current status of body composition assessment in sport · current status of body composition...
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Current Status of Body CompositionAssessment in SportReview and Position Statement on Behalf of the Ad Hoc ResearchWorking Group on Body Composition Health and Performance,Under the Auspices of the I.O.C. Medical Commission
Timothy R. Ackland,1 Timothy G. Lohman,2 Jorunn Sundgot-Borgen,3 Ronald J. Maughan,4
Nanna L. Meyer,5 Arthur D. Stewart6 and Wolfram Muller7
1 University of Western Australia, Perth, WA, Australia
2 University of Arizona, Tucson, AZ, USA
3 The Norwegian School of Sport Sciences, Oslo, Norway
4 Loughborough University, Loughborough, Leicestershire, UK
5 University of Colorado and United States Olympic Committee, Colorado Springs, CO, USA
6 Robert Gordon University, Aberdeen, UK
7 Karl-Franzens University and Medical University of Graz, Graz, Austria
Contents
Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2271. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2282. Review of Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
2.1 Reference Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2302.1.1 Cadaver Dissection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2302.1.2 Multi-Component Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2302.1.3 Medical Imaging – MRI and CT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232
2.2 Laboratory Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2332.2.1 Dual Energy X-Ray Absorptiometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2332.2.2 Densitometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2362.2.3 Hydrometry (Body Water) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2372.2.4 Ultrasound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2382.2.5 Three-Dimensional Photonic Scanning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
2.3 Field Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2402.3.1 Anthropometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2402.3.2 Bioelectrical Impedance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2432.3.3 Body Mass Index and Mass Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245
3. Summary and Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246
Abstract Quantifying human body composition has played an important role inmonitoring all athlete performance and training regimens, but especially so ingravitational, weight class and aesthetic sports wherein the tissue composi-tion of the body profoundly affects performance or adjudication. Over thepast century, a myriad of techniques and equations have been proposed, but
REVIEW ARTICLESports Med 2012; 42 (3): 227-249
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all have some inherent problems, whether in measurement methodology or inthe assumptions they make. To date, there is no universally applicable cri-terion or ‘gold standard’ methodology for body composition assessment.Having considered issues of accuracy, repeatability and utility, the multi-component model might be employed as a performance or selection criterion,provided the selected model accounts for variability in the density of fat-freemass in its computation. However, when profiling change in interventions,single methods whose raw data are surrogates for body composition (with thenotable exception of the body mass index) remain useful.
1. Introduction
Body composition is an important health andperformance variable. In weight-sensitive sports,many athletes use extrememethods to reducemassrapidly or maintain a low body mass in order togain a competitive advantage. As a consequence,athletes with very low body mass, extreme masschanges due to dehydration or eating disorders,an extremely low percentage of body fat, or in-sufficient bone mineral density, are becomingcommon issues in many sports.[1,2] Deliberatelyinduced underweight or short-termmass reductionmay lead to severe medical problems with some-times fatal consequences.[1] The weight-sensitivesports in which extreme dieting, low percentagebody fat, frequent mass fluctuation and eatingdisorders have been reported, can be summarizedin three groups:� Gravitational sports – in which mass restricts
performance due to mechanical (gravitational)reasons. Among these are long distance running,ski jumping, high jumping and road cycling.
� Weight class sports – in which unhealthy short-term mass reduction behaviour, associated withextreme dehydration, can be observed becausethe athletes anticipate an advantage when theyare classified in a lower weight category. Thisgroup includes the sports of wrestling, judo,boxing, taekwondo, weight lifting and light-weight rowing.
� Aesthetic sports – in which athletes or theircoaches expect higher scores when their bodymass and shape conform to a perceived bodyideal. This group includes, particularly, thejudged female sports of rhythmic and artistic
gymnastics, figure skating, diving and syn-chronized swimming.Body fat may act as ballast in biomechanical
terms, but adipose tissue is a vital endocrine organin terms of general health. The different biome-chanical and health imperatives present a conflictfor athletes, for whom risks of eating disordersare exacerbated. To our knowledge, few of theinternational sport federations have consideredimplementation of programmes aimed to dis-courage athletes from extreme dieting or fromrapid mass loss by means of dehydration. TheInternational Ski Federation (FIS) has changedregulations[3-5] in order to improve the low massproblem, but more can be achieved in this area.An important step on the path toward main-taining an athlete’s health and performance bymeans of rule changes, is the ability to assess theathlete’s body composition with accuracy, preci-sion and reliability.
Understanding and quantifying human bodycomposition has formed a central part of medicalresearch for the best part of a century.While prog-ress has been significant with landmark studiesand the use of new and combined analyticalmethods, unassailable ethical and methodologi-cal limitations have precluded the identificationof an absolute standard against which methodscan be compared in humans. As a consequence,while accurate assessment of body fatness hasbeen a major goal of body composition researchover the past 50 years, much of the work to vali-date new and old methods is indirect. Despiteconsiderable advances in methods, today there isstill no gold standard for body-fat assessmentwith accuracy better than 1%.
228 Ackland et al.
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Quantification of fat has been the prime focusof attention, but many coaches and scientistsworking with elite athletes recognize that knowl-edge of the amount and distribution of leantissues, such as bone andmuscle, can be just as im-portant in determining sports performance. Forexample, the relationship between muscle cross-sectional area and force/power generation is wellknown and so change in muscle size (relative tobody mass) becomes an important assessmentparameter during preparation for high-level com-petition. Making sense of the myriad of techniquesfor estimating each of the tissue components re-quires a clear framework by which these may beproperly compared.
During the development and integration ofsuch multi-component methods, the last threedecades have also been witness to a dramatic in-crease in research on elite athletes from a wholerange of sports. As training methods have be-come more sophisticated, each athletic group hasbecome more specialized, modifying its typicalphysique imperatives away from general mor-phological norms. As a consequence, many of theassumptions on which some techniques rely areno longer valid for athletes. For example, eliteathletes who had undergone resistance trainingwere estimated to have negative 12% fat usingdensitometry[6] and to have negative fat on thetorso using dual energy x-ray absorptiometry(DXA).[7] Furthermore, athletes are reluctant tointerrupt what for many is a full-time occupationfor the sake of body composition assessment,thereby making the more involved laboratorytechniques less appealing. These factors all con-spire against the scientist seeking to make accuratemeasurements on athletes, with the inevitableconsequence that data may be misleading, mis-interpreted or perhaps used inappropriately. Thisreality has forced researchers to consider accept-able surrogate measures for fatness, such as a sumof skinfolds, without recourse to quantifying tissuemass.
The choice of body composition techniqueoften depends on the intended purpose for whichdata are to be used, as well as the available tech-nology. In regard to high-performance sport, theassessment of body composition may define a
performance or selection criterion, be used to as-sess the effectiveness of an exercise or dietary in-tervention, or be used to monitor the health statusof an athlete. Individual body composition goalsshould be identified by trained healthcare person-nel (e.g. athletic trainer, physiologist, nutritionistor physician) and body composition data shouldbe treated in the same manner as other personaland confidential medical information.
In addition to the published journal articles,books and book chapters written by the authorsof this review, several online databases (includingMEDLINE and SPORTDiscus�) were searchedto provide the most current publications to in-form this review paper.
2. Review of Techniques
Though many techniques exist for describingthe constituent components of the body, in prac-tice, the techniques in current use fall into Ref-erence, Laboratory and Field method categories,which include both the Chemical (Molecular) orAnatomical (Tissue/Systems) approaches (figure 1).Within these approaches, we must also understand
Fat mass(lipid)
Protein
Water
Other
Chemical(molecular)
4-C
Adiposetissue
Skeletaltissue
Muscle andconnective
tissue
Other
Anatomical(tissue)
4-C
Fat mass(lipid)
Fat-freemass
Chemical(molecular)
2-C
Fat mass(lipid)
Otherleanmass
Bonemineral
Chemical(molecular)
3-C
Fig. 1. Chemical and anatomical body composition models. 4-C,3-C and 2-C models, respectively. C = component.
Body Composition Assessment in Sport 229
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that techniques can be categorized as being Di-rect, for example, via cadaver dissection; Indirect,where a surrogate parameter is measured to estimatetissue or molecular composition; or Doubly In-direct, where one indirect measure is used topredict another indirect measure (i.e. via regres-sion equations). The use of regression equationsalso means that these approaches are sample-specific. Hawes and Martin[8] refer to these cate-gories as levels of validation.
In both the Chemical and Anatomical ap-proaches, we may also employ multi-componentmodels (figure 1). Thus, it has been common forauthors to refer to 2-component models (fat mass[FM] and fat-free mass [FFM]), 3-componentmodels (fat, bone mineral and lean content), or4-component models (adipose, bone, muscle andother tissues).
A review of body composition methods mustalso consider the implications of techniques thatmerely sample the body as opposed to thosethat attempt to assess the whole body. Severalcommonly employedmethods (e.g. skinfolds, ultra-sound) sample the subcutaneous adipose tissue(SAT) at standardized sites and assume that thereis some fixed and direct relationship between thiscompartment and fat depots deep within thebody. Furthermore, it is assumed in these meth-ods, that the standardized sites provide a repre-sentative estimate of the total subcutaneous fat inthe body.
Finally, mention must be made regarding in-dividual versus group results. Some techniquesthat supposedly assess body composition (e.g.body mass index [BMI]) are often cited as beingsignificantly correlated with important health in-dicators, or values from other assessment proce-dures. Readers are cautioned to understand thatdemonstration of a strong association at the popula-tion level is not the same as a technique providingaccurate, precise and reliable body compositiondata for an individual.
2.1 Reference Methods
The reference methods are, by definition, themost accurate techniques for assessing bodycomposition and have often been employed as
criterion against which other techniques arecompared. Nevertheless, these reference methodsmay have limited applicability for monitoringathletes. Limitations include feasibility (e.g. cada-ver dissection), time and financial costs involved(e.g. MRI scanning), a lack of published norma-tive data (e.g. multi-component models), and un-necessary radiation exposure (e.g. CT scanning).There are also questions regarding sensitivity(acuteness) of some of the accepted referencemethods. A summary of the important features ofthese techniques is provided in table I.
2.1.1 Cadaver Dissection
Human body composition analysis is unique inthat validated measures can be ascertained onlyvia cadaver dissection. Even so, this approachdoes have several limitations (table I). Aside fromthe use of porcine carcasses to validateDXA, time,cost and for cadavers, inescapable ethical barriers,limit the use of this technique. Results from theBrussels Cadaver Study were employed to testseveral assumptions related to the anthropometryfield method of body composition analysis.[9-11]
Since the cadaver dissection method cannot beutilized for individual analysis, practitioners haveturned to other reference, laboratory and fieldmethods for estimating body composition.
2.1.2 Multi-Component Models
The best reference methods for estimation ofbody fat are the multi-component models. Boththeir precision and accuracy are in the order of1–2%. Elaborate 6-, 5-, 4- and 3-component mod-els are available for body-fat estimation.[12] The4-componentmodel using body density, bodywaterand bone mineral is the most often used methodand is, at present, the leading reference methodfor body composition. Wang et al.,[12] presented13 different 4-component equations, each withdifferent assumptions for the various compo-nents. The 4-component equation is always in theform of (equation 1):
FM¼C1 BV�C2 TBWþC3M�C4 BM (Eq: 1Þ
where BV is body volume, TBW is total bodywater, M is bone mineral and BM is body mass.
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Tab
leI.
Featu
res
ofbody
com
positio
nre
fere
nce
meth
ods
Meth
od
Levelof
analy
sis
Appro
ach
No.of
com
ponents
Outc
om
em
easure
sA
ssum
ptions
/cautions
Advanta
ges
Lim
itations
Cadaver
dis
section
DA
nato
mic
al
5T
issue
masses:
Skin
Adip
ose
Bone
Muscle
Oth
er
Lim
ited
num
ber
of
dis
secte
dspecim
ens
cannotbe
repre
senta
tive
oft
he
range
ofb
ody
types
and
com
positio
ns
am
ong
ath
lete
s
May
be
used
tovalid
ate
oth
er
indirectm
eth
ods
Sm
all
num
bers
ofcadavers
None
were
ath
letic
Lim
ited
range
ofstr
uctu
res
Long
and
tedio
us
meth
od
Loss
ofbody
fluid
Cannotbe
used
for
indiv
iduala
naly
sis
Multi-com
ponent
models
DC
hem
ical
3–6
Fatm
ass
Body
density
Tota
lbody
wate
r
Bone
min
era
l
Pro
tein
Bone
min
era
lin
clu
des
oth
er
min
era
lin
oth
er
tissues
Consta
ntpro
port
ion
of
pro
tein
tow
ate
r
Assum
econsta
nt
densitie
sofeach
com
ponent
Mostappro
priate
refe
rence
meth
od
to
date
Accom
modate
s
variabili
tyofboth
bone
min
era
land
wate
r
conte
nt,
whic
h
invalid
ate
sth
etw
o
com
ponentm
odel
Long
analy
sis
pro
cess
Expensiv
ete
chnolo
gy
Lack
ofpublis
hed
norm
ative
data
Medic
ali
magin
g:
MR
Iand
CT
DA
nato
mic
al
4T
issue
thic
kness
/are
a/v
olu
me:
Adip
ose
Bone
Muscle
Oth
er
Both
machin
es
desig
ned
prim
arily
for
dia
gnostic
use
rath
er
than
quantify
ing
tissue
dim
ensio
ns
Rela
ting
anato
mic
al
dim
ensio
ns
totissue
masses
requires
assum
ptions
abouttissue
densitie
s
More
assum
ptions
and
vastcom
puting
pow
er
required
for
assessin
g
deep
fatdepots
No
exposure
toio
niz
ing
radia
tion
with
MR
I
Expensiv
ete
chnolo
gy
Long
analy
sis
pro
cess
Hig
hexposure
toio
niz
ing
radia
tion
with
CT
Confined
space
ofsom
e
appara
tus
may
induce
cla
ustr
ophobia
Lack
ofpublis
hed
norm
ative
data
D=
direct.
Body Composition Assessment in Sport 231
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The most practical multi-component modelmeasures body density and body water and canestimate fatness within standard errors of estimate(SEEs) of 2.0–2.5% (3-component model). Preci-sion of multi-component models is high.[12-14]
Technical errors of estimating body volume, bodywater and bone mineral have been combined toyield a percentage of fat error of about 1%. Ac-curacy is in the order of 2%,[15] and even betterwhen a 5-component model is used.[12] However,when body water estimations are not accuratelyassessed, as in the case of Clasey et al.,[16] thenlarger errors in the multi-component modelsare apparent. Compared with the data by Wanget al.,[17] for example, the variation (standarddeviation squared [SD2]) between water variabil-ity (variance) is 3.2-times greater in the Claseyet al.[16] sample. This variability exceeds the bio-logical variation in water/FFM content underusual hydration conditions, thereby signifying alarge technical contribution. Multi-componentassessment models are time consuming and re-quire access to expensive and sophisticated tech-nology, which often places them out of reach forpractical applications in sport.
2.1.3 Medical Imaging – MRI and CT
MRI is a highly sophisticated and costlytechnique that has become the premier medicalimaging technique during recent years. It requiresa powerful main (usually superconducting) mag-net, a magnetic field gradient system, which isessential for signal localization, and a radio fre-quency system, which is used for signal genera-tion and processing. Like other tomographicalimaging techniques, MRI scanning results in adata array (MRI image), which represents thespatial distribution of some measured physicalquantity. The values of the image pixels dependon various parameters of the tissue under study:MRI produces images of internal physical andchemical characteristics of an object from ex-ternally measured nuclear magnetic resonance(NMR) signals. The effects of these tissue char-acteristics on the NMR signal can be enhanced orsuppressed by using appropriate data acquisitionprotocols. The flexibility of MRI in data acqui-sition can result in quite different images for
the same anatomical region, depending on theparameter setting. The soft-tissue contrast, whichdepends largely on the design of the pulsingsequence, exceeds that of CT and of ultrasound.No ionizing radiation is involved, so the methodis not invasive, although the confined space ofthe scanner may induce claustrophobia. It isbeyond the scope of this review to describe MRIin detail, and readers are referred to Rungeet al.[18] or Liang and Lauterbur[19] for furtherinformation.
Despite its sophistication, this technique re-quires powerful software for analysis involvingsetting thresholds for different tissues. Mostsoftware in clinical use is designed for diagnosticpurposes, not for quantifying tissue dimensionsbeyond the organ level. Whole-body scans arepossible, but need to be acquired as a series ofstacks and subsequently integrated. Currently,the pixel size of 2mm· 2mm in slices used in total-body scans limits the accuracy of measurement,particularly in lean athletes. Difficulties in dis-criminating boundaries between tissue layers,further limits sensitivity.
CT is also capable of high resolution internalimages of the body, but involves a high radiationdose because its image acquisition is based onx-rays, which are configured in a perpendicularplane to the supine participant. The x-ray tubeand detector follow a rotational path-enablingimage reconstruction following the measured at-tenuation relative to air and water, quantified inHounsfield units. Tissues vary in their radio-graphical density; skeletal muscle has a muchhigher range than adipose tissue, enabling easydistinction and quantification of each. However,whole-body scanning in living humans is notfeasible due to an unjustifiably high radiationdose, and most studies rely on interpolationbetween slices of measured composition. BothMRI and CT produce tissue distances, areas andvolumes which, if related to multi-componentmodels of body composition, require assump-tions to relate to mass via assumed density and/orchemical composition. As a consequence of theseveral limitations associated with MRI and CT,neither represents a practical method for every-day body composition assessment.
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2.2 Laboratory Methods
The laboratorymethods are used extensively forassessing body composition of athletes (thoughperhaps not to the same degree as field methods),but there exists wide variation in their accuracyand precision. A summary of the important fea-tures of these techniques is provided in table II.
2.2.1 Dual Energy X-Ray Absorptiometry
For over two decades, DXA has been the di-agnostic method of choice for osteoporosis andhas been used increasingly in the quantificationof soft tissue. It achieves this by passing filteredx-ray beams at two different photon energiesthrough the participant that are attenuated dif-ferentially by the material in their path. With theparticipant lying on the scanning table, the pro-cess maps the mass and composition of each pixelin terms of bone mineral, fat and fat-free softtissue. FM is determined by the ratio of soft-tissue attenuation at the two energies, and in-vivoelemental composition supports the underlyingphysical concept of this being accurate.[17] DXAhas been criticized because it assumes segmentconstancy in tissue composition; however, bothwater and lipid content of skin, adipose, muscleand bone tissue exhibit regional variation.[20]
Despite a low radiation dose (that varies accord-ing to the scanner type and beam configuration,and consequentially requires a pregnancy test inwomen of child-bearing age), this method isviewed as a laboratory reference method andcontributes to the bone mineral assessment formulti-component models. Utility of DXA andthe widespread proliferation in current practice,has rested on the convenience of acquiring re-gional composition data without recourse to themore costly and scarce medical imaging tech-niques. However, we must caution against usingDXA on multiple occasions (perhaps no morethan four times per annum), not only due to thecumulative radiation dose (including all othersources from medical imaging), but also due tothe error of measurement, which limits the abilityto detect small composition changes over time.Although effective doses associated with DXAmeasurements are low when compared with x-ray
imaging, we do not encourage frequent or indis-criminate DXA testing.
Multi-component models as a referencemethodto validateDXAare now extensive[15] with SEE forpredicting percentage of fat falling between 2% and3%, representing a major advance in laboratoryand clinical practice for estimating body composi-tion. The theoretical basis and assumptions thatDXA makes in deriving composition estimates arediscussed in detail in the review of Pietrobelli andcolleagues.[21] These relate to beam hardening (dueto the depth of tissue encountered by the x-raybeam) and errors of estimating fat quantity in ap-proximately 40% of scan pixels that contain bone(estimated by the measured composition of neigh-bouring pixels with no bone).With the more recentfan-beam scanners, magnification errors may alsolimit accuracy in larger subjects.
For athletes, DXA measurement has severaladvantages over other reference and laboratorytechniques, due to its speed and convenience, andbecause the measurement is minimally influencedby water fluctuation. However, measurement ofathletes who are excessively small, large or leanmay introduce errors greater than for subjects ofstandard size and composition. Individuals greaterthan ~192 cm may be too tall for the scan bed,while the soft tissue of very obese people may mi-grate beyond the available width of the scan area.The more recent scanners can accommodateindividuals of 120 kg, but strength athletes mayexceed the mass permitted by older models.
DXA has been used to derive regional and totalfat estimates, which outperformed densitometryrelative to a 4-component model.[22] This led someauthors to use it as a reference method in pref-erence to densitometry, yielding FM and FFMpredictions fromothermethods. In a study ofmaleathletes, SEE predicting DXA-derived FM fromskinfolds was 1.7 kg, although the seven leanestathletes showed negative fat on the torso.[7] Thehigh muscle mass and low FM of these individualsappears to fall beyond the calibrated range. Ofsome considerable concern, then, is the ever-increasing access to DXA by commercial sportsorganizations that seek to measure incrementallylean and muscular individuals, meaning the scopefor misinterpretation of data is also increasing.
Body Composition Assessment in Sport 233
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PR
INT
INT
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ES
T O
NL
Y A
ND
SH
OU
LD
NO
T B
E D
IST
RIB
UT
ED
EIT
HE
R IN
TE
RN
AL
LY
OR
EX
TE
RN
AL
LY
VIA
PR
INT
OR
EL
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IC M
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Tab
leII
.F
eatu
res
ofbody
com
positio
nla
bora
tory
meth
ods
Meth
od
Levelof
analy
sis
Appro
ach
No.of
com
ponents
Outc
om
em
easure
sA
ssum
ptions
/cautions
Advanta
ges
Lim
itations
DX
AD
Chem
ical
3C
om
ponentm
ass:
Fat(lip
id)
Bone
min
era
l
Oth
er
fat-
free
soft
tissue
Inte
rpola
tion
forsoft
tissues
inare
as
where
bone
is
dete
cte
d
Assum
es
magnific
ation
err
ors
and
beam
hard
enin
g
are
insig
nific
ant
Whole
-body
appro
ach
Costand
tim
eeff
icie
ncy
Min
imalsubje
ctaction
needed
Sm
all
radia
tion
dose
Min
imalopera
tor
train
ing
Regio
nalcom
part
ment
analy
sis
Independent
ofhydra
tion
sta
tus
Good
pre
cis
ion
Valid
ation
again
stporc
ine
models
Calc
ula
tion
alg
orith
ms
diffe
rbetw
een
manufa
ctu
rers
and
are
not
publis
hed
Pencil
vs
fan-b
eam
diffe
rences
inaccura
cy
Lim
ited
scan
bed
siz
e
Cannotscan
ifpre
gnant
Regio
nall
egis
lative
requirem
ents
diffe
r
Densitom
etr
y
(UW
W)
DC
hem
ical
0W
hole
-body
density
UW
Wre
quires
estim
ation
ofre
sid
ualvolu
me
and
oth
er
entr
apped
air
spaces
Whole
-body
appro
ach
UW
Wre
quires
consid
era
ble
subje
ct
involv
em
ent
Requirem
entto
rem
ain
still
thro
ughoutand
issues
of
wate
rconfidence
cre
ate
difficultie
sfo
rm
easuring
child
ren
Estim
ation
ofentr
apped
air
spaces
ispro
ble
matic
Body
density
does
not
pro
vid
ein
form
ation
about
indiv
idualt
issue
com
ponents
Densitom
etr
y
(UW
W)
IC
hem
ical
2%
Fat
%F
atfr
ee
Uses
invalid
assum
ptions
regard
ing
the
density
of
fat-
free
tissues
Whole
-body
appro
ach
Sim
ple
calc
ula
tion
Meth
od
notsupport
ed
in
str
ength
train
ed
indiv
iduals
and
oth
er
popula
tions
inclu
din
goste
oporo
tic,
child
ren
and
ath
lete
s
Densitom
etr
y
(AD
P)
IC
hem
ical
2%
Fat
%F
atfr
ee
AD
Pm
akes
assum
ptions
aboutth
eth
erm
ala
ir
pro
pert
ies
within
the
cham
ber
AD
Pre
quires
estim
ate
of
oth
er
entr
apped
air
spaces
Uses
invalid
assum
ptions
regard
ing
the
density
of
fat-
free
tissues
AD
Pis
easie
rto
adm
inis
ter,
ism
ore
tim
eeff
icie
ntand
requires
less
part
icip
ant
action
and
/or
dis
com
fort
than
UW
W
Does
notre
quire
wate
r
confidence
Sim
ple
calc
ula
tion
Requirem
entto
rem
ain
still
thro
ughoutcre
ate
s
difficultie
sfo
rm
easuring
child
ren
Meth
od
notsupport
ed
in
str
ength
train
ed
indiv
iduals
and
oth
er
popula
tions
inclu
din
goste
oporo
tic,
child
ren
and
ath
lete
s
Continued
nextpage
234 Ackland et al.
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INE
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PR
INT
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ES
T O
NL
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ND
SH
OU
LD
NO
T B
E D
IST
RIB
UT
ED
EIT
HE
R IN
TE
RN
AL
LY
OR
EX
TE
RN
AL
LY
VIA
PR
INT
OR
EL
EC
TR
ON
IC M
ED
IA F
OR
OT
HE
R T
HA
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PU
RP
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Tab
leII
.C
ontd
Meth
od
Levelof
analy
sis
Appro
ach
No.of
com
ponents
Outc
om
em
easure
sA
ssum
ptions
/cautions
Advanta
ges
Lim
itations
Hydro
metr
y
(body
wate
r)
DC
hem
ical
0%
Wate
rT
he
tracer
isdis
trib
ute
d
hom
ogeneously
equally
acro
ss
all
com
ponents
and
isnotm
eta
boliz
ed
Whole
-body
appro
ach
Min
imalsubje
ctaction
needed
Can
use
saliv
a,urine
or
blo
od
toestim
ate
the
dilu
tion
Easy
toadm
inis
ter
Tim
eto
reach
equili
brium
(3–4
hours
)
Expensiv
e
Acute
ingestion
ofa
larg
e
bolu
soffluid
aff
ects
the
assessm
ent
Hydro
metr
y
(body
wate
r)
DC
hem
ical
2%
Fat
%F
at-
free
Uses
invalid
assum
ptions
regard
ing
the
hydra
tion
of
fat-
free
tissues
Easy
toadm
inis
ter
Meth
od
notsupport
ed
in
people
with
card
iac,kid
ney
dis
ease
and
those
with
oedem
aand
oth
er
fluid
rete
ntion
pro
ble
ms
Ultra
sound
DA
nato
mic
al
3T
issue
layer
thic
kness
Skin
Adip
ose
Muscle
No
image
dis
tort
ion
Corr
ectspeed
ofsound
used
for
giv
en
tissue
Corr
ectdete
ction
oftissue
layer
boundaries
Hig
haccura
cy
and
pre
cis
ion
Applic
able
inth
efield
Non-invasiv
eand
no
ioniz
ing
radia
tion
No
tissue
com
pre
ssio
n
Tis
sue
thic
kness
from
1m
mto
300
mm
measure
able
Many
thic
kness
measure
ments
from
each
image
Rapid
data
acquis
itio
n
Min
imalsubje
ct
involv
em
ent
Low
costcom
pare
dw
ith
MR
Ior
CT
Sam
ple
sth
esubcuta
neous
fatdeposit
only
Consid
era
ble
skill
necessary
Meth
od
isnotsta
ndard
ized
yet
Ultra
sound
techniq
ue
inclu
des
inhere
ntart
efa
cts
3D
photo
nic
scannin
g
DA
nato
mic
al
0B
ody
surf
ace
are
a
Body
volu
me
Shape
para
mete
rs
Clo
thin
gtightn
ess
does
not
aff
ectth
ebody’s
conto
ur
or
pro
file
Alw
ays
overe
stim
ate
sbody
volu
me
due
tohair
or
clo
thin
g
Min
imum
subje
ct
involv
em
ent
Rapid
data
acquis
itio
n
(10–15
sec)
Som
eclo
thin
gcolo
urs
and
textu
res
aff
ectim
age
qualit
y
Hirsuitis
maff
ects
body
volu
me
3D
photo
nic
scannin
g
IC
hem
ical
2%
Fat
%F
atfr
ee
Uses
invalid
assum
ptions
regard
ing
the
density
ofF
M
and
FF
M
As
above
Meth
od
notsupport
ed
in
str
ength
train
ed
indiv
iduals
and
oth
er
popula
tions
inclu
din
goste
oporo
tic,
child
ren
and
ath
lete
s
3D
=th
ree
dim
ensio
nal;
AD
P=
air
dis
pla
cem
ent
ple
thysm
ogra
phy;
D=
direct;
DX
A=
dual
energ
yX
-ray
absorp
tiom
etr
y;
FM
=fa
tm
ass;
FF
M=
fat-
free
mass;
I=in
direct;
UW
W=
underw
ate
rw
eig
hin
g.
Body Composition Assessment in Sport 235
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DXA has been compared with CT and neu-tron activation analysis for assessing skeletalmuscle mass with SEE of 1.6 kg and 4.4 kg, re-spectively.[23] Further validation studies byWanget al.[24] and Kim et al.[25] concluded that skele-tal muscle mass could be accurately predicted byDXA. However, Tothill et al.[26] showed con-siderable regional differences between machinemanufacturers and pencil versus fan-beam con-figurations for estimating fat and bone mineral.While apparent bilateral composition differencesare likely to result from positioning and regionaldivision lines falling at pixel boundaries, observedvariations in fat estimation also relate to differentassumptions of the fat distributionmodel used in thesoftware. Further, some fan-beam scanners havesignificantly overestimated the leg muscle mass de-rived byCT in elderly individuals,[27] and the effect islikely to be exacerbated amongst athletes.
In summary, DXA, though a reasonably pre-cise whole-body method, is not reliable in pro-ducing accurate fat estimates of lean athletes,although its assessment of total and regionalFFM is generally acceptable if total scannedmassequates to scale mass. Intermanufacturer differ-ences in hardware and software algorithms pre-clude straightforward or meaningful comparisonsbetween apparatus.
2.2.2 Densitometry
Body density measurements, using either un-derwater weighing (UWW) or air displacementplethysmography (ADP) to estimate percentageof fat, are based on the 2-component model. Thisdivides the body into FM and FFM, assumes aconstant density of each, then relates the mea-sured whole-body density to a percentage of bodyfat.[28,29] Lipid is the only constituent of the bodywhose specific gravity is less than that of water(1.0) and its buoyant force is opposed by allother, denser constituents. Variations in waterand bone mineral content of the FFM amongpopulations and individuals affect its densityand, therefore, limit the utility of this approach asa reference method.[30]
UWW requires a participant (on a submersibleseat suspended from a load cell) to exhale maxi-mally during submersion. Calculating body den-
sity relies on dividing body mass by the measuredvolume. Although less subject involvement is re-quired using plethysmography, both methods re-quire estimation of residual lung volume withadditional equipment and expertise. In UWW thisis routinely performed using oxygen dilution,[31]
and should be done in the water because hydro-static pressure affects measured lung volumes.
ADP follows a similar approach by measuringbody volume, but in a sealed air capsule, ratherthan under water. By comparison, ADP is rapid,does not require water confidence and is suitablefor a wider range of individuals. Currently, theavailable ADP technology is referred to as theBodPod (Life Measurement Inc., Concord, CA,USA). In this system, a measuring chamber and areference chamber (beneath the seat) are linkedby a flexible airtight diaphragm, which is per-turbed to induce small pressure changes betweenboth chambers. Using Poisson’s Law, the pres-sure-volume relationship at a fixed temperature isused to calculate the volume of the participant inthe measuring chamber. After the system hasbeen calibrated with a known volume, the parti-cipant is weighed wearing swimwear and a cap,and then occupies the measuring chamber for~2minutes for volumetric measurement. Breathingnormally during measurement, the participantis then prompted to execute a breathing man-oeuvre for residual gas calculation. Adjustmentsfor thoracic gas volume and skin surface areaare necessary because of the behaviour of the airinside the chamber.
Despite its advantages over UWW in partici-pant acceptability and throughput, several meth-odological issues require consideration. Moistureon the skin or hair affects compressibility ofair next to the body surface, leading to an un-derestimation of the percentage of body fat. Thebehaviour of air close to the skin surface is pre-dicted by a surface area artefact, based on esti-mated body surface area. Such estimates mayunder- or overestimate an athlete’s true surfacearea. Clothing is also important with swimwearrecommended; wearing gym apparel reduces test-retest reliability and leads to an underestimate offatness.[32] Peeters and Claessens[33] also demon-strated that a lycra cap compresses the hair less
236 Ackland et al.
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effectively than a silicon cap, not fully eliminatingthe effect of isothermal air trapped in scalp hair,which also results in an underestimation of fat-ness. While such issues of participant presenta-tion are easily addressed, more problematic maybe locating the capsule, which requires a separateroom, with closely regulated temperature andhumidity. Changes in ambient pressure throughwindows or doors may cause the system to requirerecalibration.
Variations in the percentage of body fat havebeen reported for gender between UWW andADP.[34] Compared with results from UWW,ADP underestimated the percentage of body fat(in absolute terms) by 8% in lean female ath-letes,[35] underestimated the percentage of bodyfat at lower fat values and overestimated at thehigher fat values in boys,[36] and under-predictedthe percentage of body fat by an average of 2% inmale college football players.[37] In summary,despite the popularity of densitometry techniquesover many decades, both UWW and ADP tech-niques adopt the 2-component model that assumesdensity of FFM to be constant. This assumptionis clearly violated in many groups of athletes.Therefore, caution is essential when interpretingbody-fat results from these methods, especiallyfor lean athletes.
2.2.3 Hydrometry (Body Water)
Except in the very obese, water is the largestsingle component of the body, typically ac-counting for 50–70% of total mass. The watercontent of different tissues varies, but lean tissueis generally 70–80% water, while adipose tissue isgenerally about 20% water.[38] There is not,however, any agreement on this and the Instituteof Medicine[39] used a value of 10% for the watercontent of adipose tissue.
Total body water can be used to estimate bothFM and FFM assuming a constant hydration of72–73%. Variation in hydration levels amongsubjects is the main limitation of this method forthe athletic population. Body water can be usedto estimate fatness within 3% and when combinedwith body density, to within 2%. The primaryapproach for body water measurement is thedeuterium dilution method, which was well de-
scribed by Schoeller et al.[40] However, bodywater assessment relies on the purchase of deu-terium oxide (a stable isotope), expert measure-ment skills and expensive laboratory equipment,so is not generally available for wide-spread bodycomposition assessments.
An understanding of hydration status has im-plications for all body composition assessmenttechniques. Although the rate of turnover ofbody water is typically about 2–3L/day, it can bemuch higher than this, with losses through faecesreaching 1L/hour during acute infectious diar-rhoea and sweat losses in excess of 3L/hour beingsustained for relatively short periods during phy-sical activity in hot environments. Daily fractionalturnover can, therefore, reach 30–50% of total bodywater. Acute changes in body water can confoundthe use of standard methodologies for the assess-ment of body composition. For a 70kg individualwith 14kg of fat (20%), a loss of 10% body waterwill increase the fraction of fat to 21.5%. All mea-sures of body composition should, therefore, bemade under standardized conditions of hydrationstatus (e.g. after fasting, prior to an exercise bout,with an empty bladder). Euhydration, however, isdifficult to define, as it is a dynamic state.
The literature indicates that a number ofmethods have been used to determine hydrationstatus. Body mass changes, urinary indices (vol-ume, colour, protein content, specific gravity andosmolarity), blood borne indices (haemoglobinconcentration, haematocrit, plasma osmolarityand sodium concentration, plasma testosterone,adrenaline, noradrenaline, cortisol and atrial na-tiuetic), bioelectrical impedance analysis (BIA),and pulse rate and systolic blood pressure re-sponse to postural change are discussed. Theurinary measures of colour, specific gravity andosmolarity may be more sensitive at indicatingmoderate levels of hypohydration than are bloodmeasures of haematocrit and serum osmolarityand sodium concentration.
All methods, however, are subject to errors asa result of recent fluid intake; acute ingestion of abolus of water can produce relatively dilute urineeven in a hypohydrated individual. Currently, no‘gold standard’ hydration status marker exists,particularly for the relatively modest levels of
Body Composition Assessment in Sport 237
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hypohydration that frequently occur during ex-ercise. The choice of marker for any particularsituation will be influenced by the sensitivity andaccuracy with which hydration status needs to beestablished, together with the technical and timerequirements, and the expense involved.
2.2.4 Ultrasound
Ultrasound imaging is based on the pulse-echotechnique. A short ultrasound pulse is appliedand travels with the speed of sound (c) in the giventissue. Most diagnostic ultrasound machinesuse c = 1540m/s for calculating the distance fromthe source to the boundary between two tissueshaving different acoustic impedances: d= c T/2(T is the echo time). For 2-dimensional imaging,ultrasound beams are sent sequentially into thetissue for creating an image in which the bright-ness of the screen (B-mode) corresponds to theecho intensity in the plane of the scan. Diffractionlimits spatial resolution approximately to thewavelength used. Frequencies between 3–22MHzare generally employed, corresponding to wave-lengths in soft tissue of 0.5–0.07mm. Maximumresolution is limited because attenuation of soundincreases with higher frequency.
High accuracy of ultrasound fat-thicknessmeasurements in humans was described in1965[41] and 1966[42] and many studies then fol-lowed.[43] The precision of SAT measurementswas found to be excellent (technical error in bothintra- and interobserver studies was less than0.15mm at all sites investigated except for triceps[0.6mm]).[44] Ishida et al.[45] found B-modeultrasound to be a highly reliable method formeasurement of both fat and muscle thickness.Ultrasound imaging has also been suggested forvisceral fat mass evaluation.[46] An ultrasoundapproach for precise and accurate measurementof skin thickness has recently been described byMoore et al.[47] Considerable skill and anatomicalknowledge may be needed to identify, correctly,the interfaces of the tissues of interest. For SATthickness measurements, however, the adiposetissue layer is comparatively easy to find as itforms a continuous layer underneath the skinthat is bounded by the muscle fascia at the deepedge.
Recently, Horn and Muller[48] compared ul-trasound (f = 7.5MHz) SAT measurements inexcised pig tissue using a semi-automatic imageevaluation procedure with vernier caliper mea-surements (0.01mm resolution); the correlationwas very high (r = 0.998; n= 140) [figure 2] andSEE was 0.21mm. The regression coefficient was0.98 when (standard) sound velocity of 1540m/swas used and 1.00 was obtained for 1510m/s,indicating a lower speed of sound in fat. How-ever, thickness measurement error due to soundspeed deviation is small (e.g. 3% for a speed de-viation of 50m/s).
In this technique, it is important for theinvestigator to control, visually, the output ofautomatic edge detection algorithms so as toprevent erroneous image interpretations. An ex-ample for SAT measurements using recently de-veloped semi-automatic evaluation software[48] isshown in figure 3.
Many thickness measurements can be obtainedfrom a single ultrasound image, resulting in a verylow standard error of the mean (SEM). Accuracydemands beyond the capability of ultrasound are
16
14
12
10
8
6
4
2
01614121086420
dVC mm
dUS
mm
Fig. 2. Comparison of SAT thickness measurements: 140 dis-tances measured in swine carcass by means of ultrasound (dUS;f = 7.5 MHz) compared with vernier calliper measurements (dVC; re-solution: 0.01 mm). Correlation coefficient was 0.998 and regressioncoefficient was 0.98 using c = 1540 m/s and 1.00 for c = 1510 m/s.Data from Horn and Muller.[48] dUS = distance of ultrasoundmeasurement; dVC = distance of vernier caliper measurement;SAT = subcutaneous adipose tissue.
238 Ackland et al.
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of little relevance because of accuracy limitationsdue to the tissue’s plasticity.
It is another advantage of ultrasound thatmany measurements can easily be made in thevicinity of a given site where thickness variesgreatly and mean values can be used instead ofsingle-point measurements.
Ultrasound is well suited to analyse fat pat-terning, and total SAT may be determined bycombining a series of ultrasound measurementswith body-surface area measurement techniquessuch as laser scanning. It should be expected thatestimates of total body fat or total subcutaneousFM based on the ultrasound method, will result inhigher accuracies when compared with skinfolds,BIA, and backscattered light techniques. However,appropriate protocols for estimates based on ul-trasound have not yet been standardized.
Due to the high accuracy of SAT measure-ments at given sites, ultrasound can be used tocalibrate other imaging techniques like MRI orCT. It could be used for optimizing the imagesegmentation protocol, which is always a crucialproblem in MRI and CT image analysis. Pixelsize in MRI whole-body scans is typically 2mm ·2mm, and SAT thickness can be below 1mm in
lean athletes; therefore, high errors at certain sitesare to be expected for such scans. A literaturesurvey on the accuracy of MRI is given by Rossand Janssen,[49] and comparative studies of MRIand ultrasound for visceral and subcutaneous fatevaluation were published by Koda et al.[50]
In summary, it can be expected that ultrasoundthickness measurements in adipose, muscle andother tissues will gain a leading role because of thehigh measurement accuracy. However, futurestudies are needed to establish standard measure-ment sites and protocols. Small, transportable ul-trasound machines are also available, which willenable application in the field.
2.2.5 Three-Dimensional Photonic Scanning
Three-dimensional (3D) photonic scanningenables profiling of the body in unprecedentedways. Its development over the past 25 years forthe clothing and automotive industries has in-cluded approaches using structured light, class 1(eye-safe) lasers or millimetre wave technologies.These have made contributions to epidemiologi-cal research and, more recently, sport science.
Photonic scanning data have shown men andwomen to be fundamentally distinct in BMI-shape relationships,[51] have quantified the effectof age in varying shape at a given body size[52] andhave explored the contrasting shapes of those ofdifferent ethnicity, at a similar level of BMI.[53]
3D scanning has also been validated for assessingbody volume and the subsequent percentage offat prediction following appropriate accountingfor lung volumes.[54] However, application ofsuch a protocol requires subjects to breathe outfully whilst being scanned, which might be limit-ing for some individuals, as it is for UWW. Astudy of military personnel found good agree-ment between the percentage of fat derived from3D scanning (Cyberware) with inhouse softwareand DXA (GE Lunar Prodigy),[55] although thestrategy for assessing the lung volume to subtractfrom scan volume before calculation of the per-centage of fat was not stated. In this respect, 3Dand DXA scanning share the commonality of‘undisclosed algorithms’ for arriving at fatnessand, thereby, unquantified error, which futureresearch must address.
AB
C D
F
G
4mm
E
Fig. 3. Semi-automatic image evaluation: the edge detection algo-rithm for SAT thickness determination enables selecting areas ofinterest, distances ultrasound measurement series, colour-coding ofdistance values and statistical evaluations.[48] In this example of aSAT layer above the triceps muscle, with the transducer held parallelto the humerus, 119 dUS values ranging from 2.3 mm to 4.3 mm wereautomatically detected by the algorithm; the median was 3.4 mm(c = 1470 m/s). Layers and interfaces: A = gel; B = gel-epidermis;C = dermis; D = dermis-SAT; E = SAT; F = SAT-fascia of muscle;G = muscle; dUS = distance of ultrasound measurement; SAT = subcu-taneous adipose tissue.
Body Composition Assessment in Sport 239
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The requirement for participants to wear form-fitting clothing, which can be a severe limitation inobesity or body-image research, is no impedimentfor research with athletes, many of whom are re-quired to wear such clothing in training andcompetition. To date, no study assessing bodyfatness in athletes via 3D scanning has beenundertaken, because of the limited availability ofscanning facilities. However, recent access to thistechnology by elite athletes has enabled quantifi-cation of body segments amongst rowers, yieldingdata (such as segmental volumes) that quantifyvariability and effect sizes relative to controls fromthe general population.[56] These findings areclearly significant for talent identification andcould not have been assessed using conventionalanthropometry.
In summary, this novel approach does not at-tempt to quantify minimum weight or fatness, butis a potentially useful adjunct to existing measures,which may be pertinent in weight-restricted sports.Alone, the method measures body volume withsome accuracy, but incorporates the same as-sumptions and limitations as densitometry whenestimating FM and FFM. Nevertheless, the rapidprofiling enables great numbers of athletes to besurveyed within the limitations of time and cost.Finally, its combination with other measurementmodalities such as ultrasound and DXA will un-doubtedly represent a major advance in futurebody composition research.
2.3 Field Methods
Field methods are most often employed formonitoring body composition in both sports andhealth applications, but with varying degrees ofvalidity. A summary of the important features ofthese techniques is provided in table III.
2.3.1 Anthropometry
The acquisition of surface dimensional mea-surements as surrogates of composition was pio-neered by Jindrich Matiegka[57] and subsequentlyapplied to Olympic athletes at the 1928 Amsterdamgames and, thereafter, notably at the 1960 Romegames to characterize somatotype, proportions andsize variability among sports.[58] To date, well over
100 body-fat prediction equations have been de-veloped from skinfold measurements,[59] and theirinconsistent outcomes result from the differences inpopulations sampled and lack of rigour in stan-dardizing the technique. For instance, varying theskinfold site by as little as 1 cm produces sig-nificantly different results when experienced prac-titioners measure the same participant.[60] Precisedefinitions for measurement sites, in addition to astandardized technique are, therefore, of funda-mental importance for this method.
To this end, 1986 saw a national standardiza-tion conference in Airlie, Virginia, USA, whichresulted in a manual being written.[59] Simulta-neously in Glasgow, UK, the International Societyfor theAdvancement ofKinanthropometry (ISAK)was formed, which subsequently established anexam-based certification scheme for practitionersand instructors, and a closely-defined protocol.[61]
Both manuals represent significant progress in thequality of data derived from anthropometric mea-sures, usually quantified by statistics of replicatemeasures. ISAK instructional courses often resultin a 7-fold reduction in intra-tester error. How-ever, high precision with a single measurer canmask systematic differences between measurersand, crucially, only under the ISAK scheme, isinter-tester error also quantified.
Fatness has been predicted from skinfolds,circumferences and skeletal width, usually val-idated against densitometry. In some cases, SEEof the skinfold method was estimated to be lessthan 3% when variation in the reference methodwas taken into account, although in generalizedequations[62] this value approached 5%, which isan unacceptably large error. While many gen-eralized equations have been cross-validated forspecific samples, their use in determining fatnessin athletes relies on conforming to the assump-tions both of anthropometry and densitometry.Out of 18 such equations, only three were foundto be reliable for use in athletes.[63]
A cross-validated skinfold equation for UShigh-school wrestlers was produced in order tostandardize the approach to establishing mini-mum weight. Produced on 860 wrestlers acrossfive universities, the study tested the validity of16 equations, the best of which estimated the
240 Ackland et al.
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Tab
leII
I.F
eatu
res
ofbody
com
positio
nfield
meth
ods
Meth
od
Levelof
analy
sis
Appro
ach
No.of
com
ponents
Outc
om
e
measure
s
Assum
ptions
/cautions
Advanta
ges
Lim
itations
Anth
ropom
etr
y
(skin
fold
s)
IA
nato
mic
al
1S
kin
fold
sum
Skin
fold
ratios
Consis
tentfa
tpatt
ern
ing
Fix
ed
subcuta
neous
to
inte
rnalfa
tre
lationship
Consta
ntskin
fold
com
pre
ssib
ility
Consta
ntskin
toadip
ose
fraction
Lip
idfr
action
ofadip
ose
tissue
Wate
rconte
nt
ofadip
ose
tissue
Relia
ble
score
sw
ith
train
ed
technic
ians
Results
can
be
com
pare
d
with
norm
s
Legitim
ate
for
testre
-teston
indiv
iduals
Low
cost,
convenie
ntdata
colle
ction
Sam
ple
sth
esubcuta
neous
fatdeposit
only
Can
be
intr
usiv
efo
rsom
e
indiv
iduals
Som
esites
difficult
toachie
ve
Sta
ndard
ization
ofm
eth
od
essential
Anth
ropom
etr
y
(skin
fold
s
equations)
DI
Anato
mic
al
2%
Fat
%F
atfr
ee
Aggre
gate
sth
elim
itations
of
both
dependentand
independentvariable
sin
the
pre
dic
tion
equation
Meth
od
has
som
e
applic
abili
tyw
ith
som
e
popula
tions
Num
ero
us
equations
availa
ble
whic
hcan
cause
confu
sio
n
Meth
od
notsupport
ed
in
extr
em
epopula
tions
(e.g
.th
e
obese)
Equations
are
popula
tion
specific
and
need
tobe
cro
ss
valid
ate
dfo
rth
esam
ple
in
question
BIA
IC
hem
ical
2T
ota
lbody
wate
r
%F
at
%F
atfr
ee
Assum
es
subje
ctcom
plia
nce
tote
sting
pre
requis
ites
Assum
es
geom
etr
icsim
ilarity
betw
een
indiv
iduals
Assum
es
tissue
resis
tivity
is
sim
ilar
betw
een
indiv
iduals
Inputdata
(age,heig
ht,
weig
ht,
ath
letic
sta
tus)
accounts
for
hig
h(u
pto
85
%)
ofvariance
inth
edependent
variable
Pre
cis
ion
hig
h
Min
imalsubje
ctin
volv
em
ent
No
ioniz
ing
radia
tion
Min
imum
subje
ct
involv
em
ent
Rapid
data
acquis
itio
n
Appare
nt
sophis
tication
Accura
cy
poor
Results
aff
ecte
dby
hydra
tion
sta
tus
Tru
nk
under-
repre
sente
d/
limbs
over-
repre
sente
din
valu
e
Severa
ldiffe
rentm
odels
have
ele
ctr
odes
pla
ced
at
various
positio
ns
on
the
body
(arm
tole
g,le
gto
leg,arm
to
arm
)
BM
Iand
MI
IA
nato
mic
al
0In
dex
ofre
lative
weig
ht
(pondero
sity)
Assum
es
weig
htchange
is
rela
ted
sole
lyto
adip
osity
Fat-
free
mass
toheig
ht
pro
port
ion
isconsta
nt
Assum
es
consta
ntbody
segm
entpro
port
ions
Pre
cis
ion
hig
h
Min
imalsubje
ctin
volv
em
ent
Min
imum
subje
ct
involv
em
ent
Rapid
data
acquis
itio
n
Gre
atvariabili
tyin
fatconte
nt
am
ong
indiv
iduals
with
the
sam
eB
MI
Fra
me
siz
e,pro
port
ions
and
muscula
rity
independently
influence
BM
I
Mass
and
sta
ture
exhib
it
consid
era
ble
diu
rnal
variabili
ty
BIA
=bio
ele
ctr
icalim
pedance
analy
sis
;B
MI=
body
mass
index;D
I=doubly
indirect;
I=in
direct;
MI=
mass
index.
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densitometry-derived minimum weight with anSEE of 2.4kg based on the sum of three skin-folds.[64] The results indicate that the equationsdeveloped by Lohman[65] with three skinfolds,Thorland et al.[64] with seven skinfolds and Behnkeand Wilmore[66] with a combination of skinfolds,circumferences and skeletal widths; all predictedminimum weight with a total error of 2.5 kg. Thisapproach involved young adult wrestlers, butcould be generalized to other athletic groups ifa large validation study were performed. How-ever, such a study would need to ensure thatthe reference method was obtained via a multi-component model, because of the known viola-tion of the assumed density of the FFM usingdensitometry alone.
While circumferences can estimate body fatness(SEE >3%), they represent variability in frame sizeand muscularity in addition to fat. However,combining skinfolds and circumferences does notincrease the prediction of body fat over skinfoldsalone.[64] Similarly, the use of skeletal breadths toestimate body fatness has a SEE >4% and offersno improvement over skinfolds in assessing eitherfatness or estimating minimal weight. Anotherapproach that has been widely reported in theobesity literature is the use of anterior-posteriorabdominal thickness or ‘sagittal abdominal dia-meter’. This dimension has been particularly associ-ated with identifying visceral fat accumulation,[67]
metabolic syndrome,[68] cardiovascular risk[69]
and shape change during weight loss.[70] Whilethis might not initially seem a strong candidatefor use in athletes, it is possible that abdominaldimensions could provide a framework for anormal anticipated shape once normative datahave been established.
An alternative to converting skinfolds to bodyfat and minimum weight is the approach pro-moted by Marfell-Jones[71] who highlighted thevalue of using skinfolds as a valid proxy foradiposity. Individual and sum of skinfolds can becompared with published norms for Olympic orworld-class athletes.[72-76] The rationale for pro-posing the skinfold thickness as a valid measurein its own right without conversion to FM orpercentage of body fat, centres on the avoidanceof a series of assumptions that are known to be
invalid – especially so in an athletic sample. Theseinclude the assumptions of constant skin (dermisand epidermis) thickness, uniform subcutaneousadipose tissue compressibility, constant relativeadipose tissue distribution, constant fat fractionof adipose tissue, constant internal to externaldistribution of fat and, above all, the assumedconstancy of the FFM density. The resultingerror in accurately estimating the percentage ofbody fat necessarily includes the additional errorsof the reference method (usually densitometry),which have been identified to be greater amongstathletic groups.
Various regimens have summed values fromdifferent measurement sites in an attempt tocapture a representative surface adiposity. Whileit is clear that certain sites, such as the thigh andthe iliac crest, tend to be larger than others, such apattern may alter with increasing leanness. Thisintroduces a further level of complexity (explain-ing why generalized formulae may not be validfor athletes) and affords the opportunity to trackskinfold patterns, means or ratios with leanness.The first of these is best depicted in a radial plotknown as the skinfold map (figure 4), where theprofile can be used for tracking individual changeor comparing an individual to group data.
0
1.0
2.0
3.0
4.0
5.0
6.0
7.0Triceps
Subscapular
Biceps
Iliac crest
Supraspinale
Abdominal
Thigh
Medial calf
Fig. 4. A skinfold map illustrating extreme leanness in elite adultmale (dark grey lines) and female (light grey lines) endurance ath-letes of similar skinfold total. Measurements are in mm.
242 Ackland et al.
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Using the data presented byKerr and Stewart,[77]
the average skinfold magnitude across sites as-sessed by ISAK-qualified practitioners variesconsiderably by sport and is generally lower inmales than females, as depicted in figure 5.
Using the same approach and comparing thesegrouped athlete profiles with those of a cohort ofadult anorexic patients,[78] we discover that fe-male gymnasts have lower, but all other femaleathletes slightly higher, scores. It is important torecognize the probability of individuals displayingdifferent thresholds of minimum skinfolds beforehealth or performance deteriorate – so applyinggroup data to individuals requires caution.
As is the case for extreme obesity, in extremeleanness, the sexual dimorphism becomes lessapparent – in other words, the characteristic fatpatterning associated with males and femalesbecomes less distinct with reduced variability inskinfold magnitude across sites. Nevertheless,some distinctiveness remains, with the male pro-file having the highest value at the subscapularsite, while the female profile is highest at thethigh. This can be seen in greater detail by con-sidering skinfold ratios. These have been usedextensively in tracking fat patterning duringchildhood growth, but may have a role in identi-fying minimum fatness in athletes. Figure 6 depictsthe leanest of an athletic sample for selected skin-fold ratios (the same individuals as in figure 4) andalso the equivalent mean values for 106 male and33 female elite athletes from a range of sports.[80]
A number of interesting observations can bemade from figure 6, including that subscapular :triceps, thigh : abdominal and triceps : bicepsskinfold ratios all appear to exhibit a differencebetween the leanest and mean values, therebysuggestive of a ‘physique gradient’ of fat pattern-ing. On the contrary, the abdominal :medial calfratio displays no such gradient, although genderdifferences appear preserved. The abdominal : iliaccrest ratio appears to depict neither a physiquegradient, nor sexual dimorphism. While cautionmay be advised in the use of ratios as opposed toabsolute values as a result of error propagation,ratios appear ubiquitously throughout exercisescience for monitoring athletes, and a combinationof absolute and ratio scores (in conjunction with
health and performance measures), might bestserve scientists seeking to use skinfolds to estab-lish a system of flagging inappropriately lowbody-fat levels and alerting athletes, coaches andmedical staff accordingly.
In summary, anthropometry provides a simpleand highly portable field method for estimatingbody composition via surrogate measures forfatness andmuscularity. Provided the measurer iswell trained and follows a standard protocol, theassumptions of the technique are acknowledgedand the data treatments are not confounded withadditional sources of error (conversion to percent-age of FM/FFM), anthropometric techniques havewidespread utility for monitoring the body com-position of athletes.
2.3.2 Bioelectrical Impedance Analysis
The total volume of a conductor can be esti-mated from its length (L) and the resistance (R)to a single frequency electric current (L2/R). Thisprinciple has been applied to body compositionassessment using BIA. The key assumptions arethat the conductor is cylindrical in shape and thatthe current is distributed throughout the con-ductor uniformly.[81]
Multifrequency bioimpedance can be used toquantify distribution of extra- and intracellularwater with important applications to the medicalfield in the areas of fluid balance and monitoringvarious patient groups, including haemodialysisand other renal disease patients.[82] The work ofWabel et al.[83] indicates the extensive use of BIAspectroscopy in the management of fluid balanceto prevent both fluid overload and dehydration.Applications to the dehydrated athletic popula-tion have yet to be developed.
Although BIA has been used widely to esti-mate body composition, and many equationshave been reviewed,[81] its accuracy is limited inestimating body water and body fatness. In acareful comparison between BIA and skinfoldsamong wrestlers, where several laboratories dili-gently followed the same measurement protocol,both methods predicted percentage of body fat(from densitometry) with an SEE of 3.5%.[84]
This indicates clearly the accuracy limits withthese measurement techniques, and the SEE
Body Composition Assessment in Sport 243
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4.0
5.0
6.0
7.0
8.0
9.0
10.0
a
b
Ave
rage
ski
nfol
d (m
m)
4.0
5.0
6.0
7.0
8.0
9.0
10.0
Ave
rage
ski
nfol
d (m
m)
Cyclin
g tra
ck
Diving
Swimm
ing
Orient
eerin
g
Long
dist
Pole va
ult
Rowing
lw
Triath
lon
Gymna
stics
Mid
dist
Diving
Triath
lon
Cyclin
g ro
ad
SASI jum
pers
SASI spr
int ru
nner
s
SASI mid
dist
SASI long
dist
Scottis
h lon
g dis
t
Gymna
stics
Fig. 5. Average skinfold depth across seven or eight sites, according to athletic group: (a) males; (b) females. Data calculated fromsummary presented in Kerr and Stewart.[77] long dist = long-distance runners; lw = lightweight; mid dist = middle-distance track runners; SASISouth Australian Sports Institute.
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values are obtainable only ‘within’ specific groups,but not for mixed groups of athletes. When theindividual body composition of an athlete is to beassessed, one should consider that, for example, a3% deviation from an assumed true value of 8%would result in a percentage of body-fat valuesbetween 5–11%. This is far from the accuracynecessary for proper interpretation of health andperformance optimization. While other studiesusing anthropometry have lower errors than this,a further limitation of the BIAmethod for athleteslies in the measurement pre-requisites, which in-clude abstaining from exercise.
2.3.3 Body Mass Index and Mass Index
Several indices expressing mass relative tosome power function of height have been sug-gested and tested for maximum correlation withmass and minimum correlation with height.[85-87]
One that is widely used is the BMI (Quetelet’sindex), which relates body mass (m; in kg) andheight (h; in m): BMI =m/h2. Anthropometricvalues of height, body mass and sitting height caneasily be measured with high accuracy, but theseindices measure ponderosity, not fatness. Inter-pretation of mass with respect to stature (‘relative
body mass’) is not a simple task. TheWHO ExpertCommittee on Physical Status stated: ‘‘Problemsarise, however, in adults whose shape differ fromthe norm. y Care should therefore be taken ingroups and individuals with unusual leg length toavoid classifying them inappropriately as thin oroverweight.’’[88]
Therefore, leg length or sitting height (as anindirect measure for leg length) should also bemeasured when ponderosity is to be assessed. Arecently introduced extension of the BMI for-mula termed mass index (MI)[3-5] has the ad-vantage of considering the individual’s sittingheight. In the general MI formula, the h, sittingheight (s), and m determine the value of this indexfor ‘relative body mass, with C being the in-dividual Cormic Index (C = s/h) [equation 2]:
MIk¼BMI�C
C
!k
¼m
h2
�Cs=h
!k
¼m
h2� ksk�Ck
(Eq: 2Þ
where the value of 0.53 for �C, which is a value inthe middle of the Cormic index continuum, rep-resents ‘mean sitting height’. The exponent kweights the impact of the Cormic index. The unitof MI is kgm-2, as for the BMI, which is just thespecial case for k set to zero; in this specificcase we get: BMI =MI0 (no consideration of in-dividual leg length). For k = 2, the general equa-tion reduces to (MI2 = 0.532m/s2) in which bodyheight (h) does not appear. The choice of k= 2 isin accordance with anthropometric data pub-lished by Norgan,[89] when a measure that is in-dependent of C (and thus independent of leglength) is desired. However, the slope of the re-gression line in Norgan’s publication is based ongroup mean values for BMI and C, which is notnecessarily equal to the mean of the slopes of re-gression lines within individual groups. For k = 1we get (MI1 = 0.53m/hs). Non-integer values fork can also be used. Further studies are necessaryto identify the best value of k for appropriateconsideration of individual leg length.
It has to be pointed out that no weight-corrected-for-body-dimension index can distinguish be-tween fat and muscle mass of an individual. Thisinability of BMI to assess fatness or adiposity hasbeen reported in the literature.[90] Despite this
0
0.5
1.0
1.5
2.0
2.5
3.0
Ski
nfol
d ra
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Abdom
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iac cr
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Tricep
s/bice
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abdo
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Subsc
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r/tric
eps
Leanest maleLeanest femaleMean maleMean female
Fig. 6. Selected skinfold ratios in extremely lean male and femaleendurance athletes, and mean values from 106 male and 33 femaleathletes.[79]
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limitation, the use of MI instead of BMI maypermit diagnosis of underweight and assessmentof ‘optimum body weight’ for high performancein sports on a finer scale. Sitting height or leglength is as easy to measure as stature[91] andshould be included in all basic data sets of ath-letes and patients. In young athletes, this will alsoassist in understanding problems associated withindividual growth.
3. Summary and Conclusion
In summary, all of the techniques in commonusehave some inherent problems, whether in method-ology, interpreting the data, or in the assumptionsthey make. Limitations in both the 2-componentmodel (accuracy) and multi-component model(practicality) highlight the desire for an econom-ical laboratory or field approach to body com-position assessment that is both accurate andobjective. In the absence of such a criterion tech-nique, there is scope for several of the reviewedmethods to play a useful role under certain cir-cumstances. For example, where the body com-position assessment is used as a performance orselection criterion, then technique accuracy andreliability are of paramount importance. Themulti-component model might be employed hereprovided the selected model accounts for thevariability of the density of FFM in its computa-tion. In this case, healthcare and high-performancesupport staff must give due consideration tothe technical error of measurement, and notapply an absolute criterion or threshold valuefor selection unilaterally. This is of particularimportance when extremely lean athletes areexamined.
However, if the athlete’s body composition isbeing monitored to assess the effectiveness of anexercise or dietary intervention, the use of somelaboratory or field method may be more prac-tical. Depending on the availability of technologyand operator training, laboratory techniquessuch as DXA, ADP and ultrasound could beemployed. Similarly, field methods, such as an-thropometry, offer a cost-effective means of mon-itoring SAT, provided the operator has thenecessary training. Clearly, BIA and the BMI are
not supported for assessing or monitoring bodycomposition, nor are those methods that makeassumptions about the density of FFM in theircomputation.
Recent developments in ultrasound imaginghave made possible accurate and reliable esti-mates of fat thickness in multiple sites of thebody. However, interpretation of the obtainedscan image is a difficult task and further researchis necessary in this field. Many coaches and sportscientists anticipate the future development of aminimum sum of fat thickness, which corre-sponds to a minimum whole-body percentage offat, for the establishment of participation stan-dards for all athletic groups. While the availablebody composition methods do not permit this atpresent, some of the emerging medical imagingtechnologies may achieve the required accuracyto make this a reality in the future.
Regardless of the method favoured, it is im-perative that coaches, athletes and scientistsappreciate the importance necessarily attached tothe presentation of an athlete for measurement.Their adherence to fundamental pre-requisites suchas fasting, no exercise in the past 12–24 hours andstandardization of hydration, influences crucially,the body composition data on which decisionsare predicated. For instance, glycogen super-com-pensation can make a noticeable difference toskinfold compressibility, can increase conductivityas a result of water storage and can add to fat-free soft tissue registered by a DXA scan. Alter-ing fluid or electrolyte balance, which is aninevitable consequence of training and competi-tion, will adversely affect measured body com-position in several techniques so standardizationof athlete presentation prior to measuring, is ofparamount importance, and should be the aspira-tion of national laboratories for high-performancetesting.
Acknowledgements
The authors wish to acknowledge the support from theMedical Commission of the International Olympic Commit-tee in creating the Ad Hoc ResearchWorking Group on BodyComposition, Health and Performance. This review of thestatus of body composition assessment methods was one ofthe primary objectives of the working group.
246 Ackland et al.
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Correspondence: Winthrop Professor Timothy R. Ackland,School of Sport Science, Exercise and Health, The Univer-sity of Western Australia, M408, 35 Stirling Highway,Crawley, WA 6009, Australia.E-mail: [email protected]
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