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
www.IJSNEM-Journal.com ORIGINAL RESEARCH
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 scientifically 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 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) 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 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, nonelite runners completed a marathon on average 10:55 min, 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. 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 fluid. If this is not done, performance is attenuated, as previously reviewed (Jeukendrup, 2011). The intake of carbohydrate and fluid can collectively be termed a nutritional strategy. Controlled human laboratory studies have been performed to investigate how much carbohydrate and fluid 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 scientifically based nutritional strategies in real world endurance competitions such as in a marathon race. If fluid 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 cognitive abilities (Montain, 2008; Shirreffs & Sawka, 2011). The authors are with the Dept. of Health Science and Technology, Aalborg University, Denmark. Address author correspondence to Ernst Albin Hansen at [email protected].
This knowledge is obtained from laboratory studies and suggests that maintaining fluid balance is one of the keys to good performance in marathon running, which typically lasts between 2 and 4 hr. The current scientifically based recommendation regarding fluid intake is 0.400 to 0.800 L/hr, depending on individual differences and ambient influences (Sawka et al., 2007). Still for completeness, it should be added that a recent field 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 final 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 sufficient to maintain homeostasis of blood glucose during prolonged exercise can enhance performance (Tsintzas et al., 1996). The current scientifically based recommendation regarding carbohydrate intake is approx.
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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 performance enhancement by intervening with a scientifically based nutritional strategy in endurance events. Hottenrott et al. (2012) conducted a nutritional intervention study in which they compared cycling performance achieved by applying scientifically based and freely chosen nutritional strategies. Cycling was performed on an ergometer, in a laboratory. Briefly, the study showed that endurancetrained cyclists performed a 64 km time trial on average 6.3% faster when applying the scientifically based as compared with the freely chosen nutritional strategy. The study was performed as a randomized crossover study in which the cyclists first performed a 2.5-hr cycling bout at 70% of their 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% and 28% less fluid and carbohydrate, respectively, when applying their freely chosen as compared with the scientifically 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 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 carbohydrate 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). Obviously, 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 addition, GI symptoms were evaluated for all runners involved since GI symptoms can affect fluid 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 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 consent 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 subsequently be matched, the runners initially responded to a self-administered questionnaire on basic characteristics 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 selfadministered questionnaire responses, the runners were divided into comparable groups.
Two Different Nutritional Strategies Runners in FRE applied their own freely chosen nutritional 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 consisting 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
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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 maltodextrin 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 familiarization 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 strategy; SCI, scientifically based nutritional strategy. The data on body mass is self-reported. *Different from FRE (p = .023).
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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 recommended 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).
Matched Pairing
Training
The Marathon Race
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.
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. 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 officials of CPH2013 using a RFID chip (Ultimate Sport Service ApS, 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.
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 difference 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).
Table 1 Marathon Race Experience and Estimated Marathon Running Ability in the Form of SelfReported 10 km Running Time Trial Time Obtained Before CPH2013
1+15 2+16 3+17 4+18 5+19 6+20 7+21 8+22 9+23a 10+24 11+25 12+26a 13+27a 14+28 Mean ± SD
Previously Completed Marathon? FRE SCI yes yes yes yes yes no yes yes yes yes yes no yes yes no yes yes yes yes no yes no yes yes yes no no yes
Self-Reported 10 km Running Time Trial Time FRE SCI 0:38:15 0:37:52 0:39:12 0:39:25 0:41:56 0:41:39 0:42:10 0:42:15 0:42:34 0:42:45 0:44:21 0:44:22 0:44:46 0:45:01 0:45:10 0:45:15 0:47:48 0:47:46 0:48:44 0:49:02 0:49:11 0:49:17 0:49:53 0:49:56 0:50:20 0:50:41 0:55:01 0:55:01 0:45:40 0:45:44 ± 0:04:32 ± 0:04:37
Note. Included is also marathon finish time in CPH2013. aPairs consisting of females. bDifferent from FRE (p = .010).
Finish Time in CPH2013 FRE 3:03:15 3:12:47 3:22:54 4:08:33 3:43:12 3:52:07 3:38:42 3:56:44 3:54:34 3:48:28 3:56:37 3:55:55 4:09:03 4:49:00 3:49:26 ± 0:25:05
SCI 2:48:21 2:55:07 3:31:55 3:23:42 3:56:12 3:38:30 3:37:25 3:37:24 3:45:53 3:36:51 3:43:51 3:50:49 3:59:38 4:33:29 3:38:31 ± 0:24:54b
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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’ homepages 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 evaluate whether data were normally distributed. Student’s twotailed 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 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.
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 pressure 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, respectively, 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;
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
Figure 2 — Correlation between prerace 10 km running time trial time and marathon finish time in CPH2013.
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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 corresponds 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 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, respectively (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, respectively (p = .002). Blood glucose was not different before as compared with after the race for FRE (P = .644). In contrast, 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 FRE SCI
Carbohydrate (g/hr) (g/kg BM) 38.0 ± 17.5 1.9 ± 1.0 3.2 ± 0.9b 64.7 ± 12.3a
Fluid (L/hr) (L/kg BM) 0.603 ± 0.209 0.029 ± 0.012 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).
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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 runners (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 symptoms 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 symptoms 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 serious 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 symptoms 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 nutritional 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 marathon 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 FRE Symptom Abdominal symptoms reflux heartburn nausea bloating vomiting abdominal pain urge to defecate diarrhea Systemic symptoms headache dizziness urge to urinate muscle cramps
SCI
Min
Max
Mean (Median)
Min
Max
Mean (Median)
0 0 0 0 0 0 0 0
3 0 3 4 0 7 9 9
0.29 (0) 0 (0) 0.43 (0) 0.29 (0) 0 (0) 1.79 (0) 1.14 (0) 0.64 (0)
0 0 0 0 0 0 0 0
8 1 3 3 1 3 7 0
1.21 (0) 0.14 (0) 0.21 (0) 0.21 (0) 0.14 (0) 0.86 (0) 0.50 (0) 0 (0)
0 0 0
4 4 9
0.43 (0) 0.64 (0) 2.57 (2.5)
0 0 0
0 2 8
0 (0) 0.36 (0) 2.14 (1)
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.
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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 previously 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 contains 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 inrace 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 understood. 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 nervous 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 performance (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.
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GI Symptoms
Practical Perspectives
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.
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 perspective 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. Seemingly, 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).
Strengths and Limitations of the Study As part of the preparation for the marathon race, runners in SCI familiarized themselves with the scientifically 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 previously 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 compete 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 average 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.
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 average 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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www.IJSNEM-Journal.com ORIGINAL RESEARCH
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 scientifically 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 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) 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 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, nonelite runners completed a marathon on average 10:55 min, 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. 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 fluid. If this is not done, performance is attenuated, as previously reviewed (Jeukendrup, 2011). The intake of carbohydrate and fluid can collectively be termed a nutritional strategy. Controlled human laboratory studies have been performed to investigate how much carbohydrate and fluid 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 scientifically based nutritional strategies in real world endurance competitions such as in a marathon race. If fluid 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 cognitive abilities (Montain, 2008; Shirreffs & Sawka, 2011). The authors are with the Dept. of Health Science and Technology, Aalborg University, Denmark. Address author correspondence to Ernst Albin Hansen at [email protected].
This knowledge is obtained from laboratory studies and suggests that maintaining fluid balance is one of the keys to good performance in marathon running, which typically lasts between 2 and 4 hr. The current scientifically based recommendation regarding fluid intake is 0.400 to 0.800 L/hr, depending on individual differences and ambient influences (Sawka et al., 2007). Still for completeness, it should be added that a recent field 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 final 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 sufficient to maintain homeostasis of blood glucose during prolonged exercise can enhance performance (Tsintzas et al., 1996). The current scientifically based recommendation regarding carbohydrate intake is approx.
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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 performance enhancement by intervening with a scientifically based nutritional strategy in endurance events. Hottenrott et al. (2012) conducted a nutritional intervention study in which they compared cycling performance achieved by applying scientifically based and freely chosen nutritional strategies. Cycling was performed on an ergometer, in a laboratory. Briefly, the study showed that endurancetrained cyclists performed a 64 km time trial on average 6.3% faster when applying the scientifically based as compared with the freely chosen nutritional strategy. The study was performed as a randomized crossover study in which the cyclists first performed a 2.5-hr cycling bout at 70% of their 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% and 28% less fluid and carbohydrate, respectively, when applying their freely chosen as compared with the scientifically 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 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 carbohydrate 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). Obviously, 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 addition, GI symptoms were evaluated for all runners involved since GI symptoms can affect fluid 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 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 consent 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 subsequently be matched, the runners initially responded to a self-administered questionnaire on basic characteristics 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 selfadministered questionnaire responses, the runners were divided into comparable groups.
Two Different Nutritional Strategies Runners in FRE applied their own freely chosen nutritional 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 consisting 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
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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 maltodextrin 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 familiarization 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 strategy; SCI, scientifically based nutritional strategy. The data on body mass is self-reported. *Different from FRE (p = .023).
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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 recommended 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).
Matched Pairing
Training
The Marathon Race
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.
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. 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 officials of CPH2013 using a RFID chip (Ultimate Sport Service ApS, 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.
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 difference 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).
Table 1 Marathon Race Experience and Estimated Marathon Running Ability in the Form of SelfReported 10 km Running Time Trial Time Obtained Before CPH2013
1+15 2+16 3+17 4+18 5+19 6+20 7+21 8+22 9+23a 10+24 11+25 12+26a 13+27a 14+28 Mean ± SD
Previously Completed Marathon? FRE SCI yes yes yes yes yes no yes yes yes yes yes no yes yes no yes yes yes yes no yes no yes yes yes no no yes
Self-Reported 10 km Running Time Trial Time FRE SCI 0:38:15 0:37:52 0:39:12 0:39:25 0:41:56 0:41:39 0:42:10 0:42:15 0:42:34 0:42:45 0:44:21 0:44:22 0:44:46 0:45:01 0:45:10 0:45:15 0:47:48 0:47:46 0:48:44 0:49:02 0:49:11 0:49:17 0:49:53 0:49:56 0:50:20 0:50:41 0:55:01 0:55:01 0:45:40 0:45:44 ± 0:04:32 ± 0:04:37
Note. Included is also marathon finish time in CPH2013. aPairs consisting of females. bDifferent from FRE (p = .010).
Finish Time in CPH2013 FRE 3:03:15 3:12:47 3:22:54 4:08:33 3:43:12 3:52:07 3:38:42 3:56:44 3:54:34 3:48:28 3:56:37 3:55:55 4:09:03 4:49:00 3:49:26 ± 0:25:05
SCI 2:48:21 2:55:07 3:31:55 3:23:42 3:56:12 3:38:30 3:37:25 3:37:24 3:45:53 3:36:51 3:43:51 3:50:49 3:59:38 4:33:29 3:38:31 ± 0:24:54b
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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’ homepages 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 evaluate whether data were normally distributed. Student’s twotailed 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 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.
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 pressure 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, respectively, 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;
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
Figure 2 — Correlation between prerace 10 km running time trial time and marathon finish time in CPH2013.
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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 corresponds 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 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, respectively (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, respectively (p = .002). Blood glucose was not different before as compared with after the race for FRE (P = .644). In contrast, 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 FRE SCI
Carbohydrate (g/hr) (g/kg BM) 38.0 ± 17.5 1.9 ± 1.0 3.2 ± 0.9b 64.7 ± 12.3a
Fluid (L/hr) (L/kg BM) 0.603 ± 0.209 0.029 ± 0.012 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).
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 runners (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 symptoms 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 symptoms 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 serious 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 symptoms 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 nutritional 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 marathon 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 FRE Symptom Abdominal symptoms reflux heartburn nausea bloating vomiting abdominal pain urge to defecate diarrhea Systemic symptoms headache dizziness urge to urinate muscle cramps
SCI
Min
Max
Mean (Median)
Min
Max
Mean (Median)
0 0 0 0 0 0 0 0
3 0 3 4 0 7 9 9
0.29 (0) 0 (0) 0.43 (0) 0.29 (0) 0 (0) 1.79 (0) 1.14 (0) 0.64 (0)
0 0 0 0 0 0 0 0
8 1 3 3 1 3 7 0
1.21 (0) 0.14 (0) 0.21 (0) 0.21 (0) 0.14 (0) 0.86 (0) 0.50 (0) 0 (0)
0 0 0
4 4 9
0.43 (0) 0.64 (0) 2.57 (2.5)
0 0 0
0 2 8
0 (0) 0.36 (0) 2.14 (1)
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 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 previously 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 contains 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 inrace 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 understood. 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 nervous 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 performance (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.
Marathon and Nutritional Strategy 653
GI Symptoms
Practical Perspectives
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.
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 perspective 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. Seemingly, 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).
Strengths and Limitations of the Study As part of the preparation for the marathon race, runners in SCI familiarized themselves with the scientifically 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 previously 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 compete 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 average 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.
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 average 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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