on level courses, other factors may contribute to
performance when positive and negative gradients are a feature of courses. Paavolainen et
al (104) noted that VO2 max was found to contribute more to uphill than horizontal running
performance. As changes have been noted in the work performed around different joints
as a runner switches from level to uphill running (112), neuromuscular testing of strength
or endurance in the involved muscle groups may also explain inter-individual differences.
Examine the limits to downhill running speeds
In addition, there was a much larger variation in runners’ speeds on downhill sections in the
studies in Chapters Four and Five compared with other sections. Mercer et al (87) has
previously shown that runners change stride length rather than stride frequency to
attenuate shock when running downhill, while Baron et al (9) has shown that the degree of
eccentric loading influences pacing strategies during downhill sprints. Changes in
kinematics downhill have also been attributed to balancing shock attenuation with stability
of the upper extremities (32). Accordingly, future studies may benefit from including
assessments of eccentric force production and tests of balance and stability to investigate
whether inter-individual differences in downhill speeds may be due to differences in
131
neuromuscular factors or motor control rather than cardiovascular capacities. It has been
shown that two brief bouts of downhill training are sufficient to protect against muscle
soreness in a subsequent downhill run (109). Accordingly, it may be beneficial to explore
the effect of incorporating specific downhill training to see if this can assist runners in
taking more advantage of potential improvements on these sections.
Investigate the efficiency of various gradient-speed trade-offs on hills
The studies in Chapters Four and Five assessed the various gradient-speed trade- offs
naturally chosen by runners. The methodology employed resulted in the use of a single
uphill/downhill section rather than a course of multiple hills. The length and relatively
constant grade of the hills used in the current studies thus enabled an improved prediction
of speed as a function of gradient. Future research however should investigate the
efficiency of a range of gradient-speed trade-offs. This could be accomplished in one of two
ways. Firstly, by using hills of varying grade and length to assess how changing these two
variables alters the speed to grade relationship, secondly, using a single grade for uphills
and downhills, numerous combinations of speed changes could be investigated using the
same runners, to evaluate the effect on energy cost, and/or performance in a subsequent
unpaced lap.
Further exploration of pacing strategies over hills
The study in Chapter Four found that runners adopted different pacing strategies at a
macro (lap) and micro (gradient) level, while the study in Chapter Five represents the first
pacing intervention in distance running to incorporate hills. As a result, future research is
132
needed to examine these preliminary findings in this area. For example, while the present
study used a fairly homogenous group of runners, a broader description of pacing principles
may be apparent through the use of group comparisons. This could include younger vs.
older runners, males vs. females or comparing runners with widely varying levels of
fitness/ability. The ability to achieve adherence to the imposed pacing strategy limited
subject numbers in the Study in Chapter Five. Accordingly, it may also be advantageous to
revisit pacing in the laboratory setting in future studies to allow the researcher to more
accurately control pacing with larger numbers. This will enable the effect of different
strategies on performance to be gauged before their application is subsequently explored
in a field environment.
6.5 Summary Following the initial validation of a non-differential receiver across the full range of human
locomotion speeds, the second study provided the first characterization of how runners
alter speeds, gait parameters and oxygen consumption when running outdoors over hills.
These findings were subsequently used in the final study to assess the effect of providing an
individualised pacing strategy on running performance on an undulating outdoor course.
The collective findings of these studies suggest that the selection of speeds on hilly courses
requires a more specialized strategy than that previously proposed for running on level
ground. By examining the way in which runners self-regulate efforts in an environment
representative of those encountered in training and racing, the results presented
contribute an important step towards understanding the principles which influence
performance in distance running.
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APPENDIX ONE- Adherence to an imposed pacing strategy
Introduction
Previous experimental pacing studies have only rarely discussed the ability of participants
to adhere to the prescribed strategy (8, 57). A review of the pacing literature shows that
this is primarily due to the exclusive use of ergometers in studies of cycling (3, 51, 65) or
treadmills in running (55) which place limits on how far a participant can stray from the
required speed. Consequently, few studies have mentioned issues of non-adherence in
pacing interventions. Thompson et al (128) found that trained swimmers were able to
follow an even paced strategy more closely than a fast/slow or slow/fast strategy; while
Atkinson et al (8) reported that two of their seven cyclists were unable to fully adhere to a
5% variation in power in parallel with gradient variation. In the very few outdoor pacing
interventions in running, adherence is either not reported (20) or is defined by the ability to
maintain close proximity to a pacing cyclist circling a level track at a set constant pace (15,
38). Ensuring adherence to a pacing strategy which accounts for gradients thus presents a
unique methodological challenge which has never previously been attempted in
experimental pacing studies of running. The challenge of maintaining adherence was thus
twofold: firstly, the ability of the runner to achieve the required intensity and secondly, the
accuracy with which they could make the necessary adjustments to achieve the required
pace.
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Methods
Experimental design
During the first field session, runners ran a solo time trial with the aim of minimizing their
overall time but were free to select their own pacing strategy and were not given any
feedback on times to prevent external pacing. For the second and third field trials, runners
were paced for the first three laps according to two different pacing regimes, Intervention
(INT) and Control (CON), while maintaining the same overall time as that for the first three
laps in the initial spontaneous (SPON) trial. (For a full description of runners, and the course
(including gradients, section lengths and subdivisions for delivery of pacing information) the
reader is referred to Chapter Five)
The specific goal of the pacing was to regulate the runners’ pace at frequent intervals, to
account for both the effect of gradient as well as the gradual changes in speed when
transitioning between new gradients. The inclusion of gradients, the use of a natural
outdoor environment and the use of frequent pace changes (66 in ≈ 7500m) precluded the
use of a “pacing vehicle” in this study. Instead, pacing was managed by informing runners
of their split time when each subsection for the paced laps was completed (90-165 m), a
goal time for the next section (of equal grade and length) and precise instructions to vary
their pace (speed up, slow down or maintain pace) in order to achieve the desired goal time
for the next subsection. A researcher rode a moped ahead of the runner in order to sight
the runner crossing a marked line on the course which designated the end of each section
and split times were recorded manually using a stopwatch mounted on the front of the
vehicle. This enabled the researcher to deliver immediate feedback to the runner on their
split time and goal time for the following section.
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Assessment criteria
A range of criteria were considered to assess the level of individual adherence to the pacing
strategy (Table A1.1 & A1.2). Initially, the root mean square error (RMSE) was determined
based on the percentage deviation from the required time. This was calculated at the level
of gradient and section (part of gradient). In addition to RMSE, two other criteria were also
applied in order to assess adherence more robustly. These are detailed below. Ideally,
adherence scores would be high across the range of such measures.
In order to examine how consistently runners were able to adhere to the strategy across
the course, the proportion of sections and gradients that were within nominated thresholds
were also calculated. As required speed changes were given to runners in whole numbers,
rather than fractional times, the threshold for adherence at a section level was errors of
less than 10% as a lower percentage would result in a classification of non-adherence when
the runner was less than 1 second away from the goal time on the shortest sections (14-15
Table A1.6-Assessment of adherence to pacing by different criteria: INT trial
Runner/Criteria
RMSE
< 4% for section
RMSE < 3% for gradient
90% of sections within 10% of goal time
90% of gradients within 5% of goal time
Overall 3 lap error
< 2%
A
B
C
D
E
F
Table A1.7-Assessment of adherence to pacing by different criteria: CON trial
Runner/Criteria
RMSE
< 4% for section
RMSE < 3% for gradient
90% of sections within 10% of goal time
90% of gradients within 5% of goal time
Overall 3 lap error
< 2%
A
B
C
D
E
F
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Discussion
The study in Chapter Five imposed two pacing strategies on a group of runners in order to
gauge their effects on performance and the accompanying physiological responses.
Accordingly, a secondary aim of this study was to assess the ability of runners to adhere to
these pacing strategies. The main finding was that adherence was much lower on downhill
sections where runners were instructed to go faster than on equivalent sections on their
SPON trial. There was also much larger individual variation in pacing adherence on the
downhill compared with uphills and level sections.
Adherence lower on downhills
Assessment at a group level, showed no overall effect of condition on adherence to the
pacing strategies. A closer inspection of adherence by gradient, however showed that
adherence on the INT trial was clearly lowest on the downhill sections with a mean error
(3.2%) almost double that of the other gradients and the lowest number of gradients and
subsections completed within 5% and 10% of their respective goals (Table A1.4).
Conversely, during the CON trial, mean errors were consistent across all gradients (range
0.30-0.62%). This may suggest that runners are less able or willing to vary their speeds on
downhill sections according to an alternative pacing regime when compared with level or
uphill sections. It has been shown in an earlier study that spontaneous speeds on downhill
sections had a higher variability between runners than level or uphill sections (131). As
oxygen cost does not limit downhill speed other factors such as a need to minimise impact
shock (87), or maximise stability (32) may determine maximum speeds. Examination of
individual adherence shows that the higher pacing error on the downhill is primarily due to
two runners (D and E) whose mean errors were more than 2.5 times higher than the next
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lowest adherer (Table A1.3). The perceptions of these athletes as to their inability to match
speeds on these sections are addressed further in Chapter Five (see Discussion-effect of
pacing on speed and oxygen consumption).
Control trial adherence
As the CON trial used the runners’ original splits from their SPON trial, it was expected that
adherence would be higher under this condition and any variance would be a combination
of day to day variability and an ability to replicate speed changes. During level track trials,
experienced collegiate runners were shown to match goal speeds more accurately
compared with recreational runners (57). As runners in this study were both highly
experienced and had high levels of fitness (see runner characteristics, chapter Five), this
reduced the possibility that errors would be due to significant errors in adjusting speeds
accurately. Day to day variability has also been shown to be low in experienced runners,
with values of 2.7% (117) and 1.4% (113) reported in spontaneous speeds using manual and
feedback controlled treadmill trials respectively. While the inclusion of gradients hinders
direct comparisons with these values, adherence was shown to be higher on the CON trial
compared with INT for the majority of runners (Tables A1.1& A1.2) with five of six runners
having a lower RMSE at a section level and four of six at a gradient level (with one runner
approximately equal).
Environmental factors
In contrast to the other runners, Runner E was consistently slower than the prescribed split
times for his CON trial, arriving at the end of the three laps with by far the largest deficit in
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time compared to the overall goal (Table A1.2). His inability to match his original split times
may be partly explained by an analysis of daily weather conditions. In addition to the effect
of gradient, environmental factors such as temperature may affect a runner’s ability to
adhere to an imposed strategy. A difference in temperatures between runners was partially
due to seasonal variations in temperatures as trials were conducted from late summer to
late autumn. More important was the variability between the three trials for each runner.
Though assigning trials to the early morning hours (0600 to 0800) minimised inter-trial
temperature fluctuations for most runners, runner E experienced an unseasonably cold day
on the morning of his CON trial compared with his SPON and INT trials (Table A1.5). Post
trial comments from this runner reflected his perceptions of its adverse affects on his speed
and it is possible that this may have contributed to his inability to match his split times from
the original SPON trial.
Conclusion
Adherence to a pacing strategy needs to be assessed relative to the frequency with which
changes in speed are imposed. For the majority of studies, paces have been changed at
relatively infrequent intervals but a reliance on treadmills and ergometers has ensured
adherence, so deviations from goal paces are practically impossible. The current study was
unique to pacing studies of running in that it involved positive and negative gradients and
involved frequent pace changes to account for the effect of gradient transitions and
extended durations. Based on this type of pacing delivery, RMSE was found to provide the
single best indicator of adherence as it assessed adherence continually. Using this index,
individual variation was found in the ability to adhere to the imposed strategy which could
be based on a range of physiological, biomechanical and psychological factors. Adherence
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may be improved in future studies through preliminary examinations of these factors to
exclude potential non-adherers, and through the development of high precision methods
for providing continuous speed feedback.
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APPENDIX TWO - Differences in displacement of the GPS receiver at three different locomotion speeds.
Lean angles were 0, 3 and 10.5 ° for walk, run and sprint respectively. Nominal course is
represented by shaded circle.
Run- 3.3 m/s
Sprint- 5.6 m/s
Walk- 1.2m/s
10m
10m
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APPENDIX THREE - Spatial distribution of GPS positions relative to known geodetic point
-2 -1.5 -1 -0.5 0 0.5 1 0.5
0.9 1.3
1.7
2.1
0
200
400
600
800
1000
Longitude error (m)
Latitude
error
(m)
Num
ber o
f Obs
erva
tions
161
APPENDIX FOUR –Validation studies of GPS and DGPS for speed (A) and distance/position (B) SA = Selective Availability, GPS = non-differential Global Positioning system, DGPS = differential Global Positioning System, WAAS= Wide Area Augmentation System
A. Validation studies of speed during human locomotion
Study Receiver type Sampling Frequency Modality Range of speeds (km/hr)