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Overweight youths are generally recognised as being at increased risk of sustaining lower extremity injuries in sports. However, previous studies are inconclusive and choices for measuring overweight are manifold. To examine two different measures of overweight, body mass index (BMI) and total body fat percentage (TBF%), as risk factors for lower limb injuries in a school-based cohort. A longitudinal cohort study. A total of 632 school children, baseline age 7.7-12.0 years, were investigated. Whole body dual energy x-ray absorptiometry scans provided measures of TBF%. Measures of BMI were obtained by standard anthropometric methods. Musculoskeletal complaints were reported by parents answering weekly mobile phone text messages during 2.5 years. Injuries were diagnosed by clinicians. Leisure time sports participation was reported weekly using text messaging. During 2.5 years of follow-up, 673 lower extremity injuries were diagnosed. Children being overweight by both BMI and TBF% showed the highest risk of sustaining lower extremity injuries (IRR 1.38 (95% CI 1.05 to 1.81)). Children who were overweight using BMI and TBF% showed the highest risk of sustaining lower extremity injuries (IRR 1.38 (95% CI 1.05 to 1.81)). The risk of lower extremity injuries appeared to be increased for overweight children. When comparing two different measures of overweight, overweight by TBF% is a higher risk factor than overweight by BMI. This suggests that a high proportion of adiposity is more predictive of lower extremity injuries, possibly due to a lower proportion of lean muscle mass.
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Total body fat percentage and body mass index
and the association with lower extremity injuries
in children: a 2.5-year longitudinal study
Eva Jespersen,
1
Evert Verhagen,
2
René Holst,
3
Heidi Klakk,
1
Malene Heidemann,
4
Christina Trifonov Rexen,
1
Claudia Franz,
1
Niels Wedderkopp
1,5
1
Centre for Research in
Childhood Health, Institute of
Sports Science and Clinical
Biomechanics, University of
Southern Denmark, Odense,
Denmark
2
Department of Public and
Occupational Health, EMGO+
Institute for Health and Care
Research, VU University
Medical Centre, Amsterdam,
The Netherlands
3
Biostatistical Research Unit,
Institute of Regional Health
Research, University of
Southern Denmark, Odense,
Denmark
4
Hans Christian Andersen
Childrens Hospital, Odense
University Hospital, Odense,
Denmark
5
Department of Orthopaedic,
Hospital of Lillebaelt, Institute
of Regional Health Service
Research and Centre for
Research in Childhood Health,
IOB, University of Southern
Denmark, Odense, Denmark
Correspondence to
Eva Jespersen, Centre for
Research in Childhood Health,
Institute of Sports Science and
Clinical Biomechanics,
University of Southern
Denmark, Campusvej 55,
5230 Odense M, Denmark;
ejespersen@health.sdu.dk
Accepted 26 October 2013
To cite: Jespersen E,
Verhagen E, Holst R, et al.
Br J Sports Med Published
Online First: [please include
Day Month Year]
doi:10.1136/bjsports-2013-
092790
ABSTRACT
Background Overweight youths are generally
recognised as being at increased risk of sustaining lower
extremity injuries in sports. However, previous studies are
inconclusive and choices for measuring overweight are
manifold.
Objective To examine two different measures of
overweight, body mass index (BMI) and total body fat
percentage (TBF%), as risk factors for lower limb injuries
in a school-based cohort.
Study design A longitudinal cohort study.
Methods A total of 632 school children, baseline age
7.712.0 years, were investigated. Whole body dual
energy x-ray absorptiometry scans provided measures of
TBF%. Measures of BMI were obtained by standard
anthropometric methods. Musculoskeletal complaints
were reported by parents answering weekly mobile
phone text messages during 2.5 years. Injuries were
diagnosed by clinicians. Leisure time sports participation
was reported weekly using text messaging.
Results During 2.5 years of follow-up, 673 lower
extremity injuries were diagnosed. Children being
overweight by both BMI and TBF% showed the highest
risk of sustaining lower extremity injuries (IRR 1.38 (95%
CI 1.05 to 1.81)). Children who were overweight using
BMI and TBF% showed the highest risk of sustaining
lower extremity injuries (IRR 1.38 (95% CI 1.05 to
1.81)).
Conclusions The risk of lower extremity injuries
appeared to be increased for overweight children.
When comparing two different measures of overweight,
overweight by TBF% is a higher risk factor than
overweight by BMI. This suggests that a high
proportion of adiposity is more predictive of lower
extremity injuries, possibly due to a lower proportion of
lean muscle mass.
BACKGROUND
Injuries sustained in sports and leisure time activ-
ities have been established as a leading cause of the
paediatric injury burden in Western countries.
15
These induce high direct and indirect costs for chil-
dren and parents and may cause short-term disabil-
ity, absence from school and/or physical activity
(PA), lost enthusiasm for participating in PA and
long-term consequences such as osteoarthritis.
611
Both intrinsic and extrinsic risk factors have pre-
viously been investigated and some attention has
been shown to body weight and body composition
as a potentially modiable risk factor on sport
injury risk.
12 13
The importance is emphasised as
overweight and obesity is affecting an increasing
proportion of children globally.
14
Hence the
paradox is that while PA is associated with numer-
ous health benets, including lowering the levels of
overweight and obesity,
15
overweight might at the
same time cause a rise in injury rates as the preva-
lence of overweight and obesity increases.
Overweight youths are generally considered as
being at increased risk of sustaining lower extremity
injuries in sports, due to a corresponding increase
in the forces that joints, ligaments, tendons and
muscular structures must endure.
1618
However,
ndings in studies on the association between body
composition and injuries are inconclusive and
choices of measures of body composition have
been varied, such as height and weight, lean muscle
mass, body fat content and most commonly body
mass index (BMI).
18
Overweight and obesity should be dened as
excess body fat. The most widely used measure-
ment to dene obesity is BMI. It is an indicator of
overweight and obesity from a population perspec-
tive, but has limitations on an individual level and
is only a proxy measurement of body fat.
19
The
association between BMI and total body fat per-
centage (TBF%), especially in athletes, has been
shown to be lower than in non-athlete controls.
2023
Moreover, the common use of BMI as a criterion
measurement may be an issue when it involves
physically active children. A high BMI might in that
case be an expression of a high proportion of lean
muscle mass, rather than overweight or unhealthy
weight. TBF% is a measure of adiposity and in the
area of sports it has been shown to be a more
precise measure for classication of overweight.
2124
Different mechanisms by which overweight
increases the risk of injuries have been proposed.
These include a relatively higher musculoskeletal
strain and impaired postural control when control-
ling for a disproportionately large body mass in
sport activities that require rapid alterations during
changes in direction.
16
Poor physical tness and
low PA levels among overweight young people add
to this risk.
15 16
Based on this, overweight caused
by a high proportion of TBF% appears to be a
more obvious risk factor than overweight caused by
a high proportion of lean muscle mass.
Overweight, dened by measures of TBF%, pos-
sibly associates differently with PA-related injuries
than overweight dened by measures of BMI.
The objective of this study was to examine two
different measures of overweight, BMI and TBF%,
as risk factors for lower limb injuries in school chil-
dren, followed for 2.5 years in a longitudinal
Jespersen E, et al.Br J Sports Med 2013;0:16. doi:10.1136/bjsports-2013-092790 1
Original article
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setting, while considering the potential confounding effects of
gender, age, tness levels and exposure times in physical educa-
tion (PE) and leisure time sports participation.
MATERIAL AND METHODS
Setting
Data from the Childhood Health, Activity, and Motor
Performance School Study Denmark (CHAMPS Study-DK)
August 2008 to July 2011 were used.
25
This investigation is a
large prospective controlled school-based study in Denmark
using the design of a natural experiment to evaluate the effect
of increased levels of PE on childhood health in general.
26
Participants
All boys and girls from preschool to fourth grade in 10 public
schools participating in the CHAMPS Study-DK also agreed to
participate in the registration of musculoskeletal pain and injur-
ies. The study was kept open, with the possibility for new chil-
dren to enter. Owing to the novel data collection method of
automated mobile phone text messaging, the schools were
included gradually in order to allow for a phasing-in process.
A subsample of children attending second to fourth grade
(age range 7.712.0 years) was invited to a dual energy X-ray
absorptiometry (DXA) scan providing TBF% in 2008. Children
were examined at baseline and at a 2-year follow-up. Height
and weight were assessed at the same time points. Data on injur-
ies and participation in organised sport were recorded from
November 2008 to June 2011.
Measurements
Musculoskeletal pain and injuries
Weekly information on musculoskeletal pain and injuries was
measured using mobile phone text messaging. Each week,
parents answered a text message asking questions on the pres-
ence or absence of musculoskeletal pain. A report of pain eli-
cited a telephone consultation, to distinguish children with
trivial complaints from those in need of clinical examination.
Physiotherapists, chiropractors or a medical doctor clinically
examined the children during the coming fortnight and a stand-
ard medical record was performed. Injuries were diagnosed
using the International Classication of Diseases (ICD-10),
WHO.
27
If needed, the child was referred for further examin-
ation, such as X-ray, ultrasound or MR scans, or to consulting
specialist doctors. Information on children being treated else-
where during the study period, for example, emergency depart-
ment, general practitioner, was collected concurrently to get a
complete data collection on injuries in this cohort.
Physical activity
Weekly amount of PE was 4.5 h for children in sport schools
and 1.5 h for children in normal schools, corresponding to
three and one double lesson per week, respectively. Pupils at
sport and normal schools were therefore assigned three and one
sport exposure units per week, respectively. Leisure time sport
was also assessed using text messaging, by parental reports on
how many times the child had participated in leisure time sport
activities during the past week.
Total body fat percentage
TBF% was measured by DXA (GE Lunar Prodigy, GE Medical
Systems, Madison, Wisconsin, USA), ENCORE software (V.12.3,
Prodigy; Lunar Corp, Madison, Wisconsin, USA).
TBF% was calculated for each participant from the equation:
((FM (g) × 100)/total body weight (g)). Cut-offs to classify
children as normal weight or overweight were dened using the
cardiovascular health-related and gender-related TBF% stan-
dards according to Williams et al.
28
The cut-off for overweight
boys was 25 TBF% and a similar cut-off for girls was 30
TBF%.
Body mass index
Weight was measured to the nearest 0.1 kg on an electronic
scale (Tanita BWB-800S, Tanita Corporation, Tokyo, Japan).
Height was measured to the nearest 0.5 cm using a portable sta-
diometer (SECA 214, Seca Corporation, Hanover, Maryland,
USA).
BMI was calculated as (weight (kg)/height
2
(m)). BMI classi-
cations for normal weight, overweight and obese were dened
using age-specic and sex-specic cut-offs as recommended by
the International Obesity Taskforce recommendations.
29
Dichotomised categories were made for weight classes as
normal weight or overweight/obese (hereafter referred to as
overweight) for easier comparison with the dichotomous vari-
able of normal weight versus overweight as described above.
Fitness level
Fitness was assessed by the Andersen test. This is a 10 min inter-
mittent running test to estimate maximal oxygen uptake and
indicate aerobic tness.
30
The test was carried out indoors in
20 m running lanes marked by cones. Children were urged to
run as quickly as possible for 15 s, then stopped for the next
15 s and this pattern was repeated for 10 min. The total distance
measured in metres was the test result. The validity and reliabil-
ity of this eld test were tested and described thoroughly for the
age group of our cohort in a study by Ahler et al.
31
Statistical methods
Data from the text messaging system and data on diagnosed
injuries were analysed using STATA V.12.0 (StataCorp, Texas,
USA).
Excessive levels of pain and the number of injuries were
reported during the start-up-phase. This is possibly explained by
the novelty of the study and the method. Observations from the
rst 4 weeks relative to the time of inclusion were therefore
excluded.
The risk of getting injured according to absolute levels of
baseline BMI and TBF% were explored. Furthermore, the
potential effect of children changing body composition through
the 2.5 years of injury surveillance was evaluated by a logistic
regression using variables with no change,change to elevated
BMI or TBF%and change to normal BMI or TBF% valuesas
categories.
Concerns about children being underweight were addressed,
as injury patterns could possibly be different in this group.
20 32
For this reason, the prevalence of underweight was determined
in the baseline population, using the recommended
cut-offs.
33 34
An initial analysis excluding the group of under-
weight children did not change the estimates of risk of injury or
the estimated effect of other covariates. Underweight children
were therefore not considered different from normal-weight
children regarding the risk of injury. Hence, they were cate-
gorised as normal-weight children.
The calculation of incidence rates accounted for the total
exposure expressed in 1000 athletic exposure units. These com-
prised the PE exposures and the participations in leisure time
sport.
A multilevel mixed-effects Poisson regression was used to esti-
mate the incidence-rate ratios (IRR) with BMI and TBF% as the
2 Jespersen E, et al.Br J Sports Med 2013;0:16. doi:10.1136/bjsports-2013-092790
Original article
group.bmj.com on November 26, 2013 - Published by bjsm.bmj.comDownloaded from
primary risk factors. BMI and TBF% were used as dichotomised
variables (0=normal values, 1=elevated values) in separate
regression analyses. For identication of groups of potential
clinical interest, the four combinations of normal and elevated
BMI with normal and elevated TBF% were likewise tested in a
regression analysis, with normal BMI and normal TBF% being
the reference groups.
Finally, BMI and TBF% were tested as continuous variables
and used for illustrating the adjusted risk of lower extremity
injuries in relation to the two measures of body composition.
The explanatory variables included gender, age, PE/leisure
time sport and tness levels. Classes and schools were used as
random effects. The multilevel random effects model reects
the hierarchical sampling structure and was chosen to allow for
potential variation between schools and between classes within
schools and to ensure correct modelling of the variances. The
number of weeks each child participated was included as an
exposure term in the model.
Potential patterns for the missing values in injury data were
addressed by a logistic regression analysis controlling for gender,
age, school type and leisure time sports effects. Missing values
because of practicalities concerning changed or wrong mobile
numbers were dropped for analyses.
RESULTS
A total of 632 children, aged 7.712 years at baseline, partici-
pated at baseline and the follow-up DXA scan and in the regis-
tration of musculoskeletal injuries. The mean baseline BMI was
16.6 (±SD 2.1) and TBF% was 20.1% (±SD 8.0). A total
number of 673 lower extremity injuries were diagnosed during
the 2.5 years of follow-up. Some children experienced more
than one injury; the range was from zero and up to eight epi-
sodes of lower extremity injuries. The range of participation
time in injury registration was 1113 weeks, with 98.1 weeks
being the mean value. Dropouts were due to children moving
away from the municipality or changing to a non-project school,
but were counterbalanced by new children moving to project
schools. Fifteen children dropped out because answering SMS
questions every week was too bothersome. An average weekly
response rate of 96% was recorded during the study period of
113 weeks. A total number of 62 001 observations were
recorded and 2502 (4%) were missing. The analysis of missing
data did not show any patterns when looking at gender, age,
school type and leisure time sports. The mean weekly sport
exposures units in PE and leisure time sport were 3.9 (±SD 1.3)
and tness level at baseline had a mean of 930 8 m (±SD101.9).
Differences in gender are presented in table 1.
The injury rates per 1000 athletic exposures showed a trend,
albeit not signicant, towards higher risk for children being
overweight, whether dened by BMI or by TBF%. Injury rates,
95% CI and gender differences are described in table 2.
28 29
The multivariate and multilevel adjusted IRR estimates by dif-
ferent measures of overweight are summarised in table 3.
Overweight children were generally at higher risk of sustaining
lower leg injuries, by BMI: 1.28 (95% CI 0.98 to 1.66) and by
TBF% 1.34 (95% CI 1.07 to 1.68), the latter being statistically
signicant.
Looking at the four combined groups of body composition,
children with elevated BMI and TBF% showed the highest risk
of sustaining lower leg injuries: 1.38 (95% CI 1.05 to 1.81)
relative to children having a normal BMI and a normal TBF%
(gure 1).
The possible effect of children changing body composition
during the 2.5 years of injury surveillance was also accounted
for in the adjusted analysis; it did not explain the risk of lower
extremity injuries, and nor did it inuence the estimated effects
of other covariates.
Gender and age did not inuence the risk, whereas the time
participating in PE and leisure time sport and tness level
explained some of the lower extremity injury risk. The risk of
injury signicantly increased for each additional time a child
participated in PE and leisure time sport from 0 to 6.5 weekly
exposure units. For the 18 children with a mean of more than
6.5 exposures a week, the risk again decreased. A positive linear
relationship was found between risk of lower extremity injuries
and aerobic tness.
The adjusted risk of lower extremity injuries in relation to the
two measures of body composition measured on a continuous scale
are illustrated in gure 2 for girls and boys. A positive linear rela-
tionship was found between risk of lower extremity injuries and the
continuous values of TBF% and BMI across the full range.
Table 1 Sample characteristics in numbers (%) and means (±SD)
measured by gender during 2.5 years of follow-up
Girls Boys
Numbers 321 (50.8%) 311 (49.2%)
Age at baseline 9.6 (0.9) 9.6 (0.9)
Range 7.911.6 7.712.0
Baseline BMI 16.7 (2.1) 16.6 (2.1)
Baseline TBF% 23.0 (7.4) 17.1 (7.4)
Weekly exposure in PE and sports 3.9 (1.3) 3.9 (1.4)
Fitness level at baseline (metre) 892.7 (89.2) 967.8 (99.8)
Lower extremity injuries
Number of lower extremity injuries 336 337
Number of children with lower extremity
injuries
178 (55.5%) 179 (57.6%)
BMI, body mass index; PE, physical education; TBF%, total body fat percentage.
Table 2 Lower extremity injury rates by BMI/TBF% groups, overall and by gender
Lower extremity injuries
Injury rate (CI)* BMITBF%
Normal BMI Overweight by BMI cut-offs Normal TBF% Overweight by TBF% cut-offs
Overall 4.4 (4.1 to 4.8) 5.3 (4.1 to 6.5) 4.4 (4.0 to 4.7) 5.2 (4.3 to 6.1)
Girls 4.4 (3.9 to 4.9) 5.1 (3.6 to 6.5) 4.3 (3.8 to 4.8) 5.2 (4.0 to 6.5)
Boys 4.4 (3.9 to 4.9) 5.8 (3.7 to 7.9) 4.4 (3.9 to 4.9) 5.1 (3.7 to 6.6)
*Injury rate is per 1000 athletic exposures; values in parentheses are 95% CI.
Age-specific and gender-specific cut-offs according to IOTF/Cole et al.
29
Cut-offs boys 25%, girls 30% according to Williams et al.
28
BMI, body mass index; TBF%, total body fat percentage.
Jespersen E, et al.Br J Sports Med 2013;0:16. doi:10.1136/bjsports-2013-092790 3
Original article
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DISCUSSION
This study is the rst to evaluate and compare two different
measures of overweight as risk factors for lower extremity injur-
ies in a school-based cohort of children. The risk of lower
extremity injuries was observed to increase in overweight chil-
dren. Being overweight measured by TBF% or a combination of
elevated TBF% and BMI was more predictive than being over-
weight measured by BMI. This suggests that a high proportion
of adiposity is more predictive of lower extremity injuries, pos-
sibly due to a lower proportion of lean muscle mass.
In contrast, Kaplan et al
35
found that body weight was a
more powerful injury risk factor than adiposity, with no differ-
ences in injury risk between linemen and non-linemen in
American football. This was shown in a study comparing differ-
ent measures of body composition (body fat, BMI, weight,
height) to injury risk in a group of 98 high school players with
28 injuries registered by trainers. This was reproduced in
another American football study reporting injury rates by body
fat, weight, BMI and lean body mass in high school football
linemen,
36
whereas adiposity expressed as TBF% was a stronger
predictor of the magnitude and type (overuse/traumatic) of mus-
culoskeletal injuries in army cadets than BMI.
37
Direct compari-
sons may not be relevant because of differences in techniques to
measure TBF%, injury registration methods, magnitudes of
studies, ages and sports specic versus more heterogenic set-
tings. Still, it is possible that in some sports, the effect of
increased mechanical loading during weight bearing or collisions
has a more pronounced effect than in other sports.
Injury patterns might also differ in relation to different injury
types. Traumatic injuries provoked and/or aggravated by greater
collision forces due to heavy weight could be argued to be inde-
pendent of the muscle/fat distribution to a greater extent than
overuse injuries, where the quality of tissue (eg, muscle strength
and endurance) is important. The effect of overweight in rela-
tion to different injury types (overuse/traumatic), different diag-
noses, different anatomical regions and different sports still
needs to be claried.
In this study, injury risk increased with increased participation
in PE and leisure time sport. This is in accordance with the
common understanding of the need to consider exposure time
when estimating injury risk. Surprisingly, children with high
tness levels had a higher risk of sustaining lower extremity
injuries. This is in contrast to earlier beliefs where lower tness
levels have been associated with muscle fatigue and subsequent
injury.
38
A possible explanation could be that children with high
aerobic capacities are also the children with the largest amount
of exposure time. Even though analyses were carried out with
adjustment for exposure time in terms of PE and leisure time
sports, there may have been uncaptured exposure time in the
most aerobically t, as unorganised leisure time activity was
unknown.
Figure 2 Adjusted risk of lower
extremity injuries by different measures
of body composition in girls and boys
(BMI, body mass index).
Figure 1 Incidence-rate ratio estimates (95% CI) by four groups of
body compositions, adjusted for age, gender, physical education/leisure
time sport and tness level (BMI, body mass index; IRR, incidence-rate
ratio; TBF%, total body fat percentage).
Table 3 Incidence-rate ratio estimates by different body
composition measures, adjusted for age, gender, physical education/
leisure time sport and fitness level
BMI
IRR (95% CI)
TBF%
IRR (95% CI)
Normal weight 1.00 1.00
Overweight 1.28 (0.98 to 1.66) 1.34 (1.07 to 1.68)*
*Statistical significance based on p<0.05%.
BMI, body mass index; IRR, incidence-rate ratio; TBF%, total body fat percentage.
4 Jespersen E, et al.Br J Sports Med 2013;0:16. doi:10.1136/bjsports-2013-092790
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Cut-offs to classify children as normal weight or overweight
were dened using cardiovascular health-related and gender-
specic TBF% standards
28
and age-specic and gender-specic
centiles from a pooled international dataset, linked to adult
cut-offs for BMI classications.
29
It can be questioned if these
criteria have the same relevance in injury risk research, but
they permit comparison across studies and contribute to a
general evaluation of health risk among overweight children.
The presentation of data in gure 2 does not suggest any
obvious cut-off for a signicant increase in risk of lower
extremity injuries in relation to overweight. Specicover-
weight cut-offs for being at increased injury risk might be less
important in the context of injury prevention, especially at an
individual level where a more comprehensive screening of
body composition involving an expression of TBF% would be
more relevant.
CONCLUSION
The risk of lower extremity injuries in a heterogenic cohort of
schoolchildren was shown to increase in overweight children.
When comparing two different measures of overweight, a body
composition of proportionally high levels of TBF% is a higher
risk factor than overweight as measured by BMI. This suggests
that a high proportion of adiposity is more predictive of lower
extremity injuries, possibly due to a lower proportion of lean
muscle mass.
Increased levels of PE and leisure time sports participation
and tness were also associated with increased risk of lower
extremity injuries.
What are the new ndings?
This study is the rst to evaluate and compare two different
measures of overweight as risk factors for lower extremity
injuries in a school-based cohort of children.
Overweight children have an increased risk of lower
extremity injuries.
Overweight by measures of total body fat percentage is
more predictive of lower leg injuries in children than
overweight by measures of body mass index.
How might it impact on clinical practice in the near
future?
Injury prevention in children and adolescents should involve
a screening of body composition involving an expression of
total body fat percentage. While dual energy X-ray
absorptiometry scans are expensive and not feasible in most
settings, a measurement method such as waist
circumference is cheap and easy to obtain.
Further research is needed into the proposed underlying
mechanisms for overweight children being at increased
injury risk. Previously suggested mechanisms have been poor
postural controlleading to problems with balance and
co-ordination, poor physical tnessassociated with muscle
fatigue, and subsequent injury and low preparticipation
physical activity levelsassociated with impaired
neuromuscular and motor learning.
Acknowledgements The authors wish to acknowledge K Froberg and LB
Andersen, Centre for Research in Childhood Health, University of Southern Denmark.
The authors would like to thank the participants and their parents and the
participating schools, The Svendborg Project and the municipality of Svendborg.
Finally, the authors wish to acknowledge the members of the CHAMPS Study-DK
not listed as coauthors of this paper: T Junge and NC Møller.
Contributors NW was responsible for the overall study concept and design. HK
and MH were responsible for the acquisition of the body composition data. EJ, CTR,
CF and NW were responsible for the acquisition of injury data. EJ, EV, RH and NW
were responsible for the analysis and interpretation of data. EJ drafted the
manuscript. All authors took part in a critical revision of the manuscript. RH
provided statistical expertise. NW obtained the funding.
Funding This study was supported by grants from The IMK Foundation, The
Nordea Foundation, The TRYG Foundationall private, non-prot organisations,
which support research in health prevention and treatment, and TEAM Denmark, the
elite sport organisation in Denmark, that provided the grant for the text messaging
system.
Competing interests None.
Ethics approval Written informed consent was obtained from the childs parent,
including verbal acceptance from the parent and the child prior to every clinical
examination for diagnosing injuries and medical record keeping. All participation in
the data collection was voluntary with the option to withdraw at any time. The
study was approved by the Ethics Committee for the region of Southern Denmark
(ID S20080047)
Provenance and peer review Not commissioned; externally peer reviewed.
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Original article
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doi: 10.1136/bjsports-2013-092790
published online November 22, 2013Br J Sports Med
Eva Jespersen, Evert Verhagen, René Holst, et al.
longitudinal study
extremity injuries in children: a 2.5-year
index and the association with lower
Total body fat percentage and body mass
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... High body mass index (BMI) and body fat percentage (bf %) can lead to high rates of musculoskeletal sports-related injury [1][2][3][4], although certain sports continuously see elite athletes with excess body fat succeed at high levels of sport and competition. Specific to softball, pitchers possess the highest percentage of body fat on collegiate softball teams [5,6], and despite the associated risk of injury that accompanies greater fat mass [7], pitchers continue to compete at high levels successfully. ...
... Specific to softball, pitchers possess the highest percentage of body fat on collegiate softball teams [5,6], and despite the associated risk of injury that accompanies greater fat mass [7], pitchers continue to compete at high levels successfully. In lieu of the added injury-risk for those athletes who display high mass [8], body fat percentage [3,4], BMI [1,2], and the anecdotal evidence that many elite pitchers can succeed despite high body fat, there may be a potential injury/performance trade-off. Excess body fat may be beneficial in terms of performance, helping pitchers reach high levels of com-petition -a notable trend among some of the best pitchers. ...
... Pitchers of various body sizes and body compositions compete at high levels of softball; despite reports that excess body fat can increase the risk of injury for athletes [1][2][3][4]. Therefore, this study examined the relationship between body composition measures, pitch speed, and GRF during the propulsion phase of the pitch to see how pitchers with different body fat percentages might portray altered pitch characteristics. ...
Article
Softball pitchers with a high body-fat percentage (bf%) can often be successful, despite the heightened risk of injury associated with high bf%. Given the importance of propulsion during pitching, those with high bf% may have an advantage performance-wise. Therefore, the purpose of this study was to examine the differences in ground reaction force (GRF) development between two groups of pitchers: those with a high-fat percentage (≥32 bf%) and a healthy-fat percentage (<32 bf%). Thirty-two female high-school softball pitchers (1.70±.06 m, 76.09±17.50 kg, 15±1 yrs) completed dual-energy x-ray absorptiometry (DEXA) scans. GRF data were collected during pitch propulsion via a force plate, pitch speed was captured using a radar gun, BMI was calculated from pitcher height and mass, and fat free mass index (FFMI) and fat mass index (FMI) were calculated using DEXA data and pitcher height. Multivariate analysis of variance revealed pitcher group GRFs differed significantly (F3,30=3.45, p=.030). Univariate follow-up analyses showed healthy bf% pitchers presented greater weight-normalized peak medial GRF (F1,30=7.17, p=.012). BMI and FFMI were positively associated with pitch speed while bf% and FMI were negatively associated with pitch speed. While pitchers can be successful and carry excess bf%, results indicate potential performance disadvantages associated with having an increased bf%.
... Contrary, dysfunctional movement (DFM) presents suboptimal movement quality and is related to a compensatory movement pattern along the kinetic chain with associated loss in the range of motion, balance, and deficit in postural control of the specific movement pattern (26)(27)(28). The importance of FM patterns has been discussed in previous studies (23)(24)(25)(26)(27) and they are considered as fundamental 'pillars' for the exhibition of complex movements (26,27), whereas DFM has been related to higher injury incidence (29)(30)(31)(32) and potential movement pathologies in children with overweight (19,20,33). Therefore, incorporating FM patterns in exercise programs is critical for the optimal progress toward more complex movement skills (26)(27)(28). ...
... Thus, effective preventive injury strategies should be incorporated across sports clubs and schools. In adolescence, various factors can contribute to higher injury risk such as previous injury, sports participation, high body mass index (BMI), total body fat, and others (19,21,32). These factors have been well studied, but none of these studies considered movement quality to be one of the predictive factors for injury in average adolescents. ...
... Based on the current literature, we selected features that are related to injury occurrence (5,21,32). For nonathletic subjects, we selected the variables that are measured at baseline: sex, age, socioeconomic status, body fat, MVPA, and total FMS score. ...
Thesis
Full-text available
The main purpose of this doctoral thesis is to determine in which way are level of physical activity (PA), adiposity, and injury related to the quality of movement patterns among adolescent population. Within this doctoral thesis, there are three distinct studies with related research questions and aims (Study 1, Study 2, and Study 3). Study 1 examined relationship between functional movement and PA in an urban adolescent population, while study 2 strive to identify association between adiposity and quality of movement patterns among the adolescent population. Finally, in Study 3, machine learning (ML) was used to predict injuries among adolescents by functional movement testing. Participants in all three studies were part of the Physical Activity in Adolescence Longitudinal Study (CRO-PALS) cohort. In Study 1 we included 725 adolescents (aged between 16 and 17 years) from CRO-PALS cohort. Movement quality was evaluated via Functional Movement ScreenTM (FMSTM) while PA was assessed with the School Health Action, Planning and Evaluation System (SHAPES) questionnaire. From SHAPES questionnaire, vigorous PA (VPA) and moderate-to-vigorous PA (MVPA) was calculated. Confounders included chronological age, body fat and socioeconomic status (SES). Results of Study 1 indicated that after adjusting for age, body fat and SES, both VPA and MVPA showed minor but significant effects on total FMS score among girls (β=0.011, p=0.001, β=0.005, p=0.006, respectively), but not in boys (β=0.004, p=0.158; β=0.000, p=0.780). Regarding PA type, volleyball and dance improved total FMS score (β=1.003, p=0.071; β=0.972, p=0.043, respectively), while football was associated with lower FMS score (β=-0.569, p=0.118). Conclusively, results of Study 1 showed that PA level is positively associated with the functional movement in adolescent girls, but not in boys, where the type of PA moderates these associations. Because girls are more engaged in aesthetic sports activities that improve functional movement, and unlike boys are in the final stages of maturation, this could affect sexual dimorphism in the quality of movement among the adolescent population. In Study 2 participants were 652 urban adolescents (aged between 16 and 17 years). Body mass index (BMI), a sum of four skinfolds (S4S), waist and hip circumference were measured, and movement quality (i.e. functional movement – FM) was assessed via FMSTM. Furthermore, total FMSTM screen was indicator of FM with the composite score ranged from 7 to 21, with higher score indicating better FM. Multilevel analysis was employed to determine the relationship between different predictors and total FMS score. Results of the Study 2 demonstrate that, in boys, after controlling for age, MVPA, and SES, total FMS score was inversely associated with BMI (β=-0.18, p<0.0001), S4S (β=-0.04, p<0.0001), waist circumference (β=-0.08, p<0.0001), and hip circumference (β=-0.09, p<0.0001). However, among girls, in adjusted models, total FMS score was inversely associated only with S4S (β=-0.03, p<0.0001), while BMI (β=-0.05, p=0.23), waist circumference (β=-0.04, p=0.06), and hip circumference (β=-0.01, p=0.70) failed to reach statistical significance. Findings of Study 2 point out that the association between adiposity and FM in adolescence is sex-specific, suggesting that boys with overweight and obesity could be more prone to develop dysfunctional movement patterns. Therefore, exercise interventions directed toward correcting dysfunctional movement patterns should be sex-specific, targeting more boys with overweight and obesity rather than adolescent girls with excess weight. Analyses for the Study 3 were based on nonathletic (n=364) and athletic (n=192) subgroups of the cohort (16–17 years). Sex, age, BMI, body fatness, MVPA, training hours per week, FMS, and SES were assessed at baseline. A year later, data on injury occurrence were collected. The optimal cut-point of the total FMS score for predicting injury was calculated using receiver operating characteristic curve. These predictors were included in ML analyses with calculated metrics: area under the curve (AUC), sensitivity, specificity, and odds ratio (95% confidence interval [CI]). Results of the receiver operating characteristic curve analyses with associated criterium of total FMS score >12 showed AUC of 0.54 (95% CI: 0.48–0.59) and 0.56 (95% CI: 0.47–0.63), for the nonathletic and athletic youth, respectively. However, in the nonathletic subgroup, ML showed that the Naïve Bayes exhibited highest AUC (0.58), whereas in the athletic group, logistic regression was demonstrated as the model with the best predictive accuracy (AUC: 0.62). In both subgroups, with given predictors: sex, age, BMI, body fat percentage, MVPA, training hours per week, SES, and total FMS score, ML can give a more accurate prediction then FMS alone. Results of the Study 3 indicate that nonathletic boys who have lower-body fat could be more prone to suffer from injury incidence, whereas among athletic subjects, boys who spend more time training are at a higher risk of being injured. Conclusively, total FMS cut-off scores for each subgroup did not successfully discriminate those who suffered from those who did not suffer from injury, and, therefore, this study does not support FMS as an injury prediction tool.
... Two common functional tests utilized to check ankle joints are the Y Balance Test (YBT) and Single Leg Hop for Distance (SLHD), which help analyze dynamic balance and ankle function (Ageberg & Cronström, 2018;Hartley et al., 2018). Body Mass Index (BMI) is another potentially modified clinical outcome predictor of lower limb injury risk, along with reduced ADROM (Doan et al., 2010;Jespersen et al., 2014). The risk of injury is significantly higher for overweight players (Tyler et al., 2006;Yard & Comstock, 2011). ...
Article
Full-text available
Ankle sprains are prevalent among athletes, with reduced ankle dorsiflexion range of motion (ADROM) contributing significantly to these injuries. In this investigation, male collegiate athletes' body composition, ADROM, and dynamic balance, as measured by the Y Balance Test (YBT) and Single Leg Hop for Distance (SLHD), were all examined concerning one another. Fifty-two collegiate athletes were recruited after eliminating athletes with a history of injuries to the lower extremities. A goniometer and the Weight-Bearing Lunge Test (WBLT) were used to measure ADROM, and Body Impedance Analysis (BIA) was used to measure body composition. SLHD was used to evaluate lower limb function, and the YBT was used to assess dynamic balance. The study discovered strong positive correlations between YBT anterior reach and ADROM (r = 0.72) and WBLT (r = 0.64). ADROM and WBLT were found through regression analysis to be significant predictors of YBT performance, particularly in the anterior reach direction. While body composition measures like BMI and total fat did not significantly correlate with YBT scores, SLHD did show a moderate correlation with YBT performance. These results imply that improving weight-bearing lunge capacity and ankle dorsiflexion may help male collegiate athletes achieve better dynamic balance. Including specific exercises to improve WBLT and ADROM capacities in training may help lower the risk of injuries to the lower extremities and enhance overall sports performance.
... In Jespersen et al. study [19], a comparison can be drawn between the criteria that determine body com- Body fat percentage was measured based on the bioelectrical body composition They defined injury as any events that can directly hamper the participation of an athlete in on-ice activity for at least one day. ...
Article
Introduction: This study aimed to review the literature on the role of body composition as a risk factor for injury in an athletic population. Materials and Methods: We searched articles in English in Google Scholar Science direct, PubMed, WOS, Scopus, ProQuest, and Cochrane Library databases without time limit until 2020 using keywords related to "body composition" and "sports injury". Results: Considering criteria including inclusion and exclusion, 10 papers out of 1322 studies were comprehensively reviewed. It was found that body composition components are related to musculoskeletal injuries in the athletic population. Body mass index, weight and bone density are known as risk factors in the development of sports injuries. Conclusion: This systematic review provides preliminary evidence of the relationship between body composition and prediction of injury in athletes. Defects in various aspects of body composition were recognized as potential risk factors for lower extremity injuries. Likewise, body composition should be considered when screening athletes.
... Young men who had excessive body fat were 47 % more likely to experience a musculoskeletal injury and had a 49 % higher use of health care (Cowan et al. 2011). Children who were overweight in terms of both BMI and total body fat percentage showed the highest risk of sustaining lower extremity injuries (Jespersen et al. 2014). Obese children had a greater risk of extremity fracture than their non-obese counterparts (Kim et al. 2013a, Kim et al. 2016, Singer et al. 2011, Sabharwal and Root 2012. ...
Technical Report
Full-text available
Much research has been done on musculoskeletal disorders (MSDs), but most reports focus on adults. This scoping review focuses on research in children and young people — both before and after joining the labour market. As many MSD problems begin in childhood, it is important to identify how they can be prevented at an early age. Many factors influence the development of MSDs, including physical factors (e.g. obesity, lack of sleep, prolonged periods of sitting), socioeconomic factors and individual factors (e.g. gender, age). This review examines how these factors affect MSDs in children and young people, how they can be prevented and how good musculoskeletal health can become an integral part of education.
... Is adiposity associated with back and lower limb pain? A systematic review associations between fat mass and pain [50,[52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67], nine studies examined adiposity within a specific muscle [68][69][70][71][72][73][74][75], five studies did not specify a site of pain [76][77][78][79], two studies only examined multisite pain [80,81], and three studies examined pain in children [82][83][84]. ...
Article
Full-text available
Background Back and lower limb pain have a major impact on physical function and quality of life. While obesity is a modifiable risk factor for musculoskeletal pain, the role of adiposity is less clear. This systematic review aimed to examine the relationship between both adiposity and its distribution and back and lower limb pain. Methods A systematic search of electronic databases was conducted to identify studies that examined the association between anthropometric and/or direct measures of adiposity and site specific musculoskeletal pain. Risk of bias was assessed and a best evidence synthesis was performed. Results A total of 56 studies were identified which examined 4 pain regions, including the lower back (36 studies), hip (two studies), knee (13 studies) and foot (eight studies). 31(55%) studies were assessed as having low to moderate risk of bias. 17(30%) studies were cohort in design. The best evidence synthesis provided evidence of a relationship between central adiposity and low back and knee pain, but not hip or foot pain. There was also evidence of a longitudinal relationship between adiposity and the presence of back, knee and foot pain, as well as incident and increasing foot pain. Conclusions This systematic review provides evidence of an association between both body fat and its central distribution and low back and knee pain, and a longitudinal relationship between adiposity and back, knee and foot pain. These results highlight the potential for targeting adiposity in the development of novel treatments at these sites.
... Taanila et al. (32) reported a U-shaped relationship between body composition and overuse injuries in young Finnish conscripts. Also, Jespersen et al. (42) reported that children with higher body fat percentage are more prone to sustain lower extremity injuries. ...
Article
Background: Several studies have investigated risk factors for injury in different sports. However, little scientific information is available in relation to futsal injuries. Objectives: This study prospectively analyzed the influence of physical fitness parameters on the occurrence of futsal injuries in Iranian national teams. Methods: Prior to the season, all 55 players of 3 Iranian national futsal teams took part in a series of tests for physical fitness parameters such as cardio-respiratory fitness, muscle strength, body composition, flexibility, agility, and speed. Team physicians recorded all injuries, medical attention, and time loss due to the injury throughout the match and training sessions. Results: During 18 months, 54 futsal injuries were sustained by 32 players. Injured players had poorer performance at baseline in agility, speed, and vertical jump than players who did not incur an injury during the season. Lower maximal oxygen uptake (VO2max) values were associated with a higher injury risk. No differences between injured and uninjured players were observed in age, anthropometric data, flexibility, and lower extremity strength. Conclusions: Some physical fitness parameters were associated with the risk of injuries in futsal. Therefore, improving physical fitness might help to reduce the number of futsal injuries.
... Risk of remaining injury types (concussion and LE injury) were inconclusive with equal or a similar number of significant and nonsignificant findings. When all studies that reported LE injury (with the exclusion of BSI) were combined (n 5 22), 16 analyses 19,22,32,38,[40][41][42][43][44][45][48][49][50] reported a significant association between high adiposity and injury while 6 22,51-54 reported no association. Differentiation between acute and overuse mechanism of injury was possible in 14 studies. ...
Article
Objective: To determine whether high or low adiposity is associated with youth sport-related injury. Data Sources: Ten electronic databases were searched to identify prospective studies examining the association between adiposity [body mass index (BMI) or body fat] and a future time-loss or medical attention sport-related musculoskeletal injury or concussion in youth aged 20 years and younger. Two independent raters assessed the quality (Downs and Black criteria) and risk of bias (Joanna Briggs Institute Critical Appraisal Tool). Random-effects meta-analyses were used to calculate pooled odds ratio [95% confidence interval (CI)] of injury. Main Results: Of 11 424 potentially relevant records, 38 articles were included with 17 eligible for meta-analyses. In qualitative synthesis, no clear association was identified between adiposity and any sport injury; however, 16/22 studies identified high adiposity as a significant risk factor for lower-extremity injury. Meta-analyses revealed higher BMI in youth with any sport-related injury and lower BMI in youth who developed a bone stress injury (BSI) compared with non-injured controls. The pooled OR (95% CI) examining the association of BMI and injury risk (excluding bone injury) was 1.18 (95% CI: 1.03-1.34). A major source of bias in included articles was inconsistent adjustment for age, sex, and physical activity participation. Conclusions: Level 2b evidence suggests that high BMI is associated with greater risk of youth sport injury, particularly lower-extremity injury and excluding BSI or fracture. Although pooled mean differences were low, anthropometric risk of injury seems to be dependent on type and site of injury in youth sport.
Article
Full-text available
Objective: To analyze whether 13 weeks of Integrative Neuromuscular Training can benefit spatiotemporal and kinematic parameters of gait in children with overweight/obesity. Methods: This is a non-randomized controlled trial. Fifty children (10.77 ± 1.24 years, 31 girls) with overweight/obesity were allocated to an exercise group (EG) (n=25) that carried out a 13-week exercise program based on fundamental movement skills, strength activities and aerobic training, and a control group (CG) (n=25) that followed their normal lifestyle. Spatiotemporal (i.e., cadence, stance and support times, step length and stride width) and kinematic (i.e., hip, pelvis, knee and ankle angles) parameters were evaluated under laboratory conditions through a 3D analysis. ANCOVA was used to test raw and z-score differences between the EG and CG at post-exercise, adjusting for pre-exercise values. Results: The EG maintained their baseline stance and single-limb support times while the CG increased them during walking (groups difference: 3.1 and 1.9 centiseconds). The EG maintained baseline maximum foot abduction angle during the stance phase whereas the CG showed an increase (groups difference: 3.9º). Additional analyses on kinematic profiles demonstrated that the EG walked with similar pelvic tilt and ankle abduction angles at post-exercise, while the CG increased the pelvic anterior tilt in the whole stance phase (mean groups difference: 7.7º) and the ankle abduction angles in early- and mid-stance phases (mean groups difference: 4.6º). No changes were observed in the rest of spatiotemporal and kinematic parameters. Conclusions: The Integrative Neuromuscular Training stopped the progression of some biomechanical alterations during walking in children with overweight/obesity. These findings could contribute to preventing common movement-derived musculoskeletal disorders in this population, as well as preserving an optimal mechanical efficiency during walking.
Article
Youth who are obese or overweight demonstrate evidence of poor lower extremity joint health and alterations in gait characteristics compared with youth who are healthy weight. However, there is no consensus if altered movement patterns are still present during high‐impact activities. The purpose of this review was to determine if spatiotemporal and kinematic and kinetic variables during high‐impact activities were significantly different between youth who are overweight and obese compared with youth who are healthy weight. An electronic search of five databases was conducted, and a meta‐analysis and qualitative evidence synthesis was performed to determine the level of evidence, analyzing three tasks: running, jumping, and hopping. The findings of this review include the following: (1) overweight/obese (OW/OB) had higher stance phase time during running, (2) OW/OB had decreased hip flexion angles during running and stationary running, (3) OW/OB had decreased knee flexion angles during landing phase of jumping and hopping, and (4) OW/OB had increased hip abduction moments during running and jumping. These altered kinematic and kinetic variables at the hip and knee may result in mechanical inefficiency with high‐impact activities, as well as potentially increased risk of joint degradation and poor joint health into adulthood.
Article
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Predictors of work-related injuries were assessed using data from a group of Greek Army officer cadets. Cadets (n = 253) were monitored by physicians for musculoskeletal injuries resulting through a 7-week Basic Combat Training (BCT) period. Potential predictors of musculoskeletal injuries (Cadets' entry number, body mass index [BMI], body fat percentage [BFP], gender, age, sport experience, and nationality) were modeled via univariate and multivariate logistic regressions. Using odds ratio (OR) and confidence interval (CI), it was shown that older age (OR = 0.73; 95% CI = 0.56-0.96), female gender (OR = 0.13; 95% CI = 0.02-0.81), high BFP (OR = 1.21; 95% CI = 1.07-1.37), and Greek nationality (OR = 0.22; 95% CI = 0.07-0.69) were all associated with musculoskeletal injuries. These factors, except for gender, were also related to overuse injuries. During BCT, adiposity expressed as BFP and not as BMI can predict the magnitude and type (acute-overuse) of musculoskeletal injuries in Greek cadets.
Article
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Objective: To analyse the association between waist-to-height ratio (WtHR) and body fat and to develop predictive adiposity equations that will simplify the diagnosis of obesity in the paediatric age group. Design: Cross-sectional study conducted in Spain during 2007 and 2008. Anthropometric dimensions were taken according to the International Biology Program. The children were classified as underweight, normal weight, overweight or obese according to national standards of percentage body fat (%BF). WtHR differences among nutritional status categories were evaluated using ANOVA. Correlation analysis and regression analysis were carried out using WtHR as a predictor variable for %BF. A t test was applied to the results obtained by the regression model and by the Siri equation. The degree of agreement between both methods was evaluated by estimating the intra-class correlation coefficient. Setting: Elementary and secondary schools in Madrid (Spain). Subjects: Girls (n 1158) and boys (n 1161) from 6 to 14 years old. Results: WtHR differed significantly (P<0.001) depending on nutritional status category. This index was correlated (P<0.001) with all adiposity indicators. The mean %BF values estimated by the regression model (boys: %BF= 106.50 x WtHR – 28.36; girls: %BF= 89.73 x WtHR – 15.40) did not differ from those obtained by the Siri equation. The intra-class correlation coefficient (0.85 in boys, 0.79 in girls) showed a high degree of concordance between both methods. Conclusions: WtHR proved to be an effective method for predicting relative adiposity in 6–14-year-olds. The developed equations can help to simplify the diagnosis of obesity in schoolchildren.
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