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Water intake and post-exercise cognitive performance: An observational study of long-distance walkers and runners

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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). Anthropometric measurements were recorded. Cognitive tests, covering word recall, ruler drop and trail making tests (TMT) A and B were conducted pre- and post-exercise. Participants 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 measures 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.
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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
[1012], 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
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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
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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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... A local study of water consumption found a similar distribution for both men and women respondents, with the highest percentage indicating that 67.76% of the respondents consume water 6-7 days per week 18. Drinking water and remaining hydrated, especially after exercise has been found to improve cognitive function 19 and help in dehydration prevention. 20 When new cases of COVID-19 started emerging in different countries, each country responded in a similar manner by imposing a lockdown to reduce the spread. ...
... A paired samples t-test was conducted to compare the participants' drinking water, exercise intensity and exercise frequency before COVID- 19 ...
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Background. COVID-19 has brought significant changes all over the world, including Malta. These changes might have impacted people's health and lifestyle. Such changes might have limited health and fitness behaviours such as frequency of exercise, intensity of exercise and water intake. Therefore, this study aims to examine and explore how the COVID-19 pandemic has impacted fitness behaviours amongst a sample of the Maltese population. Methods. The sample (n = 995)was selected through convenience sampling. Data was collected through an online 38-item survey which was dispersed on social media during April and May 2020. The questions measured the frequency of health behaviours to provide a comparison between the participant's health behaviours in November 2019 and April 2020, during the COVID-19 pandemic. Results. The data was analysed through Factor Analysis which was conducted for dimension reduction. Factor analysis resulted in 1 factor composed of 3 variables (frequency of exercise, intensity of exercise and water consumption). Further analyses were conducted using a paired samples t-test on SPSS. Following analysis, the results showed that there was an increase in exercise frequency amongst the sample population, whereas there was a decrease in exercise intensity and water consumption. These results confirm that there was a change in health behaviours amongst the study's sample. Conclusion. This study recommends further investigation as to understand this difference in behaviours and its attributes. This can help inform health behaviours should there be further waves of the pandemic or other lockdowns
... Regarding the influence of the different ergogenic aids, increased water and caffeine intake had diverse effects on the RT. Specifically, increased water intake had no influence on RT (Benefer et al., 2013), while caffeine seemed to decrease RT duration (p = .041) (Jordan et al., 2014). ...
... In addition, several studies evaluated the influence of different variables over the RT duration. For instance, caffeine intake (Jordan et al., 2014), external focus of attention (Kovacs et al., 2018), high altitude exposure (Hydren et al., 2013), lower illumination of the court, and higher velocity of the approaching ball (Tu et al., 2010) lowered RT, while water intake seemed to have no significant influence over RT duration (Benefer et al., 2013). On the other hand, it is still unclear whether physical fatigue and strength training programs significantly affect RT (Decroix et al., 2016;Özdemir et al., 2010;Ten Haaf et al., 2018;Veness et al., 2017). ...
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This systematic review aimed to synthesize the current evidence on the feasibility of volitional reaction time (RT) tests to evaluate the information processing abilities of athletes. Four databases were searched, and, finally, 38 studies exploring the reliability, validity, or sensitivity of RT tests were included. Seven studies explored the reliability, which ranged from poor to excellent, while only three studies explored the validity of RT tests. The most important downside of the majority of the implemented RT tests is their nonspecific nature (i.e., stimulus and response did not resemble the sports actions). Sports scientists should focus on developing RT tests that are specific for each sport and refine the testing procedures to obtain accurate, reproducible, and sensitive measurements of RT.
... Less research has been carried out to determine the effect of water consumption on psychomotor tasks, which require both a cognitive and motor response. To date, studies show that water consumption increases motor speed in adults in the trail making test (Benefer et al., 2013) and improves performance in children playing a 'Wii' console game which requires a motor response in the form of the press of a button and a simultaneous downward sweep of the hand (Booth et al., 2012). However, no impact of drinking water was found on children's performance on a simple paper and pencil manual line tracing task which requires visuomotor skills (Edmonds & Burford, 2009;Edmonds & Jeffes, 2009;Chard et al., 2019) There is little consistency in the type and complexity of motor tasks included in such intervention studies to date. ...
... Our findings also supported our hypotheses that drinking water would not impact bead threading and finger-tapping. Previous studies have shown that more complex tasks, requiring more cognitive skills, such as the trail making test (Benefer et al., 2013) and a 'Wii' console game (Booth et al., 2012), are improved by consuming additional water. Conversely no effects of drinking water have been found on simpler tasks such as manual line tracing task which requires hand-eye coordination (Edmonds & Burford, 2009;Edmonds & Jeffes, 2009;Chard et al., 2019). ...
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Evidence shows that having a drink of water can improve cognitive performance in schoolchildren. This study investigated whether water consumption would improve a range of tasks requiring both cognitive and fine motor skills. Participants were 85 children (37 boys, 48 girls, mean age 10.1 years, SD = 0.6) attending a primary school in the UK. Children completed finger-tapping, bead threading, and handwriting tasks at baseline and test. They were divided into two groups; one group was offered a 500-ml bottle of water after baseline tasks were completed and the other group was not. The drink group were given 5 min to consume the water and they could choose how much to drink. We also recorded the volume of water consumed in order to consider dose response relationships. Participants in both groups were given a 25-min break, during which they could read quietly, before repeating the tasks at test. Results showed that the participants who were given a drink, regardless of volume, had faster handwriting speed at test than those who did not. Correlations between volume drunk and changes in performance from baseline to test showed there was a positive relationship between volume drunk and improvement in finger-tapping speed. These results show that the simple intervention of giving children a drink of water has a beneficial effect on fine motor skills, and handwriting, which is an integral activity in school.
... One of the common sources of simple carbohydrates (sugar) can be found in sugar-sweetened beverages, which have been associated with a multitude of NCDs, including hyperglycinemia, kidney stones, obesity, type 2 diabetes, and the onset of metabolic syndrome. An inadequate intake of plain water is linked to dehydration and increased risks of chronic kidney disease, impairment of physical activity, and disturbance of cognitive functioning [56][57][58][59]. In the current study, the water intake reported by the respondents can be described as a higher intake with 65.31% juxtaposed with all other beverages. ...
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... Adequate fluid intake is necessary for the body to be healthy [51][52][53]. This metasynthesis revealed that participants were often limiting their fluid intake [37]. ...
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Background Globally, cardiovascular disease (CVD) accounts for 45% of all chronic non-communicable disease deaths and 31% of all deaths. CVD has remained the primary cause of death in the world for the past fifteen years. Compared to other continents, CVD and its risk factors are highly prevalent in Africa, but the continent also displays a low-level of knowledge and awareness of CVD, and poor perception of its risk factors. Little research has been done on the connection between the daily lived experiences of African people and the high prevalence and poor perception of CVD and its risk factors on the African continent. The aim of this study is to provide an in-depth understanding of the daily, lived experiences of African people and the connections between these experiences and the prevention, control, and management of CVD and its risk factors. Methods A systematic search was performed in PubMed, CINAHL, EMBASE, Psych INFO, and Web of Science databases to identify published English qualitative studies of CVD and its risk factors. Qualitative metasynthesis included structured techniques of data immersion and quality appraisal, thematic synthesis, and reciprocal translation. Results Seven studies met the inclusion criteria. Four major themes were identified from the metasynthesis: 1) understanding and beliefs about CVD; 2) perceived causes/risk factors for CVD; 3) understanding and belief about obesity; 4) perceived treatment options for CVD. The metasynthesis identified a consistent disconnect among African people between seeing CVD as a deadly and chronic disease and their perceptions of the minimal signs and symptoms of the disease in the early stages. This was further compounded by the gap between traditional healers and health care professionals. Conclusions Perceptions of CVD, its risk factors, and treatments were influenced by religious and cultural factors. Given the minimal signs and symptoms experienced in the early stages of the disease, there was a consistent disconnect among African people between seeing CVD as a deadly and chronic illness. Further investigations of the religious and cultural influences and educational programs related to these areas of disconnect are needed to improve the knowledge, attitudes, and beliefs of African people.
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... By contrast, dehydration occurs when the fluid intake is insufficient to replace the free water output is more common. Even mild dehydration or a low fluid intake may impair cognitive performance [2][3][4], reduce the ability to perform physical activities [5,6] and increase the incidence and prevalence of kidney and urinary system diseases [7][8][9]. Therefore, it is necessary to develop recommendations and guidelines for adequate intake (AI) of water and raise public recognition of the importance of maintaining an adequate water intake. ...
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The hypothesis was considered that a low fluid intake disrupts cognition and mood. Most research has been carried out on young fit adults, who typically have exercised, often in heat. The results of these studies are inconsistent, preventing any conclusion. Even if the findings had been consistent, confounding variables such as fatigue and increased temperature make it unwise to extrapolate these findings. Thus in young adults there is little evidence that under normal living conditions dehydration disrupts cognition, although this may simply reflect a lack of relevant evidence. There remains the possibility that particular populations are at high risk of dehydration. It is known that renal function declines in many older individuals and thirst mechanisms become less effective. Although there are a few reports that more dehydrated older adults perform cognitive tasks less well, the body of information is limited and there have been little attempt to improve functioning by increasing hydration status. Although children are another potentially vulnerable group that have also been subject to little study, they are the group that has produced the only consistent findings in this area. Four intervention studies have found improved performance in children aged 7 to 9 years. In these studies children, eating and drinking as normal, have been tested on occasions when they have and not have consumed a drink. After a drink both memory and attention have been found to be improved.
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We investigated the cognitive effects of exercising in the heat on the field players of two football teams in a series of three matches. Different rehydration and cooling strategies were used for one of the teams during the last two games. Cognitive functions were measured before, during and immediately after each football match, as well as core temperature, body mass, plasma osmolality and glucose levels, allowing an estimate of their differential impacts on cognition. The pattern of results suggests that mild-moderate dehydration during exercise in the heat (up to 2.5%) has no clear effect on cognitive function. Instead, plasma glucose and core temperature changes appear to be the main determinants: higher glucose was related to faster and less accurate performance, whereas core temperature rises had the opposite effect. The 50% correlation between plasma glucose and core temperatures observed during exercise in the heat may help to stabilize cognitive performance via their opposing effects. The glucose-like effects of sports drinks appear to be mediated by increased plasma glucose levels, because drinks effects became non-significant when plasma glucose levels were added to the models. The cooling intervention had only a beneficial effect on complex visuo-motor speed.
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Submitted 8 healthy, endurance trained men (mean age 27.4 yrs), unacclimated to heat, to variations in body hydration. The Ss were kept euhydrated, dehydrated by controlled passive hyperthermia or exercise on a treadmill up to a weight loss of 2.8%, or hyperhydrated using a solution containing glycerol, with a total ingested volume equal to 21.4 ml/kg of body weight. On completion of a 90-min recovery period, the Ss were assigned a pedaling exercise and psychological tests of perceptive discrimination, psycho-motor skill, memory, fatigue and mood, were administered. Both dehydration conditions impaired cognitive abilities without any relative differences between them. Following arm crank exercise, further effects of dehydration were found for tracking performance only. Moreover, long-term memory was impaired in both control and hydration situations, whereas there was no decrement in performance in the hyperhydration condition. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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Dehydration, if sufficiently severe, impairs both physical and mental performance, and performance decrements are greater in hot environments and in long-lasting exercise. Athletes should begin exercise well hydrated and should drink during exercise to limit water and salt deficits. Many athletes are dehydrated to some degree when they begin exercise. During exercise, most drink less than their sweat losses, some drink too much and a few develop hyponatraemia. Athletes should learn to assess their hydration needs and develop a personalized hydration strategy that takes account of exercise, environment and individual needs. Pre-exercise hydration status can be assessed from urine frequency and volume, with additional information from urine color, specific gravity or osmolality. Changes in hydration status during exercise can be estimated from the change in body mass: sweat rate can be estimated if fluid intake and urinary losses are also measured. Sweat salt losses can be determined by collection and analysis of sweat samples. An appropriate, individualized drinking strategy will take account of pre-exercise hydration status and of fluid, electrolyte and substrate needs before, during and after a period of exercise.
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