1. A Comparison of Two Bioelectrical
Impedance Analysis Modes with DXA
for Estimating the Body Composition of
Elite Inter-County GAA Athletes
Name: Paul Sweeney
Student I.D: 11111321
A thesis submitted to the University of Limerick in fulfilment of the requirements
for the Degree of Bachelor of Science in Sport and Exercise Sciences
Department of Physical Education & Sport Sciences
Head of Department: Dr. Ann McPhail
Supervisor: Professor Phil Jakeman
Submitted: April 2015
.

2. i
Authors Declaration
I hereby declare that the work contained within this thesis is my own work, and was
completed without collaboration or assistance from others apart from the counsel
received from my supervisors, Name and department. This work has also not been
submitted to any other University of higher education institution, or for any other
academic award within this University.
Name
Date
Name
Date

3. ii
Acknowledgements
I would like to thank my parents Pat and Mary, for their great support throughout over
the years. They did well to put up with me during stressful times.
I would like to thank Linda, Anne-Marie, David, and Callum for their support and help
throughout what has been a demanding final year.
I would like to thank my supervisor Phil Jakeman for his advice and helpful knowledge
during this project.
I would like to thank Will McCormack and Katie Hughes for their advice during this
project. Thank your for responding to my many emails and always being available to
chat when needed.
Thanks to the fourth year Sport and Exercise Science class for a great four years. Best
of luck in the future.
To Murray, Tinny, Norris, O’Hare, and Leacy. The amount of craic that has been had
over the last few years will never be topped and for that I thank ye.
A special thanks must go to Tinny for the grinds in excel over the past year.

4. iii
Table of CoFntents
Authors Declaration........................................................................................................i
Acknowledgements .......................................................................................................ii
List of Figures................................................................................................................v
List of Tables............................................................................................................... vii
Abbreviations ............................................................................................................. viii
Abstract........................................................................................................................ix
Chapter 1 - INTRODUCTION....................................................................................... 1
Chapter 2 – LITERATURE REVIEW ............................................................................ 3
2.1 What is Body Composition?................................................................................................ 3
2.2 Why Measure the Body Composition of Athletes?............................................................. 3
2.2.1 Physical Demands of GAA ............................................................................................ 3
2.2.2 Body Composition and Athletic Performance.............................................................. 4
2.2.3 Seasonal Variations in Body Composition.................................................................... 6
2.3 Methods of Body Composition Assessment ....................................................................... 7
2.3.1 DXA............................................................................................................................... 7
2.3.2 BIA................................................................................................................................ 9
2.3.3 Problems Associated with BIA.................................................................................... 10
2.3.4 BIA vs. DXA ................................................................................................................. 10
2.4 Conclusion......................................................................................................................... 12
Chapter 3 - METHODS ...............................................................................................13
3.1 Participants ....................................................................................................................... 13
3.2 Preparation ....................................................................................................................... 13
3.3 Procedures ........................................................................................................................ 13
3.3.1 Anthropometric Measurements ................................................................................ 13
3.3.2 Bioelectrical Impedance Analysis (BIA)...................................................................... 13
3.3.3 Dual Energy X-ray Absorptiometry (DXA) .................................................................. 14
3.4 Statistical Analysis............................................................................................................. 14
Chapter 4 – RESULTS................................................................................................16
4.1 Descriptive Statistics ......................................................................................................... 16
4.2 BIA Athlete and Normal mode vs. DXA Analysis............................................................... 16
4.2.1 Overview .................................................................................................................... 16

5. iv
4.2.2 BFM ............................................................................................................................ 16
4.2.3 LTM............................................................................................................................. 16
4.2.4 BMC............................................................................................................................ 17
4.2.5 FFM............................................................................................................................. 17
4.2.6 BF%............................................................................................................................. 17
4.2.7 Athlete vs. Normal mode ........................................................................................... 17
Chapter 5 – DISCUSSION ..........................................................................................23
5.1 Background and Purpose .................................................................................................. 23
5.2 Findings ............................................................................................................................. 23
5.3 Systematic Errors .............................................................................................................. 24
5.4 BIA Athlete vs. Normal mode............................................................................................ 26
5.5 Limitations......................................................................................................................... 26
Chapter 6 – CONCLUSION.........................................................................................27
6.1 Summary and Future work................................................................................................ 27
References..................................................................................................................28
Appendices .................................................................................................................. A
Appendix A1..............................................................................................................................A
Appendix A2..............................................................................................................................A
Appendix A3..............................................................................................................................A
Appendix A4..............................................................................................................................A
Appendix A5..............................................................................................................................A
Appendix A6..............................................................................................................................A

6. v
List of Figures
Figure 4.1 - Bland Altman plots of Body Fat Mass (kg) with mean difference (dotted
lines) and 95% limits of agreement (dots) comparing Athlete and Normal modes to
DXA………………………………………………………………………………………….....18
Figure 4.2 - Bland Altman plots of Lean Tissue Mass (kg) with mean difference (dotted
lines) and 95% limits of agreement (dots) comparing Athlete and Normal modes to
DXA……………………………………………………………………………………..….…..19
Figure 4.3 - Bland Altman plots of Fat Free Mass (kg) with mean difference (dotted
lines) and 95% limits of agreement (dots) comparing Athlete and Normal modes to
DXA……………………………………………………………………………..………….…..20
Figure 4.4 - Bland Altman plots of Lean Tissue Mass (kg) with mean difference (dotted
lines) and 95% limits of agreement (dots) comparing Athlete and Normal modes to
DXA…………………………………………………………………………………..….……..21
Figure A1 - Lunar iDXA scanner (GE Healthcare, Chalfont St Giles, Bucks.,
UK)…………………………………………………………………………………………..…A2
Figure A2 - Fundamental principle of
DXA…………………………………………………………………………………………….A2
Figure A3 - Tanita MC-180MA Body composition Analyser (Tanita UK
Ltd.)…………………………………………………………………………………….………A3
Figure A4 - Bland Altman plots of Bone Mineral Content (kg) with mean difference
(dotted lines) and 95% limits of agreement (dots) comparing Athlete and Normal modes
to
DXA…………………………………………………………………………………………….A4
Figure A5 - Bland Altman plots of body fat mass (kg) with mean difference (dotted
lines) and 95% limits of agreement (dots) (Athlete vs.
Normal)……………………………………………………………………….………………..A5
Figure A6 - Bland Altman plots of lean tissue mass (kg) with mean difference (dotted
lines) and 95% limits of agreement (dots) (Athlete vs.
Normal)………………………………………………………………………………….……..A5
Figure A7 - Bland Altman plots of fat free mass (kg) with mean difference (dotted lines)
and 95% limits of agreement (dots) (Athlete vs.
Normal)……………………………………………………….………………………………..A6

7. vi
Figure A8 - Bland Altman plots of fat free mass (kg) with mean difference (dotted lines)
and 95% limits of agreement (dots) (Athlete vs.
Normal)……………………………………………………………………….………………..A6

8. vii
List of Tables
Table 4.1 - Anthropometrics for the 157 GAA players included in this study. Data are
reported as mean (standard deviation (SD)), median (interquartile range (IQR)) and
range (max – min); n = 157)
………………………………………….……………………..……………………..………15
Table 4.2 DXA and BIA athlete (a) and normal (n) measured body fat mass (BFM),
lean tissue mass (LTM), bone mineral content (BMC), fat free mass (FFM), and body
fat % (BF%), for all subjects (n = 157)
………………………………………………………………………………………..……..17

9. viii
Abbreviations
Air Displacement Plethysmography - ADP
Bioelectrical Impedance Analysis - BIA
Bioelectrical Impedance Spectroscopy - BIS
Body Fat Mass - BFM
Body Fat Percentage - BF%
Bone Mineral Content - BMC
Coefficient of Variance - CV
Correlation Coefficient - CC
Standard Error of Estimate - SEE
Limits of Agreement - LoA
Standard Deviation - SD
Dual Energy X-ray Absorptiometry - DXA
Extracellular Water - ECW
Fat Free Mass - FFM
Four Compartment model - 4-C model
Gaelic Athletic Association - GAA
Hydrostatic Weighing - HW
Intracellular Water - ICW
Lean Tissue Mass - LTM
Multi Frequency Bioelectrical Impedance Analysis - MF-BIA
National Collegiate Athletic Association - NCAA
Single Frequency Bioelectrical Impedance Analysis - SF-BIA
Total Body Water – TBW

10. ix
Abstract
A Comparison of Two Bioelectrical Impedance Analysis Modes with DXA for
Estimating the Body Composition of Elite Inter-County GAA Athletes
Student name Paul Sweeney Supervisor Professor Phil Jakeman
Background: The Tanita MC-180MA body compositional analyser is a multi frequency
bioelectrical impedance analysis (BIA) device used within clinical settings. The
equations provided by the manufacturer utilise gender, height, body mass, age, body
type category (Athlete or Normal) and measured impedance to obtain body
composition estimations. Purpose: The purpose of this study was to investigate which
mode (Athlete or Normal) was demonstrated better agreement in estimating the body
composition in elite inter-county GAA (football and hurling) players, compared to dual
energy x-ray absorptiometry (DXA) as the reference method. Methods: One hundred
and fifty seven inter-county GAA players aged 19-40 were recruited from five county
teams in Ireland. For each subject estimates of body fat mass (BFM), lean tissue mass
(LTM), bone mineral content (BMC), fat free mass (FFM), and body fat percentage
(BF%) were taken by BIA in Athlete and Normal mode and DXA. Results: Both BIA
modes demonstrated good relative agreement with DXA for all body composition
measures. In absolute terms, there were significant differences observed between
Athlete mode and DXA for all body composition variables, underestimating BFM, BMC,
and BF% while overestimating LTM and FFM with large biases and wide limits of
agreement found. No significant differences were observed between Normal mode and
DXA for BFM and BF%, however underestimations were shown for LTM, BMC and
FFM. Bias was smaller and limits of agreement were narrower in Normal mode
compared to DXA. Conclusion: Compared with DXA, Normal mode displayed better
accuracy than Athlete mode in estimating the body composition of elite GAA players. In
absolute terms, Athlete mode provided large biases and wide limits of agreement for all
body composition measures. Normal mode showed smaller biases, indicating that it
may be used interchangeably with DXA for group measurements, however wide limits
of agreement suggest that results of body composition assessments on individuals
should be analysed with caution.

11. 1
Chapter 1 - INTRODUCTION
Body composition is an important component of health and physical fitness that can
influence the performance of athletes (Rodriguez, 2009). Accurate assessments of
body composition are necessary in order to monitor training and nutritional status of
athletes (Moon, 2013). Strength and conditioning coaches can use body composition
measurements to evaluate the effectiveness of specific training programmes (Moon
2013). Sports nutritionists can also utilise body composition results to establish
personalised dietary interventions for their athletes (Esco et al., 2014; Segal, 1996).
Furthermore, body composition values can assist medical personnel in monitoring an
athlete’s physical and mental health, as radical changes in body composition can
indicate underlying health concerns (Fornetti et al., 1999). The Gaelic Athletic
Association (GAA) is the largest sporting organisation in Ireland and is comprised of
five distinctive sports; Gaelic football, hurling, camogie, handball and rounder’s.
Football and hurling are the most popular of these sports with an estimated 15% of
adult males participating in both codes in Ireland (Delaney and Fahey, 2005). Both
football and hurling are physically demanding contact sports in which high levels of
muscular strength, power and speed are advantageous (McIntyre et al., 2005; Reilly
and Doran, 2001). Therefore, the development of lean tissue mass (LTM) is desirable
as it is seen to enhance strength, power, and speed (Rodriguez, 2009). In contrast,
higher amounts of body fat mass (BFM) are detrimental to athletic performance,
increasing energy expenditure and reducing power to weight ratio, speed and
acceleration (Duthie et al., 2006; Sventesson et al., 2008; Harley et al., 2011).
There are many methods available for assessing and monitoring the body composition
of athletes. Laboratory methods include Dual Energy X-ray Absorptiometry (DXA),
Hydrostatic Weighing (HW) and Air Displacement Plethysmography (ADP), whereas
Bioelectrical Impedance Analysis (BIA) and Anthropometry are commonly used in field
settings (Ackland et al., 2012). DXA is frequently used method for body composition
analysis in clinical and sport settings and is considered to be a reliable and valid
method of assessing body composition in athletes (Buehring et al., 2014; Bilsborough
et al., 2014). DXA allows for a minimally invasive measurement of the three-
compartment model of body composition, consisting body fat mass (BFM), and two
components of fat free mass (FFM), i.e. LTM and bone mineral content (BMC).
Although DXA is an accepted technique for body composition measurement, it is
expensive and impractical, as each scan must be conducted by trained personnel.
Most inter-county GAA teams would not have time for, or access to DXA, therefore field

12. 2
methods are often preferred within this population due their low cost and high
practicality.
BIA has emerged as a popular field alternative to DXA for use within athletic
populations (Moon, 2013), as the method is user friendly, inexpensive, and requires no
specialised facilities or expertise to take the measurements (Sillanpaa et al., 2014). BIA
estimates body composition by applying an electric current through the human body
measuring resistance and reactance (Kushner, 1992). The resistance measured as
well as gender, height, and body mass are then integrated into a regression equation
from which BFM, FFM and TBW can be estimated. The regression equations are
usually specific to the population for which they were established therefore the choice
of equation is important (Swartz et al. 2002). Elite athletes engage in rigorous training
and tend have leaner physiques (different body types) than non-athletes (Prior et al.,
2001) thus equations derived from sedentary populations may not be suitable (Swartz
et al., 2002). Recommended prediction equations have been developed for athletes in
the past (Yannakoulia et al., 2000; Oppliger et al., 1992), however their validity in
athletic populations is still unknown.
In a bid to improve the accuracy of body composition measurements in individuals with
different body types, some BIA systems have incorporated two programmed
algorithms, one for athletes (Athlete mode) and one for the non-athletes (Normal
mode). The choice of mode is based on the volume of exercise performed by the
subject per week. Although limited research has been conducted investigating the
accuracy of these settings on BIA devices in athletes, a study by Swartz et al. (2002)
examined whether the choice of BIA algorithm altered body composition estimates
compared to HW in a cohort of highly active, moderately active, and inactive young
men. “Normal adult” mode was found to overestimate BF% (4.5 - 5%) and
underestimate FFM (3.5 - 4 kg) in subjects who participated in greater than 2.5 hours of
exercise per week. Conversely, the “athlete” mode underestimated BF% (4.5%) and
overestimated FFM (3.7 kg) in individuals who participated in less than 2.5 hours of
exercise. The results of the above study emphasise the need for population specific
BIA equations to be created in order to accurately assess body composition of athletes.
The purpose of this study was to investigate which mode (Athlete or Normal) on the
Tanita MC-180MA Multi-Frequency BIA displays the most agreement in measuring
body composition components in elite inter-county GAA players compared to DXA as
the reference method. A secondary purpose was determine whether choosing BIA in
Normal vs. Athlete mode significantly changes the output of the components of body

13. 3
composition. It was hypothesized that Athlete mode would display better agreement
than Normal mode for estimating the body composition of inter-county GAA players
compared to DXA as the criterion method
Chapter 2 – LITERATURE REVIEW
2.1 What is Body Composition?
Body composition has been described as “the chemical or physical components that
collectively make up an organisms mass, defined in a systematic way” (Stewart, 2010).
Body composition can be divided into a model consisting of 5 complex levels, (i)
Atomic; (ii) Molecular; (iii) Cellular; (iv) Tissue System; (v) Whole-Body. The majority of
research conducted on athletic populations has focused on investigating how the
quantity and distribution of molecular components including BFM and FFM (LTM, BMC,
and total body water (TBW)) can influence athletic performance (Malina et al., 2007)
2.2 Why Measure the Body Composition of Athletes?
Body composition plays an important role in the health and performance of an athlete
(Ackland et al., 2012). The measurement of body composition and changes in body
composition over time has many important applications to athletes and sport and
exercise science practitioners. Measurements can be analysed for many purposes
including, monitoring the success of training programs (Moon, 2013), establishing
individualised dietary interventions from estimating energy expenditure (Segal, 1996),
and evaluating the physical and mental well-being of the athletes (Fornetti et al., 1999).
Consequently, it is important for strength and conditioning coaches, nutrition experts
and health care practitioners working with athletes, that reliable, accurate and relatively
inexpensive methods for body compositional analysis are available.
2.2.1 Physical Demands of GAA
In order to play Gaelic football and hurling at a high level (i.e. inter-county), one must
display high levels of physical ability. Both codes are physically demanding contact
sports, characterised by intermittent changes of pace with anaerobic bouts overlapping
on moderate aerobic activity (Reeves and Collins, 2005). In a recent study on elite
adolescent GAA athletes, Cullen et al. (2013) stated that successful performance
required athletes to display several fitness components including high levels of
muscular strength, power, and speed, while also relying on the anaerobic and aerobic
systems. While literature investigating the physiological demands of hurling is limited,
research has shown that elite Gaelic footballers exhibit similar fitness profiles to

14. 4
professional soccer and Australian football athletes. (Cullen et al., 2013; Reilly and
Collins, 2008; Reeves and Collins, 2005; McIntrye, 2005). Therefore, due to the highly
physical and intense nature of the sport it is reasonable to assume that body
composition influences performance.
2.2.2 Body Composition and Athletic Performance
Numerous studies have investigated the relationship between body composition and
physical performance in lacrosse, soccer, and ice hockey; yet, there are no studies
examining this relationship within the GAA. The majority of research has investigated
the effects of BFM on physical performance with a vast amount of literature supporting
the notion that excess body fat negatively impact’s athletic performance (Ackland et al.,
2012; Rodriguez, 2009; Duthie et al., 2006).
Excess BFM is suggested to be particularly detrimental to athletes participating in
sports which involve activities requiring the maximal displacement of one’s body
through space (i.e. sprinting, running, and jumping). This is because the BFM acts as
extra weight that must be propelled against gravity, negatively effecting an athlete’s
acceleration, speed, and power to weight ratio, while also leading to an increase in
energy expenditure (Harley et al., 2008; Svantesson et al., 2008; Malina and Geithner,
2007). A study by Matilla et al. (2007) found increased BFM to be strong predictor of
aerobic performance in a sample of 140 Finish conscripts. The authors documented
inverse relationship between adipose tissue and aerobic capacity, in that for every 1%
increase in BF%, there was a 19.3 meter reduction in coopers test running distance.
Similar findings were reported in a study conducted by Collins et al. (2014)
investigating the relationship between body composition and performance tests in 54
collegiate level lacrosse athletes. The subjects underwent body composition
assessments by air-displacement plethysmography (ADP) before participating in a
battery of tests, measuring maximum power production (one repetition maximum power
clean), upper-body muscular endurance (body weight bench press and dips to failure),
and both aerobic (one mile run) and anaerobic capacity (300 yard shuttle). Moderate
correlations were found between increased BF% and number of bench press and dips
repetitions (upper body muscular endurance) (r = -0.36) and one mile run times
(aerobic capacity) (r = 0.44), while a strong relationship existed between increased
BF% and 300 yard shuttle test time (anaerobic capacity) (r = 0.69).
It can therefore be suggested that in biomechanical terms, increased BFM acts as
ballast, that can lead to negative aerobic and anaerobic performance outcomes
(Ackland et al., 2012). The findings of Collins et al. (2014) and Matilla et al. (2007) are

15. 5
in agreement with research by Potteiger et al. (2010) who investigated predictors of ice
skating performance in 21 elite ice hockey players. Body composition was assessed by
means of air displacement plethysmography (ADP). On-ice skating performance was
measured during 6 timed 89 m sprints with the results showing BF% to be moderately
correlated to average skating time (r = 0.57), such that greater relative fat levels were
associated to slower skating speed. Based on the findings from the above studies it is
clear BFM has a negative effect on many key fitness components related to hurling and
football including speed, and both aerobic and anaerobic capacities. This further
emphasises the importance of accurate body composition assessment methods for
practitioners within the sports.
Although the measurement of BFM and its effect on athletic performance has been the
main focus within the literature, many studies have also investigated the influence of
FFM components. LTM is of interest for athletes and sports practitioners, because like
BFM, its quantity and distribution is said to effect the performance of physical tasks.
This is because skeletal muscle is the tissue responsible for force production within the
body with a direct relationship existing between skeletal muscle cross-sectional area
and force generation (Ackland et al., 2012). Therefore, a phenotype displaying high
proportions of LTM is desirable for elite inter-county hurlers and footballers, as this is
directly related to higher power and strength-to-weight ratios, as well as enhanced
acceleration, speed, power, strength, and endurance (Svantesson et al., 2008; Duthie
et al., 2006). Numerous studies have been conducted on athletic populations
investigating the association between LTM and physical performance. Matilia et al.
(2007) investigated the relationship between fitness and physical performance in 140
conscripts and found lower body LTM to predict lower body explosive power in the form
of a standing broad jump test (r2
= 0.231). However, no relationship existed between
LTM and muscular strength or aerobic capacity. This was thought to be because
muscular strength was assessed using tests that involved using the subjects own body
mass as the external load (sit-ups, push-ups, back extensions, and pull-ups). The
authors concluded that they may have obtained different results it the administered
strength tests consisted of exercises involving pushing resistance away from the body
(e.g. 1 repetition maximum bench press). This may be due to the fact that absolute
FFM levels play an important role in the performance of tasks involving the projection
of objects or the movement of another individual (breaking tackles in hurling and
football) (Malina and Geitner, 2011). In contrast, a study recently conducted by
Hogstrom et al. (2012) on 48 male and female adolescent cross-country skiers (aged
15-17), showed LTM to influence aerobic capacity, reporting a positive moderate

16. 6
association between LTM%, weight adjusted maximal oxygen uptake thresholds (VO2
max), and the onset of blood lactate accumulation (r = 0.47–0.67, p < 0.05).
Furthermore, when assessing differences in physical fitness amongst female wrestlers
Pallares et al. (2012) found elite wrestlers had higher levels of FFM than amateurs,
thus further supporting the suggestion that FFM influences athletic performance. As the
majority of studies have investigated the effect of BFM on physical ability, more
research is warranted relating to FFM components.
2.2.3 Seasonal Variations in Body Composition
It is clear that body composition is an important determinant of performance therefore it
is important to assess body composition changes that occur throughout a season and
how these variations effect athletic performance. Studies on soccer, rugby, and rugby
league have investigated the body composition of athletes at different points of the
season and have noted intra-seasonal changes caused by a number of factors
including injury, illness, and dietary practices (Harley et al., 2011; Carling and Orhant,
2010; Silvestre et al., 2006; Duthie et al., 2005). Therefore, monitoring body
composition at different phases of a season may help players avoid any adverse
variations while also providing target values to achieved by the player through training
and nutritional intervention after a period of injury (Harley et al., 2011). Research also
suggests that a GAA player’s body composition tends to change throughout the course
of a season due to the physical effects of training and the stage of competition reached
(Reilly and Keane, 2001; Reilly and Doran, 2001). Much of the literature investigating
soccer has noted beneficial changes in body composition over the course of a
competitive season. Osteojic and Zivanic (2003) assessed body composition
alterations using skinfold measurements in thirty male professional soccer players and
while there was no changes noted in FFM, the results displayed a reduction in BFM
expressed as BF%, between the beginning and the end of the season. This was in
conjunction with the findings from a similar study by Casajus et al. (2001) who found a
significant decrease in BFM expressed as BF% (8.6 ± 0.91 %FM to 8.2 ± 0.91 %FM)
derived from skinfold measurements in 15 elite soccer players between the beginning
(September), and middle (February) of the season. Furthermore, Silvestre et al. (2006)
investigated seasonal body composition changes on 25 male elite level collegiate
soccer players using DXA at the beginning (pre) and end (post) of a National Collegiate
Athletic Association (NCAA) season. The authors observed a significant increase in
whole body (0.9 ± 0.2 kg), and regional LTM in the legs (0.4 ± 0.0 kg) and trunk (0.3 ±
0.1 kg) from pre to post season phases. In contrast to the above findings, Harley et al.
(2011) reported detrimental body composition changes in elite rugby league players

17. 7
during the competitive season. The authors performed measurements using DXA at
three intervals throughout the competitive season: PRE (end of pre-season), MID
(Middle of competitive season), and POST (a week after the conclusion of the season),
and found among other variables, a significant reduction in absolute LTM (-1.17 ± 1.33
kg) and BFM (0.90 ± 1.14 kg) from PRE to POST. The literature discussed in the above
paragraphs further demonstrates changes that occur throughout the course of a
sporting season once again highlighting the need for a quick, simple and reliable
method of body comp assessment in the GAA. No studies of this kind have been
conducted on a population of GAA athletes therefore, further research into seasonal
variations is required.
2.3 Methods of Body Composition Assessment
Numerous techniques to have been developed to estimate body composition and can
be divided into reference, laboratory, or field categories (Ackland et al., 2012).
Reference methods are the most accurate techniques to which all other methods are
compared. The four compartment (4-C) body composition model is considered the true
reference method for body composition assessment as it can provide estimates of FFM
without making assumptions relating to the density and hydration of individuals
(Toomey et al., 2015). The model is determined using a combination of techniques,
separating body mass into BFM, TBW, bone, and protein. BFM is measured by
hydrodensitometry, bone mass by DXA, TBW by isotope dilution, and protein from the
residual (Toomey et al., 2015). However, issues relating to time, cost, and accessibility,
limit its application within sporting populations (Santos et al. 2010). Laboratory methods
for body composition assessment include DXA, HW and ADP, while BIA and skinfolds
are commonly used field methods (Ackland et al., 2012). HW and ADP are based on
the two-compartment model of body composition measuring BFM and FFM by
estimating whole body density and relating it to BF%. For HW, the subject is
submerged in water and body density is calculated by dividing their body mass by the
volume. ADP is similar to HW however, density is estimated using a highly pressurised
air capsule and not underwater (Ackland et al., 2012). DXA and BIA were the two
techniques used within the current study therefore they will be discussed in greater
detail in the preceding paragraphs.
2.3.1 DXA
DXA is a minimally invasive three-compartment model of body compositional
assessment that estimates whole body and segmental BFM, as well as two FFM
components LTM and BMC. DXA offers many advantages to other laboratory
techniques for athletes as measurements are relatively quick (5-8 minutes), precise,

18. 8
and use low levels of radiation (Ackland et al., 2012). DXA operates by transmitting x-
rays through the body at two separate photon energies, one low and one high (i.e. 40
and 70 KeV). The x-ray beams travel through tissues within the body and are
attenuated depending on the physical make up (density and thickness) of the tissues
they pass through (Toombs et al., 2012). Soft tissues (fat, muscle, water) are lower in
density than hard tissues (bone) and therefore allow more photons to travel through,
decreasing attenuation. DXA distinguishes between BF and FFM by calculating the
ratio of low-to-high photon energy attenuation in the soft tissue (Pietrobelli et al., 1996)
(Fig. 1). (Appendix 1)
A vast amount of literature has investigated the validity of DXA against a four-
compartment (4-C) model for assessing body composition within athletic populations
(Santos et al., 2010; Withers et al., 2004; Prior et al., 1997). Many studies have found
mean differences between the methods in BF% ranging from -3.5% to 2.9%, with the
majority showing larger underestimations of relative and absolute BFM in leaner
individuals (Toombs et al., 2012). The accuracy and precision of DXA is said to vary
depending on the tissue measured, with values for lean mass demonstrating better
accuracy and precision than BFM (Toombs et al., 2012). Santos et al. (2010)
investigated the accuracy of fan DXA compared to a 4-C model in 27 elite male judo
athletes. The results showed that on a group level, DXA provided better estimations of
FFM (r > ~0.95, Standard error of estimate (SEE) <1.98, Limits of Agreement (LoA);
0.6 kg to -7.0 kg) than BFM (r > ~0.78, SEE <2.6, LoA; 6.2 kg to -1.1 kg) and BF% (r >
~0.72, SEE <2.65, LoA; 8.8 to -2.4). This was indicated by the higher correlation
coefficient (CC), lower SEE and tighter LoA. Additionally, on an individual level large
differences were found between DXA and 4-C for all variables. Similarly, Bilsborough et
al. (2012) conducted a study on thirty-six elite Australian football players and found a
fan DXA to provide greater accuracy and precision for estimates of LTM and BMC,
than BFM. DXA measures were compared to a whole body phantom to assess
accuracy, whereas the athletes completed two separate scans under the same
conditions to determine DXA precision. DXA showed better accuracy for estimating
LTM and BMC (r = 0.98-1.00) than BFM (r = 0.39-0.84) showing stronger correlations
with the reference method. Furthermore, precision was higher for LTM and BMC (%CV
0.3%-0.6%) than BFM (%CV = 2.5%). The authors suggested that these findings could
have stemmed from physiological variations within participants, as the conditions were
not adequately controlled. The results from aforementioned studies demonstrate that
measures obtained from DXA should be analysed with caution especially for BFM in
athletes at both a group and individual level. Although there remains some uncertainty

19. 9
about the accuracy of DXA obtained body composition measures, it has been found to
demonstrate similar results compared to other laboratory techniques (i.e. densitometry)
(Kohrt, 1998). Therefore, until the creation of a practical 4-C model for athletes, DXA is
the preferred method, due to its speed, practicality and precision (Ackland et al., 2012)
2.3.2 BIA
BIA is a popular field method of assessing body composition that has been widely used
in athletic populations due to its cost, accessibility, and practicality. BIA is based on a
three-component model providing estimations of FFM, BFM, and TBW (Moon, 2013).
BIA devices transmit harmless electrical currents through the body in order to calculate
impedance, (i.e. resistance and reactance of the current) (Kyle et al., 2004; Bolanowski
and Nilsson, 2001; Kushner, 1992). BIA operates on the principle that electrical
currents flow through body tissues at different velocities depending on their
composition, with the most resistance provided by BFM and the least by FFM as it is
rich in water and electrolytes (Kushner, 1992). Assuming TBW makes up a fixed
percentage of FFM (73%), body composition values can be estimated from specific
regression equations based on gender, height, body mass, and calculated resistance
(Pateyjohns et al., 2006; Kyle et al., 2004). Many choices of BIA systems are now
commercially available. Early BIA methods utilised a single frequency current (SF-BIA)
of typically 50kHz, travelling between surface electrodes placed on the hand and foot of
the subject to estimate the body composition of an individual (Kyle et al. 2004).
However, research investigating the accuracy of SF-BIA systems in athletic populations
has shown conflicting results, with some authors reporting good accuracy (Yannkoulia
et al., 2000; Fornetti et al., 1999) and others reporting poor accuracy (Esco et al.,
2011). These contradictory findings may have occurred due to the fact that single low
frequency currents (<100KHz) cannot fully penetrate through cell membranes and
therefore are unable to predict the concentration of intracellular water (ICW), and in
turn total body water (TBW) (Shafer et al., 2009). Advancements in technology have
led to BIA devices being developed using multiple frequency currents (5 to 500kHz)
(Silanpaa et al., 2014; Scharfetter et al., 2001). These Multi-Frequency BIA devices
(i.e. MF-BIA, Bioelectrical Impedance Spectroscopy (BIS)) are deemed to be more
accurate in determining distribution of ICW and extracellular water (ECW) and
therefore may be preferred to SF devices in the estimation of FFM (Matthie, 2008).
However, it has not yet been determined which method provides the most accuracy
(Kyle et al., 2004), therefore this topic requires more research.

20. 10
2.3.3 Problems Associated with BIA
A problem associated with the use of BIA within athletic populations is its high
sensitivity to variations in hydration status. BIA estimates body composition by
assuming that FFM comprises of 73% TBW, thus, changes in hydration levels can lead
to prediction errors in body composition parameters. Elite GAA athletes perform
multiple bouts of acute exercise each week, and are therefore more susceptible to
greater hydration variation than the normal individual (Segal, 1996). These individual
acute exercise bouts may lead to loses in TBW through sweating causing increased
BIA measured resistance, and in turn falsely underestimates FFM and overestimates
BFM (Segal, 1996). Saunders et al. (1998) investigated the effects of altered hydration
on BIA in 15 endurance athletes aged 19 to 56 years. The results showed that hypo-
hydration induced by exercise, was incorrectly interpreted as changes in the athletes
BFM. This was supported by Frisard et al. (2005) who suggested that BIA
overestimates FFM and underestimates BFM in overly hydrated individuals, and
underestimates FFM and overestimates BFM in those who are dehydrated. The above
findings emphasise the importance of strict adherence to pre-test guidelines (i.e.
fasting, exercise avoidance) in order for accurate body composition values to be
obtained in athletes (Moon, 2013).
BIA regression equations are usually population specific, therefore choosing the correct
equation is of fundamental importance. To date, no generalised equation with a valid
estimation of TBW exist for use on athletic populations, which could cause inaccurate
FFM values due to the variability of FFM hydration in athletes (Moon, 2013). This is a
major limitation of BIA use in athletic populations and so some BIA devices have
developed two pre-programmed algorithms in their devices, one for athletes, and one
for non-athletes. It has not yet been established which mode provides more accurate
results and hence the current study was conducted.
2.3.4 BIA vs. DXA
The majority of research comparing BIA to DXA for body composition assessment has
been conducted on non-athletic populations with only one study within the literature
comparing the Tanita MC-180MA MF-BIA to DXA. This was conducted by Leahy et al.
(2012) on a large cohort (n = 403) of healthy men and woman aged 18-29 years. The
authors found BIA to underestimate median BFM (1.3 kg) and BF% (2.1%), while
overestimating FFM (1.5 kg) (p < 0.05) compared to DXA in all subjects. When the
biases were investigated further, the authors observed that the underestimations
became more apparent as absolute and relative fat tissue levels increased, whereas,
the overestimation of FFM remained constant across the spectrum of values. Similarly,

21. 11
Sillanpaa et al. (2014) studied 882 mixed gender adults aged 18-88 years and found
MF-BIA to underestimate BFM (2.9 kg and 1.6 kg) and overestimate LTM (3.1 kg and
2.6 kg) in men and women respectively. BIA was found to overestimate LTM in leaner
subjects and underestimate BFM in those who were obese. The biases found between
BIA and DXA in the above studies could have stemmed from the algorithms used in
both BIA devices and the body geometry of the participants.
Conflicting results have been reported in studies comparing bioelectrical impedance to
DXA in athletes participating in numerous sports across several age ranges (Moon,
2013). Fornetti et al. (1999) compared a SF-BIA device to DXA for measuring BF% and
FFM in a cohort of 132 female athletes from a range of sports, and found BIA to
provide good relative agreement, as demonstrated by high correlations (r = 0.969-
0.983) and low a prediction error (1.1 kg) between the two methods. Yannakoulia et al.
(2000) created two athlete specific BIA equations derived from DXA, and found them to
provide accurate measures of body composition in a cohort of female dancers when
validated against DXA. The authors noted that cross-validation would be necessary in
order to adequately assess the accuracy of both equations in an athletic population.
The above findings are in contrast to more recent literature where BIA has been
reported to display inaccurate measures compared to DXA. In a sample of 43 highly
active male judo, karate, and water polo athletes, De Lorenzo et al. (2000) reported
that between SF-BIA and DXA, BIA underestimated BF% by 2.5% and overestimated
FFM by 2.4 kg. Similarly, Sventesson et al. (2008) studied elite male soccer (n= 17)
and ice-hockey athletes (n= 16) aged (18+) and found a a bioelectrical impedance
spectroscopy (BIS) device underestimated BF% by 4.6% in ice hockey players and
1.1% in soccer players with large differences also being reported at an individual level.
Esco et al. (2011) found further support for the above studies in a similar investigation
on 40 collegiate level female athletes using a hand-to-hand SF-BIA device. While good
relative agreement existed between both methods for BF% (r = 0.74, R2
= 0.55, SEE =
3.60, and p < 0.01), and FFM (r = 0.84, R2
= 0.71, SEE = 2.45, p < 0.01), poor absolute
agreement was found. This was indicated by the large biases and wide limits of
agreement showing BIA to underestimate BF% by 5.1% and overestimating FFM 3.4%,
with the differences becoming greater at higher levels of fat and lean mass. The above
findings were concurrent with more recent research by Esco et al. (2014) on 45 female
collegiate level athletes, who demonstrated a MF-BIA device to underestimate BF% by
3.3% and overestimate FFM by 2.1 kg compared to DXA. Based on the above findings,
it appears that BIA methods underestimate both absolute and relative body fat and
overestimate FFM and LTM compared to DXA in athletic populations. However, the

22. 12
majority of studies were conducted on female athletes using SF-BIA devices; therefore,
further research is warranted into the use of MF-BIA devices on male team sport
athletes.
2.4 Conclusion
It can be concluded from the literature review that body composition can influence
athletic performance. Therefore, an easy to use, quick and accurate method for
measuring body composition of GAA players as this can provide valuable information
to athletes, coaches, and various other professionals within the field of sport and
exercise sciences. DXA and BIA are commonly utilised methods for body composition
assessment in athletic populations. While the literature suggests that BIA lacks
accuracy compared to DXA in athletic groups the majority of studies have been
conducted on female athletes using SF-BIA devices; therefore, further research is
warranted examining the accuracy of MF-BIA devices on male team sport athletes.
More research is also needed in order to evaluate the accuracy of population specific
regression equations as there is a gap in the research relating to BIA equations created
for athletes from a multi-compartment model with a valid estimate of TBW. The vast
amount of literature suggesting that BIA is inaccurate at extreme BF levels which
further strengthens the need for athlete specific equations to be developed.

23. 13
Chapter 3 - METHODS
3.1 Participants
Following written, informed consent, 157 senior inter-county GAA athletes (put in
number of hurlers and footballers here) were recruited from five inter-county teams
across Ireland. Before the study commenced, all athletes completed a pre-test health
screening questionnaire.
3.2 Preparation
Data collection took place over a four year period (2009-2013) within the Physical
Education and Sport Sciences department of the University of Limerick. The
participants were instructed to avoid any form of organised training or exercise session
of 20 minutes or longer for a period of 12 hours before testing, refrain from ingesting
food for three hours before testing, drink 500 ml of water one hour before testing, and
empty their bladder or defecate immediately before testing if required.
3.3 Procedures
3.3.1 Anthropometric Measurements
Body mass was measured in minimal clothing to the nearest 0.1 kg using a Tanita MC-
180MA Body Composition Analyzer, (Tanita UK Ltd). Height was measured to the
nearest 0.1 cm using a stadiometer (Seca, Birmingham, UK). Subjects were instructed
to remove all jewellery and metal objects prior to testing to ensure accuracy of the BIA
and DXA measurements (Sun et al. 2005).
3.3.2 Bioelectrical Impedance Analysis (BIA)
BIA measurements were carried out before DXA for all participants to determine fat
free mass (FFM), body fat mass (BFM) and lean tissue mass (LTM). Whole and
segmental body composition was assessed using an eight-contact electrode multi
frequency bioelectrical impedance analyser (Tanita MC-180MA Body Composition
Analyzer, Tanita UK Ltd). Body composition of all participants was assessed in both
Normal and Athlete mode. According to the instructions of the manufacturer, Normal
mode was designed for individuals who participated in less than 12 hours exercise per
week. Athlete mode was designed for active individuals who were over the age of 18
and participated in 12 or more hours of training (exercise) per week. In accordance with
the manufacturer’s instructions participants stood barefoot on the stainless steel metal
panel of the Tanita MC-180 with their feet parallel and soles in contact with the four
heel and toe metallic electrodes, and body mass was recorded. Gender, height, body
mass, age and physical activity mode (“normal” or “athlete”) were manually entered into

24. 14
the BIA keypad interface. Participants grasped the handgrips, with their thumbs,
fingers, and palms in contact with the four anterior and posterior placed metallic
electrodes, and with arms hanging naturally by their sides fully extended, and abducted
laterally to approximately 20 degrees to ensure contact between the arms and torso
was avoided (Hogan et al. 2011). The device obtained impedance measures from 5
different regions within the body (whole body, right leg, left leg, right arm, left arm), by
passing an electric current (less than or equal to 90uA) from the 8 polar electrodes,
through the body at various frequencies (5, 50, 250, and 500 kHz). Body composition
parameters were then estimated from specific equations using height, body mass,
physical activity and impedance values (Kyle et al. 2004). The impedance measure
had a Coefficient of variance of 0.4% (Leahy et al. 2012).
3.3.3 Dual Energy X-ray Absorptiometry (DXA)
Measurements of body fat mass (BFM), , Lean tissue mass (LTM), bone mineral
content (BMC) and fat free mass (FFM) were undertaken by a Lunar iDXA scanner (GE
Healthcare, Chalfont St Giles, Bucks., UK) with encore 2007 v.11 software.. Calibration
was performed daily according to the manufacturer’s instructions using a proprietary
phantom consisting of bone, lean, and fat tissue. Participants wore minimal clothing
and removed all jewellery prior to the scan. Measurements were performed and
supervised by trained technicians within the University of Limerick Physical Education
and Sport Sciences Department. Participants were instructed to lay supine and
motionless on the measurement table with their arms by their sides and hands in the
mid-prone position, making sure there was no contact between the arm and trunk
segments. The DXA scanner used within this study was capable of providing
segmental body analysis, splitting the body into three anatomical regions of interest
(arms, legs, and trunk). Leahy et al. (2012) defined the aforementioned regions by the
following body landmarks.The arm segment was defined as the area of tissue bisecting
the centre of the glenohumeral joint to the phalanges. The leg segment was the area of
tissue perpendicular to the axis of the neck of the femur, angled with the pelvic brim to
the phalanges. The trunk segment consisted of all remaining distal tissue from the
bottom of the skull excluding leg and arm segments. All composition data was
calculated by enCore software from DXA derived estimates of body mass. According to
Huizenga et al. (2007) the coefficient of variation for the iDXA measurement of body
composition is <1%.
3.4 Statistical Analysis
Statistical analysis were performed using PASW Statistics 18.0 for Windows (SPSS,
Inc., Chicago, IL. A Kolgomorov Smirnov test was conducted to determine whether

25. 15
data were normally or non-normally distributed. Paired t tests and Wilcoxon signed
ranks tests were used in order to compare measures from each BIA mode to DXA (i.e.
Athlete vs. DXA and Normal vs. DXA) for whole body analysis, and also to compare
both modes to one another (i.e. Athlete vs. Normal). Pearson’s and Spearman’s
correlation were used to assess the relative agreement between the methods. Bland
Altman plots (Bland and Altman 1986) were used to assess the absolute agreement
and bias between both modes and DXA and between both modes independently.
Limits of agreement were determined as the mean of the difference between each
method +/- 1.96 x SD of the difference. All tests were two-tailed and with the
significance level set at 0.01 for correlation analysis and 0.05 for all other analysis.

26. 16
Chapter 4 – RESULTS
4.1 Descriptive Statistics
Anthropometrics for the GAA players are reported in Table 4.1. Not all the data were
normally distributed; the mean, standard deviation and range are reported, as well as
the median and interquartile range (IQR).
Table 4.1 Anthropometrics for the 157 GAA players included in this study. Data are
reported as mean (standard deviation (SD)), median (interquartile range (IQR)) and
range (max – min); n = 157)
Mean (SD) Median (IQR) Range
Age (y) 25.5 (4.1) 25.3 (6.7) 19-40
Height (cm) 183.3 (4.59) 184.0 (8.0) 173-195
Mass (kg) 85.4 (7.1) 84.8 (9.4) 70-111
BMI (kg/m2
) 25.3 (1.5) 25.1 (1.8) 21-31
LTMI (kg/m2
) 20.1 (1.0) 19.9 (1.0) 18-23
ALTMI (kg/m2
) 9.9 (0.5) 9.9 (1.0) 9-12
4.2 BIA Athlete and Normal mode vs. DXA Analysis
4.2.1 Overview
Comparisons of BIA Athlete and Normal mode to DXA for all body composition
variables are displayed in table 4.2. Strong positive correlations were found between
both modes and DXA for BFM, LTM, FFM, and BF% (r > 0.6; p = 0.000), however,
moderate correlations were found for BMC (r = 0.57-0.58; p = 0.000). There were
significant differences found between Athlete mode and DXA for all body composition
variables and between Normal and DXA for LTM, BMC, and FFM (p < 0.05; Table 4.2).
4.2.2 BFM
Athlete mode underestimated median BFM by 2.8 kg (-21.3%) (p = 0.000), (LoA; -1.6
kg to 7.3 kg). Normal mode overestimated median BFM by 0.2 kg (1.5%) (p = 0.183),
(LoA; -4.6 kg to +4.1 kg). Both modes underestimated BFM in individuals with greater
than 20 kg of BFM (Figure 4.1 a & b.).
4.2.3 LTM
Compared to DXA, Athlete mode overestimated mean LTM by 2.7 kg (3.9%) (p =
0.000), (LoA; -7.0 kg to 1.6 kg) (Figure 4.2 (a)). Normal mode underestimated mean
LTM by 0.2 kg (-0.2%) (p = 0.000), (LoA; -4.0 kg to +4.4 kg) (Figure. 4.2 (b)). There
was no clear trend in the difference between DXA and BIA over the range of LTM
values (Figure 4.2 a & b.).

27. 17
4.2.4 BMC
For BMC, A Wilcoxon signed ranks test showed that there was significant differences
(p = 0.000) obtained by both modes and DXA, with Athlete mode and Normal mode
underestimating the median value by 0.2 kg (5.2%) and 0.3 kg (7.8%) respectively.
4.2.5 FFM
Athlete mode was found to overestimate mean FFM by 2.4 kg (3.3%) (p = 0.000), (LoA;
-6.9 kg to 2.1 kg) (Figure. 4.4 (a)). Normal mode underestimated FFM by 0.6 kg (0.8%)
(p = 0.000), (LoA; -3.8 kg to +5.1 kg) (Figure. 4.4 (b)). Similar to LTM there was no
obvious trend in the difference between DXA and either BIA mode with increasing FFM
(Figure 4.3 a & b.).
4.2.6 BF%
The mean difference between DXA and BIA in both Athlete and Normal modes for
BF% are illustrated in Figure 4.5. BIA Athlete mode underestimated mean BF% by
3.3% (p = 0.000), (LoA ranging from approximately -1.9% to +8.4%) (Figure. 4.5 (a)).
Normal mode overestimated mean BF% by 0.4%, (LoA -5.4% to +4.6% (Figure. 4.5
(b)). There was a trend towards BIA underestimating in individuals with greater than
20% BF (Figure 4.4 a & b.).
4.2.7 Athlete vs. Normal mode
Significant differences were noted between both modes for all body composition
estimates (p < 0.05). Athlete mode underestimated BFM (3 kg) and BF% (3.6%) and
overestimated FFM (3.1 kg) and LTM (2.9 kg) values compared to the Normal setting.
Bland-Altman plots revealed that underestimations became more evident at lower
levels of BFM, while the difference between the two modes was less at higher body fat
levels.

28. 18
Table 4.2 DXA and BIA athlete (a) and normal (n) measured body fat mass (BFM), lean tissue mass (LTM), bone mineral content (BMC), fat
free mass (FFM), and body fat % (BF%), for all subjects (n = 157)
Variable Method Mean SD Median Range IQR r-value p-value
BFM DXA
BIA (a)
BIA (n)
13.9
11.1*
14.1*
4.4
4.0
3.9
13.1
10.3b,c
13.3b
7-31
4-27
5-30
5.0
5.0
5.0
0.74
0.77
0.000
0.183
LTM DXA
BIA (a)
BIA (n)
67.5a,
*
70.2a,b,c,
*
67.3a,b,c,
*
4.7
4.8
4.6
67.3
70.0
67.2
56-79
61-81
58-77
7.0
7.0
7.0
0.89
0.89
0.000
0.000
BMC DXA
BIA (a)
BIA (n)
3.9
3.6*
3.5*
0.3
0.2
0.2
3.8
3.6b,c
3.5b,c
3-5
3-4
3-4
1.0
0.0
0.0
0.58
0.57
0.000
0.000
FFM DXA
BIA (a)
BIA (n)
71.4a
73.8a,b,c,
*
70.8a,b,c
*
4.9
5.0
4.8
71.2
73.6
70.7
59-84
64-84
61-81
7.0
7.0
7.0
0.99
0.89
0.000
0.000
BF% DXA
BIA (a)
BIA (n)
16.1
12.8a,
*
16.5a,
*
4.0
3.7
3.5
15.3
12.6b,c
16.2b
8.7-27.9
5.1-24.7
6.1-26.8
5.3
5.4
4.7
0.77
0.69
0.000
0.077
(
a
= normal distribution; b = significant correlation between BIA and DXA measurement (p < 0.01); c = significant difference between BIA and
DXA measurements; r value = correlation coefficient; p = statistically significant at (p < 0.05); * = significant difference between BIA Athlete and
Normal mode measurements)

29. 19
-0.2
4.1
-4.6
-8.0
-4.0
0.0
4.0
8.0
12.0
5.0 10.0 15.0 20.0 25.0 30.0 35.0
BFMdifferencebetweenmethods
(DXA-BIANormal(kg)
BFM (mean of methods) (kg)
BFM Bland Altman (DXA vs BIA Normal) (b)
Figure 4.1 a & b Bland Altman plots of Body Fat Mass (kg) with mean difference
(dotted lines) and 95% limits of agreement (dots) comparing Athlete and Normal modes
to DXA
2.8
7.3
-1.6
-8.0
-4.0
0.0
4.0
8.0
12.0
5.0 10.0 15.0 20.0 25.0 30.0 35.0
BFMdifferencebetweenmethods
(DXA-BIAAthlete(kg)
BFM (mean of methods) (kg)
BFM Bland Altman (DXA vs BIA Athlete) (a)

30. 20
0.2
4.4
-4.0
-12.0
-8.0
-4.0
0.0
4.0
8.0
55.0 60.0 65.0 70.0 75.0 80.0
LTMdifferencebetweenmethods
(DXA-BIANormal(kg)
LTM (mean of methods) (kg)
LTM Bland Altman (DXA vs BIA Normal) (b)
Figure 4.2 a & b Bland Altman plots of Lean Tissue Mass (kg) with mean difference
(dotted lines) and 95% limits of agreement (dots) comparing Athlete and Normal modes
to DXA
-2.7
1.6
-7.0
-12.0
-8.0
-4.0
0.0
4.0
8.0
55.0 60.0 65.0 70.0 75.0 80.0
LTMdifferencebetweenmethods
(DXA-BIAAthlete(kg)
LTM (mean of methods) (kg)
LTM Bland Altman (DXA vs BIA Athlete) (a)

31. 21
0.6
5.1
-3.8
-12.0
-8.0
-4.0
0.0
4.0
8.0
55.0 60.0 65.0 70.0 75.0 80.0 85.0
FFMdifferencebetweenmethods
(DXA-BIANormal(kg)
FFM (mean of methods) (kg)
FFM Bland Altman (DXA vs BIA Normal) (b)
Figure 4.3 a & b Bland Altman plots of Fat Free Mass (kg) with mean difference
(dotted lines) and 95% limits of agreement (dots) comparing Athlete and Normal modes
to DXA
-2.4
2.1
-6.9
-12.0
-8.0
-4.0
0.0
4.0
8.0
55.0 60.0 65.0 70.0 75.0 80.0 85.0
FFMdifferencebetweenmethods
(DXA-BIAAthlete(kg)
FFM (mean of methods (kg)
FFM Bland Altman (DXA vs BIA Athlete) (a)

32. 22
-0.4%
4.6%
-5.4%
-8.0%
-4.0%
0.0%
4.0%
8.0%
12.0%
5.0% 10.0% 15.0% 20.0% 25.0% 30.0%
BF%differencebetweenmethods
(DXA-BIANormal)
BF% (mean of methods)
BF% Bland Altman (DXA vs BIA Normal) (b)
Figure 4.4 a & b Bland Altman plots of Body Fat % with mean difference (dotted lines)
and 95% limits of agreement (dots) comparing Athlete and Normal modes to DXA
3.3%
8.4%
-1.9%
-8.0%
-4.0%
0.0%
4.0%
8.0%
12.0%
5.0% 10.0% 15.0% 20.0% 25.0% 30.0%
BF%differencebetweenmethods
(DXA-BIAAthlete)
BF% (mean of methods)
BF% Bland Altman (DXA vs BIA Athlete) (a)

33. 23
Chapter 5 – DISCUSSION
5.1 Background and Purpose
Body composition has gained particular interest within the GAA because athletic
performance is influenced by and dependent on the quantity and proportion of BFM
and LTM (Pritchard et al., 1998). BFM negatively influences many fundamental fitness
components of Gaelic football and hurling, including speed, aerobic and anaerobic
capacity (Collins et al., 2014; Potteiger et al., 2010; Matilla et al., 2007). Conversely,
LTM has been positively associated with athletic performance (Hogstrom et al., 2012).
Therefore there is a growing need for convenient and accurate assessment methods
within the GAA. DXA is now accepted as a reference method for estimating LTM and
BFM in athletes (Bilsborough et al., 2014; Stewart and Hannon, 2000). However, the
majority of inter-county GAA teams do not have access to this method it is expensive
and most often found in clinical or laboratory settings. BIA methods on the other hand,
offer an attractive alternative for athletes, as they are cost and time effective, non-
invasive, and easy to use. The accuracy of BIA is limited in populations displaying
extreme levels of body fat, and has been found to overestimate and underestimate fat
values in lean and obese cohorts respectively (Segal et al., 1988). Athletes tend to be
leaner and more active than the normal population This has led many manufacturers to
incorporate body types into their equations. The primary purpose of this study was to
determine which mode (Athlete or Normal) on the Tanita MC-180MA MF-BIA had
better agreement in measuring body composition components in elite inter-county GAA
players compared to DXA. A secondary purpose was to investigate whether choosing
one BIA mode over the other significantly changed the output of the components of
body composition.
5.2 Findings
The principle finding of this investigation was that, relative to DXA, BIA Normal mode
provided more accurate measurements of the body composition components than
Athlete mode of elite GAA athletes. Values obtained in Athlete mode displayed larger
biases and wider limits of agreement for all body composition components. Both BIA
modes showed good relative agreement with DXA as demonstrated by strong
correlation coefficients for BFM, FFM, LTM, and BF%, as well as moderate correlations
for BMC. Although the high correlation coefficients indicate a strong relationship
between methods, this type of analysis does not imply that the two methods agree with
one another. In absolute terms, Normal mode demonstrated good agreement with DXA
for BFM and BF% as indicated by small non-significant biases between the methods.

34. 24
These findings are comparable with numerous other studies carried out on athletes that
have shown close agreement between BIA and DXA for body composition measures
(Yannaoulia et al., 2000; Fornetti et al., 1999). Athlete mode significantly
underestimated median BFM 2.8 kg and mean BF% by 3.3% compared to DXA.
Although significant differences were found between both BIA modes and DXA for FFM
and LTM values, the biases exhibited in Normal mode were small, underestimating
FFM by 0.2 kg and LTM by 0.6 kg in contrast to Athlete mode which overestimated
FFM and LTM by 2.4 kg and 2.7 kg respectively, with the overestimations remaining
consistent over the range of FFM and LTM values. The measures obtained from
Athlete mode agree with previous research conducted on athletic populations finding
SF and MF-BIA devices to display good relative agreement with DXA, but provide
underestimations of BF% and overestimations of FFM respectively in collegiate level
female athletes (Esco et al., 2011; Esco et al., 2014). The results of the current study
demonstrate that Normal mode was superior to Athlete mode in assessing body
composition of elite GAA athletes. The non-significant biases and high correlation
coefficients between Normal mode and DXA for BFM and BF%, suggest it to be a
reliable method for group level body fat analysis in athletic cohorts. However, the limits
of agreement were wide for all measures, therefore limiting its applicability to
estimating body composition in individual athletes (Pateyjohns et al., 2006).
5.3 Systematic Errors
On closer interpretation of the Bland Altman plots, systematic errors existed between
both BIA modes and DXA, with underestimations for BFM and BF% becoming more
apparent as body fat levels increased (>20%; >20 kg respectively). These observations
are in agreement with findings reported in similar studies comparing MF- BIA to DXA in
normal healthy populations (Sillanpaa et al., 2014; Leahy et al., 2012; Sun et al., 2005).
Leahy et al. (2012) showed Tanita MC-180MA MF-BIA to significantly underestimate
BFM and BF% compared to DXA, and noted that the underestimations increased in
subjects with higher body fat levels. The underestimations of BFM and BF% in the
current investigation and the above studies may be due to hydration status at varying
levels of body fat (Pateyjohns et al., 2006; Frisard et al., 2005). Overweight individuals
have been found to exhibit greater TBW and Extracellular water (ECW) than their
leaner counterparts (Steijaert et al. 1997) Therefore, given the highly sensitive nature
of BIA to variations in hydration, higher levels of TBW could be wrongly interpreted as
greater FFM and lower BFM levels in subjects.
To the authors knowledge this is the first study to compare the accuracy of the BIA
modes on the Tanita MC-180MA MF-BIA to DXA, in a group of elite athletes, therefore

35. 25
no direct comparisons could be made. However, in a similar study Swartz et al. (2002)
investigated the accuracy of Athlete and Normal modes against HW as they criterion
method in 57 middle-aged men of varying levels of physical activity. Subjects were
divided according to activity level with seventeen who participated in greater than 10
hours aerobic exercise per week categorised as highly active. All participants were
scanned in both modes and the Athlete equation was found to display greater accuracy
for estimates of BF% and FFM in the highly active subjects. While no significant
differences existed between Athlete and HW for BF% and FFM (p = 0.309), Normal
mode overestimated BF% by 5% and underestimated FFM by 4kg in highly active
individuals (p < 0.001). The findings of this investigation one again demonstrated the
importance of choosing the correct BIA equation in highly active populations. These
results were in contrast to the current study where Normal mode displayed better
accuracy than Athlete mode for all body composition estimates compared to DXA. The
inconsistent findings of the two studies could stem from many explanations. Firstly, the
current study used a MF-BIA device opposed to the SF-BIA utilised by Swartz et al.
(2002). Previous research has shown differences between SF and MF BIA devices for
body composition estimations (Thompson et al., 2007; Pateyjohns et al., 2006). For
instance, Thompson et al. (2007) found SF-BIA to display larger bias and wider LoA
than MF when compared to DXA as the criterion method. Secondly, the two studies
were carried out on different population groups. While similar age ranges were
reported, Swartz et al. (2002) studied highly active men while current investigation was
conducted on elite athletes. Although Swartz et al. (2002) suggested that those who
were highly active had comparable activity levels to athletes, elite GAA players perform
specialised regimens each week involving many types of training (i.e. aerobic,
resistance, anaerobic), which can modify their physical make up away from
morphological norms (Ackland et al., 2012). Furthermore, the multiple bouts of acute
exercise performed during training and competition, make athletes more susceptible to
variations in fluid and electrolyte balance (Ackland et al., 2012). As BIA assumes the
constant hydration of FFM (73%), variability of impedance within participants may be
greater in elite athletes. Despite the participants in the current investigation being given
clear pre-test guidelines regarding fluid consumption and exercise avoidance, time
since last exercise bout was not measured. As GAA is an amateur organisation, many
players would be reluctant to interrupt their training schedules for a body composition
assessment, which may be a further reason for the contradictory findings of the two
studies.

36. 26
5.4 BIA Athlete vs. Normal mode
The secondary purpose of this study was to determine whether choosing one BIA
analysis mode over the other significantly altered body composition estimates. Results
showed that Athlete mode significantly underestimated BFM and overestimated LTM
compared to Normal mode. Swartz et al. (2002) similarly reported differences in the
outputs of the BIA physical activity settings showing Normal mode to significantly
overestimate BF% by 6.8% (p < 0.001) and underestimate FFM by 5.5 kg (p < 0.001)
compared to Athlete mode in highly active individuals. These conflicting results
between the two studies further emphasise the need for more research to be
conducted on BIA devices that incorporate equations based on body types and
physical activity levels. This is because inaccurate body composition estimations could
provide GAA coaches and practitioners with false data relating to the effectiveness of
training programmes and nutritional interventions.
5.5 Limitations
Several limitations of the investigation should be noted. Subjects were given strict pre-
test guidelines (e.g. refrain from any form of organised training or exercise session of
greater than 20 minutes for a period of 24 hours before testing, refrain from ingesting
food for three hours before testing, consume 500ml of water one hour before testing,
empty bladder or defecate immediately before testing if required). However, we could
not determine the hydration status of the participants. Variations in hydration levels can
lead to errors in predicting body composition components as BIA assumes that FFM
comprises of 73% water (Esco et al., 2014). As previously mentioned, elite inter-county
GAA players are predisposed to deviation in FFM hydration due to rigorous training
regimens, therefore this could have affected the accuracy of BIA estimates in the
current study. Another possible limitation of the current study was that DXA was used
as the criterion method instead other laboratory techniques such as hydrodensitometry
(HD) or a 4-C model. Although studies in the past have found DXA demonstrates
similar accuracy to HD, (Kohrt, 1998) research on athletic cohorts comparing DXA to 4-
C models have found mean biases in BF% ranging from -3.5% to 2.9%, with the most
studies reporting larger BFM underestimations in leaner individuals (Toombs et al.,
2012). However, many other studies on athletic cohorts have utilised DXA as the
criterion method (Esco et al., 2014; Esco et al., 2011; Sventesson et al., 2008), due to
its speed and precision therefore, until the development of a practical 4-C model for
assessing athletes, DXA is an adequate reference method (Stewart and Hannon,
2000).

37. 27
Chapter 6 – CONCLUSION
6.1 Summary and Future work
Based on the available literature, it was hypothesized that Athlete mode would display
better agreement than Normal mode for estimating the body composition of inter-
county GAA players compared to DXA as the criterion method. Although both settings
provided acceptable relative agreement with DXA, Normal mode was found to be more
accurate for all measures, showing excellent absolute agreement with DXA for BFM
and BF%. This indicates that Normal mode may be used interchangeably with DXA for
group comparisons of body composition, however, the wide limits of agreement
suggest that results of individual body composition assessments should be analysed
with caution.
The results of this study may have practical implications to practitioners within the GAA
(Strength and Conditioning Coaches, dieticians/sports nutritionists). As DXA is
expensive and inconvenient for use in field settings, BIA Normal mode may serve as a
practical alternative for measuring body composition of groups. This could save inter-
county GAA teams time and money, while also allowing body composition to be
assessed frequently throughout the season in order to evaluate the effects of training
and nutritional interventions. As the current study did not assess the validity of BIA for
assessing body composition over a period of time, future research could assess the
suitability of BIA to measure changes in body composition over the course of the
training year. As hydration levels in athletes can fluctuate more than non-athletes, and
no generalised equation with a valid estimation of TBW exists for use on athletic
populations, future work should also focus on creating athlete specific BIA equations
from multi-compartment models that can accurately assess TBW.

38. 28
References
 Ackland, T. R., Lohman, T. G., Sundgot-Borgen, J., Maughan, R. J., Meyer, N.
L., Stewart, A. D. and Muller, W. (2012) 'Current status of body composition
assessment in sport: review and position statement on behalf of the ad hoc
research working group on body composition health and performance, under
the auspices of the IOC Medical Commission'.
 Bilsborough, J. C., Greenway, K., Opar, D., Livingstone, S., Cordy, J. and
Coutts, A. J. (2014) 'The accuracy and precision of DXA for assessing body
composition in team sport athletes', J Sports Sci, 32(19), 1821-1828.
 Bolanowski, M. and Nilsson, B. E. (2001) 'Assessment of human body
composition using dual-energy x-ray absorptiometry and bioelectrical
impedance analysis', Medical Science Monitor, 7(5), 1029-1033.
 Buehring, B., Krueger, D., Libber, J., Heiderscheit, B., Sanfilippo, J., Johnson,
B., Haller, I. and Binkley, N. (2014) 'Dual-energy X-ray absorptiometry
measured regional body composition least significant change: effect of region of
interest and gender in athletes', Journal of Clinical Densitometry, 17(1), 121-
128.
 Carling, C. and Orhant, E. (2010) 'Variation in Body Composition in Professional
Soccer Players: Interseasonal and Intraseasonal Changes and the Effects of
Exposure Time and Player Position', Journal of Strength & Conditioning
Research, 24(5), 1332-1339.
 Casajús, J. A. (2001) 'Seasonal variation in fitness variables in professional
soccer players', J Sports Med Phys Fitness, (41), 463-9.
 Collins, S. M., Silberlicht, M., Perzinski, C., Smith, S. P., & Davidson, P. W.
(2014) ‘The Relationship Between Body Composition and Preseason
Performance Tests of Collegiate Male Lacrosse Players’ Journal of Strength &
Conditioning Research, 28(9), 2673-2679.
 Cullen, B. D., Cregg, C. J., Kelly, D. T., Hughes, S. M., Daly, P. G. and Moyna,
N. M. (2013) 'Fitness Profiling of Elite Level Adolescent Gaelic Football
Players', Journal of Strength & Conditioning Research, 27(8), 2096-2103.
 De Lorenzo, A., Bertini, I., Iacopino, L., Pagliato, E., Testolin, C. and Testolin,
G. (2000) 'Body composition measurement in highly trained male athletes. A
comparison of three methods', J Sports Med Phys Fitness, 40(2), 178-83..
 Duthie, G., Pyne, D., Hopkins, W., Livingstone, S. and Hooper, S. (2006)

39. 29
'Anthropometry profiles of elite rugby players: quantifying changes in lean
mass', Br J Sports Med, 40(3), 202-207.
 Esco, M. R., Olson, M. S., Williford, H. N., Lizana, S. N. and Russell, A. R.
(2011) 'The accuracy of hand-to-hand bioelectrical impedance analysis in
predicting body composition in college-age female athletes', Journal Of
Strength And Conditioning Research / National Strength & Conditioning
Association, 25(4), 1040-1045.
 Esco, M. R., Snarr, R. L., Leatherwood, M. D., Chamberlain, N., Redding, M.,
Flatt, A. A., Moon, J. R. and Williford, H. N. (2014) 'Comparison of total and
segmental body composition using DXA and multi-frequency bioimpedance in
collegiate female athletes', Journal of Strength & Conditioning Research.
 Fornetti, W. C., Pivarnik, J. M., Foley, J. M. and Fiechtner, J. J. (1999)
'Reliability and validity of body composition measures in female athletes',
Journal of Applied Physiology, 87(3), 1114-1122.
 Frisard, M. I., Greenway, F. L. and DeLany, J. P. (2005) 'Comparison of
Methods to Assess Body Composition Changes during a Period of Weight
Loss', Obesity Research, 13(5), 845-854.
 Harley, J. A., Hind, K. and O'Hara, J. P. (2011) 'Three-Compartment Body
Composition Changes in elite Rugby League Players During a Super League
Season, Measured by Dual-Energy X-ray Absorptiometry', Journal of Strength &
Conditioning Research, 25(4), 1024-1029.
 Hogstrom, G. M., Pietila, T., Nordstrom, P. and Nordstrom, A. (2012) 'Body
Composition and Performance: Influence of Sport and Gender Among
Adolescents', Journal of Strength & Conditioning Research, 26(7), 1799-1804.
 Kohrt, W. M. (1998) 'Preliminary evidence that DEXA provides an accurate
assessment of body composition', Journal of Applied Physiology, 84(1), 372-
377.
 Kyle, U. G., Bosaeus, I., De Lorenzo, A. D., Deurenberg, P., Elia, M., Gómez, J.
M., Heitmann, B. L., Kent-Smith, L., Melchior, J.-C., Pirlich, M., Scharfetter, H.,
Schols, A. M. W. J. and Pichard, C. (2004) 'Bioelectrical impedance analysis—
part I: review of principles and methods', Clinical Nutrition, 23(5), 1226-1243.
 Leahy, S., O’Neill, C., Sohun, R. and Jakeman, P. (2012) 'A comparison of dual
energy X-ray absorptiometry and bioelectrical impedance analysis to measure
total and segmental body composition in healthy young adults', European
Journal of Applied Physiology, 112(2), 589-595.
 Malina, R. M. and Geithner, C. A. (2011) 'Body Composition of Young Athletes',

40. 30
American Journal of Lifestyle Medicine, 5(3), 262-278.
 Malina, R.M., (2007) ‘Body composition in athletes: assessment and estimated
fatness’. Clin Sports Med, 26(1): p. 37-68.
 Martin Bland, J. and Altman, D. (1986) 'Statistical methods for assessing
agreement between two methods of clinical measurement', The lancet,
327(8476), 307-310.
 Matthie, J. R. (2008) 'Bioimpedance measurements of human body
composition: critical analysis and outlook', Expert Review of Medical Devices,
(5), 239-61.
 Mattila, V. M., Tallroth, K. A. J., Marttinen, M., & Pihlajamäki, H. (2007)
‘Physical fitness and performance. Body composition by DEXA and its
association with physical fitness in 140 conscripts’, Medicine and science in
sports and exercise, 39(12), 2242-2247.
 McIntyre, M. C. (2005) 'A comparison of the physiological profiles of elite Gaelic
footballers, hurlers, and soccer players', Br J Sports Med, 39(7), 437-439.
 Moon, J. R. (2013) 'Body composition in athletes and sports nutrition: an
examination of the bioimpedance analysis technique', Eur J Clin Nutr, 67 Suppl
1, S54-9.
 Oppliger, R. A., Nielsen, D. H., Shetler, A. C., Crowley, E. T., & Albright, J. P.
(1992) ‘Body composition of collegiate football players: bioelectrical impedance
and skinfolds compared to hydrostatic weighing’, Journal of Orthopaedic &
Sports Physical Therapy, 15(4), 187-192.
 Ostojic, S. M. (2003) 'Seasonal alterations in body composition and sprint
performance of elite soccer players', Journal of Exercise Physiology, 6(3), 11-
14.
 Pallarés, J. G., López-Gullón, J. M., Torres-Bonete, M. D. and Izquierdo, M.
(2012) 'Physical fitness factors to predict female Olympic wrestling performance
and sex differences', Journal of Strength & Conditioning Research, 26(3), 794-
80
 Pateyjohns, I. R., Brinkworth, G. D., Buckley, J. D., Noakes, M. and Clifton, P.
M. (2006) 'Comparison of three bioelectrical impedance methods with DXA in
overweight and obese men', Obesity, 14(11), 2064-2070.
 Pichard, C., Kyle, U. G., Gremion, G., Gerbase, M. and Slosman, D. O. (1997)
'Body composition by x-ray absorptiometry and bioelectrical impedance in
female runners', Medicine & Science in Sports & Exercise, 29(11), 1527-1534.
 Pietrobelli, A., Formica, C., Wang, Z. and Heymsfield, S. B. (1996) 'Dual-energy

41. 31
X-ray absorptiometry body composition model: review of physical concepts',
American Journal of Physiology-Endocrinology and Metabolism, 271(6), E941-
E951.
 Potteiger, J. A., Smith, D. L., Maier, M. L. and Foster, T. S. (2010) 'Relationship
between body composition, leg strength, anaerobic power, and on-ice skating
performance in division I men's hockey athletes', Journal of Strength &
Conditioning Research, 24(7), 1755-1762.
 Prior, B. M., Cureton, K. J., Modlesky, C. M., Evans, E. M., Sloniger, M. A.,
Saunders, M. and Lewis, R. D. (1997) 'In vivo validation of whole body
composition estimates from dual-energy X-ray absorptiometry', Journal of
Applied Physiology, 83(2), 623-630.
 Reeves, S. and Collins, K. (2003) 'The nutritional and anthropometric status of
Gaelic football players', Int J Sport Nutr Exerc Metab, 13(4), 539-48.
 Reilly, T. and Collins, K. (2008) 'Science and the Gaelic sports: Gaelic football
and hurling', European Journal of Sport Science, 8(5), 231-240.
 Reilly, T. and Doran, D. (2001) 'Science and Gaelic football: A review', J Sports
Sci, 19(3), 181-193.
 Reilly, T. and Keane, S. (2013) ‘SEASONAL VARIATIONS IN THE FITNESS
OF ELITE GAELIC FOOTBALLERS', Science and football IV, 86.
 Rodriguez, N. R., DiMarco, N. M., & Langley, S. (2009). Nutrition and athletic
performance. Medicine and science in sports and exercise, 41(3), 709-731.
 Santos, D. A., Silva, A. M., Matias, C. N., Fields, D. A., Heymsfield, S. B. and
Sardinha, L. B. (2010) 'Accuracy of DXA in estimating body composition
changes in elite athletes using a four compartment model as the reference
method', Nutr Metab (Lond), 7(22), 7075-7.
 Saunders, M. J., Blevins, J. E. and Broeder, C. E. (1998) 'Effects of hydration
changes on bioelectrical impedance in endurance trained individuals', Medicine
and science in sports and exercise, 30(6), 885-892.
 Scharfetter, H., Schlager, T., Stollberger, R., Felsberger, R., Hutten, H. and
Hinghofer-Szalkay, H. (2001) 'Assessing abdominal fatness with local
bioimpedance analysis: basics and experimental findings', Int J Obes Relat
Metab Disord, 25(4), 502-11.
 Segal, K. R. (1996) 'Use of bioelectrical impedance analysis measurements as
an evaluation for participating in sports', The American journal of clinical
nutrition, 64(3), 469S-471S.
 Segal, K., Van Loan, M., Fitzgerald, P., Hodgdon, J. and Van Itallie, T. B.

42. 32
(1988) 'Lean body mass estimation by bioelectrical impedance analysis: a four-
site cross-validation study', The American journal of clinical nutrition, 47(1), 7-
14.
 Shafer, K. J., Siders, W. A., Johnson, L. K. and Lukaski, H. C. (2009) 'Validity of
segmental multiple-frequency bioelectrical impedance analysis to estimate body
composition of adults across a range of body mass indexes', Nutrition, 25(1),
25-32.
 Sillanpää, E., Cheng, S., Häkkinen, K., Finni, T., Walker, S., Pesola, A., &
Sipilä, S. (2014). Body composition in 18‐to 88‐year‐old adults - comparison of
multifrequency bioimpedanceand dual‐energy X‐ray
absorptiometry. Obesity,22(1), 101-1
 Silvestre, R., Kraemer, W. J., West, C., Judelson, D. A., Spiering, B. A.,
Vingren, J. L., Hatfield, D. L., Anderson, J. M. and Maresh, C. M. (2006) 'Body
composition and physical performance during a national collegiate athletic
association division 1 men’s soccer season’, Journal of Strength & Conditioning
Research, 20(4), 962-970.
 Steijaert, M., Deurenberg, P., Van Gaal, L. and De Leeuw, I. (1997) 'The use of
multi-frequency impedance to determine total body water and extracellular
water in obese and lean female individuals', International journal of obesity,
21(10), 930-934.
 Stewart, A. D. and Hannan, W. J. (2000) 'Prediction of fat and fat-free mass in
male athletes using dual X-ray absorptiometry as the reference method', J
Sports Sci, 18(4), 263-74.
 Stewart, A.D., (2010) ‘Kinanthropometry and body composition: a natural home
for three-dimensional photonic scanning’, J Sports Sci, 28(5): p. 455-7.
 Sun, G., French, C. R., Martin, G. R., Younghusband, B., Green, R. C., Xie, Y.-
g., Mathews, M., Barron, J. R., Fitzpatrick, D. G., Gulliver, W. and Zhang, H.
(2005) 'Comparison of multifrequency bioelectrical impedance analysis with
dual-energy X-ray absorptiometry for assessment of percentage body fat in a
large, healthy population', The American journal of clinical nutrition, 81(1), 74-
78.
 Svantesson, U., Zander, M., Klingberg, S. and Slinde, F. (2008) 'Body
composition in male elite athletes, comparison of bioelectrical impedance
spectroscopy with dual energy X-ray absorptiometry', Journal of Negative
Results in Biomedicine, 7, 1-1.
 Swartz, A. M., Evans, M. J., King, G. A. and Thompson, D. L. (2002) 'Evaluation

43. 33
of a foot-to-foot bioelectrical impedance analyser in highly active, moderately
active and less active young men', British Journal of Nutrition, 88(02), 205-210.
 Thomson, R., Brinkworth, G. D., Buckley, J. D., Noakes, M. and Clifton, P. M.
(2007) 'Good agreement between bioelectrical impedance and dual-energy X-
ray absorptiometry for estimating changes in body composition during weight
loss in overweight young women', Clinical Nutrition, 26(6), 771-777.
 Toombs, R. J., Ducher, G., Shepherd, J. A. and De Souza, M. J. (2012) 'The
impact of recent technological advances on the trueness and precision of DXA
to assess body composition', Obesity (Silver Spring), 20(1), 30-9.
 Toomey, C. M., Cremona, A., Hughes, K., Norton, C. and Jakeman, P. (2015)
'A Review of Body Composition Measurement in the Assessment of Health',
Topics in Clinical Nutrition, 30(1), 16-32.
 Wattanapenpaiboon, N., Lukito, W., Strauss, B., Hsu-Hage, B., Wahlqvist, M.
and Stroud, D. (1998) 'Agreement of skinfold measurement and bioelectrical
impedance analysis (BIA) methods with dual energy X-ray absorptiometry
(DEXA) in estimating total body fat in Anglo-Celtic Australians', International
journal of obesity, 22(9), 854-860.
 Withers, R., Smith, D., Chatterton, B., Schultz, C. and Gaffney, R. (1992) 'A
comparison of four methods of estimating the body composition of male
endurance athletes', Eur J Clin Nutr, 46(11), 773-784.
 Yannakoulia, M., Keramopoulos, A., Tsakalakos, N. and Matalas, A. L (2000)
'Body composition in dancers: the bioelectrical impedance method', Medicine &
Science in Sports & Exercise, 32(1), 228.

44. A1
Appendices
Appendix A1
Subject Consent Form

45. A2
Appendix A2
Dual Energy X-ray Absorptiometry
Figure A1 Lunar iDXA scanner (GE Healthcare, Chalfont St Giles, Bucks., UK)
Figure A2 Fundamental principle of DXA

46. A3
Appendix A3
Bioelectrical Impedance Analysis
Figure A3 Tanita MC-180MA Body composition Analyser (Tanita UK Ltd.)

47. A4
0.4
1.0
-0.2
-0.90
-0.40
0.10
0.60
1.10
1.60
3.00 3.20 3.40 3.60 3.80 4.00 4.20 4.40 4.60
BMCdifferencebetweenmethods
(DXA-BIANormalkg)
BMC (mean of methods) (kg)
BMC Bland Altman DXA vs BIA Normal (b)
Appendix A4
BMC Bland Altman plots DXA vs BIA Athlete and Normal modes
Figure A4 a & b Bland Altman plots of Bone Mineral Content (kg) with mean difference
(dotted lines) and 95% limits of agreement (dots) comparing “athlete” and “normal”
modes to DXA
0.3
0.9
-0.3
-0.90
-0.40
0.10
0.60
1.10
1.60
3.00 3.20 3.40 3.60 3.80 4.00 4.20 4.40 4.60
BMCdifferencebetweenmetods
(DXA-BIAAthlete(kg)
BMC (mean of methods) (kg)
BMC Bland Altman DXA vs BIA Athlete (a)

48. A5
Appendix A5
Athlete mode vs. Normal mode Bland Altman plots
Figure A5 Bland Altman plots of body fat mass (kg) with mean difference (dotted lines)
and 95% limits of agreement (dots) (Athlete vs. Normal)
Figure A6 Bland Altman plots of lean tissue mass (kg) with mean difference (dotted
lines) and 95% limits of agreement (dots) (Athlete vs. Normal)
-3.1
-2.0
-4.1
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
4.0 9.0 14.0 19.0 24.0 29.0 34.0
BFMdifferencebetweenmethods
(Athlete-Normal)
BFM (mean ofmethods) (kg)
BFM Bland Altman (Athlete vs. Normal)
2.9
3.9
1.9
0.0
1.0
2.0
3.0
4.0
5.0
57.0 62.0 67.0 72.0 77.0 82.0
LTMdifferencebetweenmethods
(Athlete-Normal)
LTM (mean of methods) (kg)
LTM Bland Altman (Athlete vs. Normal)

49. A5
Appendix A6
Figure A7 Bland Altman plots of fat free mass (kg) with mean difference (dotted lines)
and 95% limits of agreement (dots) (Athlete vs. Normal)
Figure A8 Bland Altman plots of body fat percentage with mean difference (dotted
lines) and 95% limits of agreement (dots) (Athlete vs. Normal)
3.1
4.1
2.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
60.0 65.0 70.0 75.0 80.0 85.0
FFMdifferencebetweenmethods
(Athlete-Normal)
FFM (mean of methods) (kg)
FFM Bland Altman (Athlete vs. Normal)
-6.0%
-5.0%
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
4.0% 9.0% 14.0% 19.0% 24.0% 29.0%
BF%differencebetweenmethods
(Athlete-Normal)
BF% (mean of methods)
BF% Bland Altman (Athlete vs. Normal)