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European Journal of Nutrition
ISSN 1436-6207
Eur J Nutr
DOI 10.1007/s00394-012-0364-y
Water intake and post-exercise cognitive
performance: an observational study of
long-distance walkers and runners
Martin D.Benefer, Bernard M.Corfe,
Jean M.Russell, Richard Short & Margo
E.Barker
1 23
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ORIGINAL CONTRIBUTION
Water intake and post-exercise cognitive performance:
an observational study of long-distance walkers and runners
Martin D. Benefer •Bernard M. Corfe •
Jean M. Russell •Richard Short •Margo E. Barker
Received: 11 January 2012 / Accepted: 18 April 2012
ÓSpringer-Verlag 2012
Abstract
Purpose The impact of diet on endurance performance
and cognitive function has been extensively researched in
controlled settings, but there are limited observational data
in field situations. This study examines relationships
between nutrient intake and cognitive function following
endurance exercise amongst a group of 33 recreational
runners and walkers.
Methods All participants (mean age of 43.2 years) took
part in a long-distance walking event and completed diet
diaries to estimate nutrient intake across three-time periods
(previous day, breakfast and during the event). Anthropo-
metric measurements were recorded. Cognitive tests, cov-
ering word recall, ruler drop and trail making tests (TMT)
A and B were conducted pre- and post-exercise. Partici-
pants rated their exercise level on a validated scale.
Nutrient intake data were summarised using principal
components analysis to identify a nutrient intake pattern
loaded towards water intake across all time periods.
Regression analysis was used to ascertain relationships
between water intake component scores and post-exercise
cognitive function, controlling for anthropometric mea-
sures and exercise metrics (distance, duration and pace).
Results Participants rated their exercise as ‘hard-heavy’
(score 14.4, ±3.2). Scores on the water intake factor were
associated with significantly faster TMT A (p=0.001)
and TMT B (p=0.005) completion times, and a tendency
for improved short-term memory (p=0.090). Water
intake scores were not associated with simple reaction time
(assessed via the ruler drop test).
Conclusion These data are congruent with experimental
research demonstrating a benefit of hydration on cognitive
function. Further field research to confirm this relationship,
supported with precise measures of body weight, is needed.
Keywords Cognitive function Memory
Reaction time Trail making test Hydration
Introduction
Experimental evidence as to the effects of dehydration on
cognitive function is unclear. Gopinathan et al. [1] reported
a decline in cognitive performance with water restriction
and exercise; as dehydration increased from 1 to 4 % loss
in body weight, there was a corresponding decline in scores
on several cognitive tests, including trail making test
(TMT), completion speed, word recall and serial addition.
Similarly, Cian et al.[2] demonstrated impairment in short-
and long-term memory, visual-spatial function, perceptive
discrimination and reaction time in dehydrated subjects.
Recently, Ganio et al. [3] reported that exercise-induced
mild dehydration (1.6 % loss in body weight) without
hyperthermia resulted in detriment in some aspects of
M. D. Benefer R. Short M. E. Barker (&)
Human Nutrition Unit, Department of Oncology,
University of Sheffield, The Medical School,
Beech Hill Road, Sheffield S10 2RX, UK
e-mail: m.e.barker@sheffield.ac.uk
B. M. Corfe
Molecular Gastrointestinal Group, Department of Oncology,
University of Sheffield, The Medical School, Beech Hill Road,
Sheffield S10 2RX, UK
J. M. Russell
Corporate Information and Computing Service,
University of Sheffield, Sheffield, UK
123
Eur J Nutr
DOI 10.1007/s00394-012-0364-y
Author's personal copy
cognitive performance, specifically visual vigilance and
visual working memory response time, compared to a
control condition that comprised equivalent exercise with
fluid replenishment.
However, not all research is in accord in this area. Mild
dehydration in young women did not result in decrements
in cognitive performance [4] unlike men [3]. Sharma et al.
[5] reported a dose-dependent decline in hand–eye coor-
dination with increasing dehydration (1–3 % body weight
loss) in a hot–dry environment, but dehydration had less
impact in a humid or thermo-neutral environment, indi-
cating an independent impact of heat on cognition [6,7].
Indeed, Cian et al.[8] noted that cognitive performance
rebounded after a 3.5-h period of recovery following
exercise-induced dehydration even when fluid balance was
not fully restored, although presentation of data in this
study did not allow confirmation of this assertion. Fur-
thermore, Szinnai et al.[9] found no effects of dehydration,
induced through water deprivation alone, on manual
tracking, paced auditory, serial addition or Stroop test
performances. However, in this study, caffeine withdrawal,
known to affect cognitive performance detrimentally
[10–12], may have confounded the results, as caffeine was
not completely restricted in the control condition. Inter-
pretation of these studies is difficult given that there is
often residual confounding from fatigue and heat stress. A
recent detailed review by Benton [13] concluded that there
was no evidence for dehydration per se having a detri-
mental effect on cognition in adults.
Caffeine has specifically been shown to improve reac-
tion time [14,15] and attention [14,16], but not memory
scanning or delayed free word recall [15]. There have been
contradictory reports of positive [16] and no effects of
caffeine on measures of sustained attention [17]. Also,
excessive intakes of caffeine can have a negative impact on
cognitive performance [10,11], whilst low doses
(\100 mg) have been shown to have no impact on short-
term memory.
Exercise per se can have an impact on cognitive func-
tion, independent of its ability to induce hypoglycaemia
[18] or dehydration [19]. Moderate-intensity aerobic
exercise improves choice reaction time (CRT) during
exercise [20]. In addition, both CRT [21] and simple
reaction time (SRT) have been shown to improve following
exercise [22].
Research on diet and cognitive performance has typi-
cally attempted to test the influence of a single nutrient
through controlled experimentation. This study examined
the influence of multiple nutrient factors in relation to time
of consumption through principle components analysis, in
order to gain a holistic view of relationships between
nutrient intake, exercise and cognitive performance in an
observational setting.
Methods
Subjects
Thirty-five recreational runners and walkers, of whom 22
were men and 13 were women, were recruited from six
different long-distance walking events in England and
Wales throughout the summer of 2010. Most participants
(n=25) were recruited through the Long Distance
Walking Association (LDWA), having registered to com-
pete in their summer events. The remainder of the sample
were recruited via local posters throughout the University
of Sheffield. Interested individuals received an information
pack, which gave an overview of the study and explained
exactly what was involved to participate. People who were
happy to take part returned a questionnaire covering
information on demographics, general health, prior expe-
rience of long-distance events and usual activity level and
also completed a written statement giving informed con-
sent. The study was approved by the University of Shef-
field Research Ethics Committee (SMBRER160).
Over half of the sample (20; 56 %) were members of
running and walking clubs. Over half of the sample (19;
55 %) had more than 10 years’ experience of long-distance
walking or running. All subjects considered themselves to
be in a state of good health, although two participants
reported metabolic conditions (Type 2 diabetes, gastritis
and colitis). Four subjects were smokers.
Data collection process
On the morning of the event, prior to the walk, participants
made themselves known to one of the two investigators
(MDB and RS), and anthropometric measuring and cog-
nitive testing were carried out. Self-reported height and
weight information was collected, and percentage of body
fat was assessed using the Omron BS 306 Body Fat
Monitor Compact (Omron Health Care Limited, Milton
Keynes). Such body composition devices have been reported
to have accuracy of between 1 and 4.8 % [23]. Body Mass
Index was calculated as body weight (kg) over height squared
(m
2
). Fully loaded bag weight was assessed using Design Go
Luggage Scales (Design Go, London), which measures
weights up to 22 kg, and is accurate to 500 g.
Immediately following the event, subjects completed the
Borg scale [24], which assessed perceived levels of exer-
tion, and cognitive testing was again carried out. Partici-
pants provided information on distance covered in
accordance with the event entered. The research team
recorded times of event completion.
All events were held under similar weather conditions;
maximum area temperatures varied from 20 to 25 °C.
Because of this homogeneity and the crudeness of
Eur J Nutr
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measurement, we did not include temperature data in the
analysis.
Cognitive tests
Simple reaction time (SRT) was assessed via the ruler drop
test [25]. A ruler was placed between the outstretched fore-
finger and thumb of the participant around the 0 cm mark.
The investigator then dropped the ruler and participants
reacted by seizing the ruler as it fell. Reaction time was
represented as the point along the ruler in cm where it was
caught. Misses were scored as 30 cm. The test was conducted
on five occasions in succession and an average calculated.
Short-term memory (STM) was assessed via word recall
of 28 abstract four-letter words [26]. Participants were
given a list of 28 abstract words to study for 1 min and were
then given 1 min to recall as many of these as possible.
Different words were used in pre- and post-event tests.
Trail making tests (TMT) A and B were used to assess
visual attention, scanning and speed of processing [27].
Both tests require participants to join the dots on a sheet of
paper as quickly as possible; TMT A requires participants
to join numbers 1–25, whereas TMT B involves joining
numbers 1–13, interspersed with letters A-L.Performance
was recorded in seconds and number of errors was noted.
Prior to use in the field, all cognitive tests were validated
in 17 non-athletic subjects for learning effect. SRT showed
no learning effect, as did STM and TMT A. For TMT B, a
learning effect was noted; to counteract this, the post-
exercise test was presented as an inverted mirror image of
the baseline test.
Dietary intake
Before the event all participants were mailed a food diary,
a food portion size booklet [28] and instructions on how to
complete an estimated food record. All food and drink
consumed on the day previous to the walking event, that
consumed on the morning of the walk and all food and
drinks brought to the event for intended consumption were
recorded in the diary. Participants estimated food quantities
using the photographic food portion size booklet [28],
household measures and packet/bottle sizes. At the outset
of the walk, following cognitive testing, participants
returned these food diaries to the research team. These
were scrutinised for missing detail whilst participants were
competing. At the finish of the event following cognitive
testing, investigators clarified food detail, portion size and
possible missing items in the diary, noted how much of the
recorded food and drink had been consumed over the
duration of the walk, and estimated additional consumption
made at occasional food and drink stations during the
event. Nutrient analysis of diet diaries was performed using
NetWisp version 3.0 software (Tinuviel Software, War-
rington), which uses the UK Nutrient databank (McCance
and Widdowson’s Composition of Foods Integrated Data-
set) to give intake estimates for the following nutrients:
energy (kJ), total water from food and beverages (ml),
carbohydrate (g), sugar (g), non-milk extrinsic sugars
(NMES) (g), glucose (g), protein (g), fat (g), saturated fatty
acids (g), alcohol (g), caffeine (mg) and sodium (mg).
Nutrient intake was calculated by the time period of con-
sumption: previous day, breakfast and during the event.
Therefore, for each subject, we had 36 nutrient intake
measures.
Statistical analysis
Principle components analysis with Varimax rotation was
carried out on the entire nutrient intake data matrix, with
the exception of alcohol intake which was omitted from the
analysis because very few subjects consumed alcohol, and
those that did consumed very little. Principal components
analysis is a statistical technique, which produces linear
combinations of the variables that account for as much of
the variance as possible, and thus describes major patterns
in the data. The first component is the linear combination
that explains as much of the variance as possible, the
second component is the linear combination of the vari-
ables which is independent of the first component and
explains as much as possible of the remaining variance,
and so on. The coefficients in these linear combinations are
called the factor loadings, and by looking at the loadings of
the variables represented in a component, we build up a
picture of the pattern, which the component is describing.
The chief advantage of the technique is that it summarises
a large number of variables into a small number of
underlying patterns.
Five components were extracted explaining 94.8 % of
the variance. Table 1shows the loadings above 0.2 for
each of the components and the amount of the variation
explained by each component. Component 4 indicated a
positive water intake, hereafter referred to as water intake.
This water intake component had positive factor loadings
of [0.2 for the following variables: previous water intake
(0.883), breakfast water intake (0.505) and during the event
water intake (0.428). Breakfast NMES intake and breakfast
caffeine intake had negative factor loadings (-0.274 and
-0.271, respectively) in the water intake component. The
water intake component accounted for 7.5 % of total var-
iation of nutrient intake. A water intake component score
was calculated for each participant based on intakes of
previous water, breakfast water, during the event water,
breakfast NMES and breakfast caffeine.
All pre-exercise cognition scores were adjusted for
water intake component score, in order to exclude the
Eur J Nutr
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influence of water intake on pre-exercise score, and address
specifically the influence of diet on cognitive performance
after exercise. Regression analysis was used to model
influences on cognitive function. The dependent variable in
all analyses was post-exercise cognitive score. The final
model was developed in stages: firstly by forced entry of
adjusted pre-exercise cognitive scores, secondly by for-
ward stepwise entry of other possible covariates (age,
gender, height, body weight, bag weight, % body fat, Body
Mass Index, distance (km), duration (hours) and pace
(km/h) and thirdly by forced entry of water intake score.
Residuals from the final model were checked for normality.
Alpha was set at 0.05 for all analyses. The analysis was
carried out using the statistical software package SPSS for
Windows version 18.0.1.
Results
Subjects characteristics
One participant failed to provide comprehensive dietary
information and one participant did not complete the walk
due to injury, giving a final sample size of 33. Participants
Table 1 Factor loadings ([0.2) of various nutrients for each component (percentage of variation explained by each component in parentheses)
Component 1 (54.4 %) Component 2 (4.1 %) Component 3 (22.0 %) Component 4 (7.5 %) Component 5 (6.9 %)
Nutrients Loading Nutrients Loading Nutrients Loading Nutrients Loading Nutrients Loading
Previous energy
(kJ)
0.978 Breakfast
energy (kJ)
0.929 During
carbohydrate
(g)
0.902 Previous water
(ml)
0.883 Previous
sodium (mg)
0.672
Previous fat (g) 0.885 Breakfast fat (g) 0.717 During energy
(kJ)
0.865 Breakfast water
(ml)
0.505 Breakfast
sodium (mg)
0.462
Previous
saturated fatty
acids (g)
0.785 Breakfast
carbohydrate
(g)
0.701 During sugars
(g)
0.814 During water
(ml)
0.424 During
caffeine (mg)
0.321
Previous
protein (g)
0.777 Breakfast
protein (g)
0.691 During non-
milk extrinsic
sugars (g)
0.686 Breakfast non-
milk extrinsic
sugars (g)
-0.274 During sodium
(mg)
0.308
During fat (g) 0.724 Breakfast sugars
(g)
0.645 During glucose
(g)
0.631 Breakfast
caffeine (mg)
-0.271 Breakfast
saturated
fatty acids (g)
0.217
Previous
sodium (mg)
0.68 Breakfast
sodium (mg)
0.62 During water
(ml)
0.57 Previous
carbohydrate
(g)
-0.260
During protein
(g)
0.676 Breakfast
saturated fatty
acids (g)
0.587 During protein
(g)
0.46 Previous
sugars (g)
-0.264
During
saturated fatty
acids (g)
0.658 Breakfast
glucose (g)
0.501 During sodium
(mg)
0.458 Previous
glucose (g)
-0.286
During sodium
(mg)
0.561 Breakfast non-
milk extrinsic
sugars (g)
0.438 During fat (g) 0.373
Previous
carbohydrate
(g)
0.552 Previous
carbohydrate
(g)
0.344 During
saturated
fatty acids (g)
0.309
Previous non-
milk extrinsic
sugars (g)
0.305 During water
(ml)
0.283 Previous
carbohydrate
(g)
0.209
Previous sugars
(g)
0.249 Previous sugars
(g)
0.257 Previous
saturated
fatty acids (g)
-0.208
Breakfast water
(ml)
0.226 Previous
sodium (mg)
-0.236
Previous
glucose (g)
0.212
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had an average age of 43.2 years. The average distance
covered was 31.6 km (±9.7), over a time period of 6.11 h
(±1.65) carrying a mean bag weight of 3.8 kg (±1.7)
(Table 2). Mean water intake was 3,199 ml (±1,313) on
the previous day, 654 ml (±404) at breakfast and 1,727 ml
(±962) during the event (Table 3). Ratings of perceived
exertion (RPE) were recorded immediately after exercise.
A mean RPE of 14.4 (±3.2) reflected a perception of ‘hard-
heavy’ exercise on the Borg 6–20 scale.
Cognitive performance
Ruler drop performance was not altered by exercise
(Table 4). Word recall deteriorated after exercise by 1.0
(±3.6) word. This change was statistically significant
(paired ttest, p=0.028). TMT A completion time
improved following exercise by 2.1 s (±3.6). This change
was statistically significant (paired ttest, p=0.003). There
was a trend for improvement on TMT B score by a margin
of 4.1 s (±12.7) following exercise (paired ttest,
p=0.072).
A final regression model showed that ruler drop perfor-
mance was not related to water intake score (p=0.895);
pre-exercise score (p=0.001) and pace (p=0.012) were
positively related to final score (Table 5). For word recall,
the final regression model showed that performance was
positively related to water intake score; however, the rela-
tionship fell short of statistical significance (B=0.564,
p=0.090). Water intake score accounted for 4.7 % of the
variation in word recall performance. TMT A performance
(completion time) was associated with water intake score;
there was an inverse relationship (B=-2.257, p=0.001).
In this model, water intake score accounted for 12.4 % of the
variation in TMT A performance after controlling for pre-
exercise score and body weight. Similarly, TMT B com-
pletion time was inversely related to water intake score
(B=-6.334, p=0.005); water intake score accounted for
13.6 % of the variation in performance after controlling for
pre-exercise score and BMI.
Discussion
We assessed the effects of dietary intake on cognitive
performance following endurance exercise using a statis-
tical technique, which summarised a large number of
nutrient intake variables collected across different time
periods into a small number of patterns. This approach
extends the research literature from investigations of
Table 2 Participant characteristics: demographic, anthropometric
and event metrics (n=33)
Mean SD
Age (years) 43.2 14.89
Height (m) 1.73 0.09
Body weight (kg) 70.8 11.81
Bag weight (kg) 3.8 1.75
% Body fat 22.9 6.64
BMI (kg/m
2
) 23.4 2.77
Distance (km) 31.6 9.70
Duration (h) 6.11 1.65
Pace (km/h) 5.40 1.69
Table 3 Nutrient intake by
time period (n=33) Previous day Breakfast During event
Mean SD Mean SD Mean SD
Energy intake
kJ 9,275.1 3,112.4 1,675.7 850.0 4,912.3 2,392.9
Carbohydrate
g 271.9 111.4 60.5 32.7 195.5 106.8
% Energy 47.2 15.6 60.3 17.7 63.9 15.2
Fat
g 74.7 45.3 11.7 11.2 35.9 24.9
% Energy 28.8 9.9 23.0 14.4 27.3 13.1
Protein
g 91.7 43.4 16.3 10.7 26.0 24.0
% Energy 16.8 4.7 16.7 6.9 8.4 4.9
Caffeine
mg 321.9 302.3 97.5 85.4 32.7 70.8
Water
ml 3,198.9 1,312.5 653.5 404.3 1,727.2 961.6
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isolated effects of single nutrients [29,30] and single meals
[31,32]. Critically, we identified a nutrient intake pattern
orientated towards good hydration, being defined by high
loadings for water intake across all time periods and a
negative loading for caffeine and NMES intake immedi-
ately prior to exercise. This integrative approach to
describe water intake rather than isolating the effect of a
single factor as in intervention studies allows examination
of combined effects of multiple factors in a field environ-
ment. Importantly, the method addresses the potential
confounding effect of nutrient interrelationships.
Although our cognitive measures were pen and paper
tests, which lack precision relative to equivalent computer-
based tests, nevertheless, they were sufficiently sensitive to
detect differences in performance for several measures in
relation to water consumption. A learning effect was likely
across the testing period as evidenced in test development
with non-athletic subjects, and such an effect may relate to
hydration. Indeed, the superior post-race cognitive perfor-
mances of subjects with high water intake scores do not
exclude a learning effect related to water intake, but if so,
that raises interesting questions about hydration.
Word recall deteriorated following exercise, lending
support to the post-exercise deterioration in STM reported
in other studies [2,8] but contradicting the research of
Coles and Tomporowski [33] who reported an improve-
ment in visual STM after exercise. Given that exercise
duration was only 40 min in the Coles and Tomporowski
study [33] relative to 6.1 h in the current study and 2 h in
the study of Cian et al. [8], it is plausible that there is an
inverted U in cognitive performance with exercise duration
[18,34]. The strong trend in the current study for high
water intake combined with low breakfast caffeine content
to benefit short-term memory supports a protective role of
hydration against an exercise-induced decline in STM.
Interestingly, impairments in STM observed in subjects
dehydrated to -2.8 % of body weight [8] were no longer
apparent 3.5 h post-exercise irrespective of whether or not
subjects were actively rehydrated. It seems that, as
observed in this study, the adverse effect of exercise on
STM is better offset by the consumption of fluid during, as
opposed to, after exercise.
The positive associations between a high water intake
and superior performance in both TMT tests are concom-
itant with the dose-dependent decline in TMT performance
in response to induced dehydration [1]. Although hydration
status was not assessed in the current study,indeed, we
lack an accurate biomarker for hydration in a field situation
where variations in posture, food intake, muscle mass and
total body water confound interpretation of both urine and
plasma indices [35,36], it is likely that hydration status
was compromised given that exercise level was hard-
heavy. The limitations of both blood and urine hydration
biomarkers to assess changes in total body water and
describe flux between intracellular and extracellular body
compartments are recognised [37], and reliance on reported
water consumption to assess hydration is justified in field
situations.
Table 4 Cognitive
performance scores in relation
to exercise (n=33)
a
Paired ttest
Pre-exercise Post-exercise Change pvalue
a
Ruler drop (cm) 16.0 (3.7) 14.9 (3.2) -1.1 (3.6) 0.088
Word recall (no.) 8.6 (3.5) 7.6 (2.6) -1.0 (2.4) 0.028
TMT A (s) 23.9 (6.4) 21.8 (6.2) -2.1 (3.6) 0.003
TMT B (s) 40.3 (15.9) 36.2 (16.5) -4.1 (12.7) 0.072
Table 5 Final regression
models predicting cognition
scores after exercise
Dependent variable Independent variables R
2
change Coefficients
BSE pvalue
Ruler drop (cm) Adjusted pre-exercise score 0.222 0.487 0.131 0.001
Pace (km/h) 0.170 -0.786 0.292 0.012
Water intake score 0.000 -0.065 0.484 0.895
Word recall (no.) Adjusted pre-exercise score 0.491 0.526 0.093 \0.000
Water intake score 0.047 0.564 0.322 0.090
TMT A (s) Adjusted pre-exercise score 0.540 0.724 0.107 \0.000
Body weight (kg) 0.061 0.086 0.055 0.129
Water intake score 0.124 -2.257 0.623 0.001
TMT B (s) Adjusted pre-exercise score 0.316 0.575 0.140 \0.000
Body mass index (kg/m
2
) 0.123 1.494 0.769 0.062
Water intake score 0.136 -6.334 2.083 0.005
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Accurate measures of body weight to assess the level of
dehydration would have been of value in the current study, as
we would have been able to contextualise our findings in
relation to other studies. Several reviews [13,19,38] have
noted that a 2 % loss in body weight seems to be a threshold
which invokes decrements in cognitive performance. It is
possible that in our observational study that this threshold
would have been reached for some subjects, as an average
distance raced was 32 km over a 6-h period, exercise level
was rated as hard-heavy, with a very variable total water
intake during the event (mean 1,727 ml, ±962 ml).
No post-exercise change was seen in ruler drop perfor-
mance offering no support for previously demonstrated
exercise-induced improvements in SRT [22,39]. We also
observed no relationship between high water intake and
ruler drop performance. These results support those of
Jime
´nez-Pavo
´n et al. [40] who found no change in SRT in
subjects running for 50 min in a hot humid environment
without water. They did, however, find improvements in
choice reaction time, multiple reaction time and peripheral
reaction time, which was attributed to improvements in
complex tasks requiring a greater degree of processing and
attention, in line with the theory that sub-maximal exercise
increases arousal and beneficially narrows attention [19].
These results indicate water intake may have a positive
role to play in maintaining effective cognitive functioning,
adding to the body of evidence demonstrating that dehy-
dration induced by exercise, water restriction and exposure
to heat impairs cognitive function [19]. Field assessment of
hydration through accurate body weight measures would
strengthen this conclusion. The extent to which water
restriction impacts on different aspects of cognitive func-
tioning remains unclear, as does the complex relationship
between exercise, core temperature, glycaemia and
hydration [41]. It may be that task complexity interacts
with hydration status, with less cognitively demanding
tasks being unduly affected by exercise-induced dehydra-
tion [18]. Interaction between hydration status and status of
other nutrients, including sugar and electrolytes, is also
probable [41]. Further research to unravel these complex-
ities is needed, as is confirmation of the benefits of water
intake on cognition in the field.
Acknowledgments Thanks to all Long Distance Walking Associ-
ation event organisers who not only granted permission for this
research to be conducted, but were also of great assistance in
recruitment of participants.
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