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What makes girls participate in sport? An analysis
of biological correlates of sport participation.
Elizabeth Theresa Vandenborn
University of Windsor
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What makes girls participate in sport? An analysis of biological correlates of sport
participation.
by
Elizabeth Vandenborn
A Thesis
Submitted to the Faculty of Graduate Studies
through the Department of Kinesiology
in Partial Fulfillment of the Requirements for
the Degree of Master of Human Kinetics
at the University of Windsor
Windsor, Ontario, Canada
2017
© 2017 Elizabeth Vandenborn
What makes girls participate in sport? An analysis of biological correlates of sport
participation.
by
Elizabeth Vandenborn
APPROVED BY:
______________________________________________
C.J. Miller
Department of Psychology
______________________________________________
K.A. Kenno
Department of Kinesiology
______________________________________________
K.J. Milne, Advisor
Department of Kinesiology
May 24th 2017
DECLARATION OF ORIGINALITY
I hereby certify that I am the sole author of this thesis and that no part of this
thesis has been published or submitted for publication.
I certify that, to the best of my knowledge, my thesis does not infringe upon
anyone’s copyright nor violate any proprietary rights and that any ideas, techniques,
quotations, or any other material from the work of other people included in my thesis,
published or otherwise, are fully acknowledged in accordance with the standard
referencing practices. Furthermore, to the extent that I have included copyrighted
material that surpasses the bounds of fair dealing within the meaning of the Canada
Copyright Act, I certify that I have obtained a written permission from the copyright
owner(s) to include such material(s) in my thesis and have included copies of such
copyright clearances to my appendix.
I declare that this is a true copy of my thesis, including any final revisions, as
approved by my thesis committee and the Graduate Studies office, and that this thesis has
not been submitted for a higher degree to any other University or Institution.
iii
ABSTRACT
Introduction: Females are currently participating in sport at a lower rate than males. It
has been determined that girls who participate in sport gain many advantages (i.e. better
bone health, greater cardio-respiratory fitness, a better quality of life). Therefore, it’s
important to determine why some females choose to continue sport participation, while
others do not. Objective: To determine if 2DR and salivary testosterone (sT) are
correlates of sport participation. Methods: A cross-sectional analysis of resting and
indirect prenatal androgen concentrations (i.e. second to fourth digit ratios) were obtained
from a sample of 18-30y females. Participant demographic (via questionnaire),
anthropometric, behavioural (via questionnaire), and retrospective sport participation (via
questionnaire) information were collected on one occasion and saliva was collected on
two occasions. Results: 2DR ratio (r = -0.650, p = 0.538) was not significantly correlated
with total sport participation, nor was sT (r = 0.094, p = 0.387). Secondary analysis
revealed significant correlations between sport participation and max hand grip (r=.406, p
= 0.000), sport competitiveness (Sport Orientation Questionnaire) (r = 0.475, p = 0.000)
and Sport Aggression (Scale of Children’s Action Tendencies in Sport) (r = 0.240, p =
0.021). Conclusion: It does not appear that androgens (whether prenatally or current)
have an impact on female sport participation. Given that females participate in sport at
lower rates than males, and that sport provides multiple social and health advantages,
continuing to determine what factors influence sport participation is necessary.
iv
TABLE OF CONTENTS
DECLARATION OF ORIGINALITY.......................................................................................... iii
ABSTRACT ................................................................................................................................. iv
LIST OF FIGURES ..................................................................................................................... vii
LIST OF TABLES ........................................................................................................................ ix
ABBREVIATIONS ....................................................................................................................... x
Chapter One ................................................................................................................................... 1
Introduction ................................................................................................................................ 1
Steroids ...................................................................................................................................... 7
Sex Steroids ........................................................................................................................... 7
The HPG Axis .......................................................................................................................... 11
Androgens ................................................................................................................................ 17
Formation ............................................................................................................................. 17
Circulation ........................................................................................................................... 18
Role of Testosterone in the Body ......................................................................................... 19
Alternative Sources of Sex Steroids ......................................................................................... 27
Female Sport Participation ....................................................................................................... 29
Potential Biological Correlates of Participation ....................................................................... 32
Testosterone and Skeletal Muscle ........................................................................................ 32
Testosterone and Cardiorespiratory Fitness .......................................................................... 34
Alternative Influences on Sport Participation........................................................................... 37
The Problem of Sex in Sport .................................................................................................... 39
Pilot Data ................................................................................................................................. 42
Chapter Two ................................................................................................................................ 44
Introduction .............................................................................................................................. 44
Design ...................................................................................................................................... 47
Methods ................................................................................................................................... 48
Definition of Sport and Sport Participation .......................................................................... 48
Sport Participation Score ...................................................................................................... 48
2D:4D Collection ................................................................................................................. 52
Salivary Testosterone (sT) ................................................................................................... 57
Physical Testing ................................................................................................................... 58
v
Godin Leisure Time Exercise Questionnaire ........................................................................ 59
The Scale of Children’s Action Tendencies in Sport ............................................................ 59
The Buss-Perry Aggression Questionnaire ........................................................................... 60
The Sport Orientation Questionnaire .................................................................................... 61
Statistical Analyses .................................................................................................................. 61
Participants........................................................................................................................... 65
Primary Analyses ................................................................................................................. 67
Secondary Analysis .............................................................................................................. 71
Discussion ................................................................................................................................ 77
Limitations ............................................................................................................................... 85
Conclusion ............................................................................................................................... 87
Future Directions ..................................................................................................................... 88
References.................................................................................................................................... 89
Appendix A ................................................................................................................................ 103
Appendix B ................................................................................................................................ 104
Appendix C ................................................................................................................................ 105
Appendix D ................................................................................................................................ 106
Appendix E ................................................................................................................................ 108
Appendix F ................................................................................................................................ 110
Appendix G ................................................................................................................................ 111
Appendix H ................................................................................................................................ 112
Appendix I ................................................................................................................................. 113
Appendix J ................................................................................................................................. 117
Appendix K ................................................................................................................................ 119
Appendix L ................................................................................................................................ 120
Appendix M ............................................................................................................................... 121
Appendix N ................................................................................................................................ 123
Vita Auctoris .............................................................................................................................. 126
vi
LIST OF FIGURES
Figure 1: Depiction of the three realms associated with sport particiaption. Each realm
contains examples which are representative of associated factos. This figure is not meant
to be exhaustive, but merely a representation of the cohesive nature of factors which are
invovled in the decision of sport participation. ................................................................... 5
Figure 2: Process of sex steroid synthesis in the body, initiating at cholesterol. The
hormones highlighted by the boxes (progesterone, testosterone, estradiol/estrogen) are the
primary sex hormones. Testosterone, the main hormone under consideration, was
discussed in this literature review (adapted from (Bernstein, 2015)). ................................ 9
Figure 3: Visual representation of an ovarian follicle (left), with an enlarged version of
the theca and granulosa cells (right), along with the associated hormones secreted from
each, as adapted from (Journal of Mid-Life Health)......................................................... 14
Figure 4: Depiction of the female hormone fluctuation across the menstrual cycle, as
adapted from (Womack)(The woman’s health program, 2010). The luteal phase of the
menstrual cycle is characterized by large amounts of progesterone and estrogen, released
from the corpus luteum, which inhibit LH and FSH release. The late luteal stage is when
LH and FSH begin to rise due to the degradation of the corpus luteum, concurrent with a
decrease in circulating estrogen and progesterone concentrations. The decline in estrogen
and progesterone begin the initiation of a new follicle. The new follicle subsequently
begins to grow, releases estrogen which exerts an inhibitory effect on LH and FSH
release (i.e. early follicular phase). During the pre-ovulatory stage (i.e. late follicular
phase), there is a spike in LH and FSH, likely caused by a switch from estrogen-induced
inhibition to stimulation, that is necessary for ovulation. Circulating testosterone
concentrations in females are more consistent across the menstrual cycle, but tend to
follow that of estrogen given that the stimulus for estrogen production results in steroidal
synthesis of which androgens occur upstream (Gupta & Chia, 2013). ............................. 16
Figure 5: Sport participation rates for 2005 and 2010 in Canada in A, males and B,
females. Black box is indicative of the largest change between groups for females, as
adapted from (Canadian Heritage Sport Participation Research Paper, 2010). ................ 31
Figure 6: Age versus average sport participation. Sport participation, expressed as
arbitrary units, for each year is the sample mean of tSPi of the number of sports played in
each year, number of months playing sports in each year, highest sport level attained in
each year, and the weekly frequency of sport participation in hours for each participant.
Points represent mean scores across that age ± SD, N=92. A one-way repeated measures
ANOVA revealed a significant main effect for age. Post hoc analysis revealed several
differences between ages, however, for clarity, significance is only shown at 13y which
was the only age observed to significantly differ from all other ages ( , p<0.05). ......... 51
Figure 7: Representative hand scan image. Second digit (2D) and fourth digit (4D)
finger lengths were determined using the line and measure function on the Image J
vii
program. Land marks for each finger consisted of the distal, center apex of each finger
(upper white lines). BC – basal crease. ............................................................................. 54
Figure 8: Scatterplot of adolescent sport participation (TSP 9to18) and 2DR. AU = arbitrary
units. Line of best fit and r value are displayed (p>0.05). ................................................ 69
Figure 9: Scatterplot of salivary testosterone and total sport participation (TSP 9to18). AU =
arbitrary units. Line of best fit and r value are displayed (p>0.05). ................................. 70
viii
LIST OF TABLES
Table 1: Intra-rater reliability for 2DR measurements. .................................................... 56
Table 2: Anthropometric and demographic data of participants ...................................... 66
Table 3: Pearson Correlation between TSP (4 dimensions), 2DR, and salivary
testosterone (sT) ................................................................................................................ 68
Table 4: Alternative Biological Correlates of TSP9to18, TSP early and TSP late.............. 72
Table 5: Alternative Behavioural Correlates of TSP9to18, TSP early and TSP late .......... 73
Table 6: Model Equation 1 for TSP9to18 ............................................................................ 75
Table 7: Model Equation 1 for TSP Early ........................................................................... 75
Table 8: Model Equation 1 for TSP Late ............................................................................ 75
Table 9: Model Equation 2 for TSP 9to18 ........................................................................... 76
Table 10: Model Equation 2 for TSP early.......................................................................... 76
Table 11: Model Equation 2 for TSP late ........................................................................... 76
Table 12: Progesterone Reference Ranges ..................................................................... 110
Table 13: Pre-Menopausal Progesterone Reference Ranges .......................................... 110
Table 14: Estradiol Concentrations ................................................................................. 111
Table 15: Correlation between 4 components of Composite Sport Participation Score. 120
ix
ABBREVIATIONS
AR – Androgen Receptor
DHEA - Dehydroepiandrosterone
DHEA-S - Dehydroepiandrosterone sulfate
DHT – Dihydrotestosterone
DNA – Deoxyribonucleic Acid
E – Epinephrine
ER – Estrogen Receptor
FSH - Follicle Stimulating Hormone
GPR30 – G-protein Coupled Receptor 30
HPG – Hypothalamus-Pituitary-Gonadal
LH - Luteinizing Hormone
MeApd – Medial Nucleus of the Amygdala
NE – Norepinephrine
NO – Nitric Oxide
pT – Prenatal Testosterone
RNA – Ribonucleic acid
ROS – Reactive Oxygen Species
SHBG – Sex Hormone Binding Globulin
StAR – Steroidogenic Acute Regulatory Protein
sT – Salivary Testosterone
TSP – Total Sport Participation
3β-HSD – 3Beta Hydroxysteroid Dehydrogenase
17β-HSD – 17Beta Hydroxysteroid Dehydrogenase
x
Chapter One
Introduction
The birth of modern sport was likely initiated by Pierre de Coubertin, the man
responsible for the modern Olympic Games, held in Athens Greece in 1896. However,
sport as we know it today is very different than in the late 1800’s. During the first modern
games, women were not allowed to be competitors because de Coubertin thought that
including women would be, “impractical, uninteresting, unaesthetic and incorrect,”
(Edwards, 2012). The majority of the 241 participants at the Games of the I Olympiad
were men. Today, it is easy to see that much has changed in the Olympics, and sport in
general. There are more people than ever, from more diverse populations of both sexes,
participating in sport today. For example, immigrants in Canada who arrived after 1990
participate in sport at approximately the same rate (29%) as Canadian-born citizens
(27%) (Canadian Heritage Sport Participation Research Paper, 2010). At the 2012
London Olympics, women competed in every sport, could have represented every
country involved in the games (i.e. no restriction), and there were a greater percentage of
women athletes than any previous Summer Olympics (Donnelley & Donnelley, 2013).
Reports from the 2016 games will likely surpass these milestones. Contrary to de
Coubertin’s view, the participation of women in sport is practical, interesting, athletic,
and important, not only for women’s health, but the health of the entire population. With
respect to the latter, the benefits of female sport participation include, but are not limited
to, the observations that girls and women who are physically active or engage in sport:

exhibit better bone health than those who don’t at all ages (Deal, 2001);
1

have greater cardiorespiratory and cardiovascular fitness across the
lifespan (Yusuf et al., 2004);

report greater quality of life and health in later years (Buman et al., 2010);

are less likely to engage in risky sexual behaviours (e.g. multiple partners,
unprotected sex, unwanted pregnancies) (Lehman & Koerner, 2004);

are less likely to report anxious and depressive episodes (De Moor, Beem,
Stubbe, Boomsma, & De Geus, 2006);

tend to perform better in academics (Fox, Barr-Anderson, NeumarkSztainer, & Wall, 2010).
It is not surprising, then, that greater than 90% of executive women reported in
one survey that they had played organized sports during some level of schooling, over
half at the university varsity level (female executives say participation in sports helps
accelerate leadership and career potential, 2014). Nonetheless, males have and continue
to participate in sport in significantly higher numbers than females. According to the
Canadian Heritage Sport Participation 2010 Report, one-third of Canadian men
participate in sport whereas only one-sixth of Canadian women are currently participating
(Canadian Heritage Sport Participation Research Paper, 2010). This observation holds
true today, even in the face of the multitude of physiological, psychological, and social
benefits of female sport participation.
Understanding sport participation involves examining the motivations and barriers
to the problem and this can take multiple approaches that culminate in addressing (or
attempting to address) barriers in order to increase participation. However, while
resultant programs show success at allowing more children to participate in sport
2
(Dishman & Buckworth, 1996), these programs are not 100% effective and the effects on
lifetime physical activity are relatively unknown. For example, a meta-analysis of afterschool programs focused on reducing the body mass index (BMI) of children and
adolescents, found an overall effect size of only 0.068 (Vasques et al., 2014). Moreover,
over the past decade, with significant government backed initiatives to increase physical
activity, the percentage of Canadian men and women who engage in leisure time physical
activity that meets recommended guidelines has remained relatively consistent
(approximately half the population)(Canadian Heritage Sport Participation Research
Paper, 2010). As well, there is discrimination in leisure time physical activity (LTPA)
between athletes and non-athletes, with athletes having higher LTPA scores on average
(Taylor et al., 1978). Consequently, even with the creation of these programs, there are
still people, particularly women and girls, who do not undertake adequate physical
activity. In fact, there is some evidence to suggest that there is a genetic basis for both
sport participation and LTPA (Maia, Thomis, & Beunen, 2002). Both intuitively and
observationally, leisure-time physical activity in young women is associated with sport
participation (Mota & Esculcas, 2002, Mota, Santos, & Ribeiro, 2008). Trying to address
barriers is a valid strategy, however, since structured physical activity is a more important
component of a young girls physical activity than boys (Mota & Esculcas, 2002), it is
also important to understand who are the females who choose to stay in sport. Knowledge
of the success stories could help with strategies that encourage prolonged female sport
participation.
Human behaviour is a complex subject and an action such as sport participation
has many influences that change across the lifespan. Consequently, it is difficult to
3
imagine that a single factor influences this behaviour. As outlined in figure 1, biological,
psychological and sociological factors likely impact sport participation together.
4
Figure 1: Depiction of the three realms associated with sport particiaption. Each realm
contains examples which are representative of associated factos. This figure is not meant
to be exhaustive, but merely a representation of the cohesive nature of factors which are
invovled in the decision of sport participation.
5
It is important to note that biological, psychological, and sociological
determinants of behaviour are fields of study that possess their own inherent complexity.
It is beyond the scope of this thesis to address themes that require entire University
faculties to teach. However, as will become apparent later, it is hypothesized that, from
a biological perspective, the major androgen, testosterone, may be responsible for female
success in sport, and consequently drive young women to participate. For example, it is
well known that testosterone has a ubiquitous effect on the body that is favourable to
increased lean body mass, muscular strength and power, and many traits such as
aggression, which would make an individual prone to success in athletic endeavours.
The classical theory of operant conditioning states that behaviours that are reinforced
tend to be repeated (Skinner, 1953) and it appears that modifying behaviours, including
reinforcement, is most effective (more effective than environmental and cognitivebehaviour modification) in increasing physical activity participation (Dishman &
Buckworth, 1996). As such, greater success in sport, simply as a result of biological
advantages and the consequent rewards, accolades, encouragement, social gain, etc. may
reinforce participation. From an athlete perspective, this is exemplified by the premier
league football player, Benoit Assou-Ekotto. He stated that although he did not ‘hate’
playing football (soccer), it was not his passion. Instead he viewed football simply as a
job, however one that he continued because it afforded him significant material and social
gains (David Hytner, 2010). There are likely many successful athletes who would state
the same.
6
Steroids
A steroid is a lipophilic, low-molecular weight compound that is derived from
cholesterol (McVeigh, 2013). There are many different steroids within the human body
that can be classified by site of production (i.e. gonadal or adrenal steroids), biological
function (i.e. glucocorticoids, mineralocorticoids, sex steroids), molecular action (i.e. an
estrogen receptor agonist), and/or biochemical effects. The three main sites of production
of steroids are the adrenal glands (produce androgens and corticosteroids), the ovaries
(secrete estrogens and progestins) and the testes (secrete androgens) (Hu, Zhang, Shen, &
Azhar, 2010).
The formation of steroids begins from the conversion of acetate to cholesterol that
can occur in all steroid-producing tissues (Hu et al., 2010). Further, cholesterol can be
derived from circulating low density and high density lipoproteins, hydrolyzed
cholesterol esters or interiorized from the plasma membrane (Ruiz-Cortes, 2012).
Steroidogenic acute regulatory protein (StAR) is responsible for the transfer of
cholesterol into the inner mitochondrial membrane of steroid producing cells (Woods,
Schwartz, Baskin, & Seeley, 2000). The transfer of cholesterol from the outer to the inner
mitochondrial membrane via StAR is the rate limiting step in steroid formation (Stocco,
2001). Sex steroids, important to this literature review, are discussed in further detail in
the following section.
Sex Steroids
There are three main classifications of sex steroids: estrogens, progestins and
androgens (figure 2). Each family of hormones serve a specific function within the body
though their actions are typically ubiquitous. In addition to the role of estrogen in the
7
development of primary and secondary sex characteristics, estrogen also plays a role in
energy homeostasis (Qiu et al., 2006), metabolism (Clegg, 2012), prevention of bone loss
(Michael, Härkönen, Väänänen, & Hentunen, 2005), and vasoprotection (Tostes, Nigro,
Fortes, & Carvalho, 2003). Progesterone is referred to as the pregnancy hormone
(Spencer & Bazer, 2002), although it also plays a role in the menstrual cycle (Jabbour,
Kelly, Fraser, & Critchley, 2006) . Progesterone is also present in males, although this
role is less clear (Oettel & Mukhopadhyay, 2004). Reference ranges for estrogen and
progesterone, in males and females, can be found in Appendices F and G. Androgens are
responsible for male differentiation of the gonads (Sitteri & Wilson, 1974) and the male
secondary sex characteristics (Hu et al., 2010). Androgen function will be described in
more detail below.
8
Figure 2: Process of sex steroid synthesis in the body, initiating at cholesterol. The
hormones highlighted by the boxes (progesterone, testosterone, estradiol/estrogen) are the
primary sex hormones. Testosterone, the main hormone under consideration, was
discussed in this literature review (adapted from (Bernstein, 2015)).
9
Mechanism of Action of Sex Steroids
Typically, steroids have two types of actions on target cells, genomic and nongenomic.
Genomic Actions
Genomic actions of sex steroids cause long term effects within target cells. Due to
the lipophilic nature of sex steroids, they have the ability to cross cell membranes (Oren,
Fleishman, Kessel, & Ben-Tal, 2004). Once inside the target cell, steroids modulate
transcription through interactions with receptors in the nucleus or the cytoplasm (Lutz et
al., 2003). Steroid hormone receptors are typically transcription factors that have the
ability to regulate the expression of target genes (Simoncini & Genazzani, 2003b). As
described by Michels & Hoppe (2008), the binding of a steroid to a steroid receptor
creates a steroid-protein complex that can bind to steroid response elements (i.e.
nucleotide sequences specifically recognized by steroid receptors) that are situated on the
promoter region of target genes. This process regulates gene expression (i.e. the making
of new proteins) in the target cell that ultimately dictates cellular function (Michels &
Hoppe, 2008).
Non-Genomic Actions
Unlike genomic actions, non-genomic actions require the continued presence of
the hormone in order for it to have an effect. It has been well established that sex steroids
have non-genomic actions, although the specific molecular mechanisms are still under
investigation (Simoncini & Genazzani, 2003). Nonetheless, it is known that sex steroids
have the ability to influence membrane spanning ion channels. For example, all sex
steroids can act as vasodilators (Ruehlmann & Mann, 2000), but estrogen particularly,
10
acts as a vasodilator of arterial walls in both sexes, although the increased concentration
of estrogen receptors in females allows the effect to be more potent in women (R. E.
White, 2002).
Simoncini & Genazzani (2003), state that sex steroids also have important nongenomic actions on the brain. For example, estrogen can influence neuron excitability by
decreasing the rapid firing of some neurons. Similarly, administration of the androgen,
dihydrotestosterone (DHT), is able to inhibit the accumulation of cellular cyclicadenosine monophosphate (cAMP), a second messenger involved in various biological
processes including hypothalamic hormone secretion, thereby decreasing the amount of
GnRH released (Simoncini & Genazzani, 2003).
Formation of Active Metabolites
As described by Martini et al., (1990), for certain sex steroids and certain target
tissues, steroids can be metabolized into different types causing different or altered
potency of effects. One example is the conversion of testosterone to 5α-DHT at the
prostrate that allows testosterone to influence prostate growth and function (Martini et al.,
1990).
The HPG Axis
The function of the Hypothalamus-Pituitary-Gonadal (HPG) Axis and the role
that sex hormones (i.e. estrogen, progesterone and testosterone) play in the body has been
well established in several texts and reviews (Miller & Auchus, 2011, Simpson et al.,
2005, Stocco, 2001, Burger, 2002).
11
The HPG axis is initiated at the paraventricular nuclei (PVN) of the hypothalamus
(Hiller & Bartke (1998)). In the PVN, afferent nerves gather to convey stress and
homeostatic signals from the body (Yin & Gore, 2010). Initiation of the HPG hormonal
system occurs when homeostatic feedback is integrated by the hypothalamus causing
gonadotropin releasing hormone (GnRH) to be secreted. GnRH travels through the
hypophyseal portal vessels to the anterior pituitary, causing the release of the
gonadotropins, luteinizing hormone (LH) and follicle stimulating hormone (FSH), into
the systemic circulation in both males and females. LH and FSH travel through the
systemic circulation until they reach the reproductive glands (i.e. the ovaries and testes).
Steroids, specifically the sex steroids progesterone, testosterone and estrogen, are
subsequently synthesized and/or released from the reproductive glands(Hiller-Sturmhofel
& Bartke, 1998).
The HPG axis is self-governing (Hiller-Sturmhofel & Bartke (1998). The end
products of the system (i.e. the sex hormones) typically, except in the case of estrogen at
certain periods of the female menstrual cycle, have an inhibitory effect on upstream
organs of the system (i.e. the pituitary and hypothalamus). For example, a high
concentration of circulating testosterone will reduce or inhibit hypothalamic release of
GnRH, thereby reducing the output of pituitary gonadotropins, and finally testosterone
(Hiller-Sturmhofel & Bartke, 1998) in order to maintain a hormone concentration within
a fairly consistent range.
In females, the feedback loop is more complicated. As reviewed by Gupta & Chia
(2013), the function of the feedback loop changes throughout the menstrual cycle. In
females, LH exerts its effects on the theca cells to produce androgens and progesterone
12
(figure 3). Progesterone, which is synthesized in theca cells in females, has a negative
feedback control on the hypothalamus. The granulosa cells in the ovary release inhibin
and synthesize estradiol primarily from theca cell produced androgens, including
testosterone. Inhibin is responsible for the inhibition of FSH release from the anterior
pituitary (Gupta & Chia, 2013).
13
Figure 3: Visual representation of an ovarian follicle (left), with an enlarged version of
the theca and granulosa cells (right), along with the associated hormones secreted from
each, as adapted from (Journal of Mid-Life Health).
14
Depending on the phase of the menstrual cycle, estrogen and progesterone can
have a positive or negative feedback effect on the HPG axis (Gupta & Chia, 2013).
Menstrual cycle hormone changes repeat over an approximately 28 day cycle (Heitz,
Eisenman, Beck, & Walker, 1999) and are described in more detail in the legend to figure
4.
In males, the feedback loop is simpler. GnRH leads to LH and FSH release that
subsequently cause testosterone to be released from the Leydig cells in the testes
(Encyclopaedia Britannica, Smith & Walker, 2014). Testosterone and FSH-mediated
inhibin secretion from the Sertoli cells in the testes exert negative feedback on the
hypothalamus and anterior pituitary (Hiller-Sturmhofel & Bartke, 1998).
15
Figure 4: Depiction of the female hormone fluctuation across the menstrual cycle,
as adapted from (Womack)(The woman’s health program, 2010). The luteal
phase of the menstrual cycle is characterized by large amounts of progesterone
and estrogen, released from the corpus luteum, which inhibit LH and FSH release.
The late luteal stage is when LH and FSH begin to rise due to the degradation of
the corpus luteum, concurrent with a decrease in circulating estrogen and
progesterone concentrations. The decline in estrogen and progesterone begin the
initiation of a new follicle. The new follicle subsequently begins to grow, releases
estrogen which exerts an inhibitory effect on LH and FSH release (i.e. early
follicular phase). During the pre-ovulatory stage (i.e. late follicular phase), there
is a spike in LH and FSH, likely caused by a switch from estrogen-induced
inhibition to stimulation, that is necessary for ovulation. Circulating testosterone
concentrations in females are more consistent across the menstrual cycle, but tend
to follow that of estrogen given that the stimulus for estrogen production results in
steroidal synthesis of which androgens occur upstream (Gupta & Chia, 2013).
16
Androgens
Androgens play a major role in the development of male primary and secondary
sexual characteristics. The primary circulating androgen is testosterone.
Formation
In males, LH is responsible for the production of pregnenolone in the Leydig cells
of the testes (Miller & Auchus, 2011). Pregnenolone is converted to
dehydroepiandrosterone (DHEA) and then, DHEA is converted to testosterone through
the intermediates, androstenediol and androstenedione. This conversion to testosterone is
catalyzed by 17β-hydroxysteroid dehydrogenase (17β-HSD) and 3β-HSD which are
present in high concentrations in the testes. Therefore, the conversation of DHEA to
testosterone occurs very quickly. In target tissues, testosterone is converted to DHT via
the enzyme 5α-reductase. DHT is 2.5-10 times more potent than testosterone, and binds
to androgen receptors (AR) with a 2-3-fold higher affinity (Mozayani & Raymon, 2012),
however, serum DHT concentrations are 1/10th that of testosterone (Hay & Wass, 2008).
The prostrate is the major site of non-testicular production of DHT, therefore DHT
concentrations are several times higher than the concentration of testosterone at this
location (Hay, Wass, & Wiley InterScience, 2008).
In females, there are three forms of testosterone precursors including
dehydroepiandrosterone sulfate (DHEA-S) (produced in the zona reticularis of the
adrenal gland), DHEA (produced in the zona reitcularis of the adrenal gland, ovarian
theca cells, and peripherally from circulating DHEA-S) and androstenedione (produced
from the zona fasciculata of the adrenal gland, the ovarian stroma, and peripherally from
circulating DHEA) (Burger, 2002). Approximately 50% of testosterone production in
17
females is a result of peripheral conversion from androstenendione. The other 50% is
produced in the zona reitcularis and the ovarian stroma. DHT is also produced in females,
but the circulating concentration is very low (Burger, 2002). Consequently, in both males
and females, circulating testosterone concentrations are the most common measure of
androgen availability.
Circulation
Circulating testosterone is mainly (~70%) bound to SHBG (Dent, Fletcher, &
McGuigan, 2012). In males, SHBG is relatively saturated due to the higher concentration
of circulating testosterone, whereas it is significantly less saturated in females (Anderson,
1974). Although estrogen also has the ability to bind to SHBG, testosterone has a
significantly increased affinity (Hammond, 2011). In fact, at one time, SHGB was
referred to as androgen-binding protein. Therefore, if there is a significant change in
SHBG concentrations, there will be a larger difference in free testosterone than estrogen
concentrations. Testosterone can partially bind to albumin (30%), a non-specific plasma
protein hormone carrier, although the affinity for albumin is relatively low (Anderson,
1974).
The androgens not bound to plasma proteins are referred to as “free” or
bioavailable (0.5-3%) (Dent et al., 2012). Free androgens are able to have an effect on
target cells through the mechanisms described above. Consequently, free hormone
concentrations give a good indication of the likelihood of observing the actions of that
hormone. In females, circulating free testosterone concentrations fluctuate during the
menstrual cycle. There is a 30-40% increase mid cycle that coincides with the pre-
18
ovulatory spike in LH (Dent et al., 2012) (see Appendix H - reference ranges for total and
free testosterone concentrations for males and females).
Role of Testosterone in the Body
Secondary Sex Characteristics
Androgens are primarily responsible for the development of secondary sex
characteristics in males (Hu et al., 2010). During puberty, there is a significant spike in
androgens, and it is this spike that leads to physical changes associated with puberty in
males. These include, but are not limited to, the development of reproductive function,
the growth of the genitals (including scrotum appearance, growth in testicular size, and
penis enlargement), the growth of pubic hair, defined body proportions, and the
achievement of the adult fat mass/muscle mass ratio (Huang et al., 2012, Bertelloni, Dati,
& Baroncelli, 2008).
Skeletal Muscle
The major genomic action of testosterone on the musculature is to maintain and
increase muscle mass, which typically leads to an increase in muscle strength. The
increase in muscle hypertrophy occurs through an increase in whole body protein
synthesis concomitant with an increase in myocyte size (Schoenfeld et al., 2016) and
myonuclei number (Hanssen et al., 2013). Differences between satellite cell function
between males and females are innate (Manzano et al., 2011). For example, stem cells in
females exhibit a regenerative potential, although this gender difference only occurs in
the presence of oxidative stress (Deasy et al., 2007). Satellite cells attach to muscle fibers
under the basal lamina and are defined through a unique myogenic differentiation process
19
(Kuang & Rudnicki, 2008). Satellite cells are typically activated in response to exercise
or injuries. Damage causes them to proliferate and differentiate into mature muscle cells
(Kuang & Rudnicki, 2008). In a study completed by Sinha-Hikin, Cornforf, Gaytan, Lee
& Bhasin (2006), a dose dependent response of testosterone administration was found
that correlated with hypertrophy in type I and II skeletal muscle fibers, likely because of
increased satellite cell muscle entry and myonuclear hypertrophy.
Supplemental evidence for the effect of androgens on skeletal muscle is evident in
research on anabolic steroid users. The purpose of anabolic steroid use is usually to
increase muscle size and strength, although this can only occur if accompanied by proper
physical training and diet (do Carmo et al., 2011). Muscle mass increase is associated
with muscle fiber hypertrophy (occurring in both type I and II fibers) and increases in
myonuclei number (Sinha-Hikim et al., 2006). Steroid users also show increases in the
muscle fibers expressing myosin heavy chain isoforms when compared to control groups
(Yu et al., 2014). The presence of myosin heavy chain is essential for specialized muscle
function and myofibril stability (Wells, Edwards, & Bernstein, 1996). Anabolic steroids
in combination with strength training resulted in fiber hypertrophy and fiber hyperplasia
(formation of new fibers), thought to be partially due to the activation of muscle satellite
cells (Yu et al., 2014). Because the doses used by anabolic steroid users and in published
reports are typically supra-physiological, it is unclear whether basal levels of testosterone
work on muscles in a dose dependent manner.
Further, as proposed by Estrada, Espinosa, Muller et al., (2003), androgens could
impact skeletal muscle size by activating a G protein-linked membrane receptor,
increasing intramuscular calcium and consequently, muscle power and potentially
20
hypertorphy.
Activation of this receptor would result in rapid actions independent of
genomic androgen receptors, however, it is important to note that no study has directly
investigated this non-genomic role in humans.
Cardiovascular Physiology
Testosterone can directly influence cardiac hypertrophy. Cardiac myocytes are
muscle cells. This means that they can be affected by testosterone in the same manner as
skeletal muscles. The influence of testosterone on the cardiovascular system can be seen
in the difference in heart size between males and females. Males have hearts that weigh
approximately 280g to 340g and females have hearts weighing 230 to 280g (Gray, 1918).
The difference in heart size is thought to be partially due to the difference in testosterone
concentrations between males and females.
This is supported by studies in which exogenous testosterone is given to cardiac
failure patients. After administration, a significant increase in cardiac output and overall
exercise capacity can be seen (Chung et al., 2007). However, it is unknown whether
supra-physiological doses of testosterone are able to induce cardiac hypertrophy above
normal ranges, although, there are anecdotal incidences of this occurring. In the study
completed by Chung et al., (2007), nandrolone (200mg) and testosterone (200mg) was
administered to 30 male participants (age 18-45 y) weekly over a 4-week duration, but
this did not significantly increase chamber size in either group, including the placebo
group. Although small changes in chamber size were seen, the results were within the
normal limits for the age range (Chung et al., 2007). In other studies, testosterone
concentrations were negatively correlated with common carotid artery intima-media
thickness (Rai & Ramasamy, 2016). More studies on the long term effects of supra21
physiological doses of androgens need to be conducted in order to fully understand the
effect that testosterone has on heart size.
A significant amount of research into the effects of testosterone on cardiac health
has come from users of anabolic steroids. The use of anabolic steroids typically includes
the exogenous supplementation of nandrolone, testosterone, and to a lesser extent other
synthetic androgens. According to Chung et al., (2007), there are anecdotal cases of
cardiomyopathy, myocardial infarction, arrhythmia, hypertension, cardiac failure, stroke,
pulmonary embolism and sudden death which have been seen in anabolic steroid users.
However, other indirect mechanisms of testosterone on cardiac health include a positive
effect of testosterone on symptoms of angina, atherosclerosis, obesity, ventricular
repolarization and intima-media thickness of the coronary artery (Oskui et al., 2013).
Hematology (Blood)
It has been well established that testosterone is important in the regulation of
erythropoiesis (i.e. the production of red blood cells) in both males and females (Beggs et
al., 2014). In the not so distant past, androgens were used as treatment for anemia
associated with chronic diseases such as end stage renal failure (Snyder & Shoskes,
2016). It has been well established that testosterone has the ability to stimulate
erythropoiesis, but the mechanisms by which this occurs are not well understood (Fried &
Gurney, 1968). A study conducted by Guo et al., (2013) identified hepcidin (i.e. the iron
regulating hormone) as the downstream target of androgen receptors. In both males and
females, hepcidin is downregulated, which in turn increases the upregulation of
Ferroportin, thereby increasing iron export from the spleen (Yu et al., 2014). This causes
an increase in iron availability for hemoglobin synthesis, which ultimately leads to an
22
increase in red blood cells. Moreover, androgens can control erythropoiesis through
erythropoietin (EPO), the hormone responsible for erythropoiesis (i.e. red blood cell
production) in the bone marrow (Beggs et al., 2014).
Testosterone and Metabolism
Men with a testosterone deficiency typically show an increased fat mass
associated with insulin resistance. This means that a decrease in testosterone
concentration is usually associated with metabolic syndrome and other metabolic
conditions, like type II diabetes (Kapoor, Malkin, Channer, & Jones, 2005). In a study
completed by Usui et al., (2014), it was hypothesized that the cause of this reduction of
fat mass was through a testosterone mediated elevated energy expenditure. An increase in
mitochondrial biogenesis in skeletal muscle was observed in the male rats, a result the
authors attributed to testosterone.
Testosterone and the Brain
Testosterone can influence the structure and physiology of the brain, which may
impact cognitive function and behaviour. For example, differences in male and female
behaviour are at least partially due to differences in sex hormones throughout
development.
As previously discussed, testosterone is typically produced in the ovaries in
females or in the testes in males. There is also evidence suggesting that testosterone can
be synthesized directly in the brain, potentially de novo from cholesterol, or from other
steroids (progesterone or deoxycorticosterone) which enter through the blood into the
nervous system (Celec, Ostatnikova, & Hodosy, 2015).
23
More specifically, it has been shown that astrocytes have all of the cellular
machinery necessary to make sex hormones from cholesterol (Rossetti, Cambiasso,
Holschbach, & Cabrera, 2016), and there is a possibility that androgens and estrogens
might be controlled near the local blood vessels (Krause, Duckles, & Gonzales, 2011).
Therefore, cerebral blood concentrations of sex steroids might be different than what is
observed in the circulation (Krause, Duckles, & Pelligrino, 2006). It is true that gonadal
hormones also act on other cells within the neurovascular unit (e.g. astrocytes and
neurons, circulating blood elements like platelets and leukocytes), and thus androgens
can also have an indirect effect on the cerebrovascular function through these actions.
Gonadal sex steroids (testosterone for males and estrogen for females) peak in the
middle trimester during pregnancy, and then drop in the perinatal period and peak a
second time within the first 3 months of life (Knickmeyer, Woolson, Hamer, Konneker,
& Gilmore, 2011). What is defined as the “pre and postnatal stages” are thought of as
periods of permanent “organizational” sexual differentiation of the brain (Sinclair,
Purves-Tyson, Allen, & Weickert, 2014). Specifically, androgen exposure before birth
has the ability to alter the number of neurons in specific brain areas, as well as their
connectivity and neurotransmitter content (Roselli & Klosterman, 1998).When a child
reaches adolescence, there is another significant increase in sex hormones which have an
altering effect on the child through the form of pubertal physical and behavioural changes
(Sinclair et al., 2014). Therefore, testosterone has organizational, as well as activational,
effects on the brain (Falter, Arroyo, & Davis, 2006). Even during adulthood, evidence
suggests that sex steroids have the ability to remodel synapses, change the dendritic
24
structure and alter neurogenesis, discrediting the notion that the adult brain is unchanging
(McEwen, 1994).
The sexually dimorphic characteristics of the brain may not necessarily be due to
the immediate effects of testosterone on the brain. Testosterone, can be converted to
estradiol by the aromatase enzyme. It is thought that it is actually the action of estradiol
on the brain that is responsible for the sexually dimorphic characteristics between males
and females (Purve et al., 2001). Therefore, if a fetus in vitro is exposed to high prenatal
testosterone (pT), there is more opportunity for the conversion of testosterone to
estradiol. This results in an increased opportunity for estradiol to modify the developing
brain in males as opposed to females. However, it is important to note that results are
still equivocal in terms of whether this difference manifests itself into a difference in
behaviour (Manlove, Guillermo, & Gray, 2008).
One of the hypothesized effects of pT exposure is on brain lateralization. For
example, males are significantly more lateralized for language and are often more lefthanded than females. It is hypothesized that the high concentrations of testosterone
prenatally, slow the development of the left hemisphere (Lust et al., 2010). Another
theory developed by Witelson & Nowakowski (1991) (i.e. the Callosal hypothesis)
postulates that pT increases axonal pruning in the corpus callosum, which would lead to a
reduction in communication between the two brain hemispheres (Witelson &
Nowakowski, 1991). Hines & Shipley (1984) put forward the sexual differentiation
theory, which hypothesized that pT causes a more “masculine” pattern of lateralization
(i.e. an increased degree of lateralization) (Lust et al., 2010). All of the above theories
have been challenged and none are currently accepted unconditionally, but they all were
25
created to try and explain the difference in language lateralization present between males
and females.
Some of the most extensively studied dimorphic areas of the brain are the preoptic
nucleus, the ventromedial nucleus and the suprachiasmatic nucleus, which are all
divisions of the amygdala and the stria terminalis (Herrera Gutiérrez, Vergara Onofre,
Rosado-García, & Rosales Torres, 2005). The ventromedial nucleus of the hypothalamus
regulates sexual behaviour in females. The density of this nucleus is much greater in
males than it is in females. Further, the synaptic connections in females are known to
vary throughout the menstrual cycle, due to changes in estrogen concentrations
(Hagemann et al., 2011). Studies have indicated that the ventromedial nucleus showed an
increase in the nucleus, nucleoli, and cell soma cellular components (a result of increased
cell metabolism and protein synthesis) in response to estrogen administration,
demonstrating the susceptibility of this brain structure to estradiol as opposed to
testosterone (Herrera Gutiérrez et al., 2005).
The suprachiasmatic nucleus (SCN) controls the circadian (including hormonal,
physiological and behavioural) rhythms of the body. It has been hypothesized that the
SCN may be also related to sexual behaviour (Herrera Gutiérrez et al., 2005). Rodent
studies have demonstrated that the prenatal administration of anti-estrogens increases
mounting behaviours (Sodersten, Hansen, & Srebro, 1981). This change in behaviour
strengthens the hypothesis that the SCN is under the control of sex steroids.
The preoptic area of the hypothalamus is responsible in part for control of sexual
behaviour, the release of gonadotropins, and mechanisms involved in ovulation. In the
26
adult rat brain, there is no difference in the size of the preoptic area of the hypothalamus
between sexes, but there is a significant difference in the growth rate. At birth, the
volume of the preoptic area is approximately double in females. During the first 10 days
of life, there is an increase in the volume of the area in
males, whereas there is no
postnatal growth in females. This difference in postnatal growth has been attributed to the
pT exposure from the testes in males (Herrera Gutiérrez et al., 2005).
The amygdala has been identified as one area of the brain responsible for
emotions associated with aggressiveness, fear, and anxiety (Herrera Gutiérrez et al.,
2005). Animal studies have looked at the sexually dimorphic properties of the medial
nucleus of the amygdala (MeApd) in rats, which plays a large role in social behaviour.
The MeApd volume is greater in males than in females rats (Cooke & Woolley, 2005).
This difference is thought to be partly due to circulating testosterone concentrations in
male and female rats. There have been studies completed on young rats who exhibit
rough and tumble play, which show that testosterone in the MeApd has a masculinizing
effect on play whereas when the amygdala is bilaterally destructed, the rat takes on a
more feminine social behaviours (i.e. care of infants is usually performed by females rats)
(Meaney, Dodge, & Beatty, 1981). This means that the hormones exhibited in the adult
rat could be influencing the MeApd, which has already be differentiated by prenatal sex
hormones (Cooke & Woolley, 2005).
Alternative Sources of Sex Steroids
As previously discussed, one of the main alternative sites for sex steroid synthesis
is the adrenal glands. In males, DHEA and androstenedione are produced in the zona
reticulate and the zona fasciculate of the adrenal cortex. In females, DHEA is produced
27
from the zona reitcularis, and androstenedione is produced in the zona fasciculata.
Moreover, in females, about 50% of the circulating testosterone is produced in the zona
reticularis (Burger, 2002).
Another important production site of sex steroids is adipose tissue (Kershaw &
Flier, 2000) (Musi & Guardado-Mendoza, 2014). Specifically, the stromal cells of the
adipose tissue are involved in the conversion, activation, and inactivation of sex steroids.
The two most highly expressed enzymes in adipose tissue responsible for steroid
conversion are cytochrome p450- dependent aromatase and 17 β-hydroxysteroid
dehydrogenase (17βHSD). Specifically, 17βHSD mediates the conversion of
androstenedione to testosterone and estrogen to estradiol. The ratio of 17βHSD to
aromatase is correlated in a positive manner with central obesity, which implicates
increased local androgen production in visceral adipose tissue (Kershaw & Flier, 2000)
(Musi & Guardado-Mendoza, 2014).
Another less prominent site of sex hormone conversion is the bone. The
conversion of testosterone to estrogen occurs using the enzyme p450 aromatase. The
conversion to estrogen is responsible for aiding in the maturation of the epiphysis of the
bone, as well as implications in increasing bone density and reducing rates of bone
turnover (Sinnesael, Boonen, Claessens, Gielen, & Vanderschueren, 2011). Therefore,
the conversion of testosterone to estrogen occurring in the bone has important local
effects on this production site of sex hormones.
The active biosynthesis of testosterone can also take place in the brain (Celec et
al., 2015). Specifically, astrocytes have all the necessary enzymes for synthesis of
28
progesterone, testosterone and estrogen from cholesterol (Diana N. Krause et al., 2006).
As many (if not all) areas of the brain exhibit estrogen and androgen receptors, sex
steroids are able to have an important influence on behaviour as previously discussed.
Female Sport Participation
In 2013, the Canadian government released a Sport Participation Research Paper
based on the census 2012 data. In accordance with the previous reports, sport
participation in Canada is continuing to decline, although the rate of decline does seem to
be slowing. Fewer Canadians ages 15 and younger are participating in sport than before.
In 2005, only 28% of the population regularly participated in sport, and in the 2010
report, this number has dropped to 26% of the population.
In accordance with the results from the Sport Participation 2010 report, Canada
has recently been given the grade of D- in overall physical activity on the 2014
ParticiPACTION Report Card on Physical Activity for Children and Youth (Active
Healthy Kids Canada, 2015). The report card is based on 11 different indicators which
are grouped into three categories including Strategies and Investments (i.e. Government
and non-government), Settings and Sources of Influence (i.e. Family and peers, school,
community and environment) and Behaviours that Contribute to Overall Physical
Activity (i.e. organized sport and physical activity participation, sedentary behaviours,
active transportation, active play). The grade given is reflective of the balance between
age groups which are meeting the physical activity standards (3 to 4 year olds), and those
who are doing poorly (age 5-11 year olds and 12-17 year olds).
29
In both the Sport Participation 2010 report, and the 2014 ParticiPACTION Report
Card, gender differences in physical activity levels are clear. The ParticiPACTION report
highlights the difference in overall physical activity through total steps taken per day. In
2011-2014, Canadian boys took an average of 11604 steps, whereas Canadian girls took
only 10443 steps, emphasizing the difference in activity levels between the two groups.
As stated in the Sport Participation 2010 report, “In Canada, men are more likely to
participate in sport than women, although the participation rates have declined for both
genders over the years.” However, between 2005 and 2010, the participation rate for
males has remained stable at around 35%. Noteworthy, is the significant drop in
participation rate for women aged 15-19 y with a 13% drop, and for females aged 20-24 y
with a 14% drop in sport participation. Visual depictions highlighting male and female
sport participation in Canada can be viewed in figure 5.
30
A
B
Figure 5: Sport participation rates for 2005 and 2010 in Canada in A, males and B,
females. Black box is indicative of the largest change between groups for females, as
adapted from (Canadian Heritage Sport Participation Research Paper, 2010).
31
A specific recommendation from the ParticiPACTION report card stated the need
to support children and youth in adding bouts of physical activity throughout their daily
lives. In order to have the most impact on sport participation, it is important to target the
gender and age range that is at most risk of dropout. As highlighted by the black box in
figure 8 above, the largest drop off rate for sport participation between males and females
occurs between the age ranges of 15-19 and 20-24 y in the female group. Therefore, in
order to have the most impact on sport participation rates, this would be the ideal age
range to target.
Potential Biological Correlates of Participation
If a child is able to experience success in sport, they are more likely to continue
participation. The specific ways in which testosterone can influence sport are discussed in
the following sections.
Testosterone and Skeletal Muscle
As previously discussed, it is well known that testosterone has the ability to act as
an anabolic agent in order to increase muscle size through genomic actions (i.e. an
increase in myocyte size and number). There is a relationship which exists between the
size of the muscle and the ability for it to produce power (3). Therefore, the larger the
muscle, the more capable it is of moving weight (resistance). This combined with the fact
that testosterone has the ability to reduce body fat in both men and women when
combined with exercise training (Bescos et al., 2009), demonstrates that testosterone not
only has the ability to positively impact power output in explosive activities like jumping
(Markovic, Mirkov, Nedeljkovic, & Jaric, 2014) and sprinting, but it can also have a
significant impact on absolute strength (Frontera, Meredith, O’Reilly, Knuttgen, &
32
Evans, 1988) . Coincidentally, it is not hard to imagine that there has been a direct
correlation found between testosterone concentrations and muscle strength as well as
muscle power. For example, in a study which looked at basal testosterone levels in
relationship to performance within male soccer players (n=32, age = 24.6 ± 4.1 y),
testosterone was positively correlated with counter-movement jump height and running
speed, but negatively associated with results of the Cooper’s test (i.e. a test in which
participants are asked to run as far as they can on a track, within a 12 minute time frame).
Therefore, athletes that were better at explosive and sprinting performance had higher
levels of basal testosterone (Bosco, Tihanyi, & Viru, 1996). This suggested a relationship
between testosterone and the development of fast twitch muscle fibers in athletes.
Further, in a study on testosterone and squat performance on elite male athletes (age =
23.6 ± 1.6 y), participants clearly separated themselves into two groups, good squatters
and average squatters, based on the maximum weight that they could squat. The good
squatters revealed a strong predictive relationship of pre-testing salivary free testosterone
concentration, whereas this relationship was much weaker in the average squatters (B
Crewther, Cook, Gaviglio, Kilduff, & Drawer, 2012). It would be informative to explore
the possibility that a cross over point in strength exists, at which point the correlations
become strong or if this is some sort of threshold effect.
Nonetheless, as noted by Bosco, Tihanyi & Viru (1995), “Well-trained athletes do
not constitute a homogenous group by their hormonal profile. Therefore, it is justified to
ask whether individual differences in basal hormone levels in the blood are related to an
athlete’s performance capacities” (Bosco et al., 1996). Further, the majority of studies
which correlate basal testosterone concentrations and performance measures were done
33
on males. This is a major gap in the literature. Further research needs to be conducted in
order to explore if a similar relationship exists in females where basal testosterone
concentrations are considerably lower.
For the female athlete specifically, there is a fluctuation of testosterone across the
menstrual cycle. Testosterone peaks around ovulation in a female and the peak lasts for
about 2-3 days. There is currently no evidence in the literature that proves this fluctuation
in testosterone is beneficial to female athletes during the peak in concentrations.
However, Dent et al. (2012), suggest that the use of oral contraceptives (which eliminate
this testosterone peak) might reduce the optimal hormone physiology for elite female
performance. Therefore, Dent et al., (2012) suggested that athletes whose events require
strength or power, might benefit from using a contraceptive device that is not hormonally
based.
Testosterone and Cardiorespiratory Fitness
The anabolic properties of androgens impact cardiac muscle such that at all ages,
male hearts are typically larger than female hearts even when myocardial mass is
normalized to body surface area. Specifically, in young adult elite athletes (18-39 y), the
difference in left ventricular mass/body surface area approaches a 20g difference between
males and females (Prakken et al., 2010). A larger heart with corresponding increase in
left ventricular size is beneficial for endurance type exercise because of the greater stroke
volume supplied with each contraction of the heart. This observation alone could help to
explain the sex-based advantage in maximal oxygen consumption (VO2 max) between
males and females.
34
Further, testosterone has the ability to stimulate the formation of red blood cells
(Mooradian, Morley, & Korenman, 1987). It is well known that mean hemoglobin
content of the blood is greater in males (although this may not be an androgen mediated
effect alone and is likely more a function of menstrual cycle related blood loss) (Harbour
& Miller, 2001). Both an increase in stroke volume and oxygen carrying capacity of the
blood would result in increased maximal oxygen consumption (VO2max). For example,
elite female distance runners can reach VO2 max values in the range of 70+ ml/kg/min
(Pate, Sparling, Wilson, Cureton, & Miller, 1987) whereas males can reach values as high
as 80+ ml/kg/min (Pollock, 1977). Consequently, females competing with genetic and
gonadally intact males, particularly through the pubertal years, may be placed at a
disadvantage in those sports requiring high levels of muscular strength, power and/or
cardiorespiratory endurance.
To look specifically at females with increased basal testosterone concentrations,
Bermon, Vilain, Fenichel & Ritzen (2015), compared females with PCOS to a body-mass
index matched control group. The endurance athletes with PCOS demonstrated the most
anabolic body composition, highest VO2max and highest performance values (Bermon et
al., 2015), thought to be at least partially attributed to the difference in resting
testosterone concentrations.
An important study which looked at the influence of basal testosterone
concentrations in female athletes was performed by Rickenlund et al., (2003). In this
study, 39 female athletes who regularly participated in endurance sports (minimum of
70km of running a week) were included. Blood samples were taken after an overnight
fast to determine serum T and SHBG, and in a subgroup of the athletes, high androgen
35
levels were found in accordance with low levels of SHBG and an increased LH:FSH ratio
(Rickenlund et al., 2003). The suspected cause of the characteristics of this subgroup was
mild hyperandrogenism. The findings of this study were unusual because
hyperandrogenism is most often seen in female athletes where strength is a benefit. This
was the first observation of a role of testosterone in endurance athletes who rely more
heavily on aerobic metabolism. Although the mechanisms are not clear, this study would
suggest that hyperandrogenism might have a positive impact on elite female endurance
athletes.
Given that testosterone, both pre and post-natal, has been associated with more
aggressive type behaviours, it is possible that testosterone induced aggression could be a
trait that self-selects certain individuals toward sport. Aggression inserts itself into the
sporting world when the goal of the competition is to win, which typically occurs in a
travel or competitive league, but can be found in less typically competitive leagues (i.e.
house-leagues). However, although the use of aggression in sport could include the intent
to injure, that is not typical of its use. In this sense, athletic aggression is viewed in a
positive light, and seen as a positive attribute when used to describe a player or team. As
stated by Makarowski (2013), many sport journalists and coaches believe that aggression
in sport is a positive behavior, and contributes positively to the achievement of success.
Particularly at young ages, the most aggressive children will stand out as they run after a
ball or skate after a puck, and as such, these children are more likely to be selected for
higher level teams that bring more practice and competition experience. Consequently,
it is possible that testosterone would exert some influence on continued and higher level
sport participation.
36
Alternative Influences on Sport Participation
As previously discussed, the decision to participate in sport is complex.
Biological correlates of sport participation as a possible influence of behavior, is an area
of research which has not been studied extensively. Conversely, sociological correlates of
sport participation are have been the primary areas of focus. Specifically for adolescents,
familial influence has a great impact on whether a child participates in sport. The
Canadian Sport Heritage report, released by the government of Canada in 2010, outlines
several sociological determinants of sport participation.
For example, the 2010 report states that the higher the level of education a person
has, the more likely they are to participate in sport (Canadian Heritage Sport Participation
Research Paper, 2010). Approximately one-third of University graduates, 25% of people
who have a post-secondary diploma and/or some university, and 22% of people who have
a college, trade or high school diploma are currently participating in sport. This is
supported through data presented by the Canadian Fitness and Lifestyle Research
Institute in 2006, which concurs that Canadians who have received a higher level of
education (greater than secondary school) are more likely than those who only have a
secondary school diploma, to be active (Lifestyle, 2006).
For youth and adolescents, if the parent currently participates or participated in
sport during their youth, it is more likely that the child is going to participate in sport.
More specifically, the influence of the mother’s sport participation is greater than the
father’s (Ruseski, Humphreys, Hallmann, & Breuer, 2011). Parents are important to the
positive values, attitudes and behaviours toward sport, and consequently, the child is
more likely to behave in a similar way (Côté, 1999).
37
It is well known that children who participate in youth sport generally come from
mid-high income families (Canadian Heritage Sport Participation Research Paper, 2010).
Socioeconomic status (SES) has a positive correlation with sport participation, meaning
that the more income a family brings in, the more likely their children are to participate in
sport. An important statistic to consider is the relationship between childhood sport
participation and participation into adulthood. People who participate in sport during their
youth are more likely to continue to participate into adulthood. During childhood is when
SES has the biggest impact on sport participation because it is generally under the
discretion of the parent whether the child participates in sport or not. White & McTeer
(2012) examined the relationship between differing levels of SES and sport participation
at different stages of development in children. In this study, it was found that SES was a
significant predictor of sport participation for children aged 6-9 y, but there was no effect
for children aged 10-15 y. One possible explanation is that sport opportunities might
increase within the school setting as children get older. For example, organized sports
teams are initiated around grade 6 when a child is around 12 y, thereby providing more
opportunities for children of all social status’ to participate in sport. The findings of this
paper, suggest that the relationship between SES and participation in sport might be more
complicated than previously thought because the influence of SES on sport participation
varies depending on stage of life.
Similarly, the use of competitiveness in sport has also fostered positive
connotations in terms of success. Competitiveness in sport can be defined as, “the desire
to enter, and strive for success in, sport situations or the desire to win in interpersonal
situations,” (Gill & Deeter, 1988). As was noted by Gill & Dzewaltowski (1988), it
38
should make sense that an individual’s innate competitiveness would be related to sport
achievement and success (Gill & Dzewaltowski, 1988). Gill & Dzewaltowski (1998),
also commented on the fact that when a person is highly oriented towards achievement,
they are more likely to approach achievement situations, try hard, strive for success and
persist in the face of failure. All of these attributes of a competitive person would be
beneficial in leading an athlete towards sporting success (Gill & Dzewaltowski, 1988).
Both aggression (briefly described earlier) and competiveness have the ability to
aid in the achievement of success in sport. The achievement of success could bring about
an external motivation for continued sport participation and therefore drive their internal
motivation to further compete. Joy is found through success, therefore motivation could
be found through admiration of their abilities from others. Further, joy brought from
winning combined with internal and external motivations may will athletes to train and
play harder, creating a cycle of success from the inherent traits of aggression and
competitiveness.
The Problem of Sex in Sport
In the development of males and females, there is no requirement that phenotypic
sex occur in a one-to-one ratio with chromosomal sex. In an extreme example, presence
of the transcription factor, SRY (sex determining region of the Y-chromosome), in an XX
(two X chromosome) individual, could result in that chromosomal female individual
undergoing normal male phenotypic development and sex assignment (Rajender et al.,
2006). In a more common occurrence, genetic females with excess androgen production
either by the gonads or by the adrenal glands, could develop under the influence of excess
(to a typical female) circulating androgen concentrations, thereby expressing phenotypes
39
that range from typical females to ambiguous or male-like development (Azziz et al.,
2004). Although the International Olympic Committee (IOC) has provided regulations
on the processes involved with reporting, investigating, and adjudicating cases of female
hyperandrogenism (IOC Regulations on Female Hyperandrogenism, 2012), and both the
IOC and International Association of Athletics Federations (IAAF) have provided
policies on sex reassignment through their Hyperandrogenism Regulations; Explanatory
Notes, released in 2009, there is still much grey area when considering natural androgen
levels in athletic women. This is even more of a problem because of the issue with
exogenous androgen administration to many elite athletes.
This poses problems with respect to the categorical placement of intersex athletes
in competition. Previously, the policy used by IAAF, the IOC and the World Anti-Doping
Association in gender verification of females focuses on the phenotypic expression of
external genitalia in the competing athlete (IOC et al., 2012). The underlying premise in
these policies is that males are at a competitive advantage in many athletic endeavours,
primarily because pubertal and post pubertal males have higher circulating concentrations
of gonadal androgens (i.e. testosterone, and its more active metabolite,
dihydrotestosterone (DHT), in particular) than females.
Consequently, while the benefits of exogenous, supraphysiological doses of
androgens may be advantageous to the competing female athlete, it is unknown whether
naturally elevated testosterone is advantageous to athletic females.
Currently, there is data to suggest that athletic females have high androgen
concentrations. In a study completed by Hagmar, Berglund, Brismar & Linden
40
Hirschberg (2009), 90 of Sweden’s elite female athletes (mean age of 24.0 ± 3.6 y)
representing 27 different sports and who were considered to be representative of the
Swedish participation in the upcoming Summer and Winter Olympic Games were
included in this study. These women underwent various tests including measurements of
body composition, a gynecologic examination and peripheral venous blood samples in
order to determine hormonal status. It was determined that 27% of these athletes had
menstrual disturbances, and that the most common mechanism underlying this
disturbance was the hyperandrogenic condition, Poly-Cystic Ovary Syndrome (PCOS).
The instance of PCOS in this athletic population was 37% as compared to the instance in
a normative population of 20%. Therefore, it was suggested specifically that “PCOS may
reflect an anabolic state that could be advantageous for physical performance and may
thereby play a key role in the achievement of a high competitive standard by female
athletes,” (Hagmar et al., 2009).
In a study by Coste et al., (2011), younger females (n=36) with a mean age of
15.4 y were investigated. These adolescent girls were all a part of a competitive
swimming program, and similarly underwent testing to determine menstrual status and
blood sampling to determine their hormonal status. A total of 72% of the swimmers
tested had testosterone concentrations great than 0.5ng/mL, characterizing them as
hyperandrogenic, where 50% of the females showed signs of hyperandrogenism and 8 of
the 36 females showed signs of PCOS as determined through a pelvic ultrasonography.
Again, it was suggested that the PCO-like syndrome in these elite-adolescent female
athletes was perhaps not a coincidence and specifically that, “A predisposition to
41
hyperandrogenism might orient girls towards sports, such as swimming, where strength is
a performance criterion,” (Coste et al., 2011).
Bescos et al., (2009) found that female athletes exhibit lower 2D:4D (2DR) ratios
(index to ring finger ratios), a known correlate with exposure to high prenatal
testosterone, than control populations. Therefore, there is the potential for both prenatal
and basal testosterone concentrations to have an effect on female athletic participation
through physiological and psychological advantages.
Pilot Data
Pilot data extends from a study conducted in the Physical Activity and
Cardiovascular Research (PACR) lab examining resting salivary and urine testosterone
concentrations of female varsity athletes compared with self-identified non-athletes.
Although there was not a significant difference between the two populations, it was
noteworthy that within the cumulative sample population, there were noticeably higher
salivary testosterone (sT) levels (39 – 148 pg/mL) than reported in an average population
(i.e. 7.7  2.6 pg/mL) in a separate study from another lab (Karkazis, Jordan-Young,
Davis, & Camporesi, 2012). However, the data was more consistent with those reported
by other labs in reference to female athletic populations (65.3  3.2 pg/ml and ~55-100
pg/ml in 2 separate studies of athletic females) (Cook & Beaven, 2013, Crewther et al.,
2012). It was theorized that the findings were the result of the sample population; many
of the “non-athlete” participants scored high on Godin’s leisure time exercise
questionnaire (LTEQ) suggesting that even though these individuals did not play varsity
sport, they were more active than typical females. It is believed that this data, combined
42
with the aforementioned studies, is sufficient to warrant a larger scale study in this
regard.
43
Chapter Two
Introduction
Why do females participate in and continue to participate in sport? Unfortunately,
when it comes to human behaviour, the answer to this question is anything but simple.
Human behaviour has different influences at different times of life, and under different
environmental conditions. Nonetheless, lifetime sport and physical activity participation
provide many physiological and sociological benefits to the participant, and
consequently, understanding this behaviour is important to the health and well-being of
women and girls.
Although often used interchangeably, physical activity and exercise (of which
sport is a component) represent different concepts. The most basic difference is that sport
and exercise is typically structured physical exertion with the intent of improving
physical fitness and most often occurs in the leisure-time of an individual versus the
activities of daily living (sleep, chores, work, etc. as well as leisure time physical activity,
LTPA) that would encompass physical activity more generally (Caspersen, Powell, &
Christenson, 1985). Nonetheless, sport and exercise are generally, what is taught in
physical education programs to adolescents, and the difference between North American
youth sport (with its emphasis on winning, increasing time and financial commitment,
etc.) and unstructured play is a concrete example of this difference. However, individuals
who compete in youth sport are more likely to meet moderate to vigorous physical
activity (MVPA) guidelines as youth and throughout life (Silva et al., 2013).
44
Unfortunately, there is a discrepancy between the LTPA and sport participation
rates of males and females worldwide. As a population, North Americans do not meet the
guidelines of daily recommended MVPA standards in both sexes, but males are more
likely to participate in LTPA, including sport participation, than females (Stephens,
Jacobs, & White, 1985). Moreover, while there is a general decline in physical activity
and sport participation starting in the early teens, the largest drop in participation rates
occur between the mid to late teens for young adult females. This problem is well-known
and multifaceted, and generally, the implementation of programs to overcome barriers
such as a lack of opportunity or deemphasizing “winning” and encouraging “play” are
encouraging, but not 100% effective. Youth sport participation in Canada for both boys
and girls have been fairly consistent over the past decade with 75% of youth between 5
and 17 participating in sport. However, it is important to note that more boys (81%) than
girls (70%) participate in sport.
The focus of this research study is to examine the relationship between sport
participation and biological factors. In particular, it is possible that the circulating
androgen, testosterone, may predispose girls toward sport participation during these
periods. Testosterone is a steroid that may impact various traits that are beneficial in
sport. For example, testosterone is known to increase skeletal muscle mass (Yu et al.,
2014), a major determinant of strength and power, and it is able to increase cardiac size
(Gray, 1918) resulting in a larger stroke volume, manifesting into increases in VO 2max.
Testosterone also regulates erythropoiesis which increases the oxygen carrying capacity
of the blood (Beggs et al., 2014). Testosterone’s effect on energy metabolism relate
directly with fat loss (Usui et al., 2014), which is highly beneficial in most sport settings.
45
Moreover, while equivocal, prenatal (Coyne, Manning, Ringer, & Bailey, 2007) and
circulating testosterone are correlated with competitiveness and aggressiveness (Book,
Starzyk, & Quinsey, 2001), traits that may also predispose individuals to success in sport.
Success in sport may subsequently lead to more physical practice, more extrinsic
motivation (awards, praise, etc. (Vallerand & Losier, 1999)), more intrinsic motivation
(fun, winning, etc. (Vallerand, Deci, & Ryan, 1987)), and a greater belief in physical
abilities (self-efficacy (McAuley, Wraith, & Duncan, 1991)), that may increase success in
sport in a cyclical fashion. In fact, exogenous anabolic androgen administration is one of
the biggest doping concerns for male and female athletic competitions because of the
effectiveness of this hormone (Sjöqvist, Garle, & Rane, 2008).
It is important to note that very few studies have addressed the potential for
biology to be a component of sport participation. It is likely that this has not been
omitted in error, but is more due to the difficulties of performing large scale studies that
include costly biological analyses. Consequently, while it is obvious that biology is
important to athletic success, there is a gap in the literature as to the extent biology plays
in sport participation, while sometimes, it is the role of biology that can have non-obvious
forms. For example, research on the relative age effect has shown that children born
early in their respective competitive age group are more likely to make higher level teams
and play longer in youth (Andronikos, Elumaro, Westbury, & Martindale, 2015). Also,
genetics has been found to explain approximately 30% of the variability in young adult
female LTPA (Maia et al., 2002). Pilot data from our lab has found a positive
relationship between LTPA and salivary testosterone (sT) in young adult females
(Vandenborn and Milne, unpublished results).
46
Combined with the potential sport-
related benefits of testosterone on performance, this forms the basis for the main
hypothesis of the current study:
Hypothesis 1: 2DR ratio, a marker of prenatal testosterone, and salivary
testosterone will correlate with sport participation through adolescence (918 y) as well as current sport participation.
An understanding of whether such a biological relationship exists with sport
participation in females will give researchers a better idea of the type of female who
chooses to continue sport participation into adulthood. As such, a secondary objective of
this study is to examine whether athletic females exhibit higher circulating levels of
testosterone that may subsequently inform policy decisions regarding female athlete
endogenous hormone levels and competition.
Design
To tests the hypotheses above, a cross-sectional analysis of resting and indirect
pre-natal androgen concentrations using second to fourth digit (2DR) ratios of the hand
were obtained from a sample of 18-30y females recruited from the University of Windsor
and the Windsor-Essex community as a convenience sample. Participant demographic
(via questionnaire), anthropometric, behavioural (via questionnaire), and retrospective
sport participation (via questionnaire) information were collected on 1 occasion and
saliva was collected on 2 occasions. All information was collected in the Physical
Activity and Cardiovascular Research Laboratory (PACR Lab) in the Department of
Kinesiology at the University of Windsor.
47
Methods
Definition of Sport and Sport Participation
According to the Sport Participation 2010 research paper produced by the
government of Canada:
Sport involves formal rules, procedures, requires tactics and
strategies, specialized neuromuscular skills, and a high degree of
difficulty and effort. The competitive nature of sport implies the
development of trained coaching personnel. It does not include
activities in which the performance of a motorized vehicle is the
primary determinant of the competitive outcome (Canadian Heritage
Sport Participation Research Paper, 2010).
Active participation was defined as “any individual who engages in sport for the
purpose of competition with others, under a set of rules, or to improve their personal
sporting performance,” (Bloom, Grant, & Watt, 2005). A list of those events which
qualified as a sport under the government of Canada’s guidelines was used in the study
(Appendix A).
Sport Participation Score
In the determination of sport participation, only those sports recognized by the
government of Canada (Appendix A) were included. Participants were asked to
remember as best as they could all of the sports which they had participated in between
the ages of 9 to 18y. Subsequently, participants were asked to start in the current year,
then work backwards from 18y to indicate sport participation at a given age that included
48
the 4 dimensions of number of sports played, highest level of achievement in any sport,
the frequency of participation per week (in hours), and the number of the months of the
year in which the sport season(s) took place (Appendix B).
The strong positive intercorrelations among the 4 dimensions fell in the range of r
= 0.296 to r = 0.631 (Table 17, in Appendix L), indicating that they could be used to
produce a single composite sport participation variable. The composite sport
participation score was calculated using a rectified summation of individual z-scores for
correlated variables of the sport participation questionnaire and is represented in equation
1:
Eq. 1:
cSPi = zNi + zMi + zfi + zHLi
Where cSP is the composite sport participation score for a specific individual (i), z are the
individual z-scores for number of sports played in a given year (N), months of sport
participation in a given year (M), frequency of sport participation per week in a given
year (f), and highest level attained for any sport in a given year (HL).
Using z-scores
resulted in all domains being equally weighted in the calculation of sport participation.
In order to generate a participation score without negative values, each individual’s
composite sport participation score was transformed using equation 2:
Eq. 2:
tSPi = cSPi + |cSPmin|
49
Where tSPi is the transformed sport participation score for individual (i), cSP is the
composite sport participation score for a given individual, and cSPmin is the minimum
sport participation composite score across the sample. Total sport participation (TSP9to18)
in youth was initially determined as the sum of tSPi scores across and inclusive of the
ages 9-18 y (equation 3).
Eq. 3:
18
TSP9to18 = ∑ tSPi(y)
y =9
The TSP score exhibited intercorrelations of r = 0.296 to r = 0.631, p=0.000 (table 17).
The pattern of TSP across the ages of 9y to 18y (figure 6) revealed an inverted ‘U’
shaped curve suggesting a peak of TSP around the ages of 13. This was confirmed by
one-way repeated measures ANOVA with the independent repeated variable of age. At
13y, TSP was greater than at all other ages (figure 6).
50
10.0
*
Sport Participation (AU)
9.0
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
9
10
11
12
13
14
15
16
17
18
Age (y)
Figure 6: Age versus average sport participation. Sport participation, expressed as
arbitrary units, for each year is the sample mean of tSPi of the number of sports played in
each year, number of months playing sports in each year, highest sport level attained in
each year, and the weekly frequency of sport participation in hours for each participant.
Points represent mean scores across that age ± SD, N=92. A one-way repeated measures
ANOVA revealed a significant main effect for age. Post hoc analysis revealed several
differences between ages, however, for clarity, significance is only shown at 13y which
was the only age observed to significantly differ from all other ages (, p<0.05).
51
Consequently, TSP was divided into two separate scores that encompassed sport
participation across 2 equal age ranges: 9y to13y (TSP early: equation 4), and 14y to 18y
(TSPlate: equation 5).
Eq. 4:
13
TSPearly = ∑ tSPi(y)
y =9
Eq. 5:
18
TSPlate = ∑ tSPi(y)
y =14
Given that one individual indicated that she had not participated in sport across
the retrospective timeline, all sport participation scores were truly 0 bounded at the lower
end (i.e. a score of 0 is representative of no sport participation).
2D:4D Collection
The 2D:4D ratio (2DR) was measured as an indicator of pre-natal androgens
(Lutchmaya, Baron-Cohen, Raggatt, Knickmeyer, & Manning, 2004), however the 2DR
ratio does not appear to be predictive of sT in adult females (Crewther, Cook, Kilduff, &
Manning, 2015). The 2DR data was collected using a flatbed scanner (Cannon CanoScan
LiDE 110 Flatbed Scanner), in which the participants were asked to place both of their
hands, with their fingers spread out onto the scanner bed. Subsequently, a black towel
was placed over the hands of the participants to increase the contrast of the final scan.
Prior to collection, participants were asked to remove all rings and jewelry, which might
have been detrimental to the later analysis. Scans were performed in colour at a
52
resolution 2400 dpi and the images were subsequently saved with only their unique
identifiers as filenames.
Analysis of the 2DR ratio was performed by 3 separate raters using the Image J
program (available for download at https://imagej.nih.gov/ij/, developed by Wayne
Rasband at the Research Services Branch of the National Institute of Mental Health),
which offers freeware picture image processing and analysis in a java format. Pictures
were uploaded into the Image J program. Images were subsequently set to an 8-bit image
type (gray scale), and rotated 90 degrees to the left so that the hands were in the upright
position. Second digit and fourth digit finger lengths were determined using the line and
measure function on the Image J program. Land marks for each finger consisted of the
distal, center apex of each finger, and the middle of the basal, proximal crease of the
finger (figure 7).
53
Figure 7: Representative hand scan image. Second digit (2D) and fourth digit (4D)
finger lengths were determined using the line and measure function on the Image J
program. Land marks for each finger consisted of the distal, center apex of each finger
(upper white lines). BC – basal crease.
54
Intra and inter rater reliability were determined and a mean of the 3 raters was used in
statistical analyses.
Intra-rater reliability was assessed with intraclass correlation coefficients. An ICC
two-way mixed effects model with absolute-agreement definition was used. The intrarater reliability test was performed a minimum of 2 weeks after the final analysis on 3
random hands (Table 1).
55
Table 1: Intra-rater reliability for 2DR measurements.
ICC Agreement
95% CI
Examiner A
0.988
0.825 - 1.000
Examiner B
0.930
0.760 - 1.000
Examiner C
0.996
0.883 - 1.000
Note. ICC – Intraclass correlation, CI – Confidence Interval
56
Inter-rater reliability was assessed with intraclass correlation coefficients. An ICC
two-way mixed-effects model with absolute-agreement definition was used (Bescos et al.,
2009) and turned out to be excellent (Average Measures ICC = 0.917, Confidence
Interval 0.878-0.944, p=0.000).
Salivary Testosterone (sT)
Saliva was collected using the SalivaBio Oral Swab SOS from Salimetrics
(product # 5001.02). Saliva samples were taken between 9-12am in order to account for
the circadian fluctuation in androgen concentrations. Participants were instructed not to
brush, floss or have dental work done at least 30 minutes before testing. Further, they
were asked to avoid food for one hour before collection as food particles may skew the
results. Participants were asked to rinse their mouth in order to ensure no food particles
are present. After waiting 10 minutes (to avoid sample dilution) the swab was placed
under the participant’s tongue by the researcher for exactly two minutes. Participants
were sitting in a chair during collection and were instructed not to talk while the swab is
in their mouth. After collection, the Oral Swab was placed in the swab collection tube
from Salimetrics (product # 5001.05) and subsequently centrifuged for 15 minutes at
1000xg. The saliva sample was immediately stored at -70°C until analysis was
performed.
A Testosterone Salivary Immunoassay Kit (ELISA) was used for analysis of the
saliva samples (Salimetrics, product #1-2312). The correlation between serum and sT, as
determined by Salimetrics, is r=0.61.
57
The hormones which show cross reactivity for the ELISA Kit greater than 1% are:
Androstenedione (1.157%), Dihydrotestosterone (36.4%), 11-Hydroxytestosterone
(1.90%) and 19-nortestosterone (21.02%). However, salivary DHT concentrations in
normal, healthy adults, are less than 10pg/mL (Wang, Wakelin, White, & Wood, 1986)
(i.e. well below the concentration of 500ng/ml used to test cross reactivity) and 19nortestosterone is absent in healthy males and females (Reznik, Herrou, Dehennin,
Lemaire, & Leymarie, 1987).
Analysis of sT was conducted according the kit instructions. Briefly, 25L of
each saliva sample were pipetted into antibody coated wells of a 96-well microplate
along with standards and controls. After incubation with a conjugate solution (contains:
testosterone conjugated to Horseradish Peroxidase, preservative) for 1 hour, the plate was
washed and subsequently incubated with 200L of TMB substrate for 30 minutes in the
dark. After stopping the enzyme reaction, the plate was read on a plate reader at 450 nm.
These methods for sT measurement are in accordance with the Testosterone Salivary
Immunoassay kit (ELISA/EIA), and have been performed in our lab previously.
Physical Testing
Subsequent to anthropometric measurements of height and weight, muscular
power was assessed using a hand dynamometer (Cardio Grip IBX H-101, Zona Health).
The hand grip protocol was adapted from the Tufts, Brown and John Hopkins Hand Grip
Strength Protocol. While standing, participants were asked to grip the dynamometer with
their non-dominant hand so that the valley between their thumb and pointer finger was
directly centered over the force gauge, with their other fingers wrapped around the
device. Participants were asked to stand with their hands at their sides, arms not touching
58
their body. Participants were instructed to perform a maximal voluntary contraction for 35 seconds in duration. After a 2 minute rest, this procedure was performed an additional 3
times. An average of all 3 scores was used for the final analysis. The hand dynamometer
was chosen due to the efficiency and practicality of the measure. Further, hand
dynamometer scores have been used previously in order to characterize overall strength
(Bohannon, 2001).
Godin Leisure Time Exercise Questionnaire
The LTEQ is a simple, self-explanatory questionnaire that estimates leisure time
physical activity, and can be viewed in Appendix C. It has been validated and proven
reliable in a number of studies (G Godin, 1997). The LTEQ required that participants
report on how many days in the previous 7 days they had engaged in strenuous physical
activity (e.g. jogging, running, basketball), moderate physical activity (fast walking,
baseball, volleyball) and mild exercise (e.g. yoga, golf, fishing) for more than 15 minutes
of their free time. Weekly frequencies of strenuous, moderate, and light activities were
multiplied by 9, 5, and 3 METs, respectively. Total weekly leisure activity was calculated
in arbitrary units by summing the products of the separate components, the results of
which correlate strongly with overall fitness levels (G. Godin & Shepard, 1985). Total
leisure activity is correlated with maximal oxygen consumption, although the time spent
in vigorous exercise appears to be even more predictive of maximal oxygen consumption
(G Godin & Shephard, 1985).
The Scale of Children’s Action Tendencies in Sport
The Scale of Children’s Action Tendencies in Sport (SCATS) was used to
measure self-reported aggression tendencies in a sport specific context (Appendix D).
59
Although the scale was primarily used for children, and the participants in the study will
be aged 18-30. The SCATS aggression score was divided into physical and non-physical
responses. Scoring of the SCATS involved summing the number of times the aggressive
responses were chosen. On this version of the SCATS, the possible aggression scores
ranged from 0-12. The validity of the SCATS was tested by its relation to the CATS
subscale, which yielded medium to high correlations including Assertion (r=0.53, p
>0.001), Physical, (r=0.74, p< 0.001), and Non-Physical Aggression (r=0.68, p<0.001),
and Submission (r=0.58, p<0.001). The validity of SCATS during its creation was also
tested against a behavioral index provided by teacher ratings. Correlations between the
average teacher behavioral ratings and SCATS subscales were significant for all but the
submission subscale and included Assertion (r=.24, p<0.01), Physical Aggression
(r=0.33, p<0.01), Verbal Aggression (r=0.22, p<0.01), and Submission (r=0.06, p<0.01).
The internal consistency reliability of the SCATS as a whole was 0.85.
The Buss-Perry Aggression Questionnaire
The Buss-Perry Aggression Questionnaire measures aggression by separating it
into four subcategories: physical aggression, verbal aggression, anger and hostility. The
Bus-Perry Aggression Questionnaire can be viewed in Appendix E. It has been validated
and proven reliable in a number of studies (Buss & Perry, 1992).There are 29 total
statements in which each participant was asked to give a rating of 1-5 as a rating of how
either uncharacteristic (1) or characteristic (5) each statement best describes themselves.
Questions 9 and 16, as indicated by an asterisk on the questionnaire, was scored in
reverse. The total of all of the responses are summed and this gives an overall rating of
aggression as a score out of 145 (29 questions x 5 possible marks per question).
60
The Sport Orientation Questionnaire
The sport orientation questionnaire (SOQ) measured three factors in regards to the
competitiveness in sport, which was used to discriminate those who are competitive in a
sport specific setting and those who are not (Appendix I). The SOQ contains 25 items and
uses a 5-point Likert scale in order to measure the participant’s agreement to each item.
The items were divided into three subscales including competitiveness (13 items), win
orientation (6 items) and goal orientation (6 items) which are all separate, but related
factors in the questionnaire. Competitiveness was defined as the desire to enter and strive
for success in sport competition. Goal orientation is an orientation towards personal
standards, regardless of the situation. Lastly, win orientation is the desire to win in a sport
specific situation although it was not related to general individual achievement
orientation. The internal consistency reliability of the subscales are as follows:
competitiveness 0.94, win orientation 0.86 and goal orientation 0.80 (Petroczi, 2007).
Statistical Analyses
All statistical analyses were performed using IBM SPSS Statistics (Version 24).
In all analyses, a p-value of less than 0.05 was set for statistical significance. Prior to
statistical analyses, the raw data was assessed to make sure entry methods were congruent
across all participants. For example, The LTEQ asked participants to indicate the number
of times per week that they participated in Strenuous Physical Activity for bout of 15mins
or more. If the participant entered the number in word form, the entry was transformed to
a numerical entry. For example, the entry “two” would be transformed to 2. Further, if
the participant entered a range into the same question, the average of the range was used
61
for analysis. For example, if the participant entered “4-5” as a response, the value of 4.5
was used.
During the data assessment, incidences of missing data also needed to be
accounted for. For example, one participant did not have a hand scan done in order to
analyze 2D:4D. Due to the missing data, this person was excluded from all analyses
where 2D:4D was included. The number of participants (N) was reported for each
analysis. There were no other issues concerning missing data within the thesis.
The following question was asked during the demographic questionnaire “Are
you currently on any supplements, medications (prescribed or not) that could potentially
alter your testosterone concentrations?” There were 5 individuals who answered YES to
this question. Upon further analysis, none of these participants had testosterone values
which were outside of the mean (32.53, 42.60, 23.75, 93.31 and 79.26 pg/ml,
respectively). Therefore, the decision was made to include these participants in the
analyses which included salivary testosterone.
An Analysis of Variance (ANOVA) was performed in order to determine the
difference between TSP across all age groups (9-18 years old). Prior to performing the
ANOVA, the following assumptions were tested.
1. Assumption 1: The normality of the dependent variable for each age group
was tested using the Shapiro-Wilks test.
2. Assumption 2: The homogeneity of variances was tested using the Levene’s
Test for Homogeneity of Variances
62
3. Assumption 3: The assumption of independence of observations was met
based on the fact that the TSP of one person would have no impact on the TSP
of another person.
Salivary testosterone concentrations were taken on 2 separate occasions,
approximately 2 weeks a part. Of the 92 total participants, there were 15 people who did
not return for a second saliva sample. However, for the 77 participants who did have 2
saliva samples, there was a significant correlation found between the first and second
sample (r=0.630, p=0.000). As such, the decision was made to use the first saliva sample
for the 92 participants in the primary data analyses.
A bivariate Pearson correlation (p<0.005) was computed between the independent
variables (IV), salivary testosterone concentration and 2D:4D ratio (2DR), and the
dependent variable (DV), sport participation in each of its 4 dimensions, TSP 9to18,
TSPearly, TSPlate, and TSPNOW. All variables were continuous and there were no outliers
(defined as 2.5 standard deviations above or below the mean). The assumption of
linearity was checked using a scatter plot of the data.
Subsequent to the initial correlations, Bivariate Pearson correlations were
performed on continuous supplemental data collected, including alternate biological (max
hand grip, body mass index (BMI), age of first menarche, and birth month) and
psychological (SOQ, Buss-Perry AQ, SCATS) IVs with the main outcome variables of
TSP9to18, TSPearly, TSPlate, and TSPNOW. All IVs that were found to be significantly
correlated (p<0.005) with the outcome variables were included in an enter-method
multiple linear regression analysis. Further, the following potential social confounding
63
variables were dummy coded into the following dichotomous (0,1) variables: use of birth
control (none = 0), presence of older brothers (none = 0), ethnicity (Caucasian = 0), and
mother’s highest education level (above high school = 0). Prior to performing linear
regression, the following assumptions were tested:
1. Assumption 1: A linear relationship between the independent and dependent
variables was tested by performing correlations (above) and analyzing scatter
plots.
2. Assumption 2: Normality between residuals in the regression was tested by
reviewing Q-Q plots (Appendix M).
3. Assumption 3: To determine if there is multicollinearity in the data, the
Variance Inflation Factors (VIFs) were analyzed. Any VIFs over 10 were
deemed unsatisfactory for a multiple linear regression. VIFs for the
regressions in the present study ranged from 1.030-1.338.
4. Assumption 4: To test for autocorrelation, the Durbin-Watson test in SPSS
was used. The acceptable range for the Durbin –Watson test is 1.5-2.5, Durbin
– Watson values in the current study ranged from 1.776 – 1.847.
5. Assumption 5: To test for homoscedasticity, scatter plots of the IVs vs. the
DV were inspected to make sure the error terms along the regression line were
equal (Appendix N).
Since a regression allows the possibility for a set of variables to have joint
predictive capabilities even when individually, they may not, and because 2DR and
testosterone formed the main hypotheses of this thesis, in all regression analysis, 2DR
and testosterone were included in the multiple linear regressions. Power analysis was
64
performed post-hoc using SPSS.
Results
Participants
Ninety-two participants (females aged 18-30 y) were recruited from the
University of Windsor and surrounding area following University of Windsor research
ethics board clearance (REB # 33486, Appendix K). Anthropometric and demographic
data of the participants are outlined in Table 2. All participants had, or were in the
completion of a post-secondary education. Only 1 participant indicated that she had not
participated in sport in any year in the retrospective timeline.
65
Table 2: Anthropometric and demographic data of participants
Anthropometric Data
Age
22.06 ± 2.56
2
BMI (kg/m )
24.40 ± 4.56
Height (cm)
168.29 ± 6.93
Weight (kg)
66.94 ± 11.54
Ethnicity (% )
Caucasian
75.56 (70)
Southeast Asian
1.11 (1)
South Asian
3.33 (3)
Latin American/Hispanic
1.11 (1)
Filipino
5.56 (5)
Chinese
1.11 (1)
Black/Caribbean/African-descent
6.67 (6)
Arab/West Asian
2.22 (2)
Other
3.33 (3)
University Major
Human Kinetics
70
Other
22
Oral Contraceptive (OC) Use
Currently Using OC
48
Not Using OC
44
Note: Anthropometric data is presented as means ±SD. Ethnicity is presented as percent
of the sample (number of participants, N=92). University major and contraceptive use
are presented as number of participants.
66
Primary Analyses
Salivary testosterone (sT) and 2DR were analyzed against the 4 dimensions of
sport participation (i.e. TSP9to18, TSPearly, TSPlate and TSPcurrent) using a bivariate Pearson
correlation. None of the correlations were significant. Results of the correlation are in
Table 3. 2DR (figure 8) and sT are shown graphically in plots against TSP9to18 (figure 9).
67
Table 3: Pearson Correlation between TSP (4 dimensions), 2DR, and salivary
testosterone (sT)
TSP
TSP9to18
TSP9to18
Pearson
Correlation
Sig. (2-tailed)
TSP NOW
.660**
.945**
TSP late
.896**
sT
0.032
2DR
-0.065
0.000
0.000
0.000
0.765
0.538
92
92
92
92
91
**
**
0.044
-0.162
0.000
0.000
0.679
0.125
92
92
92
91
**
0.020
-0.047
0.000
0.849
0.659
92
92
91
0.042
-0.079
0.694
0.456
92
91
N
TSP NOW
Pearson
Correlation
Sig. (2-tailed)
.518
N
TSP early
early
Pearson
Correlation
Sig. (2-tailed)
.702
N
TSP late
Pearson
Correlation
Sig. (2-tailed)
N
sT
Pearson
Correlation
Sig. (2-tailed)
-0.102
0.338
N
2DR
.736
91
Pearson
Correlation
Sig. (2-tailed)
N
Note: ** Correlation is significant at the 0.01 level (2-tailed).
68
Figure 8: Scatterplot of adolescent sport participation (TSP9to18) and 2DR. AU = arbitrary
units. Line of best fit and r value are displayed (p>0.05).
69
Figure 9: Scatterplot of salivary testosterone and total sport participation (TSP9to18). AU =
arbitrary units. Line of best fit and r value are displayed (p>0.05).
70
Secondary Analysis
Results of Bivariate Pearson Correlations between TSP9to18, TSPearly, and TSPlate
revealed significant correlations between all three dimensions and maximum hand grip
(HGmax), SOQ total, and SCATS total. For TSP9to18, significant correlations were: HGmax
(r=0.406, p = 0.000), SOQ (r=0.475, p = 0.000) and SCATS (r=0.240, p = 0.021). For
TSPearly, significant correlations were: HGmax (r=0.272, p = 0.009), SOQ (r=0.362, p =
0.000) and SCATS (r=0.200, p = 0.055). For TSPlate, significant correlations were: HGmax
(r=0.518, p = 0.000), SOQ (r=0.545, p = 0.000) and SCATS (r=0.251, p = 0.016). Results
of all correlations are in Tables 4 and 5.
71
Table 4: Alternative Biological Correlates of TSP9to18, TSP early and TSP late
TSP9to18
TSP9to18
Pearson Correlation
Sig. (2-tailed)
N
TSP early
Pearson Correlation
Sig. (2-tailed)
N
TSP late
Pearson Correlation
Sig. (2-tailed)
N
HGmax
Pearson Correlation
Sig. (2-tailed)
N
72
sT
TSP early
.945**
0.000
TSP late
.896**
0.000
HGmax
.406**
0.000
92
92
**
0.030
0.779
BMI
-0.023
0.829
2DR
-0.065
0.538
Birth Month
0.041
0.696
92
92
92
91
92
.702
0.000
**
.272
0.009
0.075
0.477
-0.044
0.677
-0.047
0.659
0.007
0.949
92
92
92
92
91
92
**
-0.038
0.722
0.010
0.923
-0.079
0.456
0.081
0.443
.518
0.000
92
sT
92
92
91
92
-0.093
0.376
.231*
0.027
-0.056
0.601
-0.079
0.452
92
Pearson Correlation
Sig. (2-tailed)
N
BMI
Pearson Correlation
Sig. (2-tailed)
N
2DR
Pearson Correlation
Sig. (2-tailed)
N
Birth Month
Pearson Correlation
Sig. (2-tailed)
N
Note: ** Correlation is significant at the 0.01 level. * Correlation is significant at the 0.05 level.
92
91
92
0.158
0.132
-0.101
0.340
-0.010
0.927
92
91
92
0.049
0.646
0.064
0.544
91
92
0.179
0.089
91
Table 5: Alternative Behavioural Correlates of TSP9to18, TSP early and TSP late
TSP9to18
TSP9to18
Pearson Correlation
Sig. (2-tailed)
TSP early
N
Pearson Correlation
Sig. (2-tailed)
TSP late
N
Pearson Correlation
Sig. (2-tailed)
sT
N
Pearson Correlation
Sig. (2-tailed)
73
2DR
N
Pearson Correlation
0.030
2DR
-0.065
SOQ SUM
.475**
Buss-Perry AQ
SUM
-0.002
SCATS SUM
.240*
0.000
0.779
0.538
0.000
0.981
0.021
92
.702**
92
0.075
91
-0.047
92
.362**
92
0.025
92
0.200
0.000
0.477
0.659
0.000
0.811
0.055
92
92
-0.038
91
-0.079
92
.545**
92
-0.040
92
.251*
0.722
0.456
0.000
0.705
0.016
92
91
-0.101
92
0.007
92
0.047
92
0.046
0.340
0.944
0.656
0.663
91
92
-0.068
92
0.011
92
-0.155
0.519
0.916
0.143
91
91
0.121
91
.318**
0.252
0.002
92
92
.266*
TSP early
.945**
TSP late
.896**
0.000
92
sT
Sig. (2-tailed)
SOQ SUM
N
Pearson Correlation
Sig. (2-tailed)
Buss-Perry AQ
SUM
SCATS SUM
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
** Correlation is significant at the 0.01 level. * Correlation is significant at the 0.05 level.
0.010
92
Regression Analyses
Factors significantly correlated with TSP9to18, TSPearly and TSPlate were included
into a standard enter method multiple linear regression as predictor variables (i.e. HGmax,
SOQ and SCATS). The main biological variables of study (sT and 2DR) were also
included. Potential confounders of testosterone (i.e. ethnicity and age of first menarche)
as well as known sociological confounds (mother’s education level and older brothers)
were included as control factors into the regression. Results of the initial regression
(tables 6, 7 and 8) were as follows: TSP9to18 (F(9,81) = 4.609, p = 0.000), TSPearly
(F(9,81) = 2.482, p = 0.015), and TSPlate (F(9, 81) = 7.192, p = 0.000.). After initial
regression analyses, due to no change in the relationships between 2DR and salivary
testosterone, a backwards multiple linear regression was performed (tables 9, 10 and 11).
Final and best predictor models were as follows for TSP9to18 (F(9,81) = 13.252, p =
0.000), TSPearly (F(9,81) = 7.373, p = 0.000), and TSPlate (F(9,81) = 21.512, p = 0.000).
74
Table 6: Model Equation 1 for TSP9to18
Unstandardized
Standardized
Coefficients
Coefficients
Std.
Model
B
Error
Beta
(Constant)
21.800
87.987
2DR
-0.408
0.886
-0.043
sT
-0.032
0.113
-0.027
HGmax
0.497
0.251
0.206
SCATS SUM
0.305
1.198
0.025
SOQ SUM
0.530
0.150
0.385
Ethnicity
-13.141
5.934
-0.206
Birth Control
1.388
5.020
0.026
Older Brothers
-8.343
5.407
-0.145
Mother's Education
-0.927
7.073
-0.013
Note: Dependent Variable: TSP9to18. F(9,81) = 4.609, p = 0.000.
Table 7: Model Equation 1 for TSP Early
Unstandardized
Standardized
Coefficients
Coefficients
Std.
Model
B
Error
Beta
(Constant)
20.483
59.733
2DR
-0.212
0.601
-0.036
sT
-0.019
0.077
-0.026
HGmax
0.145
0.170
0.097
SCATS SUM
0.245
0.813
0.033
SOQ SUM
0.283
0.102
0.330
Ethnicity
-7.985
4.028
-0.201
Birth Control
0.479
3.408
0.015
Older Brothers
-5.784
3.671
-0.161
Mother's Education
1.698
4.802
0.037
Note: Dependent Variable: TSP Early. F(9,81) = 2.482, p = 0.015.
Collinearity
Statistics
t
0.248
-0.461
-0.282
1.979
0.255
3.544
-2.215
0.276
-1.543
-0.131
Sig.
0.805
0.646
0.779
0.051
0.799
0.001
0.030
0.783
0.127
0.896
VIF
1.056
1.084
1.325
1.201
1.449
1.064
1.107
1.076
1.148
Collinearity
Statistics
t
0.343
-0.353
-0.250
0.853
0.302
2.789
-1.982
0.141
-1.576
0.354
Sig.
0.733
0.725
0.803
0.396
0.764
0.007
0.051
0.889
0.119
0.724
VIF
1.056
1.084
1.325
1.201
1.449
1.064
1.107
1.076
1.148
Table 8: Model Equation 1 for TSP Late
Unstandardized
Standardized
Coefficients
Coefficients
Std.
Model
B
Error
Beta
(Constant)
2.325
36.812
2DR
-0.196
0.370
-0.045
sT
-0.013
0.047
-0.023
HGmax
0.351
0.105
0.319
SCATS SUM
0.060
0.501
0.011
SOQ SUM
0.247
0.063
0.393
Ethnicity
-5.157
2.483
-0.178
Birth Control
0.911
2.100
0.038
Older Brothers
-2.559
2.262
-0.097
Mother's Education
-2.623
2.959
-0.079
Note: Dependent Variable: TSP Late. F(9, 81) = 7.192, p = 0.000.
75
Collinearity
Statistics
t
0.063
-0.528
-0.268
3.346
0.120
3.946
-2.077
0.434
-1.131
-0.886
Sig.
0.950
0.599
0.790
0.001
0.905
0.000
0.041
0.666
0.261
0.378
VIF
1.056
1.084
1.325
1.201
1.449
1.064
1.107
1.076
1.148
Table 9: Model Equation 2 for TSP 9to18
Unstandardized
Coefficients
Model
(Constant)
HGmax
SOQ SUM
Ethnicity
Standardized
Coefficients
B
-19.767
Std. Error
13.536
.514
.242
Collinearity
Statistics
Beta
.213
t
-1.460
Sig.
.148
VIF
2.122
.037
1.275
.516
.137
.375
3.766
.000
1.258
-12.969
5.697
-.204
-2.276
.025
1.015
Note: Dependent Variable: TSP 9to18. F(9,81) = 13.252, p = 0.000.
Table 10: Model Equation 2 for TSP early
Unstandardized
Coefficients
Standardized
Coefficients
Model
(Constant)
B
5.104
Std. Error
8.214
SOQ SUM
.325
.083
-8.631
-6.164
Ethnicity
Older Brothers
Collinearity
Statistics
Beta
t
Sig.
VIF
.621
.536
.378
3.917
.000
1.017
3.806
-.217
-2.268
.026
1.003
3.479
-.171
-1.772
.080
1.020
Note: Dependent Variable: TSP Early. F(9,81) = 7.373, p = 0.000.
Table 11: Model Equation 2 for TSP late
Unstandardized
Coefficients
Model
(Constant)
Standardized
Coefficients
B
-19.845
Std. Error
5.649
HGmax
.349
.101
SOQ SUM
.253
-5.158
Ethnicity
Beta
Collinearity
Statistics
t
Sig.
VIF
-3.513
.001
.317
3.452
.001
1.275
.057
.403
4.420
.000
1.258
2.378
-.178
-2.169
.033
1.015
Note: Dependent Variable: TSP late. F(9,81) = 21.512, p = 0.000
76
Discussion
The primary aim of the current study was to determine whether biological
androgen and/or androgen exposure could predict female sport participation. It was
hypothesized that early exposure to testosterone, as inferred from the 2DR, may
predispose girls toward sport participation. In fact, this has been shown to some extent in
males and females, where lower ratios appear to relate to fitness and level of sport
(Bescos et al., 2009; Hönekopp, T. Manning, & Müller, 2006; Ranson, Stratton, &
Taylor, 2015, Golby & Meggs, 2011), Testosterone is known to impact many traits that
are beneficial to sport success, such as an increase in skeletal muscle mass (a determinant
of strength and power), increase in cardiac size (resulting in a larger stroke volume), and
a regulation of erythropoiesis which could potentially increase the oxygen carrying
capacity of the blood.
In contrast to previous studies, neither testosterone nor 2DR was related to
TSP9to18 alone (r = 0.032, p = 0.765 and r = -0.065, p=0.538, respectively) or when the
potential for covariates and confounders were added in a linear regression. Previous
research has shown an association between the digit ratios (2DR) of elite female fencers
(n=87) and current and highest past rankings (Bescos et al., 2009). However, the athletes
in the Bescos et al. study (2009) had all achieved world class ranking at some point in
their past and this relation was true only when ethnicity and the rankings were accounted
for (Bescos et al., 2009). Similarly, Paul et al., (2006) analyzed the 2DR ratio of 607
females (53.8 ± 8.5 y). Participants were asked to rate their highest level achieved (on a
scale of 1 – 5 ranging from social participation only, to national level) for a total of 12
sports. When adjusted for age, the highest level achieved in any sport was significantly,
77
negatively associated with average 2DR (b = -4.93, p = 0.01). Consequently, it is
possible that the relation between 2DR and sport success might only manifest in elite
level athletes. Moreover, the average age of participants in this study were 22.32 ± 2.68
y, much younger than those in the aforementioned study (Paul et al., 2006). As such,
some of the participants in the present study may not have reached their full potential and
achieved to their highest level within the timeframe analyzed. Indeed, the average age of
female Olympians is in the mid to late 20’s in many sports (Arti Patel, 2012).
Nonetheless, only a small fraction of the population and present sample, specifically,
would be expected to reach Olympic level at any point in their lives, and further, one of
the central aims of the current study was to determine the relationship between androgens
and sport participation, not simply highest level. Even though no significant relationship
was found, it is interesting to note that the direction of the relationship was negative
(TSP9to18: r = 0.065, TSPearly: r = -0.047, TSPlate: r = -0.079) which is suggestive of a
lower 2DR ratio (i.e. indicative of higher prenatal testosterone) negatively influencing
female sport participation.
Moreover, Paul et al., (2006) suggested that the performance of childhood
athletes may not correlate well with best adult performance due to variability in
development. Therefore, it may not be valid to look at adolescent sport participation as a
sum, due to ranges in age of development (Paul et al., 2006). With respect to
testosterone, although pubertal testosterone and adult testosterone appear to correlate
(Apter & Vihko, 1990), the Mayo Clinic indicates that total testosterone values can range
from 7-44ng/dl at the age of 10-11 y to 8-60ng/dl at 19 y. In order to control for this, TSP
was divided into TSPearly (9-13) and TSPlate (14-18) and age of menarche was included as
78
a covariate in regression analyses in order to determine whether biological influences
may have had a greater impact at later stages or for girls who began development later.
Nonetheless, in all cases, neither testosterone nor 2DR was significantly related to sport
participation. Further, neither sT nor 2DR was significantly correlated with TSPNOW (r =
0.116, p = 0.317, and r = -0.162, p = 0.125, respectively), though it should be noted that
for both, the r values were the strongest and p values the lowest when analyzed against
TSPNOW. Interestingly, traits that would otherwise correlate with sport participation such
as youth fitness have been shown to correlate with 2DR ratio in young boys and girls
(Hönekopp et al., 2006), however, that and recent evidence suggests that this effect
appears much stronger in boys (Ranson et al., 2015).
The mean sT values of the participant sample (60.18 ± 23.44 pg/ml) were within
the reference ranges for females provided by the Salimetrics ELISA Kit (48.47 ±38.44
pg/ml). Although the sT concentrations are slightly higher than average, they were
similar to previous studies which have analyzed sT in athletes (Cook & Beaven,
2013)(Blair Crewther et al., 2015), and the pilot data collected for this thesis. This
supports the validity of the sT concentrations found. To the knowledge of the researcher,
this is the only study thus far which has analyzed sT and sport participation in a sample
that included recreational athletes, as most studies have been completed on elite athletes
or sport participation has not been recorded (Cook & Beaven, 2013)(B. T. Crewther,
Hamilton, Casto, Kilduff, & Cook, 2015). Moreover, only one participant in the sample
reported that they had not participated in sports at any time during their life. The mean
moderate-strenuous leisure time physical activity score of the group (as determined by
the Godin Leisure-Time Physical Activity Questionnaire), was 40 ± 24.2 units. Godin,
79
(2011) developed three categories to group activity levels based on the subtotal of
moderate and strenuous activity: active (24 units or more) moderately active (14-23 units)
and insufficiently active (less than 14 units). On average, participants from the current
sample showed ranges in the active category. Approximately 72% of the participants in
the study were either currently or had been previously enrolled in a university
Kinesiology program. Results from the pilot data are in accordance with the current
study, which found that students in Kinesiology exhibit greater activity levels than other
majors. Further, those who participate in organized sport during adolescence, have on
average, higher weekly hours of moderate-to-vigorous physical activity into young
adulthood (Walters, Barr-Anderson, Wall, & Neumark-Sztainer, 2009). Therefore, the
active categorization of the current study sample is not surprising, considering all
participants excluding one, had participated in sport at some point in their life. However,
the variation in total sport participation across all participants, does allow for some
speculation as to the influences of sport participation on athletes through adolescence.
Potential confounders of the current participant sample were indicative of a higher
sport participation rate. For example, ethnicity was a significant predictor of TSP9to18,
TSPearly and TSPlate. Interestingly, European females who are a part of an ethnic minority,
participate in sport less frequently than females who belong to an ethnic majority (Elling
& Knoppers, 2005). In the present sample, approximately 77% of participants in the
current sample self-identified as Caucasian.
Also, socio-economic status (SES) is known to influence sport participation such
that the higher the SES, the greater the sport participation (White & McTeer, 2012). A
proxy for SES of children and adolescents is parental education (Walters et al., 2009)
80
however, maternal education appears to be the strongest predictor of childhood SES
(Hoff & Tian, 2005). In the current sample, approximately 72% of participants had
mothers who had attended post-secondary education (ranging from a college to a PhD
degree). This is greater than the national average of Canadians who have acquired a postsecondary degree at 64.1% (Statistics Canada). The higher maternal education level of
this sample could have had a positive effect on the total sport participation of the current
females.
One of the strongest correlations found was the association of TSP9to18, TSPearly
and TSPlate with a marker of upper body strength [i.e. maximum hand grip (HGmax)]. Not
only was HGmax correlated on its own, but according to structure coefficients, it
contributed 35% and 43.3% to the predicted sport participation score in the regression
models for TSP9to18 and TSPlate, respectively. However, causality is unknown. It is
possible that by participating in sport, an athlete is exposed to environments and
situations in which strength may increase. Conversely, a person who is stronger initially
might do better in a sport situation, thereby increasing their sport success. Indeed, it has
been proposed that androgens help to develop grip strength in men and women, although
this relation may be subdued in females (Isen, McGue, & Iacono, 2014)). Interestingly,
HGmax was removed from the final model in the prediction for TSPearly. This may
suggest that strength is a result of sport participation given that TSP late would have been
much closer in time to participation in this study. Nonetheless, the relationship between
HGmax and sport participation was clear even without a categorization of sport, which
suggests that, strength is an important component of sport participation and achievement.
81
Noteworthy, is the correlation between TSP9to18 (r = 0.240, p = 0.021) and TSPlate
(r = 0.251, p = 0.016) and the Scale of Children’s Action Tendencies in Sport (SCATS).
As discussed previously, the SCATS is a questionnaire used to determine sport specific
aggression tendencies. A significant amount of research has studied aggressive
tendencies in athletes, and whether the aggressive tendencies are viewed as being
inherent to the athlete, or are developed through the nature of the sporting environment
(Kimble, Russo, Bergman, & Galindo, 2010). Results of this correlation were not
surprising, considering aggression has previously been defined as a positive factor of
performance (Rascale, Coulomb, & Pfister, 1998, Widmeyer & Birch, 1984). However,
drawing conclusions across studies including sport aggression and sport
participation/success should be taken with caution, as many studies define aggression in
different ways. For example, a study assessing aggression in ice hockey players defined
aggression as “an overt verbal or physical act that has the potential to physically or
psychologically injure a person,” (Visek & Watson, 2005). However, some studies have
chosen to segregate aggression into two forms: hostile and instrumental, (which defines
the intent of the aggressive act as trying to gain a competitive advantage, rather than for
malicious intent). Therefore, even though this classic definition still involves the intent to
injure, based on the differentiation between competitive and malicious intent, it can still
be viewed in a positive light, leading a player towards sport success. The SCATS score
was also significantly correlated with the total score of the Buss-Perry Aggression
Questionnaire (AQ), however, the AQ was not correlated with any range of sport
participation and when the SCATS was added into regression analyses, it was not found
to be a significant component of the final model, unlike the SOQ score. This suggests
82
that aggression is not as important as competitiveness to sport participation even though
aggression can be used to gain a competitive advantage.
It has been suggested in the literature that prenatal and basal testosterone may be
associated with aggressive tendencies. The majority of literature in this field uses the
definition of aggression which implies malicious behaviour and intent to injure.
Moreover, historically, males were thought to be more aggressive than
females(Giammanco, Tabacchi, Giammanco, Di Majo, & La Guardia, 2005). The
increased concentration of testosterone in males is taken as evidence for the link between
testosterone and aggression (Book et al., 2001). There has been a positive relationship
between testosterone and aggression established in non-humans, but this relationship is
significantly less established in humans. In contrast, prenatal testosterone has been linked
to aggressive tendencies, although these results have primarily be found in males (Bailey
& Hurd, 2005, Dogan, Barut, Konuk, & Bilge, 2007, Kuepper & Hennig, 2007).
However, in the current study, there were no correlations found between standard
measures of aggression (Buss-Perry AQ) and sports aggression (SCATS) and either sT
concentrations or 2DR (although once again, the negative direction of the relationship
between 2DR and the SCATS should be noted).
As noted above, a strong relationship between competitiveness in sport situations
(SOQ) and TSP9to18, TSPearly, and TSPlate was found (r=0.473, p=0.000, r = 0.362,
p=0.000, and r = 0.545, p = 0.000, respectively), suggesting that the higher a female’s
measure of competitiveness, the higher their sport participation score. There are three
subscales (i.e. Win Orientation, Goal Orientation and Competitiveness) associated with
the SOQ and the correlations of the subscales with TSP9to18 were as follows: Win (r =
83
0.324, p = 0.002), Goal (r = 0.331, p = 0.001) and Competitiveness (r = 0.544, p = 0.000).
To the knowledge of the author, the SOQ has not been used as a measure to predict sport
participation. The majority of studies have used the SOQ to determine the
competitiveness levels in existing groups of athletes (Jamshidi, Hossien, Sajadi, Safari, &
Zare, 2011, Manouchehri & Tojari, 2013). The original purpose of the SOQ in the current
study was to aid in predicting sport participation, but due to the fact that all participants
(excluding one) had or currently were participating in sport, comparisons to previous
studies can be made.
For example, a study conducted by Finkenberg & Moode (1998), compared the
sport orientation of a group of collegiate athletes (N=40) and non-athletes (N=36). The
author found that collegiate athletes scored higher on the SOQ than non-athletes. When
compared to the current study, the average scores on the subscales although slightly
higher, were quite similar. The differences are defined as follows:
Table 12: Comparison between SOQ scores of Finkenberg et al., (1998) and the current study.
Competitiveness
Goal Orientation
Win Orientation
Finkenberg et al.,
48.2 ± 10.03
24.6 ± 3.7
20.6 ± 4.9
49.8 ± 12.0
21.8 ± 5.3
25.8 ± 4.0
(1998)
Current Study
It was interesting to note that of the psychological questionnaires used as
supplementary data in this study, sport participation was highly correlated with the
positive attributes (sport aggression and competitiveness) and not correlated with the
84
negative attribute (standard aggression). This result helps to support the notion that
participating in sports contributes to fostering positive attributes in females, which has
been shown in many previous studies (Fraser-thomas, Côté, Deakin, & Co,
2007,Troutman & Dufur, 2008, Holt, Tamminen, Tink, & Black, 2009).
Post-hoc power analyses resulted in power values ranging from 0.842 – 0.999 for
all regression models. The power analyses were performed including all 92 participants,
although a study utilizing a larger sample size would result in a stronger overall power.
Moreover, a participant pool which included more females who did not participate in
sport at any time in their life would add to the strength of the study.
Limitations
92 females were recruited for the present study. For standard multiple linear
regression, a sample size of 15 individuals per variable entered in a prediction model is
normally accepted. In our secondary analyses, 6 predictors and 3 dummy coded
confounding variables were entered into regression analyses. However, using backward
regression, the final model consisted of only 3 predictors and measures of statistical
validity were deemed acceptable. Further, in retrospect, one aspect of the study design
that might have skewed the type of individual who volunteered for the study was the
study title. Due to the fact that the title included the words “sport participation” this might
have deterred females who had not ever participated in sport from volunteering, thereby
attracting only females with a sporting history. Nonetheless, a greater sample size and
one that included more women who had never participated in sport would provide greater
insights into female adolescent sport participation.
85
Another limitation of this study is the fact that our sport participation scores were
primarily retrospective, while the questionnaires and sT measure were current.
However, participants were asked to fill out the sport participation questionnaire for the
current year, and therefore, TSPNOW could be compared with the independent variables.
In the majority of outcomes, correlations were similar (i.e. between variables) and final
regression models included the same variables.
Measuring any hormone in humans comes with its own limitations. Testosterone
varies throughout the day as well as throughout the menstrual cycle in females (although
these changes are small in comparison to the changes in estrogen across the menstrual
cycle). Therefore, testosterone samples were taken between 9-12am when testosterone
concentrations were the highest to take into account its circadian fluctuation. Moreover,
sT is not 100% correlated with free testosterone in serum (r = 0.61) and is less correlated
to serum concentrations in females than males (Shirtcliff, Granger, & Likos, 2002). The
saliva sample used to determine testosterone was only indicative of free testosterone
within the body. A more accurate measure could have been determined through serum
blood draws (which could have measured total testosterone), although this method may
have negatively impacted the total number of participant volunteers. Further, free
testosterone concentrations may not be indicative of testosterone’s impact on tissue due
to potential ranges in testosterone receptor sensitivity or SHBG concentration (see
literature review). Nonetheless, the use of sT in females has been used previously in
sport literature (Cook & Beaven, 2013, B Crewther et al., 2012, Cook & Beaven, 2013,
Gatti & De Palo, 2011).
86
The cSP i developed for this thesis also presents limitations. The aim of the cSP i
was to try to present an arbitrary value to sport participation. There were four dimensions
chosen for this thesis, but it is possible that more than four factors could be relevant to a
person’s sport participation (e.g. multiple leagues or overlap of different sports). It is a
challenge of research to try and put an arbitrary number to a complex phenomenon.
However, the current cSPi was modelled after a published report with similar dimensions.
Lastly, a limitation of this study was the use of simple proxies in order to try and
quantify complex subjects. For example, 2DR was used as a proxy for prenatal androgen
exposure, although this relationship has only been established in a few high impact, broad
scientific journals (Voracek & Loibl, 2009), which is suggestive that this measure is not
yet widely accepted (Lutchmaya et al., 2004). Another example of simple proxies is the
use of single questionnaires to try and determine complex behavioural aspects (i.e.
standard aggression, competitiveness, sport aggression). This is common practice in
literature, and all questionnaires used had high validity and reliability, although to capture
the true behavioural nature of any human is most likely impossible.
Conclusion
In summary, it does not appear that androgens, whether prenatally or current,
impact female sport participation. However, it is interesting to note that the relationship
between 2DR ratio and sport participation were negative in all relations though not
significant. Participation in sport is a result of a complex relationship including but not
limited to biological advantages, familial and peer influences, socioeconomic status,
positive and negative experiences within sport, and chance. Given that women and girls
participate in sport at lower rates than men and boys, and given that sport participation
87
provides social and health advantages throughout life for those who participate,
attempting to determine what factors most influence sport participation is a necessary
field of study.
Future Directions
Future studies should include a greater number of participants who have never
been involved in sport. This would contribute to a better understanding of whether the
correlates of sport participation found in this thesis are due to sport participation itself, or
simply due to chance. Moreover, the current study was retrospective, a study with a cross
sectional design across the ages of 9 to 18 may provide better insights into the biological
correlates of female sport participation.
88
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the origin of handedness and functional asymmetry. Neuropsychologia, 29(4), 327–333.
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102
Appendix A
ELIGIBLE SPORTS
Snow sports
Biathlon
Bobsleigh
Broomball
Curling
Dog Sledding
Figure Skating
Hockey
Ice Sailing
Luge
Ringette
Skeleton
Ski Jumping
Skiing
Downhill/Alpine
Skiing, Cross
Country/Nordic
Skiing, Nordic
Combined
Snowboarding
Snowshoeing
Speed Skating
Combat
Sports
Boxing
Judo
Karate
Taw Kwon Do
Wrestling
Fencing
Water sports
Canoeing
Diving
Kayaking
Steeple Chase
Hurdles
rowing
Sailing/Yachting
Swimming,
Synchronized
Wakeboarding
Water Skiing
Water Polo
NonTraditional
Sports
BMX
Cheerleading
Climbing
Cricket
Orienteering
Skateboarding
Mountain
Boarding
Wakeboarding
Large Team
Sports
Ball Hockey
Baseball
Basketball
Broomball
Cheerleading
Cricket
Field Hockey
Football - tackle,
flag, touch
In-line hockey
Lacrosse
Ringette
Rugby
Softball
Skill Sports
Archery
Bowling (5 pin)
Golf
Lawn bowling
Badminton
Handball (4
walls)
Netball
Racquetball
Squash
Table Tennis
Team Handball
Artic sports
Traditional
Aboriginal Sports
Track and
Field
Endurance
Sports
Shot, Discuss,
Hammer Throw
High Jump, Long
Jump, Javelin,
Pole Vault
Adventure racing
Canoeing
Kayaking
Cycling
Track and Field
Shot, Discuss, Hammer Throw
High Jump, Long Jump, Javelin, Pole Vault
Steeple Chase, Hurdles
103
In-line skating
Kayaking
Modern
Pentathlon
Mountain Biking
Orienteering
rowing
Running, Cross
Country
Running, Road
Triathlon
Race Walking
Equestrian
Lifting
Power Lifting
Urban Road
Sports
BMX
Cycling
Inline Skating
Running, Road
Skateboarding
Race Walking
Multiple
Sport
Adventure
Racing
Modern
Pentathlon
Appendix B
Sport Participation Questionnaire
104
Appendix C
Godin Leisure-Time Exercise Questionnaire
1. During a typical 7-Day period (a week), how many times on average do you do the
following kinds of exercises for more than 15 minutes during your free time (write on
each line the appropriate number).
Times Per Week
a. Strenuous Exercise
(HEART BEATS RAPIDLY)
(e.g. running, jogging, hockey, football, soccer,
squash, basketball, cross country, skiing, judo,
roller skating, vigorous swimming,
vigorous long distance bicycling).
b. Moderate Exercise
(NOT EXHAUSTING)
(e.g. fast walking, baseball, tennis, easy bicycling,
volleyball, badminton, easy swimming, alpine skiing,
popular and folk dancing)
c. Mild Exercise
(MINIMAL EFFORT)
(e.g. yoga, archery, fishing from river bank, bowling
Horseshoes, golf, snow-mobiling, easy walking)
During a typical 7-Day period (a week), in your leisure time, how often do you engage in any
regular activity long enough to work up a sweat (heart beats rapidly)?
Often
Sometimes
105
Never/rarely
Appendix D
Scale of Children’s Action Tendencies in Sport
Imagine that …
1. You're playing basketball with some friends and a kid on the other team keeps
shoving you to get the ball. What would you do?
(Pick one choice from each pair of choices below)
A. Shove the kid back
Or B. Tell the kid to stop
(circle A or B)
C. Pretend it didn't bother me
Or D. Shove the kid back
(circle C or D)
E. Tell the kid to stop
Or F. Pretend it didn't bother me
(circle E or F)
2. You're playing soccer and a player from the other team trips you on purpose. What
would you do?
(Pick one choice from each pair of choices below)
A. Tell the player not to do that
again
Or B. Stay away from that player the rest of
the game
(circle A or B)
C. Get even by tripping the player
Or D. Tell the player not to do that again
later
(circle C or D)
E. Tell the player not to do that
again
Or F. Stay away from that player the rest of
the game
(circle E or F)
3. You are playing hockey and the kids on your team really want to win. Some kids
on your team want to try to hurt the best player on the other team. They ask you to
help them. What would you do?
106
(Pick one choice from each pair of choices below)
A. Help the kids on my team hurt
the other player
Or B. Tell the kids on my team I don't hurt
people
(circle A or B)
C. Just pretend to help, but not
really try to hurt the player
Or D. Help the kids on my team hurt the
other player
(circle C or D)
E. Tell the kids on my team I don't
hurt people
Or F. Just pretend to help, but not really try
to hurt the player
(circle E or F)
4. You are playing hockey and a fight starts. Some kids from your team are fighting
kids from the other team. What would you do?
(Pick one choice from each pair of choices below)
A.
Stay far away from
Or B.
Try to stop the kids from
fighting
the fight
(circle A or B)
C.
Join the fight
Or D. Stay far away from the fight
(circle C or D)
E.
Try to stop the kids
from fighting
Or F. Join the fight
(circle E or F)
107
Appendix E
Buss-Perry Aggression Questionnaire
Using the 5-point scale shown below, indicate how uncharacteristic or characteristic each of the
following statements is in describing you. Check off the box which corresponds with your rating in
the box to the right of the statement.
1 = extremely uncharacteristic of me
2 = somewhat uncharacteristic of me
3 = neither uncharacteristic nor characteristic of me
4 = somewhat characteristic of me
5 = extremely characteristic of me
1
1. Some of my friends think I am a hothead.
2. If I have to resort to violence to protect my rights, I
will.
3. When people are especially nice to me, I wonder what
they want.
4. I tell my friends openly when I disagree with them.
5. I have become so mad that I have broken things.
6. I can’t help getting into arguments when people
disagree with me.
7. I wonder why sometimes I feel so bitter about
things
8. Once in a while, I can’t control the urge to strike
another person.
9. I am an even-tempered person.
10. I am suspicious of overly friendly strangers.
11. I have threatened people I know.
12. I flare up quickly, but get over it quickly.
13. Given enough provocation, I may hit another
person.
14. When people annoy me, I may tell them what I think of
108
2
3
4
5
them.
15. I am sometimes eaten up with jealousy.
16. I can think of no good reason for ever hitting a
person.
17. At times I feel I have gotten a raw deal out of
life.
18. I have trouble controlling my temper.
19. When frustrated, I let my irritation show.
20. I sometimes feel that people are laughing at me
behind my back.
21. I often find myself disagreeing with people.
22. If somebody hits me, I hit back.
23. I sometimes feel like a powder keg ready to
explode.
24. Other people always seem to get the breaks.
25. There are people who pushed me so far that we
came to blows.
26. I know that “friends” talk about me behind my back.
27. My friends say that I’m somewhat argumentative.
28. Sometimes I fly off the handle for no good reason.
29. I get into fights a little more than the average person.
109
Appendix F
Table 12: Progesterone Reference Ranges
Males
Females
0-23 months
0.87-3.37 ng/mL
0.87-3.37 ng/mL
2-9 y
<0.15 ng/mL
0.20-0.24 ng/mL
10-17 y
Adult levels are
attained by puberty
Values increase
through puberty and
adolescence
≥ 18 y
0.20-1.40 ng/mL
Levels vary
throughout the
menstrual cycle, see
table 2.
*Adapted from Lippe, LaFranchi, Lavin et al., (1974).
Table 13: Pre-Menopausal Progesterone Reference Ranges
0.20-1.50ng/mL
Follicular Phase
Ovulation Phase
0.80-3.00 ng/mL
Luteal Phase
1.70-27.00 ng/mL
Postmenopausal
<0.15-0.80 ng/mL
*Adapted from Lippe, LaFranchi, Lavin et al., (1974).
110
Appendix G
Table 14: Estradiol Concentrations
Free Estradiol – Serum
Adult Males
0.2 – 1.5pg/mL
Adult Females
0.6 – 7.1pg/mL
Total Estradiol – Serum
Newborns (Males + Females)
Concentrations are elevated at birth
and fall rapidly during the first week
to values < 15pg/mL
Concentrations increase to 1032pg/mL between days 3-60, with a
subsequent decline to pre-pubertal
levels of <15pg/mL during the first
year
Concentrations increase to 5.0 –
50pg/mL between days 30-60, then
decline to pre-pubertal values of <
15pg/mL during the first year.
8.0 – 35pg/mL
Males < 6m
Females < 1 year
Adult Values
Males
Females
Follicular
30 – 100pg/mL
Luteal
70 – 30pg/mL
Postmenopausal <15pg/mL
*Adapted from Elmlinger, Kuhnel & Ranke (2002).
111
Appendix H
Table _: Free Testosterone Reference Values
Table _: Total Testosterone Reference Values
Males
Males
0-5 months
6 months - 9 years
10-11 years
12-13 years
14 years
15-16 years
17-18 years
≥ 19 years
Females
0-5months
6 months - 9 years
10-11 years
12-16 years
17-18 years
≥ 19 years
20-25 years
25-< 30 years
30-< 35 years
35-< 40 years
40-< 45 years
45-< 50 years
50-< 55 years
55 -<60 years
60-< 65 years
65-< 70 years
70-< 75 years
75-< 80 years
80-< 85 years
85-< 90 years
90-< 95 years
95-100+ years
Females
5.25
5.05
4.85
4.65
4.46
4.26
4.06
3.87
3.67
3.47
3.28
3.08
2.88
2.69
2.49
2.29
-
2.07
19.80
19.00
18.10
17. 10
16.40
15.60
14.70
13.90
13.00
12.20
11.30
10.50
9.61
8.76
7.91
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
20-25 years
25-< 30 years
30-< 35 years
35-< 40 years
40-< 45 years
45-< 50 years
50-< 55 years
55 -<60 years
60-< 65 years
65-< 70 years
70-< 75 years
75-< 80 years
80-< 85 years
85-< 90 years
90-< 95 years
95-100+ years
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
-
1.09
1.06
1.03
1.00
0.98
0.95
0.92
0.90
0.87
0.84
0.82
0.79
0.76
0.73
0.71
0.68
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
112
75
<7
<7
<7
<7
100
300
240
-
400
20
130
800
1200
1200
1200
950
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
20
<7
<7
<7
20
8
-
80
20
44
75
75
60
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
ng/dl
Appendix I
Name: ________________________________________________
Current School Year: _________________
Major:
_________________________________
Date of Birth (month and year only):
_______________________________________________
Email: _____________________________________________
(For project use only – ID # _____________________________)
113
1. Demographic Information
Today’s Date (DD/MM/YY)
Date of Birth (MM/YY) ___/___
____/____/____
Age: _____
Race/Ethnic Background (please circle)
 Aboriginal

Asian or Asian Descent

Hispanic/Latino

Non-Hispanic Black or African Descent

Non-Hispanic White or Caucasian

Other/Mixed (please describe) _________________

Prefer not to answer
Are you currently pregnant? (please circle)
NO
YES
Do you have any children? (please circle)
NO
YES
Are you left handed or right handed? (please circle)
RIGHT
LEFT
2. House Hold/Family Information
Parent Information:
Parent
Highest Level of Education
Completed
114
Sibling Information:
Please list the gender, age and order of your BIOLOGICAL siblings only (if any)
including yourself in the list. For example, if you have one older and one younger brother
you would put …. Brother (24 years old), Me (22 years old), Brother (19 years old), on
the lines below.
________________________________________________
________________________________________________
________________________________________________
________________________________________________
________________________________________________
____________________
Are you a twin or part of a multiple birth?
(please check one box)
YES
NO
If so, please indicate whether you are fraternal or paternal, Further as the gender of your
siblings.
______________________________________________________
______________________________________________________
______________________________________________________
______________________________________________________
________________
3.
Pubertal Information
Age of Menarche
(As best as you can remember, indicate how old you were when you first started
menstruating on the line below)
115
________________________________________________________________________
______
Date of last menarche
(As best as you can remember, please indicate the last day of menstruation (i.e. bleeding)
of your most previous period. If you aren’t sure of the last day, please guess the
approximate day)
________________________________________________________________________
______
YES
NO
Are you currently on birth control?
(please circle)
YES
NO
If yes, please indicate what brand:
________________________________________________
Do you have any pre-existing conditions which may alter your testosterone
concentrations?
(please circle)
YES
NO
Are you currently on any supplements, medications (prescribed or not) that could
potentially alter your testosterone concentrations?
YES
NO
If yes, please indicate the specific medication ______________________________.
If yes to the above question, please indicate the number of years you have been taking
the above supplement, medication or drug since the age of 9.
116
Appendix J
Sport Orientation Questionnaire
The following statements describe reactions to sport situations. We want to know how
you usually feel about sports and competition. Read each statement and circle the letter
that indicates how much you agree or disagree with each statement on the scale: A, B, C,
D, or E. There are no right or wrong answers; simply answer as you honestly feel. Do not
spend too much time on any one statement. Remember, choose the letter that describes
how you usually feel about sports and competition.
Strongly Slightly
agree
agree
Neither Slightly Strongly
agree
disagree disagree
nor
disagree
1.
I am a determined competitor.
A
B
C
D
E
2.
Winning is important.
A
B
C
D
E
3.
I am a competitive person.
A
B
C
D
E
4.
I set goals for myself when I
compete.
A
B
C
D
E
5.
I try my hardest to win.
A
B
C
D
E
6.
Scoring more points than my
opponent is very important to me.
A
B
C
D
E
7.
I look forward to competing.
A
B
C
D
E
8.
I am most competitive when I try
to achieve personal goals.
A
B
C
D
E
9.
I enjoy competing against others.
A
B
C
D
E
10. I hate to lose.
A
B
C
D
E
11. I thrive on competition.
A
B
C
D
E
12. I try hardest when I have a
specific goal.
A
B
C
D
E
13. My goal is to be the best athlete
possible.
A
B
C
D
E
117
14. The only time I am satisfied is
when I win.
A
B
C
D
E
15. I want to be successful in sports.
A
B
C
D
E
16. Performing to the best of my
ability is very important to me.
A
B
C
D
E
17. I work hard to be successful in
sports.
A
B
C
D
E
18. Losing upsets me.
A
B
C
D
E
19. The best test of my ability is
competing against others.
A
B
C
D
E
20. Reaching personal performance
goals is very important to me.
A
B
C
D
E
21. I look forward to the opportunity
to test my skills in competition.
A
B
C
D
E
22. I have the most fun when I win.
A
B
C
D
E
23. I perform my best when I am
competing against an opponent.
A
B
C
D
E
24. The best way to determine my
ability is to set a goal and try to
reach it.
A
B
C
D
E
25. I want to be the best every time I
compete.
A
B
C
D
E
118
Appendix K
Today's Date: November 03, 2016
Principal Investigator: Ms. Elizabeth Vandenborn
REB Number: 33486
Research Project Title: REB# 16-195: "Why do girls play? Can circulating and prenatal testosterone predict
sport participation in adolescent females?"
Clearance Date: November 3, 2016
Project End Date: August 31, 2017
Milestones:
Renewal Due-2017/08/31(Pending)
______________________________________________________________________________
This is to inform you that the University of Windsor Research Ethics Board (REB), which is organized and
operated according to the Tri-Council Policy Statement and the University of Windsor Guidelines for
Research Involving Human Subjects, has granted approval to your research project on the date noted above.
This approval is valid only until the Project End Date.
A Progress Report or Final Report is due by the date noted above. The REB may ask for monitoring
information at some time during the project’s approval period.
During the course of the research, no deviations from, or changes to, the protocol or consent form may be
initiated without prior written approval from the REB. Minor change(s) in ongoing studies will be
considered when submitted on the Request to Revise form.
Investigators must also report promptly to the REB:
a) changes increasing the risk to the participant(s) and/or affecting significantly the conduct of the study;
b) all adverse and unexpected experiences or events that are both serious and unexpected;
c) new information that may adversely affect the safety of the subjects or the conduct of the study.
Forms for submissions, notifications, or changes are available on the REB website:www.uwindsor.ca/reb.
If your data is going to be used for another project, it is necessary to submit another application to the REB.
We wish you every success in your research.
Dr. Suzanne McMurphy, Ph.D.
Chair, Research Ethics Board
2146 Chrysler Hall North
University of Windsor
519-253-3000 ext. 3948
Email: [email protected]
The information contained in this e-mail message is confidential and protected by law. The information is
intended only for the person or organization addressed in this e-mail. If you share or copy the information
you may be breaking the law. If you have received this e-mail by mistake, please notify the sender of the email by the telephone number listed on this e-mail. Please destroy the original; do not e-mail back the
information or keep the original.
119
Appendix L
Table 15: Correlation between 4 components of Composite Sport Participation Score
Number of
Sports
Number of
Sports
Pearson
Correlation
Sig. (2-tailed)
N
Highest
Pearson
Level
Correlation
Sig. (2-tailed)
N
Weekly
Pearson
Frequency
Correlation
Sig. (2-tailed)
N
Months of
Pearson
Sport Season Correlation
1
Highest
Level
.296**
1012
.296**
0.000
1012
1
0.000
1012
.541**
0.000
1012
.481**
0.000
1012
.484**
1012
.541**
0.000
1012
1
0.000
1012
.631**
0.000
1012
.619**
0.000
1012
.481**
1012
.631**
0.000
1012
1
0.000
1012
1012
Sig. (2-tailed)
0.000
0.000
N
1012
1012
Note: **. Correlation is significant at the 0.01 level (2-tailed).
120
Weekly
Months of
Frequency
Sport Season
.484**
.619**
Appendix M
Q-Q Plots for Regression Model 1
TSP9to18
TSPearly
TSPlate
121
Q-Q Plots for Regression Model 2
TSP9to18
TSPearly
TSPlate
122
Appendix N
Regression Model 1 Standardized Predicted Value vs. Standardized Residual
TSP9to18
TSPearly
123
TSPlate
Regression Model 2 Standardized Predicted Value vs. Standardized Residual
TSP9to18
124
TSPearly
TSPlate
125
Vita Auctoris
NAME:
Elizabeth Vandenborn
PLACE OF BIRTH:
Chatham, ON
YEAR OF BIRTH:
1993
EDUCATION:
Chatham-Kent Secondary School, Chatham, ON,
2011
University of Windsor, BHK, Windsor, ON,
2015
University of Windsor, MHK., Windsor, ON,
2017
126