3. 646 Hansen et al.
60 g/hr when glucose is consumed and up to 90 g/hr when
a combination of glucose and fructose is consumed (Burke
et al., 2011; Jeukendrup, 2011).
A previous study has shown that marathon runners
in general consume less fluid and carbohydrate during
competition than the scientifically based recommended
amounts. Thus, Pfeiffer et al. (2012) reported a fluid intake
of 0.354 ± 0.187 L/hr and a carbohydrate intake of 35 ±
26 g/hr during a marathon with a mean finish time of 3:46
hr. This suggests that there could be a potential for perfor-
mance enhancement by intervening with a scientifically
based nutritional strategy in endurance events.
Hottenrott et al. (2012) conducted a nutritional inter-
ventionstudyinwhichtheycomparedcyclingperformance
achieved by applying scientifically based and freely chosen
nutritionalstrategies.Cyclingwasperformedonanergome-
ter,inalaboratory.Briefly,thestudyshowedthatendurance-
trained cyclists performed a 64 km time trial on average
6.3% faster when applying the scientifically based as com-
pared with the freely chosen nutritional strategy. The study
wasperformedasarandomizedcrossoverstudyinwhichthe
cyclistsfirstperformeda2.5-hrcyclingboutat70%oftheir
maximal oxygen uptake and subsequently the 64 km time
trial. The scientifically based nutritional strategy consisted
of a target intake of 60 g maltodextrin and glucose, 30 g
fructose, 0.5 g sodium, and 0.05 g caffeine per hour. The
study also revealed that the cyclists on average consumed
20%and28%lessfluidandcarbohydrate,respectively,when
applying their freely chosen as compared with the scientifi-
cally based nutritional strategy. It is unknown whether it
is possible to achieve a similar performance enhancement
through a nutritional intervention with marathon runners
during real world competition conditions.
Gastrointestinal(GI)symptomsduringrunningmight
cause runners to reduce intake of fluid and carbohydrate.
Runners competing in marathon races have been reported
to suffer from GI symptoms (Rehrer et al., 1989). On the
other hand, studies have also shown that during intense
16 km endurance runs, where the runners had a high car-
bohydrate intake through energy gels, GI symptoms were
generally low. At the same time, there was a correlation
between GI symptoms during the runs and history of GI
symptoms (Pfeiffer et al., 2009; Pfeiffer et al., 2012). Obvi-
ously, serious GI symptoms can influence performance in
a marathon race.
The main purpose of the current study was to test the
hypothesis that a marathon race could be completed faster
by applying a scientifically based nutritional strategy as
compared with a freely chosen nutritional strategy. In addi-
tion, GI symptoms were evaluated for all runners involved
sinceGIsymptomscanaffectfluidandcarbohydrateintake
during a marathon race and eventually affect performance.
Methods
Participants and Experimental Design
Following approval by the ethical committee ofThe North
Denmark Region Committee on Health Research Ethics,
104 nonelite marathon runners, who fulfilled the study’s
inclusion criteria, volunteered. Inclusion criteria were that
volunteers should be healthy men or women between 18
and 60 years and planning to run Copenhagen Marathon
2013 (CPH2013). The volunteers signed informed con-
sent forms. Their characteristics are included in Figure
1. The study was designed as a matched pairs design
(Figure 1) that has a relatively large statistical power
compared with the number of participants. A substantial
dropout during the training and familiarization period
before the marathon race was anticipated. In addition, it
was necessary to have an ample number of runners for
a strict pairing process. Consequently, it was assessed
necessary to initially recruit a considerable number of
just over one hundred runners at the very beginning of the
study. These runners were subsequently divided in two
groups and eventually, after a training and familiarization
period, pairs were matched with one runner from each
group, as described in details below. One of the groups
(FRE) had to apply a freely chosen nutritional strategy
in CPH2013, while the other group (SCI) had to apply a
scientifically based nutritional strategy in the same race.
Division of Runners Into Two Groups
As a part of the process of creating two comparable
groups of runners from which the pairs could subse-
quently be matched, the runners initially responded to
a self-administered questionnaire on basic characteris-
tics like body mass, height, and age. In addition, they
answered questions about their previous marathon race
experience and their self-estimated finish time in the
upcoming marathon race. Furthermore, the runners
performed a 10.0 km running time trial approximately 7
weeks before CPH2013. For this running time trial, the
runners were instructed to run in a flat terrain without
traffic lights or other hindrances and perform the trial
in a nonfatigued condition. The time to complete the
10 km running time trial was reported to the authors.
Based on the 10 km running time trial time and the self-
administered questionnaire responses, the runners were
divided into comparable groups.
Two Different Nutritional Strategies
Runners in FRE applied their own freely chosen nutri-
tional strategy in the marathon race. Further, runners in
FRE were not informed about the nutritional strategy
applied by runners in SCI. For comparison, runners in SCI
applied a scientifically based nutritional strategy consist-
ing of a combined intake of energy gels (H5 EnergyGel+,
H5 Ltd, Leicestershire, UK) and water. Runners in SCI
were instructed to consume two energy gels and 0.200
L of water 10 to 15 min before the start of CPH2013.
Furthermore, these runners were instructed to consume
one energy gel at the 40th min after the start of the race
and subsequently one gel every 20th min in the remainder
of the race.A single gel contained 20 g maltodextrin and
glucose, 0.02 g sodium, and 0.03 g caffeine. With regard
4. Marathon and Nutritional Strategy 647
to the water intake, runners in SCI were instructed to drink
at the official race depots.An intake of 0.750 L water per
hr was the recommended target. Depending on estimated
finish time and the distance between the water depots,
runners were presented with an individualized plan for
their water intake. This plan consisted of a table in which
the runners were able to see how many cups (0–2 cups)
of water, they should consume at each of the 10 official
race depots. Each cup contained approximately 0.200
L of water. Runners were recommended to stop while
consuming water, to minimize spill and thereby secure
an adequate intake. By following the scientifically based
nutritional strategy strictly, each runner would consume
close to the target intake of 0.750 L water, 60 g malto-
dextrin and glucose, 0.06 g sodium, and 0.09 g caffeine
per hour.
Familiarization
Four to five weeks before CPH2013, all runners in both
groups were asked to complete a half marathon. For the
runners in SCI, the half marathon served as a familiar-
ization session in which they gained experience with the
scientifically based nutritional strategy that they should
Figure 1 — Flowchart illustrating the progress of runners in the study. FRE, freely chosen nutritional strat-
egy; SCI, scientifically based nutritional strategy. The data on body mass is self-reported. *Different from
FRE (p = .023).
5. 648 Hansen et al.
apply later in the marathon race. Thus, they applied the
same nutritional strategy in the half marathon as in the
marathon race.As a part of the strategy, energy gels were
carried by the runners in belts. For further familiarization
during training, each runner in SCI was supplied with 20
energy gels 30 days before CPH2013. It has been recom-
mended that athletes practice their nutritional strategy
to train the gut’s capacity to absorb carbohydrate during
exercise and thereby increase exogenous carbohydrate
oxidation (Jeukendrup, 2011).
Training
All runners in both groups were asked to follow their
own personal training regimen in the run-up to CPH2013.
Runners submitted a training journal by the end of each
week during the last 11 weeks before the marathon race.
In the weekly training journal the runners had to report
the following three training variables regarding the last
week’s training: total number of covered km, total number
of running sessions, and number of running sessions that
involved interval run. For each runner, a single mean
value was initially calculated across the 11 weeks for
each of the three variables by summing all the weekly
submitted values and dividing this sum by the number of
weeks that the runner had submitted a training journal.
Subsequently, the overall mean (and standard deviation,
SD) for each group across the entire 11-week period was
calculated for each training variable.
Matched Pairing
The day before the marathon race, runners from SCI were
paired with runners from FRE according to gender as
well as their reported 10 km running time trial time. The
strict matching criteria were that pairs had to consist of
runners 1) of the same gender and 2) with a maximal dif-
ference of 1% in the 10 km running time trial time. Only
pairs that fulfilled these matching criteria were included,
and that resulted in a total of 14 matched pairs (Table 1).
The Marathon Race
Between 90 and 15 min before the start of CPH2013, all
runners were weighed (Tanita, Model HD-351, Tokyo,
Japan) wearing their running clothes and shoes. At the
same time, blood glucose was measured in a single drop of
blood taken from a fingertip. A Contour XT Meter (Bayer
HealthCare, Toronto, Canada) was used for this blood
analysis.Anearlierversionofthisbloodglucosemeasuring
apparatus has been reported to have a very high accuracy
(Pfützner et al., 2012). Finish time and split times for each
runner were measured by the race officials of CPH2013
usingaRFIDchip(UltimateSportServiceApS,Svendborg,
Denmark). Approximately 5 to 10 min after finishing the
marathon race, the runners were weighed again, wearing
the same clothes as during the weighing before the race.
In addition, at the same time, blood glucose was measured
again, applying the same method as before the race.
Table 1 Marathon Race Experience and Estimated Marathon Running Ability in the Form of Self-
Reported 10 km Running Time Trial Time Obtained Before CPH2013
Previously Completed
Marathon?
Self-Reported 10 km Running Time
Trial Time
Finish Time in CPH2013
FRE SCI FRE SCI FRE SCI
1+15 yes yes 0:38:15 0:37:52 3:03:15 2:48:21
2+16 yes yes 0:39:12 0:39:25 3:12:47 2:55:07
3+17 yes no 0:41:56 0:41:39 3:22:54 3:31:55
4+18 yes yes 0:42:10 0:42:15 4:08:33 3:23:42
5+19 yes yes 0:42:34 0:42:45 3:43:12 3:56:12
6+20 yes no 0:44:21 0:44:22 3:52:07 3:38:30
7+21 yes yes 0:44:46 0:45:01 3:38:42 3:37:25
8+22 no yes 0:45:10 0:45:15 3:56:44 3:37:24
9+23a yes yes 0:47:48 0:47:46 3:54:34 3:45:53
10+24 yes no 0:48:44 0:49:02 3:48:28 3:36:51
11+25 yes no 0:49:11 0:49:17 3:56:37 3:43:51
12+26a yes yes 0:49:53 0:49:56 3:55:55 3:50:49
13+27a yes no 0:50:20 0:50:41 4:09:03 3:59:38
14+28 no yes 0:55:01 0:55:01 4:49:00 4:33:29
Mean 0:45:40 0:45:44 3:49:26 3:38:31
± SD ± 0:04:32 ± 0:04:37 ± 0:25:05 ± 0:24:54b
Note. Included is also marathon finish time in CPH2013.
aPairs consisting of females.
bDifferent from FRE (p = .010).
6. Marathon and Nutritional Strategy 649
Intake and Gastrointestinal Symptoms
Same evening after the marathon race, all runners received
a self-administered questionnaire regarding their intake
of water, energy drink, energy gels, fruit, and any other
products from 15 min before the start and throughout the
race. The carbohydrate content of the different products
was assessed from the product manufacturers’ homep-
ages or from standard tables (Hark & Deen, 2006). The
questionnaire also addressed GI symptoms during the
race, with respect to abdominal symptoms such as reflux,
heartburn, nausea, bloating, vomiting, abdominal pain,
urge to defecate, and diarrhea—as well as such systemic
symptoms as headache, dizziness, urge to urinate, and
muscle cramps. Runners assessed the GI symptoms on a
10-point scale ranging from 0, no problem at all, to 9, the
worst it has ever been.This way of assessing GI symptoms
is based on the method applied by Pfeiffer et al. (2009).
Statistical Analysis
A statistical power analysis applying an alpha level of
0.05, a power of 0.80, and a SD of 24 min estimated that
an 8% difference in performance could be detected with 16
pairs.The Kolmogorov–Smirnov test was applied to evalu-
ate whether data were normally distributed. Student’s two-
tailed unpaired and paired t tests were applied whenever
appropriate. To test for differences between FRE and SCI
in running velocity throughout the marathon race, two-way
repeated-measuresANOVA with section of the marathon
course as within-subject factor and nutritional strategy as
between-subject factor was performed. As post hoc test,
Student’s paired samples two-tailed t tests with step-down
Holm-Bonferroni adjustment (Ludbrook, 1998) were
applied. GI symptoms were evaluated with Wilcoxon’s
signed-rank tests since most data were mainly recorded
in the no problems at all category and were therefore not
normally distributed. GI symptoms that were scored >4
were termed serious. Pearson’s correlation coefficients
were calculated for correlations between 10 km running
time trial time and finish time for CPH2013 for FRE and
SCI separately. Spearman’s correlation coefficient was
calculated for correlations between nonparametric data,
such as GI symptoms and history of GI symptoms. Effect
size (ES) was calculated as: ES = (Me—Mc)/SDc, where
Me and Mc represent mean of experimental and control
group, respectively. SDc represents standard deviation of
the control group. Classification of ES was as follows:
0.2, small difference; 0.5, moderate difference; 0.8, large
difference.Version 20 of IBM SPSS Statistics was applied
(SPSS Inc., Chicago, IL, USA). Data are presented as
mean ± SD unless otherwise indicated. The significance
level was set at p < .05.
Results
Environmental Race Conditions
CPH2013 took place in Copenhagen 19th May 2013
between 9:30 a.m. and 3:30 p.m. Conditions were cloudy
and rainy. Air temperature was 15°C at 9:30 a.m., 17°C
at 12:00 a.m., and 19°C at 2:00 p.m. Barometric pres-
sure was 1019 hourPa. Wind speed was on average 3
m/s, and relative humidity was 93%, while 7 mm of rain
was registered during the race. The 42.195 km marathon
course in CPH2013 can be described as relatively flat.
Baseline
Height, body mass, and gender distribution were not
different between FRE and SCI (p = .179 and p = .427,
respectively). However, runners in FRE were younger
than runners in SCI (p = .023; Figure 1). There was no
significant difference between the two groups in the 10
km running time trial time (p = .246; Table 1). Pearson’s
correlation coefficient showed high correlation between
the 10 km running time trial time and finish time in
CPH2013 for both FRE (r = .842, p < .001) and SCI (r =
.865, p < .001; Figure 2). Training regimen in the run-up
to CPH2013 was not different between FRE and SCI.
This applies to both total number of covered km (FRE:
31.9 ± 10.6 km/week, and SCI: 35.0 ± 12.2 km/week;
p = .462), total number of running sessions (FRE: 2.6 ±
0.6 running sessions/week, and SCI: 2.6 ± 0.7 running
sessions/week; p = .817), as well as number of running
sessions that involved interval running (FRE: 0.7 ± 0.4
sessions/week, and SCI: 0.4 ± 0.3 sessions/week; p =
.081). There was no difference between FRE and SCI
with regard to compliance of reporting training, which
amounted to 94 ± 10% and 96 ± 8%, respectively (p =
.864). Twelve and 14 runners in FRE and SCI, respec-
tively, performed a half-marathon in the preparation phase
before the marathon race.
Intake of Carbohydrate and Fluid
Carbohydrate intake was 145.6 ± 70.3 g and 234.3 ± 46.6
g for runners in FRE and SCI, respectively (p = .003;
Figure 2 — Correlation between prerace 10 km running time
trial time and marathon finish time in CPH2013.
7. 650 Hansen et al.
Table 2). Runners in FRE had their carbohydrate from
energy drinks, gels, and fruit. Fluid intake was 2.34 ±
0.93 L and 2.44 ± 0.65 L for runners in FRE and SCI,
respectively, and not different between groups (p = .740;
Table 2).
Performance
The self-reported best marathon finish time (3:43 ± 0:22
hr, performed 1.5 ± 0.8 years before CHP2013) of the
runners who had previous marathon experience (n = 20)
was comparable with the finish time in the current study
(Table 1). Finish time for runners in SCI was 10:55 ±
13:09 min shorter than for runners in FRE, which corre-
sponds to a 4.7 ± 5.6% difference (p = .010; Table 1). The
effect size was –0.43. Figure 3 represents an illustration
of the development of running velocity throughout the
marathon race for the two groups. The ANOVA showed
that there was a significant interaction between section of
the marathon course and nutritional strategy (p < .001).
The post hoc analysis showed that running velocity was
significantly different between FRE and SCI from section
30 to 35 km and through the rest of the race (p = .003
to .005). The correlation coefficient (r) was –0.205 (p =
.295) for correlation between carbohydrate intake (g/hr)
and finish time when including all 28 runners.
Body Mass and Blood Glucose
Before the marathon race, the measured body mass in FRE
andSCIwas79.0±10.8kgand75.5±15.2kg,respectively
(p = .679).After the race, body mass in FRE and SCI was
78.9 ± 10.7 kg and 75.4 ± 14.9 kg, respectively (p = .662).
Body mass was not different before as compared with after
the race, which applies to both FRE (p = .888) or SCI (p
= .589). The changes in body mass from before to after
the race were not different between the groups (p = .665).
Before the marathon race, blood glucose in FRE and
SCI was 4.8 ± 0.5 mmol/l and 5.1 ± 0.5 mmol/l, respec-
tively (p = .419).After the race, blood glucose in FRE and
SCI was 4.9 ± 0.7 mmol/l and 6.3 ± 0.9 mmol/l, respec-
tively (p = .002). Blood glucose was not different before as
compared with after the race for FRE (P = .644). In con-
trast, blood glucose was higher after than before the race
for SCI (p = .0003). The changes in blood glucose from
before to after the race were different between the groups
(p = .001). The effect size of these changes was 2.39.
GI Symptoms
GI symptoms, as experienced in the marathon race and
subsequently reported by the runners, were not different
between FRE and SCI (p = .140 to 0.823; Table 3). None
Table 2 Intake of Carbohydrate and Fluid in CPH2013 (Mean ± SD)
Nutritional Strategy Carbohydrate Fluid
(g/hr) (g/kg BM) (L/hr) (L/kg BM)
FRE 38.0 ± 17.5 1.9 ± 1.0 0.603 ± 0.209 0.029 ± 0.012
SCI 64.7 ± 12.3a 3.2 ± 0.9b 0.681 ± 0.193 0.034 ± 0.009
Note. Data are mean ± SD. BM is body mass measured before the start of CPH2013. Regarding data for FRE: n = 14
for intake per hr, and n = 12 for intake per kg body mass. Regarding data for SCI: n = 14 for intake per hr, and n = 13
for intake per kg body mass. Different from FRE:
ap = .002.
bp = .021.
Figure 3 — Development of running velocity throughout the marathon race. *Different from FRE (p
= .003 to .005).
8. Marathon and Nutritional Strategy 651
of the mean scores exceeded 4 that in the current study
would have been termed serious. Runners in both groups
reported no problem at all or very minor problems during
the race with regard to headache, dizziness, heartburn,
nausea, bloating, and vomiting.
Runners in FRE had no problem at all or very minor
problems with regard to reflux. Three runners (21%) in
FRE reported serious abdominal pain during the race with
scores ranging between 6 and 7. One participant (7%) in
FRE reported serious symptoms in urge to defecate and
diarrhea with a score of 9 in both symptoms. Two run-
ners (14%) in FRE reported serious urge to urinate with
scores of 5 and 9. Three runners (21%) in FRE reported
serious muscle cramps with scores ranging between 6
and 9. Spearman’s correlations coefficient showed fair
correlation between abdominal symptoms during the race
and history of abdominal symptoms (r = .613, p = .020),
while there was no correlation between systemic symp-
toms during the race and history of systemic symptoms
(r = .356, p = .212) for runners in FRE.
Runners in SCI reported no problem at all or very
minor problems with regard to abdominal pain during the
race. One participant (7%) in SCI reported serious symp-
toms in reflux with a score of 8. One participant (7%) in
SCI reported serious symptoms in urge to defecate with
a score of 7. Three runners (21%) in SCI reported seri-
ous urge to urinate during the race with scores ranging
between 5 and 8. Three runners (21%) in SCI reported
serious muscle cramps with scores ranging between 6
and 7. Spearman’s correlations coefficient showed a high
correlation between abdominal symptoms during the race
and history of abdominal symptoms (r = .765, p < .001),
while there was no correlation between systemic symp-
toms during the race and history of systemic symptoms
(r = .106, p = .718) for runners in SCI.
Discussion
The current study focused on nutritional strategy, intake,
performance, and GI symptoms in nonelite runners
performing a marathon race. It resulted in three major
findings. First, runners who applied a freely chosen nutri-
tional strategy consumed considerably less carbohydrate
than runners applying a scientifically based strategy did.
Second, finish time in the race was longer for runners
applying the freely chosen nutritional strategy. Third, GI
symptoms were not different between runners applying
the two different nutritional strategies.
Fluid Intake and Hydration State
Fluid intake was not different between FRE and SCI, and
at the same time it was within a recommended range of
0.400 to 0.800 L/hr (Sawka et al., 2007). In addition, the
fluid intake was larger than previously reported voluntary
intake (Pfeiffer et al., 2012). This indicated that both
groups in the current study apparently were hydrated, and
that dehydration did not play a key role for performance.
Measurements of body mass before and after the mara-
thon race supported this. Hence, neither in FRE nor in SCI
was the body mass different after the race as compared
with before. Still, one important note should be made
regarding the body mass. Runners were weighed in dry
conditions before the race, while the runners were wet
at the weighing after the race due to rain during the race.
A test performed in our laboratory after the race showed
that an estimated 0.90 L of fluid, or 0.90 kg, could be
contained in a runner’s wet clothes, typically consisting of
just shirt, shorts, socks, and shoes. Still, subtracting this
mass from the runners’ body mass after the race resulted
in body mass losses of less than 2% that is considered
Table 3 Self-Reported Scores of GI Symptoms in CPH2013
Symptom
FRE SCI
Min Max Mean (Median) Min Max Mean (Median)
Abdominal symptoms
reflux 0 3 0.29 (0) 0 8 1.21 (0)
heartburn 0 0 0 (0) 0 1 0.14 (0)
nausea 0 3 0.43 (0) 0 3 0.21 (0)
bloating 0 4 0.29 (0) 0 3 0.21 (0)
vomiting 0 0 0 (0) 0 1 0.14 (0)
abdominal pain 0 7 1.79 (0) 0 3 0.86 (0)
urge to defecate 0 9 1.14 (0) 0 7 0.50 (0)
diarrhea 0 9 0.64 (0) 0 0 0 (0)
Systemic symptoms
headache 0 4 0.43 (0) 0 0 0 (0)
dizziness 0 4 0.64 (0) 0 2 0.36 (0)
urge to urinate 0 9 2.57 (2.5) 0 8 2.14 (1)
muscle cramps 0 9 2.21 (0.5) 0 7 1.79 (0)
Note. Min and max are lowest and highest reported scores in the groups, respectively. n = 14 for FRE; n = 14 for SCI.
9. 652 Hansen et al.
a threshold below which endurance performance is not
affected negatively (Montain, 2008; Shirreffs & Sawka,
2011).A rough estimate of the sweat rate, assuming a 0.9
kg loss of body mass and a fluid intake of 2.3 l, amounts
to 0.9 L/hr that compares reasonable well with sweat
rates in comparable activities and weather conditions
(Sawka et al., 2007).
Carbohydrate Intake and Performance
Carbohydrate intake in FRE was comparable to previ-
ously reported voluntary intake (Pfeiffer et al., 2012).
More importantly though, it was considerably lower
in FRE than in SCI. And it is likely that this difference
in carbohydrate intake between FRE and SCI was the
major reason for the difference in performance between
the two groups in the current study. The lower intake
of carbohydrate might have resulted in less effective
metabolic processes for runners in FRE including less
glucose supply to the brain (Nybo, 2003a; Nybo et al.,
2003b) and working muscles (McConell et al., 1999).
During the first part of the marathon race, the body con-
tains a storage of glycogen which is gradually broken
down. This storage is only sufficient for a limited time
when applying a particular workload. Thereafter, running
velocity will decrease because of insufficient supply of
glucose. This has previously been shown for prolonged
cycling (Widrick et al., 1993). It should also be noted
that marathon finish time has previously been correlated
with carbohydrate intake indicating better performance
with larger intake (Pfeiffer et al., 2012).
The maximal uptake of glucose is approx. 60 g/hr as
previously summarized (Burke et al., 2011; Jeukendrup,
2011). That amount is in line with the intake in SCI in
the current study. Still, it should be noted that research
has shown that it is possible to enhance performance by
ingesting even larger total amounts of combined multiple
transportable carbohydrates of glucose and fructose in
a ratio of 2:1 (Currell & Jeukendrup, 2008). The latter
was found by having cyclists cycling for 2 hour while
ingesting a total amount of carbohydrate of 1.8 g/min
consisting of either a glucose-only beverage or a glucose
and fructose beverage. An argument for not producing
energy gels consisting of combined glucose and fructose
is that the latter causes the gels to have a (too) sweet
taste, which might cause that athletes ingest inadequate
amounts of gels.
Of note is that elite marathon runners apparently
over the last several years have increased focus on in-
race nutrition and hydration practices and actually have
an intake that corresponds to the present intake by the
runners in SCI (Stellingwerff, 2012; Stellingwerff, 2013).
Caffeine
The gels that were used in the current study contained
caffeine. Thus, the better performance that was observed
for runners in SCI as compared with runners in FRE
was obtained with a combined intake of carbohydrate
and caffeine in SCI. Caffeine has been shown to be
able to enhance running (Wiles et al., 1992) and cycling
performance (Kovacs et al., 1998). The reason for the
performance enhancing effect is, however, not fully under-
stood. It has been shown that caffeine intake increases the
overall concentrations of plasma free-fatty acids, which
potentially could have a sparing effect on the carbohydrate
storage in the body during prolonged exercise (Cox et al.,
2002). Others have shown that caffeine intake increased
the exogenous carbohydrate oxidation rate and suggested
that this was mediated through increased intestinal glucose
absorption and eventually could result in performance
enhancement (Yeo et al., 2005). In addition, it has been
speculated that caffeine has an impact on the central ner-
vous system, causing signals of fatigue during exercise to
be overridden (Cox et al., 2002). In the study byYeo et al.
(2005) exogenous glucose oxidation was investigated in
cyclists during 2 hour of cycling. It was reported that this
was 26% higher when adding a caffeine intake of 5 mg/
kg/hr to a glucose drink intake (48 g/hr) as compared with
ingesting the same glucose drink without caffeine. In the
study by Kovacs et al. (1998), time trial cycling perfor-
mance (lasting about 1 hr) was investigated in a group of
triathletes and cyclists. It was reported that performance
was enhanced when adding a caffeine intake of 3.2 mg/
kg/hr to the intake of a fluid that contained 68 g/L glucose.
However, when only adding a caffeine intake of 2.1 mg/
kg/hr, the performance was not different from performance
obtained by intake of the glucose drink without caffeine
(Kovacs et al., 1998). In the current study, caffeine intake
was on average 1.00 mg/kg/hr for the runners in SCI. This
amount was thus considerably lower than the amounts
applied in the studies by Yeo et al. (2005) and Kovacs et
al. (1998). It is therefore suggested that the difference in
performance between the two groups in the current study
was primarily caused by the difference in carbohydrate
intake between runners in SCI and runners in FRE. In
further support of this, it occurs unlikely that all runners
in FRE ingested complete caffeine free products. In other
words, it is likely that at least some of the runners in FRE
did also have some caffeine intake, although this can
unfortunately not be documented.
Blood Glucose
Runners in SCI had higher blood glucose concentrations
after the marathon race than before the race. That may
intuitively appear surprising. However, it has previously
been reported that blood glucose increases in the initial
phase of recovery following intense exercise, and that
this possibly is a result of an imbalance between glucose
production and utilization in which production exceeds
utilization for the initial 5 min (Calles et al., 1983). One
interpretation of the higher blood glucose values after the
race in SCI, while not in FRE, could be that the higher
total intake of carbohydrate throughout the race in SCI
caused carbohydrate availability in only that group to be
large enough for excess glucose production in the initial
phase after the finish.
10. Marathon and Nutritional Strategy 653
GI Symptoms
GI symptoms in both FRE and SCI were generally low,
which indicated that runners overall had a high level of
GI tolerance. Notably, the higher carbohydrate intake
in SCI, as compared with that in FRE, did not result in
more GI symptoms. The GI symptoms in the current
study are comparable to those reported by Pfeiffer et
al. (2009, 2012). Furthermore, the individually reported
abdominal symptoms from the marathon race in the
current study were positively correlated with history
of abdominal symptoms. This is a finding that has also
reported previously (Pfeiffer et al., 2009, 2012). An
interpretation is that the prevalence and severity of GI
symptoms does not seem to be affected by the intake
of carbohydrates during a marathon race but rather
by individual tolerance and history of GI symptoms.
Whether individual GI tolerance is trainable remains
to be investigated more thoroughly.
Strengths and Limitations of the Study
As part of the preparation for the marathon race, run-
ners in SCI familiarized themselves with the scientifi-
cally based nutritional strategy. This was done in a half
marathon 4–5 weeks before CPH2013 and in addition
during training before the marathon race. It has previ-
ously been advised that athletes test their tolerance
during hard training sessions, ideally under conditions
similar to those of the race that they are going to com-
pete in (Pfeiffer et al., 2009). The runners’ own training
regimens were not interfered with, since this was not a
training intervention study. Merely, to be able to describe
the training that was performed, runners were asked
to report training diaries. Based on these reports, it is
suggested that training was similar in FRE and SCI and
therefore not influencing the difference in performance
observed between the two groups. The two groups
were similar with regard to height, body mass, gender,
self-estimated marathon finish time, and 10 km running
time trial time. However, runners in FRE were on aver-
age approximately 8 years younger than runners in SCI
were. It is though suggested that the age difference was
not in favor of SCI with regard to performance. Direct
observation of intake was not an option in the current
study due to limited resources. Therefore, the memory
of the runners was relied on, which can be a challenge
in prolonged exercise such as a marathon (Rutishauser,
2005). The target carbohydrate intake was the same for
all runners in SCI regardless of individual factors. It is
thus possible that more individualized strategies taking
into account for example body mass and history of GI
symptoms would have resulted in even larger difference
in performance between FRE and SCI than observed.
Carbohydrate loading before prolonged running can
enhance performance (Fallowfield & Williams, 1993)
and there could have been a difference in carbohydrate
loading between the two groups. However, the current
study did not focus on this aspect.
Practical Perspectives
It is unknown why nonelite runners apparently ingest
too little carbohydrate during marathon races. Possible
reasons for an inadequate intake could include fear of GI
symptoms and inadequate availability of carbohydrate
during the race. It is also possible that runners do not have
sufficient knowledge about scientifically based nutritional
strategies and that they do not familiarize themselves
sufficiently with adequate intake during training. Still,
the current study indicates that all these aspects are either
exaggerated or can largely be dealt with.A practical per-
spective of the current study is that, apparently, it requires
an informational and perhaps even pedagogical effort by,
for example, coaches, trainers, or other influential persons
to close the performance-deteriorating gap between the
freely chosen and the scientifically based intake. Seem-
ingly, nonelite runners are not by themselves focusing
sufficiently on their nutritional strategy and its association
with their performance as it has been reported previously
(O’Neal et al., 2011).
Conclusions
It was tested whether a marathon race was completed
faster by applying a scientifically based rather than a
freely chosen in-race nutritional strategy. It was found
that nonelite runners completed a marathon race on aver-
age 11 min, corresponding to 5%, faster by applying a
scientifically based nutritional strategy as compared with
a freely chosen nutritional strategy. Furthermore, average
values of gastrointestinal symptoms were low and not
different between the two groups of runners that applied
the two different nutritional strategies.
Acknowledgments
Runners are thanked for their enthusiastic participation in the
study. H5 Ltd, Leicestershire, United Kingdom, EnergySport,
Langvad, Denmark, Sport24, Aalborg, Denmark, and Aalborg
University, Denmark, are all thanked for their support in form
of grants. Copenhagen Marathon race organizers are thanked
for their kind cooperation.
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