Influence of hydration status on pacing during trail running in the heat.

Abstract

The purpose of this study was to determine the influence of hydration status on pacing of trail runners in the heat (wet bulb globe temperature = 26.2 +/- 1.8 degrees C). A randomized, crossover design was used and the participation occurred within a 2-week period. Seventeen competitive, well-trained distance runners (9 men, 8 women, age 27 +/- 7 years, height 171 +/- 9 cm, weight 64.2 +/- 9.0 kg, body fat 14.6 +/- 5.5%) completed the study. Subjects started maximum effort trials that were either hydrated (HYR) and dehydrated (DHR). Each trial subjects ran three 4-km loops with a 4-minute rest between loops. Significance was set at p < or = 0.05. The DHR had a significantly greater body mass loss at the pre- and posttrial time points (-2.05 +/- 1.25 and -4.3 +/- 1.25%, respectively) vs. HYR (-0.79 +/- 0.95 and -2.05 +/- 1.09%, respectively). Subjects ran the 12 km faster (p < 0.001) in HYR (3,191 +/- 366 seconds) vs. DHR (3,339 +/- 450 seconds). Differences between fastest and slowest loops during HYR (54 +/- 40 seconds) were significantly smaller than DHR (111 +/- 93 seconds; p = 0.041). Additionally, loop times were slower for loop 1 (HYR 1,039 +/- 116 seconds vs. DHR 1,071 +/- 123 seconds; p = 0.028), loop 2 (HYR 1,066 +/- 123 seconds vs. DHR 1,105 +/- 148 seconds; p = 0.01) and loop 3 (HYR 1,081 +/- 132 seconds vs. DHR 1,168 +/- 189 seconds; p = 0.003) when dehydrated. Percent of the race completed by loop as calculated by finishing time was significantly different at loop 2 between HYR (33.6 +/- 0.36%) and DHR (33.1 +/- 0.35%, p = 0.002) and loop 3 (33.8 +/- 0.75% vs. 34.9 +/- 1.35%, respectively, p = 0.01). Total variation from the mean pace for the duration of the HYR compared to the DHR approached significance (p = 0.064). Average percent of variance approached significance between trials (p = 0.057). Differences between the fastest and slowest loops between trials demonstrated an increased ability for hydrated individuals to evenly pace themselves. While total variation from the mean pace was not significantly different, it could have practical applicability. These findings reveal that dehydration is associated with decreases in a runners' ability to evenly pace themselves during a competitive situation.

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INFLUENCE OF HYDRATION STATUS ON PACING
DURING TRAIL RUNNING IN THE HEAT
REBECCA L. STEARNS,1DOUGLAS J. CASA,1REBECCA M. LOPEZ,1BRENDON P. MCDERMOTT,2
MATTHEW S. GANIO,1NORA R. DECHER,3IAN C. SCRUGGS,4ANGELA E. WEST,5
LAWRENCE E. ARMSTRONG,1AND CARL M. MARESH1
1Human Performance Laboratory, Department of Kinesiology, University of Connecticut, Storrs, Connecticut;
2Department of Health and Human Performance, University of Tennessee at Chattanooga, Chattanooga,
Tennessee;3University of Virginia Health System, Charlottesville, Virginia;4Avon Old Farms School,
Avon, Connecticut; and5Steadman Hawkins Clinic, Vail, Colorado
ABSTRACT
Stearns, RL, Casa, DJ, Lopez, RM, McDermott, BP, Ganio, MS,
Decher, NR, Scruggs, IC, West, AE, Armstrong, LE, and
Maresh, CM. Influence of hydration status on pacing during trail
running in the heat. J Strength Cond Res 23(9): 2533–2541,
2009—The purpose of this study was to determine the influence
of hydration status on pacing of trail runners in the heat (wet
bulb globe temperature = 26.2 6 1.8?C). A randomized, cross-
over design was used and the participation occurred within a
2-week period. Seventeen competitive, well-trained distance
runners(9men,8women,age 276 7 years,height1716 9cm,
weight 64.2 6 9.0 kg, body fat 14.6 6 5.5%) completed the
study. Subjects started maximum effort trials that were either
hydrated (HYR) and dehydrated (DHR). Each trial subjects ran
three 4-km loops with a 4-minute rest between loops. Signifi-
cance was set at p # 0.05. The DHR had a significantly greater
body mass loss at the pre- and posttrial time points (22.05 6
1.25 and 24.3 6 1.25%, respectively) vs. HYR (20.79 6 0.95
and 22.05 6 1.09%, respectively). Subjects ran the 12 km
faster (p , 0.001) in HYR (3,191 6 366 seconds) vs. DHR
(3,339 6 450 seconds). Differences between fastest and
slowest loops during HYR (54 6 40 seconds) were signifi-
cantly smaller than DHR (111 6 93 seconds; p = 0.041).
Additionally, loop times were slower for loop 1 (HYR 1,039 6
116 seconds vs. DHR 1,071 6 123 seconds; p = 0.028), loop
2 (HYR 1,066 6 123 seconds vs. DHR 1,105 6 148 seconds;
p = 0.01) and loop 3 (HYR 1,081 6 132 seconds vs.
DHR 1,168 6 189 seconds; p = 0.003) when dehydrated.
Percent of the race completed by loop ascalculated byfinishing
time was significantly different at loop 2 between HYR
(33.6 6 0.36%) and DHR (33.1 6 0.35%, p = 0.002) and
loop 3 (33.8 6 0.75% vs. 34.9 6 1.35%, respectively,
p = 0.01). Total variation from the mean pace for the duration
of the HYR compared to the DHR approached significance (p =
0.064). Average percent of variance approached significance
between trials (p = 0.057). Differences between the fastest and
slowest loops between trials demonstrated an increased ability
for hydrated individuals to evenly pace themselves. While total
variation from the mean pace was not significantly different, it
could have practical applicability. These findings reveal that
dehydration is associated with decreases in a runners’ ability to
evenly pace themselves during a competitive situation.
KEY WORDS performance, dehydration, pace
INTRODUCTION
M
environmental factors that influence finishing time, few
studies have focused on the impact of pacing strategy, which
is considered an important performance factor. Specific
pacing strategies have been shown to benefit different race
durationsrangingfrom800mtoultraenduranceevents(1,30).
Since the top finishers in elite races finish within seconds of
each other, it is important to closely consider factors affecting
finishing time.
Athletes will use different pacing strategies in competition
depending on the distance covered. For the purpose of this
paper, distances will be defined by the following, commonly
accepted ranges: short duration/sprint event (,30 seconds),
middle distance event (1.5–2 minutes), prolonged/endurance
event (.2 minutes but ,4 hours), ultraendurance event
(.4hours)(1). An athlete might use a pacing strategy termed
‘‘negative race pace.’’ A performance is defined as having
negative pacing when the average speed of the athlete
gradually increases throughout the duration of the event.
any competitive endurance sports are based
upon athletes performing a given distance in
the fastest time possible. While there have
been studies examining physiological and
Address correspondence to Rebecca L. Stearns, rebecca.stearns@uconn.
edu.
23(9)/2533–2541
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? 2009 National Strength and Conditioning Association
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Negative refers to the fact that split times with this strategy
continue to decrease as the race continues. An ‘‘all-out’’
pacing strategy is when an athlete spends the majority of the
race accelerating and eventually reaches peak speed and
gradually but slightly slows until the finish. An athlete utilizes
a positive pacing strategy when his or her speed gradually
decreases over the duration of the event. Lastly, even pacing
occurs when an athlete performs at a constant speed
throughout (1).
In endurance events, it has been widely demonstrated that
an even pacing strategy is the most effective in both cycling
(2,13,26) and running (10,16). Previous studies have indicated
that even pacing produces the fastest times (10,13,16,26).
When Padilla et al. (26) examined cyclists on a 1-hour world
record performance attempt, cyclists maintained a pace
within an average range of 2 km?hr-1from their average pace.
Foster et al. (13) determined that during a 2-km bike race the
overall finishing difference between a slow start and an even
start averaged 7.2 seconds or a 4.3% difference. In the world
of running, such as during the Olympic 1.5 km, this would
translate to the difference between first place and last place.
While a relatively constant pace has been shown to be
effective,justhowlittlevariabilitythereiswithinthatpacecan
be a concern. Billat et al. (6) demonstrated that while runners
completed a 10 km run to exhaustion, a greater physiological
strain was demonstrated during a constant paced run, as
opposed to when the runners were allowed to freely pace
themselves. Allowing the runners to freely pace themselves
resulted in slight variations in pace and a better performance,
however it may be important to note that this study only
included three subjects. In another study by Cottin et al. (10)
world record performances ranging from the 1.5 km to the
10 km were analyzed. It was determined that the range of
coefficients of variation in velocity was 1%–5%. This demon-
strates that a perfectly constant pace may not be completely
efficient and therefore is not necessary to result in world
record performances. Physiologically speaking, it is unclear
as to why a perfectly constant pace may not be optimal but
straying too far from this is detrimental. However, few studies
have examined this point. Other factors including terrain,
wind, or competition could potentially influence pacing.
In further analyzing variations in pace, Gosztyla et al.(15)
performed a study with 5 km runners starting at different
speeds (3% faster, 6% faster, even pace, 3% slower, or 6%
slower than average pace). These athletes were able to start
from 3%–6% faster than their average 5 km pace without
performancedecrements.Somestudieshavefurthersupported
a slightly varied pace (4), while others have demonstrated
decreases in performance from such pacing strategies (16).
One last option that has shown to be effective for distance
runners is a negative pacing strategy. Studies by both Noakes
(24) and Robinson (29) demonstrate that when completing
a race between 1200 m and 1600 m, athletes were more
successful when using a negative pacing strategy while
deviating from this resulted in slower finishing times.
Accumulating evidence supports endurance runners utiliz-
ing either a constant or possibly a negative pacing strategy
(6,15,24,29). There is also the possibility that a negative pace
strategy becomes more effective as the event decreases in
length (30). However, the impact that uncontrollable factors,
such as the environment, might have on pacing ability must
also be considered. Martin et al. (19) discusses the evident
decrease in pace during the 10-km run at the World
University Games in Kobe, Japan in 1985. It was 32?C dry
bulb with 73% relative humidity that day. Given ideal
conditions running at the ideal pace would not be a problem,
however in such a stressful environment this pace would
have led to intolerable heat accumulation. Therefore, pacing
strategies were forced to change and consequently the overall
pace slowed. There are many other examples of slow race
finishes due to nonideal environmental conditions (12,21).
However, we know of no studies that have examined the
effect of hydration status on the ability of runners to properly
pace themselves.
It has been shown that dehydration negatively affects
performance and increases core body temperature, possibly
by reaching a ‘‘limiting core body temperature’’ and causing
performance decrements (5,28,31). Performance decrements
are seen as early as 2% dehydration and are reflected in a 3%–
6% decrease in running velocity (5). This has been mirrored
in ratings of perceived exertion (RPE) values. As dehydration
increases RPE values increase and are significantly greater
than the RPE values reported in a hydrated state (17). This
increased RPE along with greater core temperatures asso-
ciated at a given intensity could play a part in the chosen pace
and the variability of that pace throughout competition.
Another proposed mechanism discussed later is the influence
of thirst and anticipatory regulation on performance.
Both pacing and hydration have been shown to be
important factors in performance. However the factors that
may positively or negatively affect an athlete’s ability to pace
themselves is not clear. Hydration has been shown to effect
areassuchasRPE(17), heart rate, and core body temperature
(5,29,31), which may then effect cognitive functioning, per-
ception of effort, and ultimately the pace at which the athlete
participates. Therefore dehydration may affect a runner’s
abilitytoproperly judgeeffortandtherefore a pace that would
optimize performance. However, studies have yet to examine
the effect of hydration on the ability of runners to accurately
pace themselves during race conditions. Accordingly, the
purpose of this study was to examine the effects of hydration
status on running pace during maximal effort performances.
METHODS
Experimental Approach to the Problem
Subjectsreportedtoanearbystateparkforfamiliarizationand
baseline measurements. In order to familiarize the subjects
with the 12-km course, they completed two practice runs on
the course with a researcher familiar with the course. These
runs were performed between 2 and 4 weeks before trial 1.
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One of these familiarization runs included a 4-km time trial to
gauge their ability as a runner. This timed practice run was
usedtogroupsubjectsintothreegroupsoffourandonegroup
of five runners with similar running abilities. Based on these
groups monetary incentives were included to encourage
maximal effort. The experimental trials consisted of com-
pleting two 12-km timed trail runs on 2 separate days 2 weeks
apart: a) maximal (race effort) 12-km trial beginning
euhydrated (HYR); and b) maximal (race effort) 12-km trial
beginning hypohydrated (DYR). Note that 400 mL of water
was provided at the 4-km and 8-km points for the euhydrated
trials.
A 12-km distance was chosen consisting of three 4-km
loops, which allowed for data collection when subjects ran by
the start/finish area. This distance is also similar to common
racing distances of 10 km and 15 km. It was also calculated
that this distance would allow for subjects to become
sufficiently dehydrated for comparison with a hydrated trial.
The trails consisted of some single track, and some technical
portions with rocky/root filled paths. Net elevation was
0 since this was a loop course with elevation gains of no
more than 50 ft.
A randomized, crossover, counterbalanced design was
used. All subjects were randomly assigned to either a
euhydrated or a hypohydrated protocol for each trial. Half
of the participants were randomly assigned to a hypohydra-
tion protocol for the first race. These participants then
followed a hydration protocol for the second race and vice
versa. All subjects received a calibrated scale (BWB-800 A,
Tanita, Tokyo, Japan) to record body mass measurements
for the duration of the study. Subjects took their own nude
body mass measures each morning for the three days before
thefirsttrialwhichservedasabaselinebodymassthroughout
the study. Subjects continued to monitor their body mass
every day until the last trial.
Subjects
Seventeen (9 men, 8 women) competitive, heat acclimatized,
well-trained distance runners (men: age 28 6 9 years, height
178 6 4.6 cm, weight 69.2 6 5.6 kg, body fat 10.2 6 2.5%;
women: age 26.8 6 4 years, height 164.2 6 6.8 cm, weight
58.6 6 7.9 kg, body fat 19.4 6 3.5%; overall: age 27 6 7 years,
height17169cm,weight64.269.0kg,bodyfat14.665.5%)
participated. Allsubjectshad aminimumof 3consistent years
of running experience and reported an exercise history of
running a minimum of 30 minutes 4 times a week for the past
3 months. The majority of subjects were either on a college
cross-country team or had participated on college teams;
however, the average participant would not be considered an
elite running competitor. Participants were excluded if they
had any of the following: a disorder that could cause com-
plications from taking Cor-Temp Disposable Temperature
Sensor(HQinc,Palmetto,Florida,USA),anywomanthatwas
pregnant at the time of either trial or had the possibility of
being pregnant; a history of exertional heat stroke or heat
exhaustion within the last 3 years; musculoskeletal injury
during the time of the running trials; an exercise/activity of
less than 30 minutes per day, 4 times per week, at a moderate
intensity for the past 3 months; chronic health problems;
a history of cardiovascular, metabolic, or respiratory disease;
feverorothercurrentillness;oroutsidetheagerangeof18–59
years. Participants completed a running history questionaire
and a medical history questionaire prior to being accepted
into the study. All participants read and signed a written
informed consent form. This study was approved by the
University of Connecticut’s institutional review board.
Procedures
The day before each trial, subjects were informed of which
hydration group they were in via individual phone calls.
Subjects were randomly grouped by treatment (hydrated or
hypohydrated). Whichever group a subject was assigned to
for the first trial, the subject then followed the other protocol
for the second trial (i.e., those that were assigned to the
hydrated group for the first trial followed the dehydration
protocol for the second trial). Subjects in the hypohydrated
group were instructed to start fluid restriction 22 hours before
their individual start time. Subjects in the hydrated group
were allowed to consume fluids ad libitum. All subjects were
required to perform a typical training run (60-minute run or
90 minutes with a combination of jogging or walking/hiking)
after 14:00 hours, which was replicated on the days prior to
both trials. All subjects were instructed to consume the same
dinner the night before each trial, and the same breakfast and
snack on the mornings of the two testing days. Subjects were
also asked to wear the same shoes and the same or similar
clothing for each of the trials. It was projected that the
dehydrated group would start the trial about 2% dehydrated
and finish about 4% dehydrated, while the hydrated group
would finish about 2% dehydrated. These values where
chosen because itis not uncommonfor runnersto finish races
;2% dehydrated; however, for longer races runners may
finish upwards of ;4% dehydrated depending on sweat rate,
water intake, and other variables. In addition, other research
has shown that as little as 2% dehydration can impair
performance (5).
Before the start of each trial, baseline measurments were
taken, including body mass as measured by a scale to
determine percent dehydration, heart rate (HR), perceived
thirst (28), and perceived thermal sensations (32). Urine was
used as a marker of dehydration, including urine color, urine
specific gravity (Usg), urine osmolality (Uosm). At this time,
subjects were also given instruction on how to use the
perceptual scales. A standardized set of instructions including
how to respond to each question was given for each scale. We
used the Borg RPE scale, which ranges from 6–20 points (7).
After baseline measurements, subjects began each trial
individually with 4-minute intervals separating each subject’s
start time. Subjects wore a heart rate monitor throughout
eachtrial.Atthe4-kmand8-kmmark,therewasamandatory
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4-minute break for all subjects, where RPE, HR, perceived
thirst, and perceived thermal sensations were measured. If at
any point during a trial a subject needed to urinate, urine was
collected into a jug, measured, and calculated into the
subject’s body mass loss.
At the conclusion of each
trial, immediate postrun mea-
surements included HR, per-
ceived thirst, perceived thermal
sensation, and RPE. Core body
temperature was taken imme-
diately post run, however these
measures are reported else-
where (9). Ten minutes after
the run, HR was taken. Twenty
minutes after the subject com-
pleted the trial, perceived thirst,
perceived thermal sensation,
and HR were measured. Partic-
ipants in the hypohydrated trial
remainedonsiteuntiltheywere
rehydrated to less than 2% of
baseline body mass measures.
Subjects receivedmonetary
compensationif the entire
study was completed. Subjects
received additional compensa-
tion based on their perform-
ances during the two trials.
Subjects were placed into four groups according to ability
(based on the times run during one all out loop which was
performed during the familiarization runs). Time to complete
the course for both trials was calculated and incentives were
based off performance within each group. The average pace
foreachloopofeverytrialwascalculated.Eachloopwasthen
analyzed to determine if a subject ran the loop closer to
his/her average pace for the hydration trial or for the average
pace during the DHR.
Wetbulb globetemperature (WBGT)wascalculated for all
hydrated and dehydrated trials occurring on the separate
days. WBGT was taken every 20 min. These values were
then averaged in appropriate time segments and averaged
for each subject.
Instrumentation
Eachsubject’s percentbodyfatwascalculated usingthreesite
skinfold measurements and the Jackson-Pollock equation
(27). Duplicate measures (Uosm) were averaged. Uosm was
determined via freezng point depression using an osmometer
(Model 3DII, Advanced Instruments, Needham Heights,
MA, USA). Urine color was determined by the urine color
chart (8). HR was measured using Polar HR monitors (Polar
E40, Polar Electro, Lake Success, NY, USA).
Statistical Analyses
Repeated measures analysis of variance (ANOVA) was used
to compare differences for performance in relation to
dehydration level, intensity, and time. Post-hoc statistical
analysis for pre- and post-values (body mass, urine color,
Uosm,) during trials was determined using a paired sample
Figure 1. Body mass changes with associated percent body mass loss.
*p # 0.05 when compared to hydrated trial at same time point (n = 17).
Figure 2. Performance time for hydrated and dehydrated trials. *p # 0.05 when compared to hydrated trial at same
time point (n = 17).
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t-test with a Bonferroni correction. Pearson’s bivariate
correlations were used to determine relationships among
variables and to assess reliability. Total variation from pace
was calculated as the sum of the difference from the mean
pace at each time point. Percent of the course completed
was calculated to further express variation in pace in terms
of how much time was spent on each loop in relation to
total time. Therefore, the percent of time spent on each
loop in relation to total time is represented. Percent of
course completed was calculated based on the average loop
time and the actual time run per loop and determining
the percentage this represented. Average percent of variance
was calculated by determining the percent difference that
existed between actual pace and average pace by loop and
taking the average from the three loops. Significance was
set at p # 0.05. To further quantify the amount of variability
between groups, a nonparametric chi-squared analysis was
performed. The number of subjects in this study is similar or
greater than previous papers that have found significant
differences in pacing (13,14,16,29) and was therefore con-
sidered an acceptable n size for statistical power. All data
analyses were performed using SPSS version 10.0.
RESULTS
Body Mass Changes
The DHR produced a significantly greater body mass loss
when comparing pre and post race (22.05 6 1.25, 24.3 6
1.25%) in relation to a 3-day baseline vs. HYR (-0.79 6
0.95,22.05 6 1.09%, Figure 1). Urine color, Usg,and Uosm
for the HYR were significantly lower (p # 0.05) at both the
pre and post time points when compared to the DHR.
Performance
Subjects ran significantly faster (p , 0.001) for the entire
12 km during the HYR vs. DHR (Figure 2). Differences
between fastest and slowest loops during HYR (54 6 40
seconds) were significantly smaller than DHR (111 6 93
seconds; p = 0.041). A two-way ANOVA revealed a signifi-
cant interaction affect over time (p = 0.024) when percentage
Figure 3. Percent of total time completed per loop for a) women (n = 8),
b) men (n = 9), and c) all subjects combined (n = 17). Represents the
percent of time each loop contributed to the total finishing time. *p # 0.05
when compared to hydrated trial at same time point. The horizontal line at
the 33.33% mark represents where an even pace for each loop would fall,
therefore a perfectly paced trial would have all three box plots along this
line.
Figure 4. Fastest and slowest loops for hydrated and dehydrated trials.
*p # 0.05 when compared to hydrated trial (n = 17).
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of the course completed at the end of each loop was com-
pared. A follow-up paired-samples t-test revealed significant
differences in HYR (vs. DHR) after loop 2 (33.6 6 0.36 vs.
33.1 6 0.35%, respectively; p = 0.002) and loop 3 (33.8 6 0.75
vs. 34.9 6 1.35%, respectively; p = 0.01) (Figure 3). A two-way
ANOVA was performed separately for men and women,
which revealed a significant interaction over time for both
the men and women (p = 0.001, p = 0.001, respectively).
A follow-up paired samples t-test revealed significant differ-
ences in the HYR vs. the DHR on loops 1, 2, and 3 for men
(32.6 6 0.61% vs. 33.5 6 0.28%, p = 0.005; 33.9 6 0.60% vs.
32.4 6 1.31%, p = 0.03; 33.1 6 0.44 vs. 34.5 6 1.08, p = 0.005,
respectively) and women (32.6 6 0.46 vs. 33.6 6 0.46, p =
0.027; 33.8 6 0.93 vs. 31.6 6 1.39, p = 0.008; 33.1 6 0.24 vs.
35.4 6 1.53, p = 0.006, respectively; Figure 3). Fastest
individual loop times were significantly faster (p = 0.028) for
HYR (1,036 6 116 seconds) than fastest loop times in DHR
(1,060 6 131 seconds). Slowest individual loop times for
HYR were significantly faster (1,090 6 132 seconds) when
compared to the slowest DHR loops (1,172 6 184 seconds;
p = 0.004; Figure 4). Total variation from the mean pace
between HYR and DHR approached significance (p = 0.064,
Figure 5). When men and women were compared separately
total variation from mean pace between HYR and DHR
was significant for the men (206 6 23.9 vs. 240 6 49.0
seconds, p = 0.042) but not the women (291 6 45.5 vs. 337 6
101.2 seconds, p = 0.296). The mean percent of variance from
average velocity approached significance between HYR
(1.7 6 1.3%) and DHR (3.3 6 2.5%, p = 0.057). When men
and women were compared separately, there was not a
significant difference (p = 0.226, p = 0.155, respectively).
No significant differences were found between the number
of subjects that ran closer to his/her pace for the HYR vs.
the DHY on loop 1 (p = 0.09) or loop 2 (p = 0.467), but
significant differences occurred on loop 3 (p = 0.046, Table 1)
and total time (p = 0.001).
Perceptual Scales
Perceived thirst was significantly greater (p # 0.05) at all time
points during the DHR. Thermal sensations were signifi-
cantly greater (p # 0.05) in the DHR after loop 2 (p = 0.027)
and at 20 minutes after (p = 0.018). RPE was significantly
greater (p # 0.05) in the DHR after loop 2 and loop 3.
Figure 5. Average pace (based on total time) compared to actual pace performed during hydrated and dehydrated trials. *p # 0.05 when compared to hydrated
trial at same time point (n = 17).
TABLE 1. Number of subjects running closer to
average pace during hydrated race versus
dehydrated race.*
Loop 1 Loop 2 Loop 3 Total
Hydrated
n
Average loop
pace (minutes)
Dehydrated
n
Average loop
pace (minutes)
121012 34
17:43 17:43 17:43 53:09
574†
16†
18:34 18:34 18:34 55:42
*Loop 3 only has 16 subjects because there was no
difference for variation from pace between hydration
states for one subject.
†p , 0.05 between trials at same time point.
Represents number of subjects that ran closer to average
pace by loop between hydration states.
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Other Indices
WBGTs for the hydrated (26.1 6 1.9?C) and dehydrated
(26.3 6 1.9?C) trials were not significantly different.
DISCUSSION
The purpose of this study was to determine the influence of
hydration status on pacing during trail running in the heat.
While previous studies have examined the importance of
pacing on performance, few have examined other variables—
specifically hydration status—that could alter the ability of
athletes to appropriately pace themselves. There are four
previously identified pacing strategies. While each one is
usually associated with specific race distances, some pacing
strategies vary depending on the skill of the athlete, envi-
ronmental factors, age, and competition. The main strategies
that this study examined were negative pace, even pace, or
positivepace.Anegativepaceoccurswhentheathlete’sspeed
gradually increases throughout the duration of the event.
A positive pace is when an athlete’s speed will gradually
decrease throughout the event. Lastly, an athlete can pace
himself or herself evenly by maintaining a constant speed
throughout the event.
The main finding from our study was the differences
identified between the fastest and slowest loop times during
a race situation. Additionally, the difference between the
fastest and slowest loops was significantly (p # 0.05) different
between hydration states. While there was not a statistical
difference between pace variability at different hydration
states, it did approach significance (p = 0.064, Figure 4) and
while variability was significant for the men, the variation
present in the women’s times may explain the lack of signifi-
cance in the overall variability analysis and may still have
clinical significance. When percent of the trial completed was
calculated, there were significant differences on loop 2 and
loop 3 demonstrating a more even pace run in the hydrated
trial. In a hydrated state, subjects demonstrated that a pace
closer to the overall average pace was more effective as
opposed to a positive pacing strategy, which was utilized by
the dehydrated subjects (Figure 2). This demonstrates that
when euhydrated and allowed to pace themselves freely
without influence from other runners, subjects attempted to
run at an even pace to attain optimal performance. The
subjects reported significantly decreased RPE values in a
hydrated state after loop 2 and at the finish, once again
demonstrating the increased strain the fluid restricted
runners were experiencing.
Since there are limited publications on the impact of
hydration on pacing, it is difficult to compare this with
previous observations. Publications on pacing support an
even pacing strategy that the hydrated runners chose for this
distance (14,16,26,30). Cottin et al. (10) and Gosztyla et al.
(15) demonstrated that variations of up to 5% of the average
velocity have no negative impact on performance and
perhaps are actually beneficial compared to a perfectly
constant pace. While both trials resulted in average velocity
variations of less than 5%, the hydrated trial resulted in
significantly less variation in velocity. Maughan et al. (20)
examined pacing strategy of marathon runners and found
that the slowest runners ran a positive pace while the fastest
runners ran at a constant pace. Based on the dehydrated
runners positive pacing strategy and past literature, it would
seem that dehydrated runners paced themselves in accor-
dance for completing an ultraendurance event or were not in
the physical condition to perform at the initial pace selected
(1). This may suggest that dehydrated runners pace
themselves for a more strenuous distance than the actual
task at hand.
It has been published (18,22,23,33) that our brain can
anticipate how much work there is to be accomplished and
therefore regulates an appropriate pace. Noakes (22) stated
that the brain is responsible for skeletal muscle motor unit
recruitment and therefore the amount of working muscle that
can cause an increase in heat production. He also argued that
the mechanism of control for this appears to be a preset
rating of perceived exertion at which exercise terminates
before dangerously high temperatures are accumulated.
However, very little discussion on this theory has focused
on hydration status and other outside factors (e.g., compe-
tition, pressure) that could play a role. At the start of the trial,
our subjects knew they were dehydrated and had a signifi-
cantly greater level of thirst. However, despite starting an
average of 27 seconds slower for the first loop when
compared to the HYR, dehydrated subjects tended to slow
over the next two loops. Near the end of the race, 14 of the 17
subjects had slowed greatly and acquired a positive pacing
strategy, suggesting that they were unable to pace themselves
appropriately while dehydrated. Only one subject ran the last
loop the fastest while dehydrated. Despite not finding
statistical significance between hydration states and race
variability, it did approach significance and can have practical
applicability. As mentioned previously, the difference
between first and last place could be a few seconds. This
may not be statistically significant, but when competing at an
elite level with other highly competitive athletes any small
decrease in finishing time is imperative for success.
It ispossible thatthe runnersdid notexpectthe dehydrated
state to impact their performance as greatly as it did; there-
fore, they did not initially start at a slower, more appropriate
pace. It is important to note that the dehydrated runners did
start at a slower pace; however, their pace still tended to slow,
as they did not continue this pace throughout the race. The
runners may also normally maintain a euhydrated state, and
were not able to account for the performance decrements
associated with dehydration. The results of this study lend
support to the anticipatory regulation theory in that with a
smallamountofdehydrationsubjectshadaslightlydecreased
pace on loop 1 and then with progressively worsening
dehydration the pace slowed at an exaggerated rate. It is
possible that the decrement in pace noted in the dehydrated
group was due to the greater physiological response (core
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body temperature increases and cardiovascular strain,
published in another article from the same study [9]).
This study has some limitations. All subjects started the
races individually with 4 minutes between each person.
Subjects also rested for 4 minutes after each loop. While this
rest was necessary to gain valuable data (9), this is not
practical for a real race scenario where all participants run
together. This 4-minute rest was imposed on every trial so
that they were identical; however, it is unusual for runners to
be allowed this small recovery, so they may have therefore
changed their pacing strategies. Instead of approaching the
three loops as one race, participants may have paced them-
selves according to each loop individually. Fluid ingestion
during the trials may have also affected pacing. Knowing that
water would be provided (vs. knowing that no consumption
of water would be allowed) may have changed the approach
taken to pacing. A study by Dugas et al. (11) observed cyclists
during an 80-km race in the heat with low vs. high fluid
intake. They reported that overall power output was sig-
nificantly lower in the low-fluid trial starting at 10 km. The
authors concluded that simply knowing the amount of fluid
intake impacted the pace selected by subjects (11).
Further studies should focus on pacing while dehydrated
and with ad libitum fluid intake. Studies may also look at
pacing strategies of athletes and correlate the level of
dehydration at the finish to the variability of a chosen pacing
strategy.Theeffectsofdehydrationonpacingmay varybased
on race distance, experience, and level of competition, which
other studies should examine.
PRACTICAL APPLICATIONS
Overall,ourresultsdemonstratethatdehydratedrunnersmay
have a slight decrease in their ability to evenly pace them-
selves during an endurance event. It has been consistently
shown that for a race at this distance, an even pace or a pace
that with less than 5% variability has the best performance
outcomes (2,10,13). The fastest recorded marathon times
have also been performed at a constant pace, while the
slowest utilize a positive pace (20). It has also been shown
that as little at 2% dehydration can have a negative impact on
race performance. It is not uncommon for runners to reach
this level of dehydration at the end of a race; however, the
impact of this upon the ability of runners to correctly pace
themselves had not previously been examined. The combi-
nation of dehydration and variability in pace could therefore
further hinder overall performance.
Despite knowing that they were dehydrated and starting at
a slower pace, dehydrated runners still adopted a positive
pace. Therefore, not only did dehydration hinder perfor-
mance, but it also affected the runner’s ability to adopt an
optimal pace. Clinically, this could explain another portion of
the performance decrements classically seen in dehydrated
athletes. Athletes may gradually become dehydrated either
from not replacing the appropriate fluids prior to or during
a competition or simply due to an extended length of
competition leading to more fluid loss. Dehydration will
decrease the performance of the athlete, but this study
demonstrates that it also effects variability in pace, which has
also been shown to decrease performance.
ACKNOWLEDGMENTS
The authors thank the Gatorade Sports Science Institute for
fundingthisstudy,aswellasthefollowingpeopleforallofthe
time and effort throughout the data collection process: Jen
Klau, Elaine Lee, Melissa Roti, Paul Boyd, Ben St. Martin,
Mike Eckert, Stephanie Mazerolle, Brittanie Volk, Ben
Keegan, Kristoffer Friend, Liz Silverberg, Kate Sanders, Kevin
Ballard, Erin Quann, Heather Mispagel, Linda Yamamoto,
TutitaCasa,PatrickAustin,HollyWisehart,KelliChristensen.
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