Poor precompetitive sleep habits, nutrients’ deficiencies,
inappropriate
body
composition and athletic performance
in
elite gymnasts
M.-R. G.
SILVA
1,2,3,4
& T.
P
AIVA
1,5
1
Institute
of Molecular Medicine, Medical Faculty of Lisbon, Lisbon, Portugal,
2
Faculty of Health
Sciences,
University
Fernando
Pessoa,
Oporto, Portugal,
3
Research
Centre for Anthropology
and Health,
University of
Coimbra, Coimbra, Portugal,
4
Scientifi
c
Commission
of the National
School
of Gymnastics, Gymnastics Federation of Portugal, Lisbon, Portugal,
5
CENC,
Sleep
Medicine Center, Lisbon, Portu
gal
Corresponding author:
M.-R. G. Silva, Rua Carlos da Maia, 296, 4200–150
Oporto, Portugal.
E-mail:
raquel@u
fp.edu.pt
Abstract
This study aimed to evaluate body
composition,
sleep,
precompetitive
anxiety and dietary intake on the elite female
gymnasts
’
performance
prior to an
international competition.
Sixty-seven rhythmic gymnasts of high
performance
level were
evaluated
in relation to sport and training practice, body
composition,
sleep
duration,
daytime sleepiness by the Epworth
Sleepiness
Scale (ESS), sleep quality by the
Pittsburgh
Sleep Quality Index
(PSQI), precompetitive
anxiety by the Sport
Competition
Anxiety Test form A
(SCAT-A)
and detailed dietary intake just before an
international competition.
Most
gymnasts
(67.2%) suffered from mild daytime sleepiness, 77.6%
presented
poor sleep quality and 19.4%
presented
high levels of
precompetitive
anxiety. The majority of gymnasts
reported
low energy availability (EA) and low intakes of
important
vitamins including folate, vitamins D, E and K; and minerals, including calcium, iron, boron and
magnesium
(p <
.05).
Gymnasts’
performance
was positively
correlated
with age (p = .001), sport practice (p = .024),
number
of daily
training
hours (p = .000),
number
of hours of training/week (p = .000), waist
circumference
(WC) (p = .008) and sleep
duration
(p = .005). However, it was negatively
correlated
with WC/hip
circumference
(p = .000), ESS (p = .000), PSQI (p =
.042),
SCAT-A (p = .002), protein g/kg (p = .028), EA (p = .002) and exercise energy
expenditure
(p = .000). High
performance
gymnasts
presented
poor sleep habits with
consequences
upon daytime sleepiness, sleep quality and low energy availability.
Keywords:
Sleep
duration,
sleep
quality, daytime
sleepiness, energy
intake,
elite
gymnasts
1.
Intro
duction
It is currently assumed that physical exercise leads
to
favourable physiological
homeostatic
regulation
o
f
sleep, is responsible for the intensity of slow wave
sleep (Fullagar et al., 2015) and helps to stabilize
the circadian rhythm and to reduce daytime
sleepi-
ness
(Halson,
2014).
Furthermore,
a good
quality
and an
adequate amount
of sleep can ensure
sign
ifi-
cant
implications
in physical, cognitive,
emotional
balance
(Erlacher, Ehrlenspiel,
Adegbesan, &
El-Din, 2011) and in the recovery, while
reducing
the risk of an
overtraining
state (Fullagar et
al.
,
2015). A study with 12 soccer players (14
hours/
week of training) and 12 controls (1.5
hours/wee
k
of training) with an average age of 16 years
found
out that athletes had a better sleep efficiency,
a
better daily
performance
and less variation in
sleep
at week days and weekend days than
controls
(Brand,
Beck,
Gerber, Hatzinger,
&
H
olsboer-
Trachsler, 2010a
).
However, recent evidence highlights that
athletes
may experience a
reduced
quality and/or
duration
of sleep
(Halson,
2014), especially before a
compe-
tition (Lastella, Lovell, & Sargent, 2014a). High
per-
formance sports are
surrounded
by agents
causing
stress and
constraints
(high
performance demands,
different time zones, sleeping in a hotel, feeding
rou-
tines,
precompetitive
anxiety and stress), which
may
affect sleep quality in athletes (Erlacher et al.,
2011
;
Schaal et al., 2011). In
addition,
it has been
observe
d
that
adolescent
and young adult’s sleep shows a
clear
variation in
duration
and variability (Maslowsky &
Ozer, 2014); this is caused by the specific
maturation
period of
adolescence
and by external factors,
among
which are increasing school and
performance
demands,
high tech gadgets, the need for social
inter-
actions and
health-related
factors (Fullagar et
al.,
2015).
Therefore, disordered
sleep can negatively
affect athletic
performance,
namely the
task
execution and mood states (Lastella et al.,
2014a
).
Although it has been
reported
that sleep quality
and
quantity are usually poor prior to
competitio
n
(Halson,
2014), and coping strategies are
certainly
required,
as
demonstrated
in boaters, before
and
during a long distance race (Léger et al., 2008), few
studies have evaluated sleep in athletes before a
com-
petition (Erlacher et al., 2011; Lastella et al.,
2014a
;
Leeder, Glaister, Pizzoferro, Dawson, &
Ped
lar,
2012; Léger et al., 2008; Silva et al.,
2012
).
On the other hand, in elite athletes,
adequate energy
availability is
mandatory
to keep both high
perform-
ance levels and long term health (Silva &
Paiva,
2014). However, in several female athletes, especially
gymnasts, the
requirements
for a lean body
(Micho-
poulou et al., 2011)
introduce
an
important
barrie
r
to the desirable health
requirements
(Silva &
Paiva,
2014). In spite of that, they are likely to stand
pro-
longed fasting periods, which, taking the example of
Ramadan,
impact on sports are not likely to have
major influence on body
composition,
but may affect
sleep, cognition and
performance
in the short
term
(Halson,
2014).
Therefore,
special
attention should
be driven to energy and
nutritional
needs related
t
o
growth and
development
of such young
athletes,
since gymnasts train intensively from very
young
ages and maintain that training regime during
adoles-
cence and early
adulthood
(Silva & Paiva,
2014
).
Besides the
above-mentioned
drawback, high
per-
formance athletes face also an
immune
function
dis-
turbance
with a higher probability of infection; this is
enhanced
by intense training, deficient
nutrition,
sleep
restriction,
travelling and changing daily
rou-
tines and logistics (Fullagar et al.,
2015
).
Considering
the data collected, it can be
suggested
that during a
competition,
young female
athletes
’
performances
will be influenced by sleep,
nutrition,
body
composition,
anxiety and intense physical
exer-
cise.
Nevertheless,
how these athletes’
precom
petitive
behaviours may relate to their
subsequent perform-
ance has been greatly
unexplored
(Lastella et
al.,
2014a). To date, there has been no study that
has
investigated these factors in gymnasts during
training
sessions or a
competition
event.
Therefore,
this
study
aims to evaluate sleep, energy intake, body
compo-
sition and
precompetitive
anxiety in high
perform-
ance athletes of Rhythmic Gymnastics (RG)
before
an
international competition,
and to analyse
the
factors impacting both negatively and positively
on
their
performance
.
2.
Methods
2.1. Participants
Sixty-seven rhythmic gymnasts (18.7 ± 2.9 years old)
o
f
high
performance
level
(36.6 ± 7.6 hours of training
pe
r
week and 11.5 ± 3.2 years of RG’
experience)
w
e
r
e
evaluated in order to collect training and
co
mp
et
itio
n
data, daytime sleepiness and sleep quality,
pr
ec
o
mp
e
t
i
-
tive anxiety and dietary intake before the FIG
W
o
r
l
d
Cup and the RG
International Tournament
in
2011
,
which took place in
Portugal.
In
accordance
wi
t
h
delegations’ arrival, athletes were
recruited
t
hr
o
u
g
h
personal contacts or through their coaches and vo
lu
n-
teered to
participate.
Study design was approved by
the Ethical
Committee
of Medical Faculty of
L
i
s
b
o
n
and written informed consent was
obtained
from al
l
participants
with 16 years old or more.
P
a
r
ti
ci
pa
ti
on
in the study
depended
on gymnasts’ own
d
ec
i
s
i
o
n
.
2.2. Training and
competition
data
Several
parameters
were
obtained:
the
number of
training sessions per week and the
number
of
hours
of training sessions per day from which the
numb
e
r
of hours of training per week was
calculated. Per-
formance was examined using the overall
perform-
ance ranking of each
participant
from the
published
final list of general
competition
results and
ordered
from 1 the highest to 67 the lowest.
Participants
were then divided into two groups: GYM1 (n =
33)
involving gymnasts with the highest scores in
compe-
tition and GYM2 (n = 34) involving gymnasts
with
the lowest
scores.
2.3. Body
composit
ion
Body mass (BM) was
measured
by a digital scale
(SECA-872, Hamburg, Germany)
to the
nearest
0.01 kg wearing T-shirt and gym shorts before
the
warming up session. Height was
determined
with
a
portable
stadiometer (SECA-213, Hamburg,
Germany)
to the nearest 0.1 cm.
Procedures were
conducted
as
recommended
by the
Inte
rnational
Society for the
Advancement
of
Kinanthr
opometry
(Marfell-Jones,
2006). Body mass index (BMI) was
calculated as a ratio of weight to the squared
height
(kg/m
2
). The waist
circumference
(WC) was
measured
with a flexible tape at the end of a
normal
exhalation at the smallest
circumference
between
the
thorax and the hips. The hip
circumference
(HC
)
was
measured
with a flexible tape at the largest
circum-
ference on
trochanters
and Waist/Hip Ratio
(WHR)
was
calculated.
Body fat (BF), fat-free mass
(FFM)
and total body water (TBW) were assessed by
bio-
impedance
analysis
(TANITABC-545,
UK).
2.4. Sleep
Daytime sleepiness was
measured
by the
Epworth
Sleepiness Scale (ESS) (Johns, 1991) and
sleep
quality by the
Pittsburgh
Sleep Quality
Inde
x
(PSQI)
(Buysse, Reynolds, Monk,
Berman,
& Kupfer,
1989). The total ESS score can range
from
0 (zero) to 24 points. A score between 0 and
9
points is
matched
as no daytime sleepiness;
between
10 and 12 points, mild sleepiness; between 13
and
16 points,
moderate
sleepiness and; above
17
points, severe sleepiness. The PSQI score
ranges
from 0 (zero) to 21 points. A total score equal to
or
less than five points is associated with a
good
quality of sleep and the total score above 5 is
con-
sidered poor sleep quality. Bed time and awake
time during the week and at weekends were
obtained
together with subjective sleep
duration. Variability
was
measured
by the difference in sleep
duration
during weekends and week
days.
2.5.
Precompetitive
anxiety
The Sport
Competition
Anxiety Test form A
(SCAT
-
A) or Illinois
Competition Questionnaire was
applied. SCAT-A was developed by Martens
(
1977
)
to evaluate the trait anxiety in a sport event, generally
defined as the
precompetitive
anxiety; it consists of
15
items, with responses classified as rarely,
sometimes
and often. A score less than 17 points is
considered
a
reduced
level of stress; a score between 17 and
24
points is a
moderate
level and a high level of
stress
whenever the score is higher than 24
points.
2.6. Energy and nutrients
assessment
Participants
were asked to record all foods
and
beverages typically
consumed
for the 24
hours
before the interview, including time of day
and
meal type. Foods were expressed in
household
measurements
and converted to grams and
millilitres
for a
quantitative
analysis.
Nutrient
data were
coded
and analysed with Food Processor SQL.
Energ
y,
carbohydrates
and proteins based on body size,
per-
centage of fat, thiamine, riboflavin, niacin,
pantoth
e-
nic acid, vitamin B-6, folate, vitamin B-12, vitamin A,
vitamin C, vitamin D, vitamin E, vitamin K,
calcium,
iron, boron,
magnesium, manganese, phosphorus,
zinc, fibre and water were
included
in the daily
dietary analyses. The
recommended
daily levels of
the
macronutrients
’
intake were: 1.2–1.6 g/kg/day
o
f
proteins,
6–10 g/kg/day of
carbohydrates
and
a
minimum
of 55% and 20–35% of fat
(R
odriguez
et al., 2009). The
Recommended
Dietary Allowances
(RDA) from the Food and
Nutrition Board/Institute
of Medicine
(FNB/IM), considering
the values of
the
estimated
average
requirement,
was applied for
the
micronutrients.
The basal metabolic rate
(BMR)
was calculated using the
Cunningham equation, as
suggested by the ACSM (Rodriguez et al.,
2009
).
Energy availability (EA) was
estimated
(Silva &
Paiva, 2014); low energy availability (LEA) was
defined as EA <45 kcal/kg
FFM/day;
and a
threshold
below 30 kcal/kg
FFM/day
was also
invest
igated,
since it is
considered
the lowest energy threshold of
EA for women
(Rodriguez
et al., 2009).
Exercise
energy
expenditure
(EEE) was calculated using
the
2011
Compendium
of Physical Activities
(Ainsworth
et al., 2011). These calculations
accounted
for ex
er-
cise
duration,
the intensity of the gymnastics
training
and BM, which were collected using a
characteriz-
ation
questionnaire.
2.7. Statistical analysis
The
characteristics
of the
participants
are
described
with
proportions
for categorical variables and
with
mean and
standard
deviation values for
continuous
variables. To test differences in training, body
com-
position, energy and
macronutrients contribution,
sleep
characterization
and
precompetitive
anxiety of
gymnasts
separated
by
performance
groups,
t
-tests
were applied.
Spearman correlation
coefficient was
used to
determine
associations between
categor
ical
and
continuous
variables; due to the
number
of
sub-
jects evaluated, the significance level used was
1%
(p < .01). Bivariate
correlations
were run on
continu-
ous measures of
demographics,
body
compositi
on,
dietary intake, sleep, anxiety and
performance.
Regression analysis using an
automatic linear
regression model, aimed to improve model
accuracy,
was used in order to evaluate
predictors
of
perform-
ance prior to
competition.
Thus, regression
standar-
dized
predicted
values and residuals were
compute
d
iteratively, assuming linear models, and adding vari-
ables
considered
significant by
correlation analysis
using the forward stepwise multiple
regression
method.
The significance level was 5% (p <
.05).
Data were analysed using IBM SPSS statistical
soft-
ware version 21.0 for Windows (New York,
USA)
.
3.
Results
Of the 115 gymnasts in
competition,
the
response
rate was 58.2%.
Participants
were from different
con-
tinents, and thus, from several
nationalities, namely:
North America, South America,
Europe,
Asia
and
Oceania. Although most gymnasts travelled
across
different time zones to compete in an
environment
that may be both geographically distant and differe
nt
from the
home-base,
there was no
association
between the
participants
’
athletic
performance and
their country of
origin.
Age, sport practice, daily training hours and weekly
training hours were significantly greater in
GY
M1
than in GYM2 (Table
I
).
During the week, the mean sleep
duration
was
8
h10 ± 1 h30 min and most gymnasts (56.7%)
slept
less than eight hours, while on weekends, most
gym-
nasts (64.2%)
presented
an
appropriate duration of
sleep and 35.8% slept less than eight hours. In
a
n
inter-group
analysis, GYM2 slept significantly less
on weekdays than GYM1 (p = .020), since
their
bedtime was significantly later (p = .029) and awake
time was significantly earlier (p = .010) than
GY
M1
(Table II). The average score for the ESS was
10.2
± 3.1; most athletes (67.2%) had mild daytime
sleepi-
ness and only 19.4% had normal scores. The average
PSQI score was 7.0 ± 2.54 and most
gymnasts
(77.6%) had poor sleep quality. Although
GYM2
demonstrated
on average a mild daytime
sleepiness
and GYM1
presented
no daytime
sleepiness
(p = .001), GYM1 showed a significantly
poorer
quality of sleep than GYM2 (p = .038; Table
II
).
The nationality influence upon the PSQI and
t
h
e
ESS suggests the dissociation between sleep
q
u
a
li
t
y
and sleepiness. For most
countries,
athletes had
hi
g
h
mean PSQI values. The best scores (3–5 points)
w
e
r
e
obtained
for Korea, Great Britain, Mexico,
S
i
n
ga
po
r
e
and Slovenia;
moderate
scores (6–10 points) for
Por
t
u
-
gal, Russia,
Thailand,
USA, China,
France, M
a
l
a
y
s
i
a
,
Uzerbaijan,
Turkey and Austria and; poor scores
f
o
r
the
remainders.
For the ESS, the worst scores
w
e
r
e
observed in the Polish, Russian, Spanish and
Br
az
il
ia
n
athletes with an average of 13 points, and
r
ea
s
o
n
a
b
l
e
scores for the
ot
he
r
s
.
In spite of the fact that both groups had
demon-
strated
moderate
levels of
precompetitive anxiety,
19.4% of the athletes
presented
high levels and sig-
nificant differences were observed between
groups
(p = .002; Table
II
).
Mean intake of protein in GYM2 was
considered
to be
adequate,
but mean intakes of
carbohydrate
were below the
recommended
levels in both
grou
ps
in
accordance
with ADA
recommendations (2009)
(Table III). In
addition,
37.3% of gymnasts
pre-
sented EA below 45 kcal/kg
FFM/day
(64.7%
in
GYM1 and 84.8% in GYM2) and 44.8% of
gymnasts
demonstrated
EA below 30 kcal/kg
FFM/day. Gym-
nasts from GYM1
presented
significantly
higher
EEE (p = .005), resulting in significantly lower EA
than GYM2 (p = .021) (Table
III
).
The majority of gymnasts
reported
low and
above
-
adequate
intakes of
important
vitamins and
minerals
(FNB/IM,
1999, 2000, 2001, 2011; Table III). In
an
inter-group
analysis, gymnasts did not show
sign
ifi-
cant group differences (p > .05) in
micronutrients
intake, with exception for calcium (p = .005),
phos-
phorus (p = .022) and zinc (p = .003; Table
III
).
Although both groups
demonstrated
fibre and
water
below the
recommended
RDA, significant
group
differences (p < .05) were found in fibre
intake
(FNB/IM,
2005a, 2005b; Table
III
).
Pearson
correlation
coefficients (r) indicate
t
h
a
t
performance
was positively
correlated
with age
Table I. Age, training volume, body
composition
and dietary intake of gymnasts (n = 67), according to
performance
groups
(GYM1
:
gymnasts with the highest scores in
competition
and GYM2: gymnasts with the lowest scores in
competition)
GYM1 (n =
33)
Mean ± s
(min.-max
.)
GYM2 (n =
34)
Mean ± s
(min.-max.)
p
Age
(years)
19.7 ± 3.1
(16
–
26)
17.8 ± 2.2
(16
–
24)
.007
∗
Years in
sport
12.4 ± 3.2
(5
–
18)
10.7 ± 3.0
(4
–
18)
.028
∗
Training
(times/week
)
5.9 ± 0.3
(5
–
6)
5.8 ± 0.4
(5
–
6)
.228
Training (hours/da
y)
6.8 ± 1.2
(5
–
9)
5.8 ± 1.0
(5
–
8)
.000
∗
Training
(hours/week
)
40.1 ± 7.7
(25
–
54)
33.2 ± 5.7
(25
–
48)
.000
∗
BM
(kg)
47.6 ± 4.8
(36
–
55)
49.1 ± 5.0
(39
–
58)
.222
Height
(m)
1.66 ± 0.1
(1.51
–
1.75)
1.67 ± 0.1
(1.58
–
1.83)
.772
BMI
(kg/m
2
)
17.2 ± 1.0
(14.5
–
19.03)
17.6 ± 1.2
(14.9
–
20.2)
.102
WC
(cm)
54.8 ± 3.9
(49
–
67)
56.6 ± 5.6
(48
–
68)
.120
HC
(cm)
65.4 ± 6.4
(59
–
87)
71.0 ± 9.9
(57
–
91)
.009
∗
WC/HC
0.8 ± 0.1
(0.6
–
0.9)
0.8 ± 0.1
(0.7
–
1.0)
.013
∗
BF
(%)
9.2 ± 2.2
(6
–
16)
8.9 ± 2.0
(6
–
15)
.579
FFM
(kg)
30.3 ± 4.7
(27
–
46)
28.6 ± 5.5
(23
–
44)
.573
BMR
(kcal/da
y)
1145.3 ± 102.7
(1027.9
–
1441.3)
1129.7 ± 121.9
(937
–
1413)
.573
TBW
(L/day)
48.2 ± 2.2
(46.1
–
52.8)
51.5 ± 6.5
(43.3
–
57.9)
.917
Note: BM: body mass, BMI: body mass index, WC: waist
circumference,
HC: hip
circumference,
BF: body fat, FFM: fat-free mass,
BMR:
basal metabolic rate, TBW: total body
water.
∗
Performance
differences
determined
by
independent-samples
t tests, p <
.05.
Table II. Sleep
characterization
and
precompetitive
stress of gymnasts (n = 67) according to
performance
groups (GYM1: gymnasts with
the
highest scores in
competition
and GYM2: gymnasts with the lowest scores in
competition)
GYM1 (n =
33)
Mean ±
s(min.-max
.)
GYM2 (n =
34)
Mean ±
s(min.-max.)
p
Bed time, weekdays
22 h43 ± 0 h55 (21 h00–01
h00)
23 h18 ± 01 h13 (21 h30–02
h30)
.029
∗
Bed time, weekend days
00 h34 ± 0 h43 (23 h00–01
h30)
00 h51 ± 1 h48 (00 h19–03
h00)
.562
Awake time, weekdays
7 h26 ± 0 h36 (06 h58–08
h00)
06 h52 ± 0 h14 (06 h00–07
h19)
.010
∗
Awake time, weekend days
08 h05 ± 1 h16 (07 h40–10
h00)
07 h49 ± 0 h55 (06 h00–09
h00)
.611
Sleep,
weekd
ays
8 h30 ± 1 h24 (7 h00–9
h30)
7 h41 ± 1 h25 (6 h00–9
h30)
.020
∗
Sleep, weekend days
8 h32 ± 1 h11 (7 h00–9
h00)
8 h27 ± 1 h18 (7 h00–9
h00)
.770
ESS
9.0 ± 0.8
(6
–
10)
11.5 ± 3.9
(8
–
18)
.001
∗
PSQI
7.7 ± 2.5
(2
–
12)
6.4 ± 2.4
(3
–
11)
.038
∗
SCAT-A
21.5 ± 2.7
(13
–
25)
23.8 ± 3.2
(19
–
30)
.002
∗
Note: ESS: Epworth Sleepiness Scale, PSQI:
Pittsburgh
Sleep Quality Index,
SCAT-A:
Sport
Competition
Anxiety Test form A.
∗
Performance
differences
determined
by
independent-samples
t tests, p <
.05.
Table III.
Micronutrients
intakes of both groups of gymnasts comparing with the RDA from the
FNB/IM
(GYM1: gymnasts with the
highest
scores in
competition
and GYM2: gymnasts with the lowest scores in
competition
)
Energy and
macronutrients
GYM1 (n =
33)
Mean ± s
(min.-max.)
GYM2 (n =
34)
Mean ±
s(min.-max.)
p
Energy (kcal/kg
FFM/day)
60.5 ±
13.4(35
–
78)
61.0 ±
17.7(30
–
91)
.898
Carbohydrate
(g/kg/
day)
5.5 ±
2.0(2.2
–
12.1)
4.8 ±
2.5(1.5
–
11.5)
.180
Protein (g/kg/day)
1.7 ±
0.4(1.2–2.8)
1.4 ± 0.4
(0.8
–
2.4)
.001
∗
Fat
(%/day)
33.7 ±
4.5(20–42)
32.3 ± 5.9
(16
–
43)
.262
EA (kcal/kg
FFM/day)
29.7 ± 12.1
(21
–
52)
32.3 ± 11.6
(16
–
59)
.021
∗
EI (kcal/day
)
1807.6 ± 232.2
(1114
–
2089)
1726.6 ± 338.6
(1047
–
2320)
.257
EEE
(kcal/d
ay)
873.7 ± 243.9
(479
–
1370)
708.6 ± 217.4
(445
–
1186)
.005
∗
Micronutrients
Mean ± s
RDA
p
Mean ± s
RDA
p
p
Thiamine (mg/day)
1.9 ±
0.8
1.1
.000
2.0 ±
1.4
1.0
.001
.449
Riboflavin
(mg/day)
2.3 ±
1.0
1.1
.000
2.4 ±
1.7
1.0
.000
.246
Niacin
(mg/day)
23 ±
13
14
.000
27 ±
19
14
.000
.397
Pantothenic
acid
(mg/day)
5 ±
2
5
.000
4 ±
0.9
5
.000
.065
Vitamin B-6
(mg/day)
2.1 ±
1.2
1.3
.001
2.5 ±
2.0
1.2
.000
.350
Folate
(
μ
g/day)
262 ±
206
400
.009
348 ±
372
400
.02
.585
Vitamin B-12
(
μ
g/day)
4.1 ±
1.6
2.4
.000
4.1 ±
2.0
2.4
.000
.476
Vitamin A
(
μ
g/day)
1411 ±
972
700
.000
1500 ±
1416
700
.001
.182
Vitamin C
(mg/day)
90 ±
49
75
.000
80 ±
57
65
.000
.537
Vitamin D
(
μ
g/day)
3 ±
2
15
.000
3 ±
3
15
.000
.688
Vitamin E
(mg/day)
8 ±
3
15
.000
6 ±
1
15
.000
.349
Vitamin K
(
μ
g/day)
17.8 ±
9.6
90
.000
13.9 ±
7.5
75
.000
.549
Calcium
(mg/day)
884 ±
246
1000
.000
686 ±
239
1300
.000
.005
b
Iron
(mg/day)
13 ±
8
18
.001
14 ±
11
15
.299
a
.537
Boron
(mg/day)
5 ±
3
20
.000
6 ±
4
17
.000
.176
Magnesium (mg/day)
293 ±
98
310
.061
a
222 ±
97
360
.002
.229
Manganese (mg/day)
3.4 ±
6.6
1.8
.041
2.7 ±
1.8
1.6
.021
.171
Phosphorus (mg/day)
1301 ±
658
700
.000
1059 ±
289
1250
.293
a
.022
b
Zinc
(mg/day)
12 ±
5
8
.001
11 ±
6
9
.001
.003
b
Fibre
(g/day)
5 ±
2
25
.000
13 ±
6
26
.000
.024
b
Water
(L/day)
1.5 ±
0.4
2.7
.000
1.4 ±
0.4
2.3
.000
.151
Note: EA: energy availability, EI: energy intake, EEE: exercise energy
expenditure.
The data do not include vitamin and mineral
supplements.
a
No
significant differences were observed between minerals and the RDA (p >
.05).
b
Significant
differences were observed in
micronutrients
intakes between the two groups of gymnasts (p <
.05).
∗
Performance
differences
determined
by
independent-samples
t tests, p <
.05.
