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It was tested whether a marathon was completed faster by applying a scientifically based rather than a freely chosen nutritional strategy. Furthermore, gastrointestinal symptoms were evaluated. Non-elite runners performed a 10 km time trial seven weeks before Copenhagen Marathon 2013 for estimation of running ability. Based on the time, runners were divided into two similar groups that eventually should perform the marathon by applying the two nutritional strategies. Matched pairs design was applied. Before the marathon, runners were paired based on their pre-race running ability. Runners applying the freely chosen nutritional strategy (n=14; 33.6±9.6 years; 1.83±0.09 m; 77.4±10.6 kg; 45:40±4:32 min:s for 10 km) could freely choose their in-race intake. Runners applying the scientifically based nutritional strategy (n=14; 41.9±7.6 years; 1.79±0.11 m; 74.6±14.5 kg; 45:44±4:37 min:s) were targeting a combined in-race intake of energy gels and water, where the total intake amounted to approximately 0.750 l water, 60 g maltodextrin and glucose, 0.06 g sodium, and 0.09 g caffeine pr. hour. Gastrointestinal symptoms were assessed by a self-administered post-race questionnaire. Marathon time was 3:49:26±0:25:05 and 3:38:31±0:24:54 h:min:s for runners applying the freely chosen and the scientifically based strategy, respectively (p=.010, effect size=-0.43). Certain runners experienced diverse serious gastrointestinal symptoms, but overall, symptoms were low and not different between groups (p>.05). In conclusion, non-elite runners completed a marathon on average 10:55 min:s, corresponding to 4.7%, faster by applying a scientifically based rather than a freely chosen nutritional strategy. Furthermore, average values of gastrointestinal symptoms were low and not different between groups.
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645
International Journal of Sport Nutrition and Exercise Metabolism, 2014, 24, 645 -655
http://dx.doi.org/10.1123/ijsnem.2013-0130
© 2014 Human Kinetics, Inc
The authors are with the Dept. of Health Science and Technol-
ogy, Aalborg University, Denmark. Address author correspon-
dence to Ernst Albin Hansen at eah@hst.aau.dk.
Improved Marathon Performance
by In-Race Nutritional Strategy Intervention
Ernst Albin Hansen, Anders Emanuelsen, Robert Mørkegaard Gertsen,
and Simon Schøler Raadahl Sørensen
It was tested whether a marathon was completed faster by applying a scientically based rather than a freely
chosen nutritional strategy. Furthermore, gastrointestinal symptoms were evaluated. Nonelite runners performed
a 10 km time trial 7 weeks before Copenhagen Marathon 2013 for estimation of running ability. Based on the
time, runners were divided into two similar groups that eventually should perform the marathon by applying
the two nutritional strategies. Matched pairs design was applied. Before the marathon, runners were paired
based on their prerace running ability. Runners applying the freely chosen nutritional strategy (n = 14; 33.6
± 9.6 years; 1.83 ± 0.09 m; 77.4 ± 10.6 kg; 45:40 ± 4:32 min for 10 km) could freely choose their in-race
intake. Runners applying the scientically based nutritional strategy (n = 14; 41.9 ± 7.6 years; 1.79 ± 0.11 m;
74.6 ± 14.5 kg; 45:44 ± 4:37 min) were targeting a combined in-race intake of energy gels and water, where
the total intake amounted to approximately 0.750 L water, 60 g maltodextrin and glucose, 0.06 g sodium, and
0.09 g caffeine per hr. Gastrointestinal symptoms were assessed by a self-administered postrace questionnaire.
Marathon time was 3:49:26 ± 0:25:05 and 3:38:31 ± 0:24:54 hr for runners applying the freely chosen and the
scientically based strategy, respectively (p = .010, effect size=-0.43). Certain runners experienced diverse
serious gastrointestinal symptoms, but overall, symptoms were low and not different between groups (p >
.05). In conclusion, nonelite runners completed a marathon on average 10:55 min, corresponding to 4.7%,
faster by applying a scientically based rather than a freely chosen nutritional strategy. Furthermore, average
values of gastrointestinal symptoms were low and not different between groups.
Keywords: carbohydrate ingestion, gastrointestinal distress, running performance
During prolonged exercise, such as in a marathon
race, athletes need to consume considerable amounts of
carbohydrate and uid. If this is not done, performance
is attenuated, as previously reviewed (Jeukendrup, 2011).
The intake of carbohydrate and uid can collectively be
termed a nutritional strategy. Controlled human labora-
tory studies have been performed to investigate how much
carbohydrate and uid that should be consumed during
prolonged exercise to obtain best possible performance
(e.g., Currell & Jeukendrup, 2008; Maughan et al., 1996;
Tsintzas et al., 1996). However, less is known about the
effects of applying such scientically based nutritional
strategies in real world endurance competitions such as
in a marathon race.
If uid balance is not maintained during exercise
lasting more than approximately one hour, it can lead to
dehydration, increased body temperature, and impaired
performance, as previously reviewed (Sawka et al., 2007).
It has further been summarized that a dehydration of more
than 2% of the body mass reduces the physical and cogni-
tive abilities (Montain, 2008; Shirreffs & Sawka, 2011).
This knowledge is obtained from laboratory studies and
suggests that maintaining uid balance is one of the keys
to good performance in marathon running, which typi-
cally lasts between 2 and 4 hr. The current scientically
based recommendation regarding uid intake is 0.400 to
0.800 L/hr, depending on individual differences and ambi-
ent inuences (Sawka et al., 2007). Still for completeness,
it should be added that a recent eld study has shown that
winners of city marathons seem to lose more than 2 to
3% of their body mass, which suggests that elite runners
may be able to perform excellently with body mass loses
greater that 2% (Beis et al., 2012).
It has previously been summarized that intake of
carbohydrate during prolonged exercise can improve
performance, possibly by conserving carbohydrate depots
as well as maintaining blood glucose and carbohydrate
oxidation in the nal phase of the exercise (Burke et al.,
2011; El-Sayed et al., 1997; Kerksick et al., 2008). During
prolonged exercise, a large turnover of carbohydrate in the
working muscles eventually challenges the homeostasis
of the blood glucose (Nybo, 2003a). A carbohydrate
intake sufcient to maintain homeostasis of blood glu-
cose during prolonged exercise can enhance performance
(Tsintzas et al., 1996). The current scientically based
recommendation regarding carbohydrate intake is approx.
www.IJSNEM-Journal.com
ORIGINAL RESEARCH
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 uid and carbohydrate during
competition than the scientically based recommended
amounts. Thus, Pfeiffer et al. (2012) reported a uid intake
of 0.354 ± 0.187 L/hr and a carbohydrate intake of 35 ±
26 g/hr during a marathon with a mean nish time of 3:46
hr. This suggests that there could be a potential for perfor-
mance enhancement by intervening with a scientically
based nutritional strategy in endurance events.
Hottenrott et al. (2012) conducted a nutritional inter-
vention study in which they compared cycling performance
achieved by applying scientically based and freely chosen
nutritional strategies. Cycling was performed on an ergome-
ter, in a laboratory. Briey, the study showed that endurance-
trained cyclists performed a 64 km time trial on average
6.3% faster when applying the scientically based as com-
pared with the freely chosen nutritional strategy. The study
was performed as a randomized crossover study in which the
cyclists rst performed a 2.5-hr cycling bout at 70% of their
maximal oxygen uptake and subsequently the 64 km time
trial. The scientically 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% and 28% less uid and carbohydrate, respectively, when
applying their freely chosen as compared with the scienti-
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) symptoms during running might
cause runners to reduce intake of uid 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 inuence 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 scientically based nutritional strategy as
compared with a freely chosen nutritional strategy. In addi-
tion, GI symptoms were evaluated for all runners involved
since GI symptoms can affect uid and carbohydrate intake
during a marathon race and eventually affect performance.
Methods
Participants and Experimental Design
Following approval by the ethical committee of The North
Denmark Region Committee on Health Research Ethics,
104 nonelite marathon runners, who fullled 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
scientically 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 nish 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 at terrain without
trafc 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 scientically 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 40
th
min after the start of the race
and subsequently one gel every 20
th
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
Marathon and Nutritional Strategy 647
to the water intake, runners in SCI were instructed to drink
at the ofcial race depots. An intake of 0.750 L water per
hr was the recommended target. Depending on estimated
nish 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 ofcial
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 scientically 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 ve 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
scientically based nutritional strategy that they should
Figure 1 — Flowchart illustrating the progress of runners in the study. FRE, freely chosen nutritional strat-
egy; SCI, scientically based nutritional strategy. The data on body mass is self-reported. *Different from
FRE (p = .023).
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 fullled 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 ngertip. A Contour XT Meter (Bayer
HealthCare, Toronto, Canada) was used for this blood
analysis. An earlier version of this blood glucose measuring
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 ofcials of CPH2013
using a RFID chip (Ultimate Sport Service ApS, Svendborg,
Denmark). Approximately 5 to 10 min after nishing 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+23
a
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+26
a
yes yes 0:49:53 0:49:56 3:55:55 3:50:49
13+27
a
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 ± 0:24:54
b
Note.
Included is also marathon nish time in CPH2013.
a
Pairs consisting of females.
b
Different from FRE (p = .010).
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 reux,
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-measures ANOVA 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 coefcients
were calculated for correlations between 10 km running
time trial time and nish time for CPH2013 for FRE and
SCI separately. Spearman’s correlation coefcient was
calculated for correlations between nonparametric data,
such as GI symptoms and history of GI symptoms. Effect
size (ES) was calculated as: ES = (M
e
—M
c
)/SD
c,
where
M
e
and M
c
represent mean of experimental and control
group, respectively. SD
c
represents standard deviation of
the control group. Classication 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 signicance
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 at.
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
signicant difference between the two groups in the 10
km running time trial time (p = .246; Table 1). Pearson’s
correlation coefcient showed high correlation between
the 10 km running time trial time and nish 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 nish time in CPH2013.
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 nish 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 nish 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 signicant interaction between section of
the marathon course and nutritional strategy (p < .001).
The post hoc analysis showed that running velocity was
signicantly different between FRE and SCI from section
30 to 35 km and through the rest of the race (p = .003
to .005). The correlation coefcient (r) was –0.205 (p =
.295) for correlation between carbohydrate intake (g/hr)
and nish time when including all 28 runners.
Body Mass and Blood Glucose
Before the marathon race, the measured body mass in FRE
and SCI was 79.0 ± 10.8 kg and 75.5 ± 15.2 kg, 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 ± 12.3
a
3.2 ± 0.9
b
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:
a
p = .002.
b
p = .021.
Figure 3 — Development of running velocity throughout the marathon race. *Different from FRE (p
= .003 to .005).
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 reux. 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 coefcient 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 reux 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 coefcient 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
ndings. First, runners who applied a freely chosen nutri-
tional strategy consumed considerably less carbohydrate
than runners applying a scientically based strategy did.
Second, nish 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
uid 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 uid, 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
reux 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.
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 uid 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 rst part of the marathon race, the body con-
tains a storage of glycogen which is gradually broken
down. This storage is only sufcient for a limited time
when applying a particular workload. Thereafter, running
velocity will decrease because of insufcient supply of
glucose. This has previously been shown for prolonged
cycling (Widrick et al., 1993). It should also be noted
that marathon nish 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 by Yeo 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 uid 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 nish.
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 nding 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 scienti-
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 inuencing the difference in performance
observed between the two groups. The two groups
were similar with regard to height, body mass, gender,
self-estimated marathon nish 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 (Falloweld & 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
sufcient knowledge about scientically based nutritional
strategies and that they do not familiarize themselves
sufciently 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 inuential persons
to close the performance-deteriorating gap between the
freely chosen and the scientically based intake. Seem-
ingly, nonelite runners are not by themselves focusing
sufciently 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 scientically 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
scientically 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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... Runners were grouped into those with an intervention target of 60 g·h −1 maltodextrin/glucose (actual intake 64.7 ± 12.3 g·h −1 ) and those who chose CHO freely (actual intake 38.0 ± 17.5 g·h −1 ). The intervention group demonstrated 5% faster finishing times (Hansen et al., 2014), suggesting that the extra CHO resulted in improved performance. Future intervention studies could investigate the performance effect of bridging this CHO gap in amateur ultra-runners. ...
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Carbohydrate (CHO) intake recommendations for events lasting longer than 3h indicate that athletes should ingest up to 90g.h. ⁻¹ of multiple transportable carbohydrates (MTC). We examined the dietary intake of amateur (males: n =11, females: n =7) ultra-endurance runners (mean age and mass 41.5±5.1years and 75.8±11.7kg) prior to, and during a 24-h ultra-endurance event. Heart rate and interstitial glucose concentration (indwelling sensor) were also tracked throughout the event. Pre-race diet (each 24 over 48h) was recorded via weighed intake and included the pre-race meal (1–4h pre-race). In-race diet (24h event) was recorded continuously, in-field, by the research team. Analysis revealed that runners did not meet the majority of CHO intake recommendations. CHO intake over 24–48h pre-race was lower than recommended (4.0±1.4g·kg ⁻¹ ; 42±9% of total energy), although pre-race meal CHO intake was within recommended levels (1.5±0.7g·kg ⁻¹ ). In-race CHO intake was only in the 30–60g·h ⁻¹ range (mean intake 33±12g·h ⁻¹ ) with suboptimal amounts of multiple transportable CHO consumed. Exercise intensity was low to moderate (mean 68%HR max 45%VO 2max ) meaning that there would still be an absolute requirement for CHO to perform optimally in this ultra-event. Indeed, strong to moderate positive correlations were observed between distance covered and both CHO and energy intake in each of the three diet periods studied. Independent t -tests showed significantly different distances achieved by runners consuming ≥5 vs. <5g·kg ⁻¹ CHO in pre-race diet [98.5±18.7miles (158.5±30.1km) vs. 78.0±13.5miles (125.5±21.7km), p =0.04] and ≥40 vs. <40g·h ⁻¹ CHO in-race [92.2±13.9miles (148.4±22.4km) vs. 74.7±13.5miles (120.2±21.7km), p =0.02]. Pre-race CHO intake was positively associated with ultra-running experience, but no association was found between ultra-running experience and race distance. No association was observed between mean interstitial glucose and dietary intake, or with race distance. Further research should explore approaches to meeting pre-race dietary CHO intake as well as investigating strategies to boost in-race intake of multiple transportable CHO sources. In 24-h ultra-runners, studies examining the performance enhancing benefits of getting closer to meeting pre-race and in-race carbohydrate recommendations are required.
... Therefore, continuous running, and increased fatigue may cause a runner to experience physiological changes that either enhance or diminish their performance or even make it impossible to continue the run 6-8 . Adequate planning of training for marathons involves selecting appropriate training methods, maintaining a rational relationship between training loads, competition loads 9 , and effective recovery, and applying appropriate pre-and postworkout supplementation 10 . ...
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The aim of this research is to evaluate marathon performance and asses the influence of this long-distance running endurance exercise on the changes of muscle stiffness in recreational runners aged 50 + years. Thirty-one male long-distance runners aged 50–73 years participated in the experiment. The muscle stiffness of quadriceps and calves was measured in two independent sessions: the day before the marathon and 30 min after the completed marathon run using a Myoton device. The 42.195-km run was completed in 4.30,05 h ± 35.12 min, which indicates an intensity of 79.3% ± 7.1% of HRmax. The long-term, low-intensity running exercise (marathon) in older recreational runners, along with the low level of HRmax and VO2max showed no statistically significant changes in muscle stiffness (quadriceps and calves). There was reduced muscle stiffness, but only in the triceps of the calf in the dominant (left) leg. Moreover, in order to optimally evaluate the marathon and adequately prepare for the performance training programme, we need to consider the direct and indirect analyses of the running economy, running technique, and HRmax and VO2max and DOMS variables. These variables significantly affect the marathon exercise.
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We investigated whether an athlete's self-chosen nutrition strategy (A), compared with a scientifically determined one (S), led to an improved endurance performance in a laboratory time trial after an endurance exercise. S consisted of about 1000 mL·h(-1) fluid, in portions of 250 mL every 15 min, 0.5 g sodium·L(-1), 60 g glucose·h(-1), 30 g fructose·h(-1), and 5 mg caffeine·kg body mass(-1). Eighteen endurance-trained cyclists (16 male; 2 female) were tested using a randomized crossover-design at intervals of 2 weeks, following either A or S. After a warm-up, a maximal oxygen uptake test was performed. Following a 30-min break, a 2.5-h endurance exercise on a bicycle ergometer was carried out at 70% maximal oxygen uptake. After 5 min of rest, a time trial of 64.37 km (40 miles) was completed. The ingested nutrition was recorded every 15 min. In S, the athletes completed the time trial faster (128 vs. 136 min; p ≤ 0.001) and with a significantly higher power output (212 vs. 184 W; p ≤ 0.001). The intake of fluid, energy (carbohydrate-, mono-, and disaccharide), and sodium was significantly higher in S compared with A (p ≤ 0.001) during the endurance exercise. In the time trial, only sodium intake was significantly higher in S (p ≤ 0.001). We concluded that a time trial performance after a 2.5-h endurance exercise in a laboratory setting was significantly improved following a scientific nutrition strategy.
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The behaviors and beliefs of recreational runners with regard to hydration maintenance are not well elucidated. To examine which beverages runners choose to drink and why, negative performance and health experiences related to dehydration, and methods used to assess hydration status. Cross-sectional study. Marathon registration site. Men (n = 146) and women (n = 130) (age = 38.3 ± 11.3 years) registered for the 2010 Little Rock Half-Marathon or Full Marathon. A 23-item questionnaire was administered to runners when they picked up their race timing chips. Runners were separated into tertiles (Low, Mod, High) based on z scores derived from training volume, expected performance, and running experience. We used a 100-mm visual analog scale with anchors of 0 (never) and 100 (always). Total sample responses and comparisons between tertile groups for questionnaire items are presented. The High group (58±31) reported greater consumption of sport beverages in exercise environments than the Low (42 ± 35 mm) and Mod (39 ± 32 mm) groups (P < .05) and perceived sport beverages to be superior to water in meeting hydration needs (P < .05) and improving performance during runs greater than 1 hour (P < .05). Seventy percent of runners experienced 1 or more incidents in which they believed dehydration resulted in a major performance decrement, and 45% perceived dehydration to have resulted in adverse health effects. Twenty percent of runners reported monitoring their hydration status. Urine color was the method most often reported (7%), whereas only 2% reported measuring changes in body weight. Greater attention should be paid to informing runners of valid techniques to monitor hydration status and developing an appropriate individualized hydration strategy.
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Endurance sports are increasing in popularity and athletes at all levels are looking for ways to optimize their performance by training and nutrition. For endurance exercise lasting 30 min or more, the most likely contributors to fatigue are dehydration and carbohydrate depletion, whereas gastrointestinal problems, hyperthermia, and hyponatraemia can reduce endurance exercise performance and are potentially health threatening, especially in longer events (>4 h). Although high muscle glycogen concentrations at the start may be beneficial for endurance exercise, this does not necessarily have to be achieved by the traditional supercompensation protocol. An individualized nutritional strategy can be developed that aims to deliver carbohydrate to the working muscle at a rate that is dependent on the absolute exercise intensity as well as the duration of the event. Endurance athletes should attempt to minimize dehydration and limit body mass losses through sweating to 2-3% of body mass. Gastrointestinal problems occur frequently, especially in long-distance races. Problems seem to be highly individual and perhaps genetically determined but may also be related to the intake of highly concentrated carbohydrate solutions, hyperosmotic drinks, as well as the intake of fibre, fat, and protein. Hyponatraemia has occasionally been reported, especially among slower competitors with very high intakes of water or other low sodium drinks. Here I provide a comprehensive overview of recent research findings and suggest several new guidelines for the endurance athlete on the basis of this. These guidelines are more detailed and allow a more individualized approach.
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1. A common statistical flaw in articles submitted to or published in biomedical research journals is to test multiple null hypotheses that originate from the results of a single experiment without correcting for the inflated risk of type 1 error (false positive statistical inference) that results from this. Multiple comparison procedures (MCP) are designed to minimize this risk. The present review focuses on pairwise contrasts, the most common sort of multiple comparisons made by biomedical investigators.
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To describe the drinking behaviors of elite male marathon runners during major city marathons. Retrospective analysis of drinking behaviors. Institutional. Ten (9 winners and 1 second position) male marathon runners during 13 major city marathons. Total drinking durations and fluid intake rates during major city marathons. The ambient conditions during the 13 studied marathon races were 15.3°C ± 8.6°C and 59% ± 17% relative humidity; average marathon competition time was 02:06:31 ± 00:01:08 (hours:minutes:seconds). Total drinking duration during these races was 25.5 ± 15.0 seconds (range, 1.6-50.7 seconds) equating to an extrapolated fluid intake rate of 0.55 ± 0.34 L/h (range, 0.03-1.09 L/h). No significant correlations were found between total drink duration, fluid intake (rate and total), running speed, and ambient temperature. Estimated body mass (BM) loss based on calculated sweat rates and rates of fluid ingestion was 8.8% ± 2.1% (range, 6.6%-11.7%). Measurements of the winner in the 2009 Dubai marathon revealed a BM loss of 9.8%. The most successful runners, during major city marathons, drink fluids ad libitum for less than approximately 60 seconds at an extrapolated fluid ingestion rate of 0.55 ± 0.34 L/h and comparable to the current American College of Sports Medicine's recommendations of 0.4-0.8 L/h. Nevertheless, these elite runners do not seem to maintain their BM within current recommended ranges of 2%-3%.
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Blood glucose meters for patient self-measurement need to comply with the accuracy standards of the ISO 15197 guideline. We investigated the accuracy of the two new blood glucose meters BG*Star and iBG*Star (Sanofi-Aventis) in comparison to four other competitive devices (Accu-Chek Aviva, Roche Diagnostics; FreeStyle Freedom Lite, Abbott Medisense; Contour, Bayer; OneTouch Ultra 2, Lifescan) at different blood glucose ranges in a clinical setting with healthy subjects and patients with type 1 and type 2 diabetes. BGStar and iBGStar are employ dynamic electrochemistry, which is supposed to result in highly accurate results. The study was performed on 106 participants (53 female, 53 male, age (mean ± SD): 46 ± 16 years, type 1: 32 patients, type 2: 34 patients, and 40 healthy subjects). Two devices from each type and strips from two different production lots were used for glucose assessment (∼200 readings/meter). Spontaneous glucose assessments and glucose or insulin interventions under medical supervision were applied to perform measurements in the different glucose ranges in accordance with the ISO 15197 requirements. Sample values <50 mg/dL and >400 mg/dL were prepared by laboratory manipulations. The YSI glucose analyzer (glucose oxidase method) served as the standard reference method which may be considered to be a limitation in light of glucose hexokinase-based meters. For all devices, there was a very close correlation between the glucose results compared to the YSI reference method results. The correlation coefficients were r = 0.995 for BGStar and r = 0.992 for iBGStar (Aviva: 0.995, Freedom Lite: 0.990, Contour: 0.993, Ultra 2: 0.990). Error-grid analysis according to Parkes and Clarke revealed both 100% of the readings to be within the clinically acceptable areas (Clarke: A + B with BG*Star (100 + 0), Aviva (97 + 3), and Contour (97 + 3); and 99.5% with iBG*Star (97.5 + 2), Freedom Lite (98 + 1.5), and Ultra 2 (97.5 + 2)). This study demonstrated the very high accuracy of BG*Star, iBG*Star, and the competitive blood glucose meters in a clinical setting.
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There is little information about the actual nutrition and fluid intake habits and gastrointestinal (GI) symptoms of athletes during endurance events. This study aimed to quantify and characterize energy, nutrient, and fluid intakes during endurance competitions and investigate associations with GI symptoms. A total of 221 endurance athletes (male and female) were recruited from two Ironman triathlons (IM Hawaii and IM GER), a half-Ironman (IM 70.3), a MARATHON, a 100/150-km CYCLE race. Professional cyclists (PRO) were investigated during stage racing. A standardized postrace questionnaire quantified nutrient intake and assessed 12 GI symptoms on a scale from 0 (no problem) to 9 (worst it has ever been) in each competition. Mean CHO intake rates were not significantly different between IM Hawaii, IM GER, and IM 70.3 (62 ± 26, 71 ± 25, and 65 ± 25 g·h(-1), respectively), but lower mean CHO intake rates were reported during CYCLE (53 ± 22 g·h(-1), P = 0.044) and MARATHON (35 ± 26 g·h(-1), P < 0.01). Prevalence of serious GI symptoms was highest during the IM races (∼31%, P = 0.001) compared with IM 70.3 (14%), CYCLE (4%), MARATHON (4%), and PRO (7%) and correlated to a history of GI problems. In all data sets, scores for upper and lower GI symptoms correlated with a reported history of GI distress (r = 0.37 and r = 0.51, respectively, P < 0.001). Total CHO intake rates were positively correlated with nausea and flatulence but were negatively correlated with finishing time during both IM (r = -0.55 and r = -0.48, P < 0.001). The present study demonstrates that CHO intake rates vary greatly between events and individual athletes (6-136 g·h(-1)). High CHO intake during exercise was related not only to increased scores for nausea and flatulence but also to better performance during IM races.