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Normative health-related fitness values for children: Analysis of 85347 test results on 9-17-year-old Australians since 1985

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Objectives To provide sex- and age-specific normative values for health-related fitness of 9–17-year-old Australians. Methods A systematic literature search was undertaken to identify peer-reviewed studies reporting health-related fitness data on Australian children since 1985—the year of the last national fitness survey. Only data on reasonably representative s amples of apparently healthy (free from known disease or injury) 9–17-year-old Australians, who were tested using field tests of health-related fitness, were included. Both raw and pseudo data (generated using Monte Carlo simulation) were combined with sex- and age-specific normative centile values generated using the Lambda Mu and Sigma (LMS) method. Sex- and age-related differences were expressed as standardised effect sizes. Results Normative values were displayed as tabulated percentiles and as smoothed centile curves for nine health-related fitness tests based on a dataset comprising 85347 test performances. Boys typically scored higher than girls on cardiovascular endurance, muscular strength, muscular endurance, speed and power tests, but lower on the flexibility test. The magnitude of the age-related changes was generally larger for boys than for girls, especially during the teenage years. Conclusion This study provides the most up-to-date sex- and age-specific normative centile values for the health-related fitness of Australian children that can be used as benchmark values for health and fitness screening and surveillance systems.
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Normative health-related tness values for children:
analysis of 85347 test results on 917-year-old
Australians since 1985
Mark J Catley, Grant R Tomkinson
The appendix to this paper
is published online only. To
view this le please visit the
journal online (http://dx.doi.
org/10.1136/bjsports-2011-
090218).
Health and Use of Time (HUT)
Group, Sansom Institute for
Health Research, University of
South Australia, Adelaide,
Australia
Correspondence to
Dr Grant R Tomkinson, Health
and Use of Time (HUT) Group,
Sansom Institute for Health
Research, University of South
Australia, GPO Box 2471,
Adelaide, SA 5001, Australia;
grant.tomkinson@unisa.edu.au
Received 13 May 2011
Accepted 22 September 2011
Published Online First
21 October 2011
To cite: Catley MJ,
Tomkinson GR. Br J Sports
Med 2013, 47,98109.
ABSTRACT
Objectives To provide sex- and age-specic normative
values for health-related tness of 917-year-old
Australians.
Methods A systematic literature search was undertaken
to identify peer-reviewed studies reporting health-related
tness data on Australian children since 1985the year
of the last national tness survey. Only data on
reasonably representative s amples of apparently healthy
(free from known disease or injury) 917-year-old
Australians, who were tested using eld tests of health-
related tness, were included. Both raw and pseudo
data (generated using Monte Carlo simulation) were
combined with sex- and age-specic normative centile
values generated using the Lambda Mu and Sigma
(LMS) method. Sex- and age-related differences were
expressed as standardised effect sizes.
Results Normative values were displayed as tabulated
percentiles and as smoothed centile curves for nine
health-related tness tests based on a dataset
comprising 85347 test performances. Boys typically
scored higher than girls on cardiovascular endurance,
muscular strength, muscular endurance, speed and
power tests, but lower on the exibility test. The
magnitude of the age-related changes was generally
larger for boys than for girls, especially during the
teenage years.
Conclusion This study provides the most up-to-date
sex- and age-specic normative centile values for the
health-related tness of Australian children that can be
used as benchmark values for health and tness
screening and surveillance systems.
BACKGROUND
Physical tness is considered to be an important
marker of current and future health in children and
adults.
1
In children, cardiovascular tness is a
weak-to-strong predictor of total and abdominal
adiposity, cardiovascular disease risk factors, cancer
and mental health.
12
Certain muscular tness com-
ponents (eg, strength and endurance) are moderate
predictors of cardiovascular disease risk factors,
skeletal health and mental health.
1
Meaningful rela-
tionships have also been reported between running
speed (another muscular tness component) and
skeletal health.
3
In adults, cardiovascular tness is a
strong and independent predictor of all-cause
mortality and cardiovascular disease mortality and
morbidity,
4
stroke,
5
cancer, mental health,
6
health-related quality of life
7
and many other cardi-
ometabolic risk factors and comorbidities.
89
Moreover, physical tness tracks moderately well
from childhood through to adulthood.
1013
This
evidence highlights the need to include
health-related tness testing (ie, the testing of
tness components such as cardiovascular and mus-
cular tness that have the strongest links with
health outcomes) as part of existing health and
tness screening and surveillance systems.
Although the most valid assessments of tness
require sophisticated laboratory equipment and a
high level of tester expertise, they unfortunately are
not suitable for mass testing. On the other hand,
properly conducted eld tests offer simple, feasible,
and practical alternatives, which typically demon-
strate good reliability and validity.
21417
In
Australia, unlike in Europe and North America
where standardised test batteries such as the
Eurot
18
or FITNESSGRAM
19
are widely adminis-
tered, a number of different eld-based tness tests
and testing protocols have been used over time. For
example, the most popular eld test of cardiovascu-
lar tness in Australia in the 1960s and 1970s was
the 549-m (600 yd) run; in the 1980s and 1990s, it
was the 1600-m run; and over the past decade or so,
it has been the 20-m shuttle run.
20
Many physical
educators and sports coaches in Australia continue
to administer tests that are no longer in favour,
largely because normative data (which are now
several decades old) are available. This makes it dif-
cult to assess the current status of health-related
tness in Australian children. Further compounding
the problem is that the last national tness survey of
Australian children was conducted in 1985,
21
and
with convincing evidence of recent temporal
changes in several components of tness,
2224
the
usefulness of such data seems to be limited.
Because there has never been a follow-up to the
1985 national survey, this study aimed to locate
large and reasonably representative datasets of
Australian children to generate normative centile
values for health-related tness. This study also
aimed to quantify sex- and age-related differences
in health-related tness. These normative data will
facilitate the identication of children with (a) low
tness in order to set appropriate goals and to
promote positive health behaviours, and (b) specic
tness characteristics that may be considered
important for sporting success.
METHODS
Data sources
A systematic review of the peer-reviewed scientic
literature was undertaken to locate studies report-
ing descriptive summary data on Australian chil-
dren tested for health-related tness using eld
tests. Candidate studies were searched for in
1 of 12 Catley MJ, et al.Br J Sports Med 2013;47:98108. doi:10.1136/bjsports-2011-090218
Original articles
November 2009 using a computer search of online bibliographic
databases (Ausport, CINAHL, Medline, PubMed, Scopus and
SPORTDiscus). The search string used for the computer search
was: ((((((((((((((((tness) OR aerobic) OR anaerobic) OR
cardio*) OR endurance) OR agility) OR exibility) OR speed)
OR power) OR strength) OR sprint*) OR jump*) OR push-up*)
OR sit-up*) OR grip strength) OR sit and reach) AND
(((((((child*) OR paediatric*) OR adolesc*) OR boy*) OR girl*)
OR youth*) OR teen*) AND (Australia*). All titles and abstracts
(when available) were assessed to identify eligible articles, with
full-text articles retrieved if there was doubt in an articles eligi-
bility. A number of Australian researchers were contacted
through email to ask whether they knew of any appropriate
studies or unpublished datasets.
Inclusion/exclusion criteria
Studies were included if they explicitly reported descriptive
health-related tness test data for apparently healthy (free from
known disease or injury) 917-year-old Australians who were
tested from 1985 onwards and if they reported data at the sex
by age by test level, on children directly measured using eld-
based tness tests for which explicit testing protocols were avail-
able. Studies were excluded if they reported descriptive data
that were published in another identied study. The reference
Table 1 Summary of the included studies that have been used to assess the health-related fitness of 917-year-old Australians since 1985
Tests reported in included studies
Study Year
Age
(years) N
Raw
data
Sampling
method
Sample
base Protocol
Push-
ups
Sit-
ups
Standing
broad
jump
Basketball
throw
50 m
sprint
Sit-
and-
reach
Hand-
grip
1.6
km
run
20 m
shuttle
run
ACHPER
50
1994 918 39104 yes School-based;
stratified,
proportional
State
(VIC)
ACHPER
50
•• •
Barnett et al
51
2007 1517 2169 no School-based;
stratified,
random
State
(NSW)
ACHPER
50
Birchall
52
1990 512 6184 yes School-based;
convenience
State
(VIC)
Pyke
21
•• • •
Booth et al
53
1997 9, 11,
13,15
399634 no School-based;
stratified,
proportional
State
(NSW)
ACHPER
50
•• •
Booth et al
54
2004 915 357466 no School-based;
stratified,
proportional
State
(NSW)
ACHPER
50
Burke et al
55
2004 1013 38117 yes School-based;
stratified,
proportional
Capital
city
(WA)
ACHPER
50
Cooley and
McNaughton
56
1998 1116 339636 no School-based;
stratified,
proportional
State
(TAS)
ACHPER
50
Dollman
et al
57
1997 1012 118450 yes School-based;
stratified,
proportional
State
(SA)
Pyke
21
••
Dollman pers.
comm.
2002 1112 19154 yes School-based;
stratified,
random
State
(SA)
Pyke
21
••
Dollman, pers.
comm.
2002 812 8389 yes School-based;
stratified,
proportional
State
(SA)
ACHPER
50
••
Pyke
21
Hands
58
2000 612 1437 yes School-based;
stratified,
random
Capital
city
(WA)
ACHPER
50
•• • •
Pyke
21
McIntyre,
pers. comm.
2009 1011 2344 yes School-based;
stratified,
random
Capital
city
(WA)
ACHPER
50
McNaughton
et al
59
1995 710 3083 no School-based;
stratified,
random
State
(TAS)
Pyke
21
••
Pyke
21
1985 715 405497 yes School-based;
stratified,
proportional
National Pyke
21
•• • •• •
Vandongen
et al
60
1990 11 485486 no School-based;
stratified,
random
Capital
city
(WA)
ACHPER
50
••
identifies test data that are available.
ACHPER, Australian Council for Health, Physical Education and Recreation; year, year of testing; n, sample size range per sex by age by test group; VIC, TAS, SA, WA, NSW
Catley MJ, et al.Br J Sports Med 2013;47:98108. doi:10.1136/bjsports-2011-090218 1 of 12
Original articles
lists of all included studies were examined and cross-referenced
to identify additional studies. Attempts were made to contact
the corresponding author of each study to request raw data and/
or to clarify study details.
Initial data analysis
The following descriptive data were extracted from each
included study: sex, age, year of testing, sample size, mean, SD,
tness test type and test protocol. Only data for commonly used
tness tests that were collected using protocols that were origin-
ally described in national or state-based tness surveys of
Australian children were retained for further analysis. Tests were
considered to be commonif they were used to measure tness
in children across a broad range of ages and in at least two sep-
arate studies. Data for each tness test were expressed in a
common metric, and protocol differences were corrected where
possible (eg, 20 m shuttle run data were expressed as the
number of completed stages using the correction procedures
described by Tomkinson et al).
25
However, if protocol correc-
tion was not possible, then only tness data collected using the
most common test protocol were retained. All available raw data
were checked for anomalies by running range checks with data
±3 SDs away from the respective study by sex by age by test
mean excluded. Age was expressed in whole years as the age at
last birthday.
Statistical analysis
Sex- and age-specic normative centile values were calculated
using a dataset comprising raw data and pseudo data that were
generated using the method described by Tomkinson.
24
Normative centile values were generated using LMSChartmaker
Light (v2.3, The Institute of Child Health, London) software,
Figure 1 Flowchart outlining the identication of the included studies.
1 of 12 Catley MJ, et al.Br J Sports Med 2013;47:98108. doi:10.1136/bjsports-2011-090218
Original articles
which analyses data using the LMS method.
26
The LMS method
ts smooth centile curves to reference data by summarising the
changing distribution of three sex- and age-specic curves repre-
senting the skewness (L: expressed as a Box-Cox power), the
median (M) and the coefcient of variation (S). Using penalised
likelihood, the curves can be tted as cubic splines using non-
linear regression, and the extent of smoothing required can be
expressed in terms of smoothing parameters or equivalent
degrees of freedom.
27
For e ach tness test, differences in means between: (a) age-
matched Australian boys and girls (eg, 10-year-old boys vs
10-year-old girls); (b) sex-matched Australian children of differ-
ent ages (eg, 10-year-old boys vs 11-year-old boys); and (c) sex-
and age-matched Australian and international children
18 2830
Table 2 1.6 km run (s) centile values and LMS summary statistics by sex and age in 9- to 17-year-old Australians
Age (year) P
5
P
10
P
20
P
30
P
40
P
50
P
60
P
70
P
80
P
90
P
95
LMS
Boys
9 750 684 618 578 547 522 499 476 452 423 401 1.042 521.963 0.183
10 732 666 602 564 535 511 489 469 447 420 400 1.284 511.053 0.175
11 710 646 585 549 523 500 480 461 441 416 397 1.466 500.394 0.166
12 682 621 563 530 505 485 467 449 430 408 392 1.721 484.819 0.157
13 643 587 535 505 483 465 448 432 415 395 380 1.895 464.529 0.148
14 605 556 509 482 462 446 431 416 401 382 369 1.987 445.569 0.140
15 575 531 490 465 447 432 418 404 390 373 360 1.979 431.504 0.133
16 552 514 477 454 437 423 410 397 383 366 354 1.865 422.693 0.128
17 534 500 467 446 430 417 404 392 379 362 350 1.707 416.545 0.123
Girls
9 829 769 706 666 635 609 584 559 533 499 475 0.779 608.674 0.167
10 820 759 697 657 626 600 576 552 526 494 470 0.878 600.149 0.166
11 801 741 680 641 611 586 562 539 514 483 460 0.929 585.820 0.165
12 784 726 666 629 600 575 552 529 505 474 452 0.921 574.682 0.164
13 771 716 658 621 593 569 546 524 500 469 447 0.852 568.706 0.163
14 763 711 655 620 592 567 545 523 498 468 445 0.737 567.486 0.162
15 760 710 656 621 594 570 547 525 500 469 446 0.591 569.809 0.161
16 757 710 658 624 597 573 550 527 502 471 446 0.428 572.723 0.160
17 753 708 658 625 598 575 552 529 504 471 446 0.263 574.536 0.159
Note, percentile data were calculated from 11 423 1.6 km run performances collected between 1985 and 1997.
L, skew; M, median; P, percentile; S, coefficient of variation.
Table 3 20 m shuttle run (completed stages) centile values and LMS summary statistics by sex and age in 9- to 17-year-old Australians
Age (year) P
5
P
10
P
20
P
30
P
40
P
50
P
60
P
70
P
80
P
90
P
95
LMS
Boys
9 11222333 456 0.213 2.573 0.568
10 12233445 578 0.373 3.537 0.543
11 12334455 678 0.520 4.131 0.517
12 12334456 689 0.643 4.460 0.486
13 22344556 789 0.744 4.888 0.453
14 23445667 8910 0.835 5.664 0.418
15 33456778 91011 0.926 6.527 0.380
16 34567788 91011 1.031 7.159 0.343
17 45667889101111 1.143 7.690 0.306
Girls
9 11112222 3450.065 1.842 0.535
10 11222233 456 0.086 2.468 0.557
11 11222334 467 0.220 2.844 0.573
12 11223334 567 0.324 3.016 0.577
13 11223344 567 0.400 3.138 0.569
14 11223344 567 0.457 3.225 0.554
15 11233344 567 0.505 3.412 0.536
16 12233445 567 0.554 3.672 0.518
17 12234455 678 0.603 4.032 0.499
Percentile data were calculated from 18 075 20 m shuttle run performances collected between 1990 and 2009.
The 20 m shuttle run can be scored in different metrics other than as the number of completed stages, such as the number of completed laps, the speed at the last completed stage
and as mass-specific peak oxygen uptake estimates (see Tomkinson et al
25
for details on how to correct 20 m shuttle run performances to different metrics).
L, skew; M, median; P, percentile; S, coefficient of variation.
Catley MJ, et al.Br J Sports Med 2013;47:98108. doi:10.1136/bjsports-2011-090218 1 of 12
Original articles
were expressed as standardised effect sizes.
31
Positive effect sizes
indicated that mean tness test performances for boys (age-
matched analysis), older children (sex-matched analysis) or
Australian children (sex- and age-matched analysis) were higher
than those for girls, younger children or international children,
respectively. Effect sizes of 0.2, 0.5 and 0.8 were used as thresh-
olds for small, moderate and large.
31
RESULTS
Table 1 summarises the 15 included studies. Of these, 12 were
identied through bibliographic database searching and word of
mouth, and three were identied through reference list search-
ing. Corresponding authors of all the studies were contacted
through email to clarify study details and/or to request raw data.
All authors satisfactorily claried study details, and seven of
them supplied raw data (gure 1).
The nal dataset comprised 85347 individual test results and
142 sex by age by test groups with a median sample size of 537
(range: 542612). Data were available for six tness compo-
nents and nine tness tests: cardiovascular endurance (20 m
shuttle run, 1.6 km run), muscular strength (hand-grip), muscu-
lar endurance (push-ups and sit-ups), muscular power (standing
broad jump and basketball throw), muscular speed (50 m sprint)
and exibility (sit-and-reach). Raw data were available for 74%
of all data points.
Normative tness data for 917-year-old Australians are pre-
sented as tabulated percentiles from 5 to 95 (P
5
,P
10
,P
20
,P
30
,
P
40
,P
50
,P
60
,P
70
,P
80
,P
90
,P
95
) in tables 210. The sex- and age-
Table 4 50 m sprint (s) centile values and LMS summary statistics by sex and age in 9- to 15-year-old Australians
Age (year) P
5
P
10
P
20
P
30
P
40
P
50
P
60
P
70
P
80
P
90
P
95
LMS
Boys
9 10.6 10.2 9.8 9.5 9.3 9.1 9.0 8.8 8.6 8.3 8.1 1.837 9.136 0.078
10 10.5 10.1 9.7 9.4 9.2 9.0 8.8 8.7 8.5 8.2 8.0 2.185 9.009 0.080
11 10.4 10.0 9.6 9.3 9.1 8.9 8.7 8.5 8.3 8.1 7.9 2.405 8.877 0.081
12 10.2 9.8 9.3 9.1 8.9 8.7 8.5 8.3 8.1 7.9 7.7 2.446 8.673 0.081
13 9.8 9.4 9.0 8.8 8.6 8.4 8.2 8.1 7.9 7.7 7.5 2.489 8.377 0.079
14 9.4 9.0 8.7 8.4 8.2 8.1 7.9 7.8 7.6 7.4 7.2 2.701 8.063 0.076
15 9.0 8.6 8.3 8.1 7.9 7.7 7.6 7.5 7.3 7.1 7.0 3.021 7.738 0.073
Girls
9 11.7 11.3 10.8 10.5 10.3 10.0 9.8 9.6 9.3 9.0 8.8 0.981 10.033 0.088
10 11.1 10.7 10.3 10.0 9.8 9.5 9.3 9.1 8.9 8.6 8.4 1.453 9.542 0.084
11 10.7 10.3 9.9 9.6 9.4 9.2 9.0 8.8 8.6 8.3 8.1 1.803 9.161 0.082
12 10.4 10.0 9.6 9.3 9.1 8.9 8.7 8.6 8.4 8.1 7.9 1.977 8.919 0.080
13 10.2 9.8 9.4 9.2 9.0 8.8 8.6 8.4 8.3 8.0 7.8 1.991 8.787 0.078
14 10.0 9.7 9.3 9.1 8.9 8.7 8.5 8.4 8.2 7.9 7.8 1.884 8.686 0.076
15 9.9 9.6 9.2 9.0 8.8 8.6 8.5 8.3 8.1 7.9 7.7 1.724 8.638 0.075
Note, percentile data were calculated from 10 104 50 m sprint performances collected between 1985 and 1999.
L, skew; M, median; P, percentile; S, coefficient of variation.
Table 5 Basketball throw (m) centile values and LMS summary statistics by sex and age in 9- to 17-year-old Australians
Age (year) P
5
P
10
P
20
P
30
P
40
P
50
P
60
P
70
P
80
P
90
P
95
LMS
Boys
9 2.3 2.5 2.7 2.9 3.1 3.3 3.4 3.6 3.8 4.1 4.4 0.623 3.260 0.198
10 2.5 2.8 3.0 3.3 3.4 3.6 3.8 4.0 4.2 4.5 4.8 0.675 3.608 0.192
11 2.8 3.1 3.4 3.6 3.8 4.0 4.2 4.4 4.7 5.0 5.3 0.733 4.026 0.188
12 3.1 3.4 3.8 4.0 4.3 4.5 4.7 4.9 5.2 5.6 5.9 0.792 4.471 0.188
13 3.5 3.8 4.2 4.5 4.8 5.0 5.3 5.5 5.8 6.2 6.6 0.843 5.012 0.187
14 3.9 4.2 4.7 5.0 5.3 5.5 5.8 6.1 6.4 6.9 7.2 0.898 5.522 0.186
15 4.2 4.6 5.0 5.4 5.7 6.0 6.3 6.6 6.9 7.4 7.8 0.943 5.975 0.185
16 4.4 4.8 5.3 5.6 6.0 6.3 6.5 6.9 7.2 7.7 8.2 0.964 6.254 0.185
17 4.5 4.9 5.5 5.8 6.2 6.5 6.8 7.1 7.5 8.0 8.5 0.966 6.467 0.187
Girls
9 2.1 2.3 2.5 2.7 2.9 3.0 3.2 3.3 3.5 3.7 3.9 1.116 3.015 0.182
10 2.3 2.6 2.8 3.0 3.2 3.3 3.5 3.7 3.8 4.1 4.3 1.024 3.336 0.181
11 2.6 2.8 3.1 3.3 3.5 3.6 3.8 4.0 4.2 4.5 4.7 0.942 3.646 0.180
12 2.8 3.1 3.4 3.6 3.8 4.0 4.2 4.3 4.6 4.9 5.2 0.873 3.970 0.180
13 3.0 3.3 3.6 3.9 4.1 4.3 4.5 4.7 4.9 5.3 5.6 0.816 4.265 0.179
14 3.2 3.4 3.8 4.0 4.2 4.4 4.6 4.8 5.1 5.4 5.7 0.739 4.410 0.175
15 3.3 3.6 3.9 4.1 4.3 4.5 4.7 4.9 5.1 5.5 5.8 0.606 4.486 0.169
16 3.4 3.7 4.0 4.2 4.4 4.6 4.7 5.0 5.2 5.6 5.9 0.394 4.557 0.162
17 3.6 3.8 4.1 4.3 4.5 4.6 4.8 5.0 5.3 5.6 5.9 0.140 4.634 0.154
Note, percentile data were calculated from 5,541 basketball throw performances collected between 1994 and 1999; L, skew; M, median; P, percentile; S, coefficient of variation.
1 of 12 Catley MJ, et al.Br J Sports Med 2013;47:98108. doi:10.1136/bjsports-2011-090218
Original articles
specic LMS values for all tness tests are also shown. The LMS
values depict the nature of the age-related distributions for boys
and girls and can be used to calculate z-scores and hence percent-
ile values by looking up a z-table, using the following formula:
z¼
x
M

L
1
LS
where zis zscore, xis performance, Lis sex- and age-specic
L-value, Mis the sex- and age-specicM-value and Sis the sex-
and age-specicS-value.
Figures 2 and 3 show the smoothed centile curves (P
10
,P
50
,P
90
).
Figure 4 shows the sex-related differences in mean tness.
Boys consistently scored higher than girls on health-related
tness tests, except on the sit-and-reach test, with the magnitude
of the differences typically increasing with age and often
accelerating from about 12 years of age. Overall, the magnitude
of differences between boys and girls was large for the 1.6 km
run, 20 m shuttle run, basketball throw and push-ups; moderate
for the 50-m sprint, standing broad jump and sit-and-reach; and
small for sit-ups and hand-grip strength. Figure 5 shows the
age-related changes in mean tness. The age-related changes
were typically larger for boys than for girls, especially during the
teenage years, and for muscular tness tests than for cardiovascu-
lar tness tests. Fitness also tended to peak from about the age of
15 years. Figure 6 shows that the differences in health-related
tness between Australian and international children were gener-
ally small, with Australian children scoring slightly higher on
hand-grip strength (mean ±95% CI: 0.20±0.03 SDs) and 50 m
sprint tests (0.24±0.02 SDs), and slightly lower on sit-and-reach
(0.36±0.02 SDs), standing broad jump (0.25±0.02 SDs) and
20 m shuttle run tests (0.49±0.01 SDs).
Table 6 Standing broad jump (cm) centile values and LMS summary statistics by sex and age in 9- to 15-year-old Australians
Age (year) P
5
P
10
P
20
P
30
P
40
P
50
P
60
P
70
P
80
P
90
P
95
LM S
Boys
9 105 113 121 127 133 138 142 147 153 161 168 1.244 137.506 0.138
10 109 117 126 133 138 143 148 154 160 168 174 1.490 143.430 0.138
11 112 121 131 138 144 149 154 160 166 174 181 1.654 149.322 0.138
12 117 126 137 144 150 156 161 167 173 182 189 1.704 155.838 0.137
13 126 136 147 154 161 166 172 178 185 194 201 1.629 166.340 0.135
14 137 146 157 165 172 178 184 190 197 206 214 1.526 177.688 0.131
15 148 157 169 177 183 189 196 202 209 219 228 1.446 189.485 0.127
Girls
9 95 102 110 116 122 126 131 136 142 150 157 1.098 126.379 0.149
10 100 108 117 123 128 133 138 143 149 158 165 1.152 133.177 0.147
11 106 114 123 129 135 140 145 151 157 166 173 1.197 140.142 0.145
12 111 118 128 135 140 145 151 156 163 171 179 1.211 145.432 0.142
13 115 123 132 139 145 150 155 161 167 176 183 1.183 150.080 0.138
14 119 127 136 143 148 154 159 164 171 180 187 1.158 153.551 0.134
15 122 129 139 145 151 156 161 166 173 181 188 1.148 155.661 0.130
Percentile data were calculated from 11 194 standing broad jump performances collected between 1985 and 2002.
L, skew; M, median; P, percentile; S, coefficient of variation.
Table 7 Push-ups (no. in 30 s) centile values and LMS summary statistics by sex and age in 9- to 15-year-old Australians
Age (year) P
5
P
10
P
20
P
30
P
40
P
50
P
60
P
70
P
80
P
90
P
95
LMS
Boys
9 4 6 8 9 11 12 14 15 17 20 22 0.846 12.310 0.452
10 4 6 8 10 11 13 14 16 18 21 23 0.894 12.943 0.447
11 4 6 8 10 12 13 14 16 18 20 22 0.940 12.942 0.438
12 4 6 9 10 12 13 15 16 18 20 22 0.980 13.200 0.422
13 5 7 9 11 13 14 16 17 19 22 24 1.020 14.255 0.399
14 6 8 11 13 14 16 17 19 21 23 25 1.070 15.954 0.370
15 7 10 13 15 16 18 19 21 23 25 27 1.126 17.697 0.337
Girls
9 23578910121316180.719 8.989 0.550
10 23567910111316180.652 8.655 0.583
11 2346789111316180.584 8.142 0.624
12 1245679101215180.518 7.395 0.672
13 1234678101215180.453 6.792 0.720
14 123456891115180.390 6.384 0.765
15 123456791114180.329 5.818 0.812
Percentile data were calculated from 7,342 push-up test performances collected between 1985 and 1991.
L, skew; M, median; P, percentile; S, coefficient of variation.
Catley MJ, et al.Br J Sports Med 2013;47:98108. doi:10.1136/bjsports-2011-090218 1 of 12
Original articles
DISCUSSION
This study provides the most up-to-date sex- and age-specic
normative centile values for 917-year-old Australians across a
range of health-related tness tests, which can be used as bench-
mark values for health and tness screening and surveillance of
children. These data complement a growing literature reporting
growth percentiles across a range of different health measures,
such as body mass index,
32
waist girth
33
and blood pressure,
28
and a range of other health-related tness measures.
29 30
It also
quanties the magnitude and direction of sex- and age-related
differences in childrens health-related tness and shows that
boys consistently scored higher than girls on tness tests (except
on the sit-and-reach test of exibility) and that boys experience
larger age-related changes in tness. The developmental patterns
of childrenstness have been well studied and extensively
reviewed (eg, for cardiovascular tness, refer to Armstrong
et al,
34
Krahenbuhl et al
35
and Rowland
36
; for muscular tness,
refer to Blimkie and Sale,
37
Froberg and Lammert
38
and De Ste
Croix
39
). Although the underlying causes of sex- and age-related
differences are clear for some tness test performances, such as
those for muscular strength, power and speed, which are largely
explained by physical differences (eg, differences in muscle mass
Table 8 Sit-ups (no. in 180 s) centile values and LMS summary statistics by sex and age in 9- to 17-year-old Australians
Age (year) P
5
P
10
P
20
P
30
P
40
P
50
P
60
P
70
P
80
P
90
P
95
LMS
Boys
9 3 5 8 11 14 17 21 25 30 40 48 0.321 17.046 0.755
10 5 8 13 17 20 24 29 34 40 50 59 0.466 24.459 0.669
11 6 10 16 21 25 29 34 39 45 55 60 0.629 29.422 0.594
12 8 14 21 26 31 36 40 45 51 60 60 0.841 35.561 0.514
13 10 17 25 31 36 40 45 50 55 60 60 1.056 40.288 0.443
14 12 20 29 34 39 43 48 52 57 60 60 1.232 43.454 0.389
15 14 22 31 36 41 45 49 53 58 60 60 1.335 44.942 0.359
16 16 24 32 38 42 46 50 54 58 60 60 1.426 46.209 0.332
17 18 26 34 40 44 47 51 55 59 60 60 1.517 47.466 0.306
Girls
9 5 8 12 15 18 21 25 29 35 43 51 0.394 21.258 0.642
10 7 10 14 18 22 26 30 34 40 50 58 0.485 25.666 0.605
11 8 11 17 21 25 29 34 39 45 54 60 0.571 29.444 0.569
12 9 13 19 24 28 32 37 42 48 57 60 0.646 32.123 0.534
13 10 15 21 26 30 34 39 44 50 59 60 0.705 34.408 0.504
14 11 15 22 27 31 35 40 45 50 59 60 0.741 35.334 0.482
15 11 16 22 27 31 35 40 44 50 58 60 0.757 35.327 0.464
16 12 17 23 28 32 36 40 44 50 57 60 0.761 35.690 0.447
17 13 18 24 28 32 36 40 45 50 58 60 0.761 36.333 0.431
Percentile data were calculated from 8 837 sit-up test performances collected between 1985 and 1999.
L, skew; M, median; P, percentile; S, coefficient of variation.
Table 9 Hand-grip strength (kg) centile values and LMS summary statistics by sex and age in 9- to 15-year-old Australians (taken as the mean
of both hands)
Age (year) P
5
P
10
P
20
P
30
P
40
P
50
P
60
P
70
P
80
P
90
P
95
LMS
Boys
9 11.5 12.5 13.8 14.8 15.6 16.4 17.2 18.1 19.2 20.8 22.1 0.600 16.415 0.197
10 13.1 14.3 15.9 17.0 18.0 19.0 19.9 21.0 22.2 23.9 25.4 0.728 18.967 0.198
11 14.5 15.9 17.7 19.0 20.1 21.2 22.3 23.5 24.9 26.8 28.5 0.764 21.217 0.200
12 15.4 17.0 18.9 20.3 21.5 22.7 23.8 25.1 26.6 28.7 30.5 0.747 22.655 0.203
13 17.5 19.3 21.5 23.1 24.5 25.8 27.2 28.6 30.4 32.8 34.9 0.738 25.819 0.205
14 20.8 22.9 25.5 27.4 29.1 30.7 32.4 34.1 36.2 39.1 41.6 0.742 30.731 0.207
15 24.6 27.1 30.3 32.6 34.6 36.5 38.4 40.5 43.0 46.5 49.5 0.752 36.517 0.207
Girls
9 9.8 10.8 12.0 12.9 13.7 14.4 15.1 16.0 17.0 18.4 19.5 0.639 14.396 0.205
10 11.4 12.6 14.1 15.2 16.2 17.1 18.0 19.0 20.1 21.8 23.1 0.842 17.072 0.210
11 12.5 13.9 15.5 16.8 17.8 18.8 19.8 20.9 22.1 23.9 25.3 0.932 18.816 0.208
12 14.4 16.0 17.8 19.1 20.3 21.4 22.5 23.6 25.0 26.9 28.5 0.922 21.374 0.200
13 16.4 18.0 19.9 21.3 22.5 23.6 24.8 26.0 27.4 29.5 31.1 0.880 23.641 0.190
14 18.2 19.7 21.6 23.0 24.3 25.4 26.5 27.8 29.2 31.3 33.0 0.828 25.390 0.178
15 19.8 21.3 23.2 24.6 25.8 26.9 28.0 29.2 30.7 32.7 34.4 0.770 26.881 0.165
Percentile data were calculated from the 3 707 hand-grip strength performances collected between 1985 and 1999.
L, skew; M, median; P, percentile; S, coefficient of variation.
1 of 12 Catley MJ, et al.Br J Sports Med 2013;47:98108. doi:10.1136/bjsports-2011-090218
Original articles
or height), they are less clear for others, such as for cardiovascu-
lar endurance, which may be explained by physiological differ-
ences (eg, differences in mechanical efciency and/or fractional
utilisation).
15 36
It is, nonetheless, beyond the scope of this
article to discuss the causes that underscore the sex- and
age-related changes in tness test performance.
International comparisons
Although several studies have previously compared the
health-related tness of Australian children with their sex- and
age-matched international peers,
20 40
comparisons have only
been made for cardiovascular tness. Figure 6 compares the
20-m shuttle run, 50 m sprint, standing broad jump, hand-grip
strength and sit-and-reach performance of 917-year-old
Australians with 1 894 971 test results from sex-, age- and test-
matched international children from 48 countries who have
been measured using the same test protocols as those referenced
in table 1 and described in Appendix 1. Figure 6 also shows typ-
ically small differences in health-related tness between
Australian and international children. Furthermore, the sex- and
age-related differences in tness of Australian children are strik-
ingly similar to those observed in international children. Given
that the differences are generally small, the normative centile
data presented in this study could be used as approximate
benchmark values for health-related tness of international
children.
Fitness thresholds for cardiometabolic risk
Fitness is widely recognised as a powerful marker of current and
future cardiovascular, skeletal and mental health. Unfortunately,
there are no universally accepted recommendations for
health-related levels of tness. In recent years however, sex- and
age-specic threshold values for cardiovascular tness (operatio-
nalised as mass-specic peak oxygen uptake in ml/kg/min) have
been established for European and US children using linked
cardiometabolic risk-based values from receiver operator
characteristic curve analyses.
4144
To estimate the prevalence of
Australian children with healthycardiovascular tness (ie,
those above the thresholds), internationalsex- and age-specic
thresholds for 917-year-old children were estimated by deter-
mining best-tting polynomial regression model (quadratic or
cubic) relating age (predictor variable) to previously reported
threshold values (response variable) in Adegboye et al,
41
Lobelo
et al,
42
Ruiz et al
43
and Welk et al.
44
Separate models were gen-
erated for boys and girls. Peak oxygen uptake values in
Australian children were estimated using 1.6 km run and 20 m
shuttle run data and the Cureton et al
45
and Léger et al
46
regression equations, respectively.
Using these thresholds, about 71% of Australian boys
(median ±95% CI: 71%±8%) and 77% of Australian girls
(median ±95% CI: 77%±10%) apparently have healthycar-
diovascular tness. Although in light of recent secular declines
in cardiovascular tness,
20 22 23 25
and with a median testing
year of 1993 in this studys cardiovascular tness dataset, it is
likely that these prevalence rates somewhat overestimate those
of today. These prevalence rates are better than (for girls), or
similar to (for boys), those observed in European (61% of boys
and 57% of girls)
29
and US (71% of boys and 69% of girls)
42
children. Geographical differences in prevalence rates may
reect differences in (a) threshold levels, (b) the year(s) of
testing, (c) sampling methodology, (d) test methodology and (e)
the way in which peak oxygen uptake was measured or
estimated.
47
Ultimately, it is important to remember that the normative
data presented in this study show how well Australian children
perform on health-related tness tests relative to their sex- and
age-matched peers. For example, using a percentile classica-
tion, children with tness in the bottom 20% can be classied
as having very lowtness; those between the 20th and 40th
percentiles as having lowtness; those between the 40th and
60th percentiles as having averagetness; those between the
60th and 80th percentiles as having hightness; and those
Table 10 Sit-and-reach (cm) centile values and LMS summary statistics by sex and age in 9- to 17-year-old Australians.
Age (y) P
5
P
10
P
20
P
30
P
40
P
50
P
60
P
70
P
80
P
90
P
95
LMS
boys
9 10.4 12.9 15.7 17.7 19.4 20.9 22.4 23.9 25.8 28.2 30.3 1.211 20.877 0.285
10 10.0 12.5 15.3 17.3 19.0 20.5 22.1 23.7 25.5 28.0 30.1 1.190 20.537 0.294
11 9.6 12.1 15.0 17.0 18.7 20.3 21.9 23.5 25.4 28.0 30.1 1.167 20.313 0.305
12 9.3 11.8 14.8 16.9 18.7 20.3 21.9 23.6 25.6 28.3 30.5 1.133 20.292 0.315
13 9.4 12.0 15.1 17.2 19.1 20.8 22.5 24.3 26.4 29.2 31.6 1.091 20.785 0.322
14 9.8 12.5 15.7 18.0 20.0 21.8 23.6 25.5 27.8 30.9 33.4 1.054 21.804 0.328
15 10.4 13.2 16.6 19.1 21.2 23.1 25.1 27.1 29.5 32.9 35.7 1.017 23.112 0.332
16 11.1 14.0 17.6 20.1 22.3 24.4 26.5 28.7 31.3 34.9 37.8 0.984 24.392 0.334
17 11.7 14.8 18.5 21.2 23.5 25.7 27.9 30.2 33.0 36.8 40.0 0.953 25.686 0.335
girls
9 13.0 15.8 18.9 21.1 22.9 24.6 26.2 28.0 29.9 32.6 34.8 1.285 24.614 0.264
10 13.0 15.7 18.8 20.9 22.7 24.4 26.0 27.7 29.7 32.4 34.6 1.259 24.402 0.265
11 13.2 15.9 19.0 21.2 23.0 24.7 26.4 28.1 30.1 32.8 35.0 1.235 24.705 0.265
12 14.0 16.7 19.9 22.2 24.1 25.8 27.5 29.3 31.3 34.2 36.4 1.230 25.790 0.262
13 15.3 18.2 21.6 24.0 25.9 27.7 29.5 31.4 33.6 36.5 38.9 1.250 27.740 0.256
14 16.5 19.5 23.1 25.5 27.6 29.4 31.3 33.2 35.4 38.4 40.9 1.293 29.440 0.248
15 17.0 20.1 23.7 26.1 28.1 30.0 31.8 33.7 35.9 38.8 41.2 1.350 29.997 0.241
16 17.0 20.0 23.5 25.9 27.9 29.6 31.4 33.2 35.3 38.1 40.3 1.412 29.647 0.235
17 16.8 19.8 23.2 25.5 27.4 29.1 30.7 32.5 34.4 37.1 39.2 1.472 29.074 0.229
Note, percentile data were calculated from 9,124 sit-and-reach performances collected between 1985 and 2000; L = skew; M = median; S = coefficient of variation. Note, a score of
20 cmcorresponds to the participant reaching their toes.
Catley MJ, et al.Br J Sports Med 2013;47:98108. doi:10.1136/bjsports-2011-090218 1 of 12
Original articles
above the 80th percentile as having very hightness. These
data are not criterion-referenced in that they do not indicate
whether children with very lowor low(or any other classi-
cation for that matter) have unhealthycardiovascular tness or
increased cardiometabolic risk. Despite the fact that previous
Australian evidence has linked low childhood cardiovascular
tness with increased cardiometabolic risk in adulthood,
48
future Australian studies are required to examine whether
childhood thresholds for cardiovascular tness (or other
health-related tness components) are signicantly associated
with clustered cardiometabolic risk (or other health outcomes,
such as mental or skeletal health outcomes).
Strengths and limitations
Despite the fact that the last national tness survey of Australian
children was in 1985, this study provides the most up-to-date
normative dataset for nine widely administered health-related
tness tests, using cumulated data from 85347 Australian chil-
dren aged 917 years collected between 1985 and 2009. This
Figure 2 Smoothed centile curves (P
10
,P
50
and P
90
) for (A) 1.6 km
run (s), (B) 20 m shuttle run (number of completed stages), (C) 50 m
sprint (s), (D) basketball throw (m) and (E) standing broad jump (cm).
Figure 3 Smoothed centile curves (P
10
,P
50
and P
90
) for (A) push-ups
(number in 30 s), (B) sit-ups (number in 180 s), (C) hand-grip strength
(kg) and (D) sit-and-reach (cm) tests.
1 of 12 Catley MJ, et al.Br J Sports Med 2013;47:98108. doi:10.1136/bjsports-2011-090218
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study used a strict set of inclusion and exclusion criteria and
rigorous initial data analysis procedures to systematically control
for any factors (eg, differences in test methodology) that might
have biased the normative values or the estimates of the sex-
and age-related differences. It used a novel pseudo-data method
to allow both descriptive and raw data to be merged before
using the LMS method to create sex- and age-specic smoothed
percentiles. It also quantied sex- and age-related differences as
standardised effects sizes, allowing for comparison between
sexes, among different ages, and with sex, age and test-matched
international children.
However, this study is not without limitations. Only one of
the 15 included studies was based on a nationally representative
sample, which obviously raises the issue of representativeness.
Most of the included studies used similar sampling frames
Figure 5 Age-related changes in mean tness expressed as effect
sizes standardised to an effect size of age 15 years=0. Data are shown
for 917-year-old boys (triangles) and girls (circles) separately tested on
the (A) 1.6 km run, (B) 20 m shuttle run, (C) 50 m sprint, (D)
basketball throw, (E) standing broad jump, (F) push-ups, (G) sit-ups, (H)
hand-grip strength and (I) sit-and-reach tests. The limits of the grey
zone represent effects sizes of 0.8 and 0.8, beyond which large
differences are observed.
Figure 4 Sex-related differences in mean tness expressed as effect
sizes. Data are shown for 917-year-old children tested on the (A) 1.6
km run, (B) 20 m shuttle run, (c) 50 m sprint, (D) basketball throw, (E)
standing broad jump, (F) push-ups, (G) sit-ups, (H) hand-grip strength
and (I) sit-and-reach tests. The limits of the grey zone represent effects
sizes of 0.8 and 0.8, beyond which large differences are observed.
Catley MJ, et al.Br J Sports Med 2013;47:98108. doi:10.1136/bjsports-2011-090218 1 of 12
Original articles
(table 1). Schools with a greater interest in sport and tness may
have been more willing to participate, and because participation
at the individual level was voluntary, it is possible that children
with low tness levels chose not to participate. This might have
resulted in tness test performances unrepresentative of the
population, but it should not have affected the sex- and
age-related differences. Fitness data were also collected at differ-
ent times during the 19852009 period, and given convincing
evidence of recent temporal declines in some (but not all) com-
ponents of Australian childrenstness,
23 49
it is possible that
the normative data presented in this study represent a better
health-related picturethan what would be observed today.
A temporal analysis of the data accumulated in this study sug-
gests that these normative data would probably overestimate the
tness of Australian children in 2009 by an average of 0.3 SDs
or 13 percentile points, assuming of course that the observed
temporal changes remained consistent across the entire 1985
2009 period. Nonetheless, these data represent the best
available and most up-to-date health-related tness data on
Australian children. It must also be remembered that despite
being simple, cheap, easy, reliable, reasonably valid and widely
used alternatives of laboratory-based criterion measures, eld
tests are affected by factors other than underlying construct
tness. For example, validity data for eld tests of cardiovascu-
lar tness suggest that (at best) only 5060% of the variance in
eld test performance is explained by the variance in underlying
peak oxygen uptake, indicating that other physiological, phys-
ical, biomechanical, psychosocial and environmental factors also
play a part.
15
In addition, although criterion-related validity has
not been established for all of the included tests, face validity is
generally accepted.
17
Most of the included tests are also consid-
ered to demonstrate good reliability, although tests requiring a
reasonable degree of subjective judgement (eg, the subjective
scoring of a properly performed sit-up or push-up) typically
demonstrate poorer reliability.
14
Conclusion
Physical tness is considered to be an excellent marker of
current and future health. In anticipation of a follow-up
national tness survey, this study provides the most up-to-date
and most comprehensive set of sex- and age-specic normative
centile values of health-related tness of Australian children,
which can be used as benchmark values for health and tness
screening and surveillance systems. These normative centile
values will facilitate the identication of children with low
tness to set appropriate tness goals, monitor individual
changes in tness and promote positive health behaviours. They
will also facilitate the identication of children who possess spe-
cictness characteristics that may be considered important for
sporting success, in the hope of recruiting the high achievers
into elite sporting development programs.
Acknowledgements The authors thank the authors of the included studies for
generously clarifying details of their studies and/or for providing raw data. The
University of South Australia Divisional Development Research Scheme supported
this study.
Correction notice This article has been corrected since it was published Online
First. The authors have noticed that the normative data in Table 10 are incorrect.
The correct table has been inserted.
Competing interests None.
Provenance and peer review Not commissioned; externally peer reviewed.
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Original articles
... Boys and girls performed similarly across most AMSC measures, with competency in the push up the only difference between the sexes. This may be due to males out performing females in health-related fitness measures [79]. There were more consistent differences in psychological constructs, with boys having significantly greater physical self-efficacy and global self-esteem. ...
... There were more consistent differences in psychological constructs, with boys having significantly greater physical self-efficacy and global self-esteem. The study population scored below the normative average in physical performance respective to age with males scoring in the 30th percentile and females in the 40th percentile for the SLJ [79]. Additionally, the population in this study scored below previous standards for AMSC (median AIMS score = 27-28) when compared with more athletic populations (mean AIMS score = 36) [45]. ...
... This study establishes relationships to AMSC, yet it is still unknown whether relationships between AMSC, sex, maturity, physical performance, and psychological constructs vary dependent on the socio-economic status of youth. Youth in areas of higher socioeconomic status may demonstrate higher levels of competency, as the population in this study scored below the 50th centile in physical performance [79]. In addition, this study sampled children aged 11-13 years old, however the development of AMSC could be established earlier as a potential intervention to counteract the drop in physical activity levels, which begin as young as 7 years old [82]. ...
Article
Full-text available
The purpose of this study was to examine the relationships between athletic motor skill competencies (AMSC), maturation, sex, body mass index, physical performance, and psychological constructs (motivation to exercise, physical self-efficacy, and global self-esteem). Two-hundred and twenty-four children aged 11–13 years old were included in the study and sub-divided by sex. The athlete introductory movement screen (AIMS) and tuck jump assessment (TJA) were used to assess AMSC, while standing long jump distance assessed physical performance. Online surveys examined participants’ motivation to exercise, physical self-efficacy, and self-esteem. Trivial to moderate strength relationships were evident between AMSC and BMI (boys: rs = −0.183; girls: rs = −0.176), physical performance (boys: rs = 0.425; girls: rs = 0.397), and psychological constructs (boys: rs = 0.130–0.336; girls rs = 0.030–0.260), with the strength of relationships different between the sexes. Higher levels of AMSC were related to significantly higher levels of physical performance (d = 0.25), motivation to exercise (d = 0.17), and physical self-efficacy (d = 0.15–0.19) in both boys and girls. Enhancing AMSC may have mediating effects on levels of physical performance and psychological constructs in school-aged children, which may hold important implications for physical activity levels and the development of physical literacy.
... Furthermore, from the perspective of prevention, these percentile values could help identify the physical fitness components in need of improvement. Some studies have suggested using the normative quintile-based framework to classify the physical fitness levels, where those below the 20th percentile are classified as "very low/poor, " 20th−40th percentiles as "low/poor, " 40th−60th percentiles as "moderate, " 60th−80th percentiles as "high/good, " and those above the 80th percentile as "very high/good" (16,(32)(33)(34). Moreover, for the individuals with "very low" or "low" level of physical fitness who were prescribed intervention, by tracking their percentile categories, the precision and influence of the intervention could be assessed (33). ...
... Compared with other countries, taking sex-and age-specific P 50 as an example, we found that the Chinese children and adolescents had better performance of SLJ than the European (3,33), Columbian (49), Greek, and Macedonian children and adolescents (35). However, regarding 50-m dash, children and adolescents from many countries, such as France (28), Germany (17), Australia (34), and Korea (12) performed better than Chinese children and adolescents. Meanwhile, the Chinese children and adolescents performed better than Polish in SR, but worse in ER (40). ...
Article
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Introduction: To develop sex- and age-specific percentile curves for seven physical fitness components for Chinese Han children and adolescents aged 7–18 years based on the total and the normal weight population using a nationally representative sample. Methods: A total of 214,228 Chinese Han children and adolescents aged 7–18 years old with all nutritional status and 161,999 with normal weight were examined. Seven physical fitness components [forced vital capacity (FVC), standing long jump (SLJ), 50-m dash, sit-and-reach (SR), grip strength (GS), body muscle strength (BMS), and endurance running (ER)] were measured, and percentile curves for each physical fitness component at the 20th, 40th, 60th, and 80th percentiles were calculated using the general additive model for location, scale, and shape (GAMLSS). Results: Physical fitness presents different characteristics in each subgroup of sex, age, and nutritional status among children and adolescents. Sex- and age-specific percentiles for the seven physical fitness components among the Chinese Han children and adolescents aged 7–18 years based on the total and the normal weight population were provided as curves. Boys performed better than girls in FVC, SLJ, 50-m dash, GS, and ER but worse in SR. The performances of FVC, SLJ, 50-m dash, GS, BMS, and ER increased with age, but the estimates of SR were at the bottom among boys aged 12 years and girls aged 11 years. The annual increments of all components were larger in boys than girls at the peak time, which was earlier in girls than boys. The gap of physical fitness components between sexes increased with age, especially during puberty (since after 11 years old). Conclusion: The present study described the percentile curves of seven physical fitness components among the Chinese Han children and adolescents based on the total and the normal weight population at the national level, which could help to chart the level of physical fitness across age span and identify the extreme populations with either health concerns or potential talents.
... Some countries have routinely collected children's anthropometric and fitness data nationwide for years [23, [55][56][57][58], or implemented nationwide monitoring systems in primary schools, such as the German Health Interview and Examination Survey for Children and Adolescents (KiGGS) [59] and the Slovenian National Surveillance System for physical and motor development (SLOFit) [60]. SLOFit is a positive example of how a monitoring system in schools can reduce the prevalence of obesity and at the same time increase the physical fitness level of children through interventions based on its results [61]. ...
... One strength is that almost all of the data collected in AUT FIT come from established and widely used motor tests that are easy to perform without requiring much additional cost, space, or time. Seven of the eight items are existing standardized tests that have been used for decades in a plethora of test batteries (FitnessGram [24], CNSPFS [56], AFEA [55], GTO [58], PFAAT [57], SLOFit [60], ALPHA [26], GMT [27], DÜMO [29]) and extensively tested for their validity. Over four physical education lessons, it was possible to collect a broad panel of anthropometric and fitness-related parameters. ...
Article
Full-text available
Monitoring of anthropometric and physical fitness parameters in primary school children is important for the prevention of future health problems. Many of the existing test batteries that are useful for monitoring require expensive test materials, specialized test administrators, and a lot of space. This limits the usefulness of such tests for widespread use. The aim of this pilot study was to design and evaluate monitoring tools for anthropometrics and physical fitness tests in primary schools, called AUT FIT. The test battery consists of height, weight, and waist circumference measurement and eight fitness tests (6 min run, V sit-and-reach, jumping sideways, standing long jump, medicine ball throw, 4 × 10 m shuttle run, ruler drop, single leg stand). Data of 821 children aged 7 to 10 years were gathered. Most AUT FIT tests showed excellent test–retest and interrater reliability and were easy to implement. Criterion-related validity was evident by a strong correlation between physical education teacher rankings and rank scores for motor fitness. Nationwide implementation in the Austrian school system could be an important component for monitoring and improving the health and fitness of primary school children.
... Boys outperformed girls in all four physical fitness components (cardiorespiratory endurance, coordination, speed, power [LOW/UP]). This is in line with other studies reporting normative values [2][3][4][5]14,[23][24][25][26] . For instance, Tambalis et al. 5 reported that boys aged 6-18 years showed significantly better performances for cardiorespiratory endurance (i.e., 20 m shuttle run test), powerLOW (i.e., standing long jump test), and agility (i.e., 10 × 5 m agility shuttle run test) compared to girls. ...
Article
Full-text available
Children’s physical fitness development and related moderating effects of age and sex are well documented, especially boys’ and girls’ divergence during puberty. The situation might be different during prepuberty. As girls mature approximately two years earlier than boys, we tested a possible convergence of performance with five tests representing four components of physical fitness in a large sample of 108,295 eight-year old third-graders. Within this single prepubertal year of life and irrespective of the test, performance increased linearly with chronological age, and boys outperformed girls to a larger extent in tests requiring muscle mass for successful performance. Tests differed in the magnitude of age effects (gains), but there was no evidence for an interaction between age and sex. Moreover, “physical fitness” of schools correlated at r = 0.48 with their age effect which might imply that "fit schools” promote larger gains; expected secular trends from 2011 to 2019 were replicated.
... As can be seen, the mean NDI for the sample was below the diagnostic cut point of 85 for LMC (McCarron, 1997). The mean values for all fitness variables, including BMI and waist girth, were observed to be lower compared to published age-similar and gendermatched adolescent populations (Catley & Tomkinson, 2011; Centers for Disease Control and Prevention, www.cdc.gov; Ervin et al., 2014;Pyke, 1987;Taylor et al., 2010). ...
Article
Purpose: Adolescent perceptions of their physical self-worth (PSW) and the component domains draw upon physical attributes, such as motor competence, physical fitness, and self-perceptions, which in turn enhance the desire to engage in physical activity. Whilst these relationships have been researched in populations with typical motor development, little is known of the interplay of these contributors to PSW with those with low motor competence (LMC). Even less is known of how importance placed on particular physical subdomains may be used by the adolescent with LMC to mitigate negative effects on their perceptions of PSW. Method: Thirty-four adolescents with low motor competence, 25 boys and 9 girls (Mage = 13.89 yrs, SD = 1.49), completed the McCarron Assessment of Neuromuscular Development (MAND), the Children's Physical Self-Perception Questionnaire (C-PSPP) and a range of physical fitness tests. Results: All self-perception subdomain score was lower than importance ratings. Physical fitness measures were also low but were not significantly associated with PSW. However, the higher importance scores relative to physical self-perceptions resulted in greater discrepancy scores in all subdomains. Conclusion: Adolescents with LMC have low PSW, and low self-perceptions relative to importance ratings for most physical self-subdomains. These discrepancies, rather than actual fitness, potentially reduce their motivation to be physically active.
... To determine the relation between aerobic fitness and quality of life in adolescence, it was important to implement the systematic measurement of adolescent wellbeing on a public health basis [39,40]. The standardized global measure of wellbeing, KIDSCREEN, according to the European consortium, available in 38 languages, in 10-, 27-, and 52-item versions [41,42] has been used to access the wellbeing of adolescents Many studies, mostly in Europe, but also in Africa, Asia, and South America, have incorporated different versions of KIDSCRREEN: the version of KIDSCREEN-10 among school-aged children [43]; the parent-rating version of KIDSCREEN-10 in 27 EU countries [10,38], and the self-report version in a sample of children with cerebral palsy in KIDSCREEN-52; on their own or with parental help [44]. Based on results, systematically developed KIDSCREEN questionnaires measured equivalent dimensions in wellbeing across a variety of cultures, allowing further monitoring and comparing differences between adolescent trends for national and international data [44]. ...
Article
Full-text available
The main aim of this study is to examine age and gender differences in cardiorespiratory fitness (CRF) among Serbian secondary school children. The secondary aim is to explore the association between CRF and quality of life in Serbian adolescents. The sample consisted of 579 adolescents (285 males), aged from 14 to 18 years old. To evaluate their anthropometric measurements, body height and body weight were examined, the 20 m shuttle run test was used to access CRF, and the standardized global measure of wellbeing KIDSCREEN was used to access the wellbeing of adolescents. The results show that the boys possessed higher CRF compared to the girls, as well as higher scores on variable distance, but there were no significant differences according to age. CRF was positively associated with physical wellbeing, psychological wellbeing, total score HRQL, body height and body weight, and negatively correlated with BMI. Conversely, physical wellbeing showed positive correlation with the other subscales of KIDSCREEN (psychological wellbeing, autonomy and parents, peers and social support, and school environment), and total score of (Health–Related Quality of Life) HRQL. The results showed that better CRF would be beneficial for quality of life among Serbian adolescents, especially among girls. Moreover, the relationship between CRF and BMI shows that adolescents with regular values of BMI have better physical fitness and wellbeing.
Article
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Timing of initial school enrollment may vary considerably for various reasons such as early or delayed enrollment, skipped or repeated school classes. Accordingly, the age range within school grades includes older-(OTK) and younger-than-keyage (YTK) children. Hardly any information is available on the impact of timing of school enrollment on physical fitness. There is evidence from a related research topic showing large differences in academic performance between OTK and YTK children versus keyage children. Thus, the aim of this study was to compare physical fitness of OTK (N = 26,540) and YTK (N = 2586) children versus keyage children (N = 108,295) in a representative sample of German third graders. Physical fitness tests comprised cardiorespiratory endurance, coordination, speed, lower, and upper limbs muscle power. Predictions of physical fitness performance for YTK and OTK children were estimated using data from keyage children by taking age, sex, school, and assessment year into account. Data were annually recorded between 2011 and 2019. The difference between observed and predicted z-scores yielded a delta z-score that was used as a dependent variable in the linear mixed models. Findings indicate that OTK children showed poorer performance compared to keyage children, especially in coordination, and that YTK children outperformed keyage children, especially in coordination. Teachers should be aware that OTK children show poorer physical fitness performance compared to keyage children.
Article
Objective The objective of this study is twofold: i) to estimate the normative values for handgrip strength and relative handgrip strength, specific to sex and age, for Colombian children and adolescents from 6 to 17 years of age using quantile regression models and ii) to compare the normative values for handgrip strength and relative handgrip strength in Colombian children and adolescents with those in children and adolescents in different countries. Method This was a cross-sectional analysis of a sample of 2647 youngsters. Handgrip strength was evaluated with a TKK 5101 digital dynamometer (Takei Scientific Instruments Co., Ltd., Tokyo, Japan). The relative handgrip strength was estimated according to weight in kilograms. The normative values were estimated to handgrip strength and relative handgrip strength through quantile regression models for the percentiles P5, P10, P25, P50, P75, P90, and P95 developed independently for each sex. All analyses were adjusted for the expansion factor. Results The values for handgrip strength were considerably higher in males than in females in all age ranges. Additionally, as age increased for both sexes, the values for handgrip strength increased. The percentiles by sex and age for relative handgrip strength show for males a proportional increase according to age; for females, this did not occur. Conclusions When making comparisons with international studies, variability is observed in the methodologies used to evaluate handgrip strength and estimation methods, which could influence the discrepancies between the different reports.
Article
We developed age- and sex-specific smoothed percentiles for vertical and long jump, as well as vertical jump power, in healthy 10–18 year olds (n = 529, 47.1% female). Jump height and distance were measured and vertical jump power was assessed via mechanography. LMS regression was used to create smoothed age-specific reference curves, separated by sex. Pearson correlations between the jumps ranged from r = 0.22 to 0.64, varying by age and sex. Comparing medians, younger males had slightly higher values for vertical and long jump compared to females. Vertical jump power was more comparable between the sexes. For all measures, differences between the sexes become more pronounced at ages associated with the transition into adolescence. Growth model coefficients are reported for calculation of Z-scores. The growth curves can be used to compare samples, track lower body power, and link tests of fitness to athletic performance or health-related outcomes.
Article
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Secular changes in anaerobic fitness test performance in healthy 6- to 17-year-old Australasians were examined by meta-analysis of 232,564 power- and speed-test performances between 1960 and 2002. Overall, power-test performance improved at a rate of +0.05% [95% confidence interval (CI) = +0.01% to +0.09%] per annum, and speed at +0.04% (CI = +0.02% to +0.06%) per annum. Results indicate that anaerobic-fitness-test performances have remained relatively stable in Australasian children and adolescents in recent decades.
Article
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Article
There is evidence that fitness has been declining and fatness increasing in Australian schoolchildren over the last generation. This study reproduced the methods of a national survey of Australian schoolchildren conducted in 1985. Anthropometric and performance tests were administered to 1,463 10- and Ii-year-old South Australians. Compared to the 1985 sample, the 1997 children were heavier (by 1.4-2.9 kg), showed greater weight for height(by 0.13-0.30 kg.m(-2.85)),and were slower over 1.6 km (by 38-48.5 s). Furthermore, the distribution of values was markedly more skewed in the 1997 data. While there was Little difference between the fittest and leanest quartiles in 1997 and their 1985 counterparts, the least fit and fattest quartiles were markedly worse in 1997. This suggests that the decline in fitness of Australian schoolchildren is not homogeneous and that interventions should target groups where the decline is most marked.
Article
Objective To report sex- and age-specific physical fitness levels in European adolescents.Methods A sample of 3428 adolescents (1845 girls) aged 12.5–17.49 years from 10 European cities in Austria, Belgium, France, Germany, Greece (an inland city and an island city), Hungary, Italy, Spain and Sweden was assessed in the Healthy Lifestyle in Europe by Nutrition in Adolescence study between 2006 and 2008. The authors assessed muscular fitness, speed/agility, flexibility and cardiorespiratory fitness using nine different fitness tests: handgrip, bent arm hang, standing long jump, Bosco jumps (squat jump, counter movement jump and Abalakov jump), 4×10-m shuttle run, back-saver sit and reach and 20-m shuttle run tests.Results The authors derived sex- and age-specific normative values for physical fitness in the European adolescents using the LMS statistical method and expressed as tabulated percentiles from 10 to 100 and as smoothed centile curves (P5, P25, P50, P75 and P95). The figures showed greater physical fitness in the boys, except for the flexibility test, and a trend towards increased physical fitness in the boys as their age increased, whereas the fitness levels in the girls were more stable across ages.Conclusions The normative values hereby provided will enable evaluation and correct interpretation of European adolescents' fitness status.
Article
Purpose: We examined the association between cardiorespiratory fitness and stroke mortality in men. Methods: This is a prospective cohort study. We followed 16,878 men, ages 40-87 yr, who had a complete medical evaluation including a maximal treadmill exercise test and self-reported health habits, There were 32 stroke deaths during an average of 10 yr of follow-up (167,961 man-yr). Results: After adjustment for age and examination year, there was an inverse association between cardiorespiratory fitness and stroke mortality (P = 0.005 for trend). This association remained after further adjustment for cigarette smoking, alcohol intake, body mass index, hypertension, diabetes mellitus, and parental history of coronary heart disease (P = 0.02 for trend). High-fit men (most fit 40%) had 68% (95% CI: 0.12, 0.82) and moderate-fit men had 63% (95% CI: 0.17, 0.83) lower risk of stroke mortality when compared with low-fit men (least fit 20%). respectively. Conclusions: Moderate and high levels of cardiorespiratory fitness were associated with lower risk of stroke mortality in men in the Aerobics Center Longitudinal study population.