PHYSICAL ACTIVITY AND FITNESS: MODERATORS OF THE STRESS-METABOLIC
SYNDROME RELATIONSHIP?

By
Megan E. Holmes

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Kinesiology
2011

ABSTRACT
PHYSICAL ACTIVITY AND FITNESS: MODERATORS OF THE STRESS-METABOLIC
SYNDROME RELATIONSHIP?
By
Megan E. Holmes
Childhood obesity and metabolic syndrome are viewed as critical public health concerns
and efforts to attenuate these conditions have focused primarily on two behavioral factors, diet
and physical activity. Recent research has shifted toward viewing these conditions as a result of
the interactions between many antecedents that can influence the balance of energy intake and
expenditure. One intriguing line of research implicates perturbations in the stress response
system and the putative role that dysregulation may have on the development of obesity and
metabolic syndrome. Researchers have observed relationships similar to those found in the adult
literature when examining the links between health and a number of ‘stress-related’ variables in
youth. An increased focus on psychosocial health determinants of metabolic heath such as stress
is of particular interest, given the favorable relationship between physical activity and/or fitness
and stress. This apparent beneficial relationship between physical activity and stress has led
researchers to examine whether physical activity may have a moderating effect (i.e., effect
modification) on the stress-metabolic syndrome relationship. Literature addressing this potential
effect modification is sparse but promising.
This dissertation aimed to examine further the relationship between psychosocial health
and metabolic health and investigate the potential moderation physical activity and health-related
fitness may have on this relationship. We examined perceived stress, problem-focused coping
levels, metabolic syndrome-related variables, physical activity levels, and health-related fitness
variables in 126 middle school students in the 2010-2011 school year. Participants approximated

th

the 75 percentile for BMI and gender differences were observed when examining systolic
blood pressure, physical activity level as estimated by questionnaire, vigorous physical activity
as estimated by accelerometer, and perceived stress. Results showed little influence of perceived
stress or problem-focused coping associated with metabolic health as determined by a metabolic
syndrome composite score and BMI. Likewise, a moderating influence of physical activity or
fitness was not observed.
Results of this study suggest physical activity, stress, and problem-focused coping have
little influence on the metabolic syndrome composite score or BMI in this sample. Although we
did not observe our hypothesized relationships, the line of inquiry examining a moderating
influence of physical activity and fitness on this relationship holds merit and should not be
abandoned. Our study highlights the need for continued methodological refinement, particularly
regarding assessment of psychosocial health indicators in this age group. A critical step in
elucidating the relationship between stress and health in youth requires identification and concise
descriptions of indices of psychosocial health that are most relevant to metabolic health.
Identification of modifiable variables with influence that transcends multiple putative
contributors to energy imbalance is particularly critical in children and adolescents where
behaviors and attitudes are still developing and timely interventions could translate into longterm, positive health outcomes in adulthood.

Copyright by
MEGAN ELIZABETH HOLMES
2011

ACKNOWLEDGEMENTS
“A positive attitude may not solve all your problems, but it will annoy enough people to make it
worth the effort.” --Herm Albright I have been fortunate to carry this sentiment as my motto
and creed throughout my academic career. The upkeep of a positive attitude is a seemingly
impossible task at times and would not have been accomplished without the support of some
very special people. Without question, my committee members are among the most supportive
people with whom I have ever had the good fortune of working. Their ability to challenge me in
such a way that encouraged my soul and cultivated my desire to learn is a characteristic I hope to
model with my own students. Jim and Karin deserve distinct recognition for their efforts. From
catching spelling errors to providing the tissues for an emotional crisis, you have supported me
and I thank you so much for all you have done. Jo Ann Janes will always have a special place in
my heart. I am so fortunate to have had you in my corner and I will be forever grateful.
I was ‘adopted’ by my dear friend Anna and her family, a life-event that has changed me
forever. I am so delighted to be a part of Hunsinger-Bratta families, without which I would not
have survived. My own family also deserves special thanks for their understanding and support.
Thank you, Mom and Dad, for your constant encouragement and reminder that this too shall
pass. Andy, you always have a warm and encouraging word for me. Traci, you gave me the book
that changed my statistical outlook and I am delighted you are part of my life now. Dave, your
passion for teaching is an inspiration to me and your thoughtfulness is something I cherish very
dearly. Connor, you are so special to me—a nephew, a little brother, and a buddy all in one!
Kristi, your love of science is an inspiration to me and I will cherish all of the love and support
you gave me through all of my challenges, mountains and mole hills alike. Thank you!

v

TABLE OF CONTENTS

LIST OF TABLES.......................................................................................................................viii
CHAPTER 1: INTRODUCTION...................................................................................................1
CHAPTER 2: REVIEW OF LITERATURE..................................................................................9
Introduction .............................................................................................................9
Obesity/Metabolic Syndrome..................................................................................9
Physical Activity, Fitness, and Their Relationship with Pediatric
Obesity/Metabolic Syndrome................................................................................13
An Alternative Hypothesis.....................................................................................20
Physical Activity, Exercise, and Stress..................................................................27
Physical Activity, Stress, and the Metabolic Syndrome........................................31
Summary and Conclusions....................................................................................32
CHAPTER 3: RESEARCH METHODS.......................................................................................34
General Procedures................................................................................................34
Anthropometry.......................................................................................................34
Fitness Assessment................................................................................................35
Blood Pressure.......................................................................................................37
Physical Activity....................................................................................................37
Assessment of Stress and Coping Resources.........................................................39
Assessment of Additional Metabolic Syndrome Variables...................................40
Derivation of the Metabolic Syndrome Score.......................................................41
Data Analysis.........................................................................................................41
Statistical Power and Sample Size Analyses.........................................................42
CHAPTER 4: RESULTS...............................................................................................................43
Participants.............................................................................................................43
Participant Characteristics.....................................................................................44
Outlier Screening...................................................................................................46
Regression Analyses (Aims 2-3: Main Effects).....................................................47
Regression Analyses (Aims 4-5: Physical Activity as a Moderator).....................47
Regression Analyses (Aims 6-7: Health-Related Fitness as a Moderator)............48
Summary of Results...............................................................................................49
CHAPTER 5: DISCUSSION.........................................................................................................51
Participant Characteristics.....................................................................................52
Psychosocial health and metabolic syndrome related variables............................56
Moderating effect of physical activity and health-related fitness..........................61
Summary and Conclusions....................................................................................65
APPENDICES...............................................................................................................................79
A: Permission.........................................................................................................80
vi

B: Consent Forms..................................................................................................82
C: Adolescent Stress Questionnaire (ASQ)...........................................................85
D: Way of Coping Questionnaire (WCQ).............................................................90
E: Physical Activity Questionnaire for Adolescents (PAQ-A)..............................93
F: Correlation Matrix.............................................................................................97
REFERENCES..............................................................................................................................99

vii

LIST OF TABLES
Table 1a:

Anthropometric and metabolic descriptive characteristics of the sample.............67

Table 1b:

Physical activity and health related fitness descriptive characteristics of the
sample ...................................................................................................................68

Table 1c:

Psychosocial descriptive characteristics of the sample .........................................70

Table 2a:

Multiple Regression of Stress on the Metabolic Syndrome Composite Score......70

Table 2b:

Multiple Regression of Stress on BMI...................................................................70

Table 3a:

Multiple Regression of Problem-focused Coping on the Metabolic Syndrome
Composite Score....................................................................................................70

Table 3b:

Multiple Regression of Problem-focused coping on BMI.....................................71

Table 4a:

Multiple Regression of Physical Activity Questionnaire Score and Stress on the
Metabolic Syndrome Composite Score.................................................................71

Table 4b:

Multiple Regression of Moderate-to-Vigorous Physical Activity and Stress on the
Metabolic Syndrome Composite Score.................................................................72

Table 5a:

Multiple Regression of Physical Activity Questionnaire Score and Stress on Body
Mass Index.............................................................................................................72

Table 5b:

Multiple Regression of Moderate-to-Vigorous Physical Activity and Stress on
Body Mass Index...................................................................................................72
Multiple Regression of Physical Activity Questionnaire Score and Problemfocused Coping on Metabolic Syndrome Composite Score..................................72

Table 6a:

Table 6b:

Multiple Regression of Moderate-to-Vigorous Physical Activity and Problemfocused Coping on Metabolic Syndrome Composite Score..................................74

Table 7a:

Multiple Regression of Physical Activity Questionnaire Score and Problemfocused Coping on Body Mass Index....................................................................74

Table 7b:

Multiple Regression of Moderate-to-Vigorous Physical Activity and Problemfocused Coping on Body Mass Index....................................................................75

Table 8a:

Multiple Regression of Aerobic Fitness and Stress on the Metabolic Syndrome
Composite Score....................................................................................................75
viii

Table 8b:

Multiple Regression of Health Related Fitness and Stress on the Metabolic
Syndrome Composite Score...................................................................................76

Table 9:

Multiple Regression of Aerobic Fitness and Stress on Body Mass Index.............76

Table 10a:

Multiple Regression of Aerobic Fitness and Problem-focused Coping on
Metabolic Syndrome Composite Score.................................................................77

Table 10b:

Multiple Regression of Health Related Fitness and Problem-focused Coping on
Metabolic Syndrome Composite Score.................................................................77

Table 11:

Multiple Regression of Aerobic Fitness and Problem-focused Coping on Body
Mass Index.............................................................................................................78

ix

CHAPTER 1
INTRODUCTION
In its most basic sense, obesity is a pathological excess of adiposity that is the result of
energy intake chronically exceeding energy expenditure (1). Approximately one-third of U.S.
children and adolescents (ages 6-19 years) are overweight or obese (2). Given its high prevalence
and the potential implications for so many other facets of life, the obesity epidemic is viewed as
a critical public health concern. Childhood obesity and the co-occurrence of elevated blood
pressure, an adverse blood lipid profile, and insulin resistance is commonly referred to as the
metabolic syndrome (3). Research efforts on metabolic syndrome have focused primarily on two
behavioral factors, diet and physical activity, which is evident by their inclusion in recent major
public health campaigns (4, 5). More recently, however, research has begun to shift towards
viewing the condition as a result of many antecedents that influence the balance of energy intake
and expenditure.
One intriguing line of research implicates perturbations in the stress response system and
the putative role that dysregulation may have on the development of obesity and metabolic
syndrome. This relationship is well established in the adult literature (6-9), and is receiving
increased attention in pediatric work. Identifying an operational and methodological definition
robust enough to capture the ubiquitous nature of stress has been difficult in adults, as noted in a
recent review by Holmes et al (10). When considering the rapid transition in all areas of
development during adolescence, which is also recognized as a stressor (11), it is not surprising
that investigators who examine the stress-obesity relationship in youth have also encountered
difficulty defining stress. Researchers have observed relationships similar to those found in the
adult literature when examining the links between health and a number of ‘stress-related’
variables such as adrenocortical activity and cortisol levels (12-15), teasing (16, 17), quality of
1

life (18), depression (19), chronic stress (20), self-esteem (17), and trait-anxiety (17). Further,
Lazarus suggests that upon recognition of a stimulus, or stressor, there is an appraisal process
which influences the coping behaviors and stress response (21). This tightly linked relationship
between stress and coping has been neglected in health-related research. An individual’s
perception of available coping resources and his/her ability to deal with any given challenge,
ultimately dictate the way by which and individual experiences stress (21). Lazarus (22) has
identified eight general coping resources (confrontive or problem-focused, distancing, selfcontrolling, seeking social support, accepting responsibility, escape or avoidance, planful
problem-solving, and positive reappraisal) which have not been thoroughly examined in youth.
Adolescence is a particularly important time period for examination of coping responses because
this is a time when adult behavior patterns are still forming. Additional research is needed to
identify if examination of relationships between coping resources and/or behaviors and health
outcomes is a viable avenue for exploration in pediatric research.
An increased focus on mental health determinants such as stress is warranted given the
marked increase of psychotropic medication prescription and physicians office visits for the
treatment of emotional and behavioral problems in youth (23, 24), particularly when considering
the favorable relationship between physical activity and/or fitness and stress. Although this is a
relatively new line of research, results generally show an inverse relationship between physical
activity and/or fitness and stress-related variables (19, 25-30). Likewise, Motl et al. used latent
class modeling to examine patterns of change in physical activity and depressive symptoms over
a two year period. Results from this study suggest that the secular decline in physical activity is
inversely associated with increases in reported depressive symptoms (31).

2

This apparent beneficial relationship between physical activity and stress has led
researchers to examine whether physical activity may have a moderating effect (i.e., effect
modification) on the stress-metabolic syndrome relationship. Literature addressing this potential
effect modification is sparse but promising. Previous work by our lab (17) and others (20) has
suggested that physical activity appears to buffer the relationship between stress and metabolic
syndrome and/or obesity. This relationship requires confirmation in a larger, diverse sample.
Likewise, the influence of fitness on the stress-metabolic syndrome relationship also
requires additional investigation. Previously, Holmes and colleagues (10) discussed the rationale
for examining physical activity as a moderator of this deleterious relationship as being grounded
in the notion that a bout of exercise (structured and purposeful physical activity) can be a stressor
and, as such, it can engage most of the same biological pathways as psychosocial stress (i.e.,
sympatho-adreano-medullary (SAM) axis, hypothalamic pituitary adrenal (HPA) axis, and
cardiovascular system). Similarly, Sothmann et al. (32) concluded that a bout of exercise that is
sufficient to elicit an improvement in aerobic fitness typically elicits a stress response as well.
Thus, the working assumption is that exercise can produce beneficial adaptations in the stress
pathways while avoiding harmful effects on health. In turn, these exercise-induced adaptations
are expected to manifest themselves as responses to psychosocial stressors that are modified in a
way that entails reduced potential for harm. This rationale is referred to in the literature as the
"cross-stressor adaptation hypothesis" (32, 33).
Most studies investigating the cross-stressor adaptation hypothesis have utilized
cardiorespiratory response measures (e.g., heart rate, blood pressure) and laboratory stressors
(e.g., mental arithmetic, Stroop word-color conflict task, hand or foot cold pressor task). Most of
these studies are also cross-sectional. Study results have been summarized in a series of recent

3

meta-analyses with limited conclusions (27-29). Sothmann expresses it best in a recent update of
the status of the cross-stressor adaptation hypothesis, "The few studies conducted to date with
humans suggest that, while exercise training for three to four months may increase key
physiological measures of fitness, it generally has not induced changes in stress reactivity as
indicated by neuroendocrine measures where a short-term psychosocial challenge is the
precipitating factor" (33). Sothmann added that "it is theoretically reasonable to postulate that ...
a beneficial effect should be present, but the experimental approaches to date have offered
limited confirming data in the human" (33).
As we continue to refine our methodological approach to studying the cross-stressor
adaptation hypothesis, it is important to consider the relationship between stress and fitness from
pediatric perspective. The premise of the cross-stressor adaptation hypothesis relies on training
responses that can be attained from physical activity, which are well-established in adults but
less clear in children. While the relationship between fitness (aerobic or health-related) and stress
has received very little attention in pediatric literature (34), it is a critical step in the investigation
of the cross-stressor adaptation hypothesis in youth.
The adult literature suggests that aerobic fitness may have an attenuating effect of 1525% on heart rate and blood pressure reactivity (28). This translates into a modest influence in
adults (a reduction of approximately 2 beats per minute in heart rate and almost 4 mm Hg in
systolic blood pressure). When we consider the impact of this potential attenuation throughout
the lifespan, the significance of exploring the fitness-stress relationship becomes clear.
The overall purpose of this dissertation was to examine the relationship between stress
and metabolic syndrome, and the moderating effect of physical activity and health-related fitness
on this relationship. There were seven major research aims and hypotheses.

4

Aim 1: To describe sources of psychosocial stress, problem-focused coping resources, metabolic
syndrome-related variables, physical activity, and health-related fitness in a sample of middle
school students.
Hypothesis: This is not a hypothesis driven aim but is required for completion of Aims 2
through 7.

Variables to be assessed in the total sample are: sources of psychosocial stress (as estimated by
the sum of the ten Adolescent Stress Questionnaire subscales), problem-focused coping
resources, metabolic syndrome-related variables (i.e., metabolic syndrome composite score,
which is comprised of waist circumference, mean arterial pressure, fasting triglycerides, high
density lipoprotein cholesterol, and fasting glucose, and body mass index), physical activity (via
questionnaire and activity monitor), and health related fitness (i.e., aerobic fitness, muscular
strength and endurance, flexibility, and body composition).
Statistical Analysis: To assess Aim One, descriptive statistics will be calculated for each
variable.

Aim 2: To examine the relationships between stress (as estimated by the sum of the ten
Adolescent Stress Questionnaire subscales) and metabolic syndrome related variables.

Hypothesis: There will be a positive relationship between stress and metabolic syndrome related
variables.

5

Statistical Analysis: To assess Aim Two, linear regression analysis will be used to determine the
relationships between stress (as estimated by the sum of the ten Adolescent Stress Questionnaire
subscales) and metabolic syndrome-related variables, controlling for chronological age, and
gender.

Aim 3: To examine the relationships between coping resources and metabolic syndrome related
variables.

Hypothesis: There will be an inverse relationship between (problem-focused) coping resources
and metabolic syndrome related variables.

Statistical Analysis: To assess Aim Three, linear regression analysis will be used to determine
the relationships between (problem-focused) coping resources and metabolic syndrome-related
variables, controlling for chronological age, and gender.

Aim 4: To examine the moderating influence of physical activity on the relationship between
stress (as estimated by the sum of the ten Adolescent Stress Questionnaire subscales) and
metabolic syndrome related variables.

Hypothesis: Physical activity will modify (attenuate) the relationship between stress and
metabolic syndrome related variables.

6

Statistical Analysis: Regression analysis will be used to assess Aim Four. An interaction term
between stress and physical activity will be created to determine the moderating influence of
physical activity, controlling for chronological age, and gender.

Aim 5: To examine the moderating influence of physical activity on the relationship between
coping resources and metabolic syndrome related variables.

Hypothesis: Physical activity will modify (enhance) the relationship between problem-focused
coping strategies and metabolic syndrome related variables.

Statistical Analysis: Regression analysis will be used to assess Aim Five. An interaction term
between coping resources and physical activity will be created to determine the moderating
potential of physical activity, controlling for chronological age, and gender.

Aim 6: To examine the moderating influence of health related fitness on the relationship
between stress (as estimated by the sum of the ten Adolescent Stress Questionnaire subscales)
and metabolic syndrome related variables.

Hypothesis: Health related fitness will modify (attenuate) the relationship between stress and
metabolic syndrome related variables.

7

Statistical Analysis: Regression analysis will be used to assess Aim Six. An interaction term
between stress and heath related fitness will be created to determine the moderating influence of
heath related fitness, controlling for chronological age, and gender.

Aim 7: To examine the moderating influence of health related fitness on the relationship
between coping resources and metabolic syndrome related variables.

Hypothesis: Health related fitness will modify (enhance) the relationship between (problemfocused) coping strategies and metabolic syndrome related variables.

Statistical Analysis: Regression analysis will be used to assess Aim Seven. An interaction term
between coping resources and heath related fitness will be created to determine the moderating
influence of heath related fitness, controlling for chronological age, and gender.

This dissertation will be organized as an introduction (Chapter 1), comprehensive review
of literature (Chapter 2) followed by a detailed account of the research methods used in this
study (Chapter 3). Chapter 4 will discuss the results as they relate to the aims and Chapter 5 will
provide discussion and conclusions as well as directions of future research. Results from this
investigation will provide a better understanding of the etiological sequelae of obesity and
metabolic syndrome, which, in turn, can be used to better formulate prevention and treatment
strategies by providing effective coping skills through positive healthy habits.

8

CHAPTER 2
REVIEW OF LITERATURE

INTRODUCTION
This review will explore the complex relationships between obesity and related comorbidities during childhood and adulthood as well as the relationship between physical activity
and aerobic fitness and their influence on these conditions. Additionally, this review will
examine the postulated influence of stress in the etiology of obesity. Perturbations in stress
response and the function of the hypothalamic-pituitary-adrenal (HPA) and sympathetic
adrenomedullary (SAM) axes have been implicated as contributing factors in the development of
obesity and this review will examine the possibility that physical activity and fitness may have a
moderating influence on the relationship between stress and obesity and related metabolic
disorders.

OBESITY/METABOLIC SYNDROME
Overweight and obesity.
The Centers for Disease Control recently began using the terms “overweight” and
“obese” (formerly, “at risk for overweight” and “overweight”) to identify weight status in
th

th

children and adolescents (> 85 percentile and >95 percentile, respectively) (35, 36). These
classifications are age- and sex-specific and have been derived from national growth data (37).
The most recent estimates of overweight and obesity among U.S. children and adolescents (ages
6-19 years) is approximately 34.7% (2). Another classification method developed by the
International Task Force on Childhood Obesity, utilizes a statistical technique which back-

9

2

2

extrapolate from the adult cut-points (i.e., BMI of 25 kg/m and 30 kg/m ) to establish age- and
sex-specific cut-points for classifying children as overweight or obese (38).

Metabolic Syndrome.
Childhood obesity is associated with several adverse physiological states such as early
maturation, orthopedic issues, sleep apnea, and polycystic ovary disease, among others (39).
Furthermore, obese children are at greater risk for cardiovascular disease (CVD) risk factors,
such as insulin resistance, hypertension, and dyslipidemia (40-44). This nebulous of comorbidities (abdominal obesity, insulin resistance, elevated triglycerides, and low high-density
lipoprotein cholesterol) constitute a condition that is referred to as the metabolic syndrome.
Although definitive classification criteria for children and adolescents have not yet been
established, some authors have adapted adult criteria of these five key components for use in
pediatrics. Using various age-adjusted NCEP criteria, metabolic syndrome among adolescents
(ages 12-19 years) is estimated between 4.2-9.2% and two thirds of US adolescents have at least
one metabolic abnormality (45-47). When examining overweight/obese adolescents specifically,
thirty percent have metabolic syndrome (47). Metabolic syndrome in adolescence affects more
males than females (6.1% vs. 2.1%, respectively) (46) and varies by ethnicity. Metabolic
syndrome is more prevalent in Mexican-American, followed by non-Hispanic white adolescents
compared with non-Hispanic blacks (12.9%, 10.9% vs. 2.5%, respectively) (47).
Metabolic syndrome diagnosis was designed to complement the classic Framingham risk
score used to estimate short-term risk (10-year) in adults which tends to underestimate the
importance of obesity in CVD risk (48). Obesity is considered to be the proximal causal factor
in development of metabolic syndrome (48, 49). Likewise, some researchers suggest that
10

impaired insulin function (50), sometimes described in combination with metabolic sensitivity
(51), is also a key precipitate of metabolic syndrome diagnosis. These attributes often overlap
and are well established as contributors in the development of the two other components of the
syndrome; atherogenic dyslipidemia and hypertension (48-50, 52).
In addition to physiological outcomes, Dietz (39) notes adverse psychosocial
consequences as the most prevalent morbidity associated with obesity. Childhood obesity is
associated with increased emotional distress (53) and one mechanism for this is observed
through teasing and/or bullying. Obese children are more likely to be teased and/or bullied
compared to their normal weight peers (54). Likewise, childhood obesity is also associated with
a decrease in quality of life (QOL) during this time period (18). Schwimmer et al. (18) showed a
significantly lower QOL in obese children and adolescents compared to normal-weight children.
Furthermore, the QOL in obese subjects was comparable to that of children and adolescents who
had been diagnosed with cancer.

Long-term consequences of childhood obesity.
While the immediate effects of childhood obesity are significant, tracking these effects
into adulthood poses an additional public health concern. Several longitudinal studies have
demonstrated the relationship between high levels of fatness during childhood and adolescence
and subsequent development of various CVD morbidities as well as mortality.
Childhood obesity tends to persist through adolescence and into adulthood (55-57). The
child-adult relationship is modest (r ≈ 0.30) and strengthened when the initial assessment is taken
at a later age (r = 0.46-0.91 and 0.60-0.78 for adolescent boys and girls, respectively) (55). The

11

degree of obesity also influences the persistence into adulthood as children or adolescents at a
higher BMI percentile are more likely to be obese as adults (56-59).
In addition to adult obesity, the literature demonstrates very clearly that childhood
obesity increases risk for other components of the metabolic syndrome in adulthood (i.e.,
hypertension (60-63), dyslipidemia (61-63), insulin resistance (64, 65)), poor vascular health (66,
67), and CVD and all-cause mortality (65, 68). Likewise, childhood obesity also increases the
risk for metabolic syndrome as an adult (69-71). Because metabolic syndrome is a progressive
condition and typically does not manifest clinically until later in life, some authors have created
composite risk scores in order to examine to what extent the clustering of characteristics
associated with metabolic syndrome track into adulthood. Although time to follow-up is
relatively short in most studies (8-12 years), the available literature consistently reports moderate
tracking of clustered risk factors (42, 72-75). Authors of the Princeton Lipid Research Followup Study recently published a twenty-five year follow-up which examined metabolic syndrome
diagnosis during childhood (using NCEP criteria) and adult disease (76). Results from this study
showed that children diagnosed with metabolic syndrome as children are 6.2 times (95% CI,
2.8-13.8) more likely to have metabolic syndrome as adults (76). When compared to their
healthy counterparts, those with metabolic syndrome during childhood were 14.6 times (95% CI,
4.8-45.3) more likely to develop incident CVD (76). The same group also examined the
sensitivity of pediatric metabolic syndrome which showed that examination of individual
components are less sensitive for predicting adult metabolic syndrome compared to examination
of all five components (77). Furthermore, it may be that different combinations of risk factors
may predict risk specific to the degree of adult obesity, as was found by a recent follow-up of the
Bogalusa Heart Study (78). In normal weight adults, abnormal metabolic risk profiles were

12

associated with higher low density lipoprotein cholesterol and insulin levels during childhood.
Contrastingly, abnormal metabolic risk profiles in obese adults were associated with higher mean
arterial pressure and glucose levels during childhood (78).
Risk factors during childhood persist through adolescence and predict risk in adulthood.
Further, examination of the clustering of risk factors suggest that the aggregated risk tends to
track stronger than individual risk factors (79). Thus, examination of metabolic syndrome is
warranted in pediatrics even in the absence of a standard definition of the condition in this age
group.

PHYSICAL ACTIVITY, FITNESS, AND THEIR RELATIONSHP WITH PEDIATRIC
OBESITY/METABOLIC SYNDROME
Physical activity
Physical activity is defined as “any bodily movement produced by skeletal muscles that
results in an increase in energy expenditure above resting rate” (80). This broad definition
allows the researcher a considerable amount of latitude in the description and categorization of
physical activity. Physical activity can be weight-bearing, non-weight-bearing, occupational,
leisure-time, continuous, intermittent, organized or non-organized. Physical activity can also be
(and is most commonly) categorized by the type, frequency, duration, and intensity of the
activity. The multi-factorial nature of physical activity makes precise and accurate assessment
problematic. Several techniques (e.g., surveys and questionnaires, pedometers, accelerometers)
are commonly used to assess physical activity; however, a detailed account of each of these
methods is beyond the scope of this review (see Medicine and Science in Sports and Exercise,
(29) 6S). Several excellent reviews are available that examine the relative precision and

13

practicality of assessment tools that are available for children (81-83). Likewise, an entire issue
of the journal Research Quarterly for Exercise and Sport, (vol. 71, issue 2) has been devoted to
articles dealing with the reliability and validity of various assessment tools in a number of
subpopulations.
Intensity of physical activity receives a great deal of attention, as it is most applicable to
health-related research. The descriptive terms "very light," "light," "moderate," "hard," "very
hard," and "maximal" have been matched to a relative percentage of maximal aerobic capacity or
assigned a metabolic equivalent (MET) value as a means of standardizing the classification of
physical activity (84). This classification system is used as the basis for recommendations
designed to improve or maintain health and cardiorespiratory (aerobic) fitness.
In 2005, results were published from an expert panel that was assembled to evaluate the
available evidence and determine physical activity recommendations to improve health and
behavioral outcomes (30). The panel concluded that children and adolescents should participate
in at least sixty minutes of moderate to vigorous physical activity daily (30). Since then, the U.S.
Department of Health and Human Services has published the 2008 Physical Activity Guidelines
for Americans, which provides more detailed recommendations for all age groups (85). Current
recommendations for children and adolescents require school-aged youth to participate in at least
sixty minutes of physical activity daily (85). It is recommended that the majority of that sixty
minutes be spent participating in activities that are of moderate-to-vigorous intensity and aerobic.
Furthermore, the recommendations suggest that at least three of the days should be at a vigorous
intensity. Muscle- and bone-strengthening activities should also be incorporated as part of the
sixty minutes of activity at least three days each week (85). Approximately 34.7% of high
school-aged youth meet the recommendation of sixty minutes of physical activity per day (86).

14

As mentioned previously, accurate measurement of physical activity is challenging.
Welk, Corbin, and Dale (87) noted that the assessment techniques must be sensitive enough to
capture the short, intermittent bouts of activity characteristic of the physical activity behaviors of
children, which is a difficult task even when using a more objective measure (e.g.,
accelerometer). Accelerometers detect and quantify motion in units of ‘counts’ by determining
the amount of disruption of a signal within the monitor during a specified interval of time. The
counts are summarized for each interval and reflect the total amount of activity that occurred
during the interval. This summary can obfuscate short bouts of vigorous activity that are
alternated with bouts of rest (87). Furthermore, MET-specific cut-points are often applied to the
counts in each interval of time to determine the amount of time spent in various intensities. MET
values are not very well established in children (88), and when those cut-points are used to
identify participation in adequate levels of physical activity (>60 minutes MVPA/day), the error
of the estimate is exacerbated even further.

Aerobic Fitness
While physical activity refers to a behavior, physical fitness refers to a set of
physiological attributes and is categorized as either health- or skill-related (89). Skill-related
fitness is comprised of agility, balance, coordination, power, reaction time, and speed and are
related to performance of motor skills associated with athletic ability (89). Health-related fitness
consists of body composition, aerobic fitness, flexibility, muscular endurance, and strength (89).
Given the well-established connection with health outcomes in adult literature, aerobic fitness
receives considerable attention relative to the other components.

15

Aerobic fitness is a physiological characteristic that reflects the maximal amount of
oxygen that can be utilized (VO2max) (80). VO2max, assessed using indirect calorimetry on
either a treadmill or cycle ergometer, is the criterion measure of aerobic fitness and is typically
expressed in absolute terms or relative to body weight (L/min and ml/kg/min, respectively). Field
assessments of aerobic capacity consist of distance or timed runs (e.g., 1-mile run, 12-minute
run) or submaximal heart rate measures to predict VO2max and generally can be used with
reasonable confidence of the validity of the estimate (90). Aerobic capacity remains relatively
stable throughout childhood and adolescence in boys at approximately 52 ml/kg/min in boys.
Aerobic capacity remains relatively stable in girls at approximately 45 ml/kg/min until age 12
when it begins to decline (91).

Physical activity-aerobic fitness relationship
Intuition would suggest that more active individuals would have higher fitness and that
the relationship between physical activity and aerobic fitness would be relatively strong, as is the
case in adults (92). This relationship is considerably more evanescent in children and
adolescents, and was examined thoroughly following the 1993 International Consensus
Conference on Physical Activity Guidelines for Adolescents in a review by Morrow and
Freedson (93). In light of this excellent review, a summary of the rationale substantiating the
modest relationship between aerobic fitness and physical activity will be provided here.
The apparently high levels and modest trainability of aerobic fitness in children and
adolescents are noteworthy contributors to the modest relationship between physical activity and
aerobic fitness in this age group. As mentioned previously, aerobic fitness remains relatively
stable through adolescence, particularly in boys. This stability appears to also apply regardless of
16

physical activity participation. Rowland examined the effects of prolonged inactivity on aerobic
fitness in a small group of children who had been confined to bed rest for nine weeks (94).
Immediately following bed rest, VO2 was approximately 37.2 ml/kg/min. Testing was repeated
monthly for the next four months and again at six and nine months and VO2 increased at each
successive test until leveling off at the third month (43.1 ml/kg/min). The difference between
initial and testing at the third month can be considered an estimate of the loss due to bed rest
(~13%) (94). Because normal variation in physical activity does not match these extremes, these
findings suggest that habitual physical activity has little influence on aerobic capacity in young
people. This is in concordant with a meta-analysis by Payne and Morrow (95) that suggests the
trainability of aerobic fitness in prepubertal children and adolescents is very modest (~5%).
However, some evidence is available that suggests a more pronounced training effect can be
observed in sedentary pubertal children and adolescents (96). Morrow and Freedson concluded
that the modest association between physical activity is conceivably explained by the high levels
of aerobic fitness combined with the inherent error associated with physical activity assessment
in youth and probable lack of a true association (93).

Physical activity, fitness, and components of the metabolic syndrome.
Several excellent reviews have examined the association between physical activity,
aerobic training and/or fitness and individual components of the metabolic syndrome or clustered
CVD risk factors (97-104) and, therefore, will only be briefly summarized here.
Some population-based research has suggested that lower physical activity levels are
modestly related to greater BMI and overweight status in children and adolescents (105-107).
Studies examining the relationship of sedentary pursuits and adiposity provide a stronger

17

association (106-110), but should also be interpreted with some caution. Time spent pursuing
sedentary behaviors does not necessarily displace time that would otherwise be spent in physical
activity (111) and can be the reflection of productive sedentary behaviors (e.g., reading,
homework) which are associated with greater physical activity (112).
The relationship between physical activity and other traditional CVD risk factors (i.e.,
blood pressure, blood lipids, and insulin resistance) has received limited attention in youth.
Aerobic training has mixed results: lowering blood pressure in normotensive and hypertensive
children (113) and normotensive adolescents (114, 115), but appears to modestly decrease blood
pressure in hypertensive (114, 115) and/or obese (116) adolescents. Physically active children
tend to have more favorable lipid profiles compared to sedentary children (98, 117, 118).
Likewise, aerobic activity and exercise training are also associated with more favorable lipid
profiles (117-119). Research examining the relationship between physical activity and/or
aerobic fitness and blood glucose or insulin function in children and adolescents generally
suggests that more active children have lower fasting insulin and aerobic training can result in
reduction in insulin levels (120-122). Furthermore, aerobic fitness appears to be an independent
predictor of insulin resistance in middle school-aged youth (123).

Physical activity, fitness, and clustered metabolic risk.
Given the lack of consensus regarding the cut-points of individual risk factors for
metabolic syndrome classification in youth and because metabolic syndrome does not typically
manifest clinically until later in life, studying the relationships between physical activity, aerobic
fitness, and metabolic syndrome in this age group is a somewhat cantankerous endeavor. Some
researchers have developed various composite scores to represent metabolic syndrome risk (124-

18

126). These methods create a continuous variable, which lends itself well to examining
associations between the severity of the metabolic syndrome with other variables. Examination
of objectively measured physical activity and aerobic fitness as independent influences is
limited, but generally suggest that both variables are inversely associated with clustered risk
factors in youth (127) and that fitness partially mediates the relationship between physical
activity and clustered risk (128).
Only a handful of studies have examined objectively measured physical activity with a
clustering of risk factors in youth. A study examining Hispanic youth found the number of risk
factors to be inversely related to total physical activity measured by accelerometry (129).
Furthermore, the number of five-minute bouts of moderate-to-vigorous activity was also
inversely related to the number of risk factors (129). The European Youth Heart Study (EYHS)
showed an inverse graded relationship between quintiles of objectively measured physical
activity and clustered metabolic risk (130). Additional analysis of the Danish arm of the study,
found that this inverse relationship was maintained even after adjusting for aerobic fitness (125).
Ekelund examined the various sub-components of physical activity (i.e., time spent in low,
moderate, vigorous, and total physical activity) in this group and observed a stronger association
for total physical activity compared to moderate-to-vigorous (131). Rizzo et al. (132) found
similar results in a smaller group of 15-yr-old Swedish girls, but the relationship was attenuated
after adjustment for fatness and aerobic fitness. The EYHS has been equally instrumental in
elucidating the inverse association between aerobic fitness and clustered risk factors. When
separated in to quartiles of aerobic fitness, the least fit were significantly more likely to have
clustered risk factor profiles compared to the most fit (OR= 15.8 and 10.4 for boys and girls,
respectively) (121). Likewise, this inverse relationship held when fitness was examined as a

19

continuous variable (133) and regardless of fatness (131). Similar associations have been
observed in other American (134-137), French Canadian (138), and Australian samples (139).

AN ALTERNATIVE HYPOTHESIS
Dysregulation of the stress response
Several authors have postulated relationships among variables in the outer valence shells
that may contribute to the central energy imbalance concept. Studies of factors such as
infections, epigenetics, maternal age, assortive mating, sleep debt, endocrine disrupting
chemicals, pharmaceutical induced weight gain, decreased variability in ambient temperatures,
greater fecundity in people with greater adiposity, and intrauterine environment have found
varying levels of support for their relation to obesity and/or the metabolic syndrome in adults
(140) and children (141). While the relative importance of these factors is yet to be established,
dysregulated stress response function has been related consistently to obesity in both adults and
children and will be further examined here.

Stress and coping
Stress is a ubiquitous term used to describe a physiological response to various
environmental, physical, and emotional stimuli. Our current understanding of stress stems from
Hans Selye’s notion of a "general adaptation syndrome" (142). According to Selye's description,
the syndrome, progresses in three stages, including an initial alarm reaction, a stage of resistance,
and, if the damage continues, exhaustion and death and manifests as a result of exposure to
damaging stimuli as diverse as cold, injury, transcision of the spinal cord, excessive exercise, or
intoxication. Enlargement of the adrenal glands, involution of the thymus and lymph nodes, and
20

ulceration of the stomach were characteristic of the process. Selye is responsible for assigning to
it the meaning stress now has in the biomedical literature. The exact definition evolved over a
period of decades. For example, "we may define stress as the state manifested by a specific
syndrome which consists of all the nonspecifically induced changes within a biologic system"
(143) or, "stress is the nonspecific response of the body to any demand made upon it" (144).
Much of Selye's work on stress was devoted to the search for the so-called "first mediator," a
substance which he believed was the single common signal for the initiation of the stress
response to various stimuli. Several substances suspected to be the "first mediator(s)" were
examined and eliminated as candidates (e.g., epinephrine, norepinephrine, acetylcholine,
histamine). Selye did not give serious consideration to the role of psychological factors until
shortly before his death (145). It is important to point out that Selye's first observation of
psychological influence came rather early, albeit as an incidental side note: "even mere
emotional stress, for instance, that caused by immobilizing an animal on a board, proved to be a
suitable routine procedure for the production of a severe alarm reaction" (146).
This observation became the starting point for critics of Selye's concept of stress. Richard
Lazarus (22) focused on the fact that the magnitude of human stress responses is typically not
proportional to the degree of objective danger. Thus, he rejected the idea of stress as a passive
response and instead proposed that the key in the stress process is the subjective appraisal of
threat. This appraisal is a cognitive inferential process about the meaning of the stimulus and its
implications for the well-being and the goals of the individual, influenced jointly by the
individual's psychological makeup (e.g., knowledge and beliefs) on the one hand and the
situation on the other. This concept gradually matured into one of the most influential cognitive
theories of stress, coping, and emotion (21, 22).

21

John Mason also influenced our present conceptualization of stress and focused his
critique of Selye's view of stress on the notion of a chemical "first mediator" and the fundamental
tenet of non-specificity (147-150). Mason agreed with Lazarus in assigning a central role to
psychological processes; he suggested that the first mediator "may simply be the psychological
apparatus involved in emotional or arousal reactions to threatening or unpleasant factors in the
life situation as a whole" (147). Mason was a pioneer in broadening the scope of investigations
beyond morphological changes in organs and tissues and beyond focusing on a single hormone
or a single endocrine system. Instead, he systematically recorded changes across multiple
endocrine systems in response to multiple challenges, including those of a psychological nature.
Mason concluded that there was evidence of patterning and specificity: "The picture emerging so
far from our study of multihormonal patterns, in fact, is one suggesting that such patterns are
organized in a rather specific or selective manner, depending upon the particular stimulus under
study, and probably in relation to the complex interdependencies in hormonal actions at the
metabolic level" (149).
It appears that stressors at each level of intensity or severity, have distinct and replicable
"signatures" in the patterning of not only hormone levels but also the activity of brain regulatory
centers (151, 152). However, the "signature" of different psychosocial stressors or emotional
states is less understood. The question of whether particular types of stressful situations or
emotions are linked to particular patterns of neuroendocrine responses remains unanswered. To
better understand this relationship, we must consider the relationships between affective states
and the activation of the hypothalamic pituitary adrenocortical (HPA) and sympathetic
adrenomedullary (SAM) axes.
This multi-dimensional concept of affect is consistent with a framework proposed

22

independently by two researchers for the specificity of endocrine responses to stress, Marianne
Frankenhaeuser and James Henry. Frankenhaeuser’s work focused on human occupational
health psychology, whereas Henry’s work was with animals and focused on adaptation to stress
and the mechanisms of cardiovascular disease (CVD). Their models suggest that the HPA axis
and cortisol are sensitive to differences along the affective valence dimension (pleasure versus
displeasure), whereas the SAM axis is primarily sensitive to differences along the activation
dimension. According to Frankenhaeuser, (153) "epinephrine is a general (non-specific)
indicator of mental arousal, increasing regardless of whether the affect is positive or negative."
In contrast, "cortisol generally increases in negative affective states only". Henry's model was
summarized as: "One system [HPA] responds with distress and with euphoria in situations
associated respectively with loss of control and with success; the other [SAM], which is
activated by situations demanding effort, decreases its response when relaxation predominates"
(154). Frankenhaeuser (153, 155) noted further that the cortisol response should be expected to
be stronger when displeasure is combined with low activation (as in exhaustion or boredom) than
when displeasure is combined with high activation (as in fear or tension). Frankenhaeuser
offered the examples of depressed patients, prisoners awaiting trial, or people who lost jobs for
the former and people under pressure to produce in low-control, coercive jobs for the latter. On
the other hand, although Frankenhaeuser (155) noted that "the pattern of [epinephrine] and
[norepinephrine] secretion from the adrenal medulla tends to be rather similar, irrespective of the
quality or nature of the emotional experience," Henry (156, 157) maintained that epinephrine is
primarily associated with fear, whereas norepinephrine is primarily associated with anger.
Today, the model proposed by Frankenhaeuser and Henry is commonly used as the conceptual
basis for investigations focusing on HPA and SAM responses to psychosocial stressors (158).

23

This conceptual framework has important implications for researchers interested in
studying the dynamics of the HPA and SAM axes in response to psychosocial stressors. For
example, if the primary target of investigation is the HPA response, the experimental situation
should involve negative affect, which can be accomplished with tasks that allow little or no
chance for the participants to successfully meet the given performance goal and combine such
elements as social evaluation, loss of control, and a sense of helplessness (159, 160). However,
if the primary target of investigation is the SAM response, the experimental situation should
mainly involve high levels of activation and effort (ideally, uncontaminated by physical effort).
Such tasks should be as engaging and engrossing as possible, challenging but also offering a
reasonable chance of success (so that they do not elicit a consistently positive or a consistently
negative outcome).

Linking stress to pathophysiology
Traditionally, researchers have examined the amplitude of the stress response (e.g., the
elevation of heart rate or hormone levels), assuming that it is the magnitude of the stress
response that is the "toxic element" or the aspect of the response most closely associated with
stress-related pathologies. However, if one considers the totality of the stress response, it is
apparent that the amplitude of the response is only one way to define it and, in many cases, it
might not have the most meaningful implications for health. The impact on the body could be
determined by several other criteria. McEwen (161) uses the concept of allostatic load, the wear
and tear of the body that occurs as a result of repeated cycles of adapting to internal and external
demands, to illustrate what these other forms might be. McEwen suggests that the normal course
of events in the process of "allostasis" (i.e., adapting to changing demands) consists of an

24

appropriately sized stress response of the SAM and HPA axes to help us deal with the demand,
immediately followed by a rapid deactivation and return to baseline. This is what "normally
happens when the danger is past, the infection is contained, the living environment is improved,
or the speech has been given" (161). However, there are at least four other scenarios, each could
exacerbate the allostatic load. These include (a) repeated activations with excessive frequency,
(b) failure to habituate (i.e., show a gradually attenuating response to a familiar stressor), (c)
delayed and slow recovery and return to baseline, and (d) failure of a system to respond,
resulting in compensatory or unregulated activation of other systems. Essentially relaying a
similar message, Chrousos and Gold (162) have focused on the "chronicity" and "excessiveness"
of the stress response as its most pathogenic elements:
Generally, the stress response is meant to be acute or at least of a limited duration.
The time-limited nature of this process renders its accompanying anti-anabolic,
catabolic, and immunosuppressive effects temporarily beneficial and of no
adverse consequences. Chronicity and excessiveness of stress system activation,
on the other hand, would lead to the syndromal state that Selye described in
1936.(162)

Stress, Obesity, and the Metabolic Syndrome: Possible Mechanisms
Hypercortisolemia has frequently been associated with adiposity, particularly visceral
adiposity (12, 163, 164). Visceral fat accumulation can be viewed as a pathological adaptation to
stress (165) as it is particularly sensitive to cortisol, perhaps due to the high density and apparent
sensitivity of glucocorticoid receptors in this region. Hypercortisolaemia creates favorable
conditions for increased lipoprotein lipase (LPL) and hormone sensitive lipase (HSL) activity,

25

the chief enzymes involved in the conversion of triglycerides to free fatty acids in circulation,
and intracellularly, respectively. LPL is responsible for increasing the amount of triglycerides at
the adipocyte (166) and because insulin resistance often manifests concurrently with visceral
adiposity, the increased circulating insulin exerts antilipolytic effects and decreased lipid
mobilization (166). Likewise, cortisol appears to have a stimulatory effect on LPL activity when
insulin is present (167). Chronic hypersecretion of cortisol may lead to impaired feedback and
resistance, which is similar to the situation with insulin resistance. HSL imparts its most
deleterious effect in the development of atherosclerotic plaque. Atherogenesis involves the
uptake of lipoproteins by macrophages, which in turn leads to cellular accumulation of
cholesterol and formation of foam cells or fatty streaks (168). This process may be exacerbated
in persons with a dysregulated or hyperactive stress response given that glucocorticoids stimulate
the esterfication of sterols in smooth muscle (169). Complementary to the dysregulation of the
HPA axis, the SAM axis also plays a role in the pathogenesis of metabolic syndrome,
particularly with regard to visceral obesity. Obese individuals with dysregualted HPA axis also
have increased SAM activity, suggesting that stress great enough to promote visceral obesity
may be in the causal pathway (170). Additionally, visceral obesity is associated with a greater
basal SAM activity compared to peripheral obesity or subcutaneous abdominal obesity (164).
The presence of hypertension within the metabolic syndrome symptomatology appears to
intensify further sympathetic reactivity compared to non-hypertensive metabolic syndrome
diagnosis (171). Although the relationships between stress and markers of the metabolic
syndrome have been well demonstrated in adults (163, 172-175), little evidence is available in
children (13, 14, 176). Additional research is necessary to confirm if the adult hypothesis may
also be applicable to youth.

26

PHYSICAL ACTIVITY, EXERCISE, AND STRESS
The cross-stressor adaptation hypothesis questions if the adaptations to one kind of
stressor (i.e., exercise or physical activity) are applicable when subjected to other sources of
stress and the generalized system is activated (33). Sothmann et al. (32) conducted an extensive
review and concluded that an acute bout of exercise sufficient to elicit an improvement in
aerobic fitness generally can also elicit a stress response. Sothmann et al. also suggest that
hormones secreted by the HPA and SAM systems are generally lower at the same exercise load
after only a few weeks of training. Likewise, training results in increased production and storage
of epinephrine and norepinephrine and subsequent increased responsiveness to maximal exercise
(32). Sothmann et al. concluded that exercise training provides a beneficial adaptation to the
threshold and also the magnitude to which the stress response is activated during exercise bouts.
Evidence regarding the applicability of an exercise-stress-training effect on non-exercise
stressors is less clear. Several adult studies have examined stress response and aerobic fitness or
activity, and results have been summarized in three separate meta-analyses (27-29). Given the
methodological immaturity of this line of research, these adult investigations are far from
conclusive, but do provide evidence that aerobic physical activity and fitness impart
physiological benefits beyond metabolic health improvements.
Very few studies have examined the relationship between physical activity and stressrelated variables (i.e., perceived stress, anxiety, depression, self-esteem, etc.) in children and
adolescents. The majority of available literature has focused on habitual physical activity or
exercise bouts without a specific focus on activity that would influence aerobic fitness. Because
these studies apparently have not focused specifically on resistance or strength training, it is
reasonable to assume that the physical activity examined was, at least to some degree, aerobic.

27

Brown et al. (25) found that as exercise frequency increased, the relationship between stress and
disease decreased. Furthermore, Strauss et al. (26) demonstrated significantly lower self-esteem
in the areas of behavior, happiness, intellectual, and popularity aspects in 9 to 16 year old
adolescents who were less active. Parfitt and Eston (19) found recently that habitual physical
activity was negatively related to anxiety and depression (r = -0.48 and -0.60, respectively,) and
positively associated with global self-esteem (r = 0.66) in children. Likewise, Motl et al. (31)
used latent class modeling to examine patterns of change in physical activity and depressive
symptoms over a two year period. Results from this study suggest that the secular decline in
physical activity is inversely associated with increases in reported depressive symptoms. These
studies provide preliminary evidence to confirm the inverse relationship between stress and
physical activity.
An exercise training study by Norris et al. (177) provides some experimental evidence for
the inverse relationship between stress and fitness in youth. Norris and colleagues (177)
compared the influence of three different training regimes (low intensity, high intensity, and
flexibility) and a control group on psychological stress in a group of adolescents (N=60). The
training protocol lasted ten weeks. The high (n=14) and low (n=15) intensity groups exercised at
70-75% and 50-60%, respectively, of their age-predicted heart rate max for 25-30 minutes, twice
per week. The flexibility group (n=15) participated in stretching exercises, accompanied by
music. Stress was assessed using Cohen’s Perceived Stress scale and anxiety was assessed using
the Multiple Affect Adjective Checklist. Results showed that the high intensity group was the
only one to show fitness improvements. Furthermore, this group reported less perceived stress
than the other groups after training (p<0.05) and less anxiety than the moderate intensity group
after training (p<0.05) (177).

28

High aerobic fitness may also modify the relationship between stress and disease. Only
one study to date has examined fitness as a moderator between stress and disease in youth.
Guszkowska (34) examined health-related fitness using the International Test of Physical
Fitness, as opposed to examining aerobic fitness exclusively. This study utilized an inventory
developed by the author to quantify the number of stressful events experienced by the
participants in the previous two weeks. Likewise, health status and major life events and daily
hassles as a source of stress perceived by adolescents were also assessed by taking an inventory
of the somatic complaints reported in the previous two weeks and an overall rating of health.
When examining this health rating as an outcome variable, a significant main effect for stress
level (F(1,250) = 8.39, p<0.0001) and gender (F(1,250) = 9.97, p<0.0001) was observed,
suggesting self-rated health was better in those who were less stressed and in boys compared to
girls. Also in this model, an interaction between physical fitness and gender was reported by the
authors (F(1,250) = 4.88, p=0.03), where boys with higher fitness exhibited higher ratings of
health. No differences were observed in girls. The authors interpreted this significant interaction
as physical fitness acting as a resource in boys that improves mood, psychological well-being,
and subjective health (34). This study provides some preliminary evidence of the effect
modifying potential of physical fitness. Because aerobic fitness is considered the most viable in
terms of warding poor metabolic health, this relationship requires additional examination with
particular emphasis on aerobic fitness and aerobic activities.
Discrepancy in the literature regarding the applicability of an exercise-stress-training
effect on non-exercise stressors may be due to the high individual variability in the perception
and appraisal of the stressor (178). Lazarus (22) suggests that the perception and appraisal of a
stressor is based on previous experiences and encounters with similar stressors and dictates the
29

stress response and subsequent coping mechanism employed. It may be that exercise and
physical activity serve as coping resources by providing an escape from a stressful condition.
This notion aligns with Dienstbier’s (179) concept of “physiological toughness," which suggests
that exercise provides a rapid and robust sympathetic nervous system and catecholamine "pulse"
that helps the individual cope effectively and efficiently with the challenge at hand, a low basal
rate and a muted HPA axis response, and a quick return to baseline. Of note in this model is that
the magnitude of the SAM reactivity is considered beneficial rather than maladaptive and having
relatively little pathogenic potential, which has been the focus of most research on the exercisestress relationship. Consistent with McEwen's (161) notion of allostatic load, Dienstbier believes
an inadequate initial response, a slow, protracted, or incomplete recovery, and the inability to
habituate across multiple exposures to the same stressor have the greatest pathogenic potential.
According to Dienstbier, the "obvious avenue toward “toughening” is a program of aerobic
exercise,"(179) which he contends can bring about most of the adaptations considered critical in
this model.
Lazarus (22) has identified eight general coping resources which have not been
examined thoroughly in youth. Very little work has explored the coping resources employed by
children and adolescents. Additional research is needed to identify if exercise and physical
activity are coping strategies used by this age group and, if so, what the implications on health
outcomes such as metabolic syndrome might be.

PHYSICAL ACTIVITY, STRESS, AND THE METABOLIC SYNDROME

30

Some researchers have begun to examine variables that could potentially modify the
stress-obesity/metabolic syndrome relationship in youth. Physical activity has been shown to
modify the relationship between stress and obesity (20) and a metabolic syndrome composite
score (17). This line of research is relatively new, and researchers have not investigated the
possibility that it may have been the aerobic benefit achieved by the physical activity that was
the modifying influence. Yin and colleagues (20) examined the relationship of personal and
community stress and physical activity with adiposity in 303 individuals, aged 12 and 24 years.
Physical activity was assessed via self-report as the number of days per week during which
physical activity was sufficient to work up a sweat and stress was assessed using the Adolescent
Resource Challenge Scale. Adiposity was assessed as waist circumference, sum of three
skinfolds, and BMI. After controlling for possible confounders, personal stress was associated
with the body mass index but not with physical activity. Further the interaction of both personal
and community stress with physical activity significantly predicted adiposity measures. These
interaction terms accounted for 2- 3% of the variance in adiposity measures, with the total
models accounting for no more than 15% and 22%.
More convincing evidence for the positive influence of aerobic activity is observed in the
study by Holmes et al. (17) In this study, physical activity was assessed via accelerometry as
minutes per day of moderate-to-vigorous physical activity. In this study, school- and sportsrelated self-esteem (negatively), as well as trait-anxiety (positively) were significantly associated
with the metabolic risk score (r = -0.64, -0.53, 0.53, respectively) in the low physical activity
group. Conversely, none of the stress variables were associated with the metabolic risk score in
the high physical activity group (17). These preliminary studies suggest that aerobic physical

31

activity may exert its beneficial effects not only by raising energy expenditure but also by
attenuating the relationship of psychosocial stress to obesity and the metabolic syndrome.

SUMMARY AND CONCLUSIONS
The irrefutable consequences of childhood obesity and metabolic syndrome make
disentangling the causal pathways involved in the etiology of these conditions a high-priority
issue for the public health community. This review examined the relationships between obesity
and related co-morbidities during childhood and adulthood as well as the relationship between
physical activity and aerobic fitness. Further, this review explored the labyrinthine relationships
that exist between these variables and entertained the notion that multiple antecedents influence
each other and contribute to the development of obesity and metabolic syndrome. Understanding
the variegated relationships between the factors that influence either side of the energy balance
equation is critical for effective prevention and treatment strategies. Future research should
continue to explore variables that augment the traditional concept of energy balance. Focus
should be directed to behaviors with influence that transcends multiple putative contributors to
obesity.
Examining the role of stress as a significant antecedent of these conditions is important
given the cyclic potential of this relationship, particularly in children. Obese children are more
likely to be teased or bullied (180, 181). Consequently, these children may experience more
frequent activation of the stress response and possibly a greater volume of stress. Identifying and
employing coping strategies through physical activity have the potential to improve the
perception and appraisal of stressful conditions while simultaneously promoting healthy
metabolic function. This is particularly important in adolescents where behaviors and attitudes

32

are still developing and timely interventions could translate into long-term, positive health
outcomes in adulthood.

33

CHAPTER 3
RESEARCH METHODS

We examined middle school students (grades 7-8) who were enrolled in physical
education in the fall/spring of 2010/11 in a public school district in close proximity to Michigan
State University. All students (n=200) enrolled in physical education in the first and second
semester were invited to participate in the study. An informational letter to obtain parental
consent for participation in the study was sent home with students. Subject assent was obtained
prior to data collection. This study was approved by the Michigan State University Institutional
Review Board. The middle school setting was chosen for reasons of ease of facilitation. We
established a rapport with the school previously during a three-year service project that involved
similar testing.
General Procedures: The graduate student investigator (MH) reviewed procedures with
all participants at the beginning of each data collection session. The initial assessment session
included measures of anthropometry and resting blood pressure, and took place during physical
education classes during a normal school day. Students also completed questionnaires designed
to assess stress and coping resources. Participants completed all questionnaires during physical
education class. A final evaluation consisted of a finger stick assessment of fasting lipids and
glucose. Additionally, accelerometers were distributed in four major waves throughout the
period of data collection, with the first wave distributed on the first day of the initial assessment.
A detailed description of each measure is provided below.
Anthropometry: Stature and body mass were measured according to standard procedures
(182). Stature was measured with a portable stadiometer with the subject standing erect, without
shoes, with weight distributed evenly between both feet, heels together, arms relaxed at the sides,
34

and the head in the Frankfort horizontal plane. Body mass was assessed and body fatness was
estimated using a foot-to-foot bioelectric impedance digital scale (Tanita Corporation, Tokyo,
Japan). Given its non-invasive nature and feasibility, bioelectric impedance is commonly used as
an indicator of body composition in youth (183, 184). Stature and body mass were used to
2

calculate BMI as kg/m . Because abdominal obesity is a key feature in the metabolic syndrome,
waist circumference was assessed as a measure of central adiposity. Waist circumference was
measured in duplicate immediately above the iliac crest (National Institutes of Health
recommendation) to the nearest 0.1 cm using a Gulick tape. All anthropometry measures were
assessed behind a privacy screen. All waist circumference measures were assessed by a single
technician (MH). Measures of height and sitting height were assessed primarily by MH and two
assistants (LH and AP) who underwent anthropometry training for other concurrent projects in
our lab. Additionally, prior to each day of data collection for the present study, assistants
reviewed measurement protocols, practiced, and confirmed their values were in agreement with
those of MH.
Because the age range of the subjects spans the period of puberty and numerous body
size and physiological functions and capacities vary by pubertal status (185), an indicator of
biological maturity status was assessed (for potential analysis as a covariate) via the maturity
offset method as outlined by Mirwald et al. (186). The maturity offset technique is a noninvasive method of indicating biological maturity. Anthropometric variables are used to create a
value that is aligned to the estimated years away from peak height velocity (APHV) (e.g., -1.5
yrs, etc.).
Fitness Assessment: MH and research assistants helped the physical education instructor
to facilitate fitness assessments using the FitnessGram test battery (187). FitnessGram protocol
35

requires assessment of cardio-respiratory fitness, muscular strength and endurance, flexibility,
and body composition.
The Progressive Aerobic Cardiovascular Endurance Run (PACER 20-meter shuttle run)
was used to determine cardio-respiratory fitness. A distance of 20 meters was measured on a
gymnasium floor with lines marking each end. Each student ran back and forth to an audible
cadence projected from a CD player. The prompts occur more rapidly as the test progresses and
each 20-meter run counts as one lap. The test ends when participants are unable to maintain the
proper cadence for two consecutive laps. Participants’ scores were recorded as the number of
laps completed during the test. Prior to testing, the PACER was explained and demonstrated by
one of the investigators.
The curl-up test was used to assess abdominal strength and endurance. With knees bent at
approximately 140°, heels flat on the floor, arms straight and parallel with the trunk, participants
brought their upper bodies forward, curling up. One curl-up was defined as curling up from the
start position and returning to the start position. Students were instructed to complete as many
curl-ups as possible at a cadence of 20 per minute until they could no longer continue or had
completed 81 curl-ups, which was the end of the recorded cadence.
Upper body strength and endurance were assessed using the 90° push-up test.
Participants began in the prone position with hands placed under or slightly wider than the
shoulders, legs straight and toes tucked under. Participants then pushed up until the arms were
straight, while maintaining straight legs and back throughout the duration of the test. One 90°
push-up was defined as lowering the body until the elbows were bent at a 90° angle and pushing
back up until the arms were straight again. The test was scored as the number of 90° push-ups
achieved before compromising form.

36

The sit and reach test was used to assess flexibility and required participants to sit on the
floor with legs out straight in front and feet (shoes off) placed with the soles flat against the base
of a sit and reach box. Arms were extended forward with the hands placed on top of each other.
Participants were instructed to reach forward as far as possible without any jerky movements.
Maximal flexibility was reported as inches reached.
FitnessGram utilizes criterion-referenced standards to dichotomize results of the testing
into two categories, “healthy fitness zone” (HFZ) or “needs improvement” (187). For data
analyses, fitness was examined as a) aerobic fitness (number of PACER laps completed) and b)
dichotomously as a student being considered fit if s/he achieved the healthy fitness zone for
aerobic fitness, body composition and one additional component (e.g., push-up, sit-ups, or sitand-reach).
Blood pressure: An automated blood pressure cuff was used to collect systolic and
diastolic blood pressures (SBP and DPB, respectively) in duplicate. SBP (mmHg) and DPB
(mmHg) were used to calculate mean arterial pressure (MAP, mmHg): (systolic BP – diastolic
BP/3) + diastolic BP. In order to insure resting values, subjects sat quietly for 5 minutes prior to
assessment and this measure was taken prior to any other measures requiring physical exertion
(188). An average of these measures was used for all calculations and analyses.
Physical activity: All participants were asked to complete the Physical Activity
Questionnaire for Adolescents (PAQ-A) (189). PAQ-A is a self-administered 7-day recall tool
that was designed to be completed in a classroom setting (189). The questionnaire consists of
eight items that are scored on a 5-point scale and used to calculate a summary physical activity
score, ranging 0-4. The summary physical activity score is calculated as the mean of these eight
items and is considered a valid measure of general physical activity level (189).

37

Participants were also invited to wear the Sensewear Armband (SWA; BodyMedia,
Pittsburg, PA) as a second indicator of habitual free-living physical activity (190). The SWA is a
wireless, non-invasive, multi-sensor activity monitor that is worn over the triceps muscle. The
SWA monitor integrates data from five sensors including a bi-axial accelerometer, heat flux
sensor, galvanic skin response (GSR) sensor, skin temperature sensor, and a near body ambient
temperature sensor to estimate energy expenditure under free-living conditions. The heat-related
sensors provide additional information about the energy cost of activity because periods of
increased work are associated with increased heat production. The GSR sensor may also
contribute to EE estimation because it detects changes in the skin’s electrical properties due to
sweat gland activity and psychological stimulus (periods of increased stimulus are associated
with increased skin conductance). Direct contributions of heat indices and GSR in the prediction
algorithms are not shared by the company but all five channels are used in estimations of EE
(BodyMedia, personal communication). The SWA has been validated in young adults in
standardized exercise sessions (191) and in children across a range of activities including:
resting, coloring, playing computer games, walking on a treadmill (2, 2.5 and 3 mph) and
stationary bicycling (192).
To ensure that the data reflected actual participant physical activity levels, detailed
screening procedures were conducted to detect non-compliance with the protocol. Data for each
day were examined to ensure that the device was worn for at least 480 minutes (8 hours). This is
a typical protocol utilized when performing research with this age group (193-195). If
compliance criteria were not achieved, then that particular day was excluded from the data
analysis. Any participant with more than two missing weekdays or one missing weekend day
was removed from analysis. Participants who missed more than two weekdays or one weekend

38

day were given the monitor a second time to obtain the missing days. We were able to obtain
complete data on 7 of 15 repeat wearers. Data were examined using a t-test to ensure that
participant physical activity did not differ between those who wore the monitor once or twice.
The outputs from the device used for data analysis included minutes of moderate and vigorous
physical activity per hour of wear time. These intensity levels were examined separately (i.e.,
MPA and VPA) and combined (i.e., MVPA) for descriptive purposes and combined for all
statistical analyses.
Assessment of stress and coping resources: Because stress is an ubiquitous term and
difficult to capture with a single indicator, we chose to use the Adolescent Stress Questionnaire
(ASQ) (196) which is designed to assess ten dimensions of stress in adolescents: stress of home
life, school performance, school attendance, romantic relationships, peer pressure, teacher
interactions, future uncertainty, school and leisure time conflict, financial pressure, and emerging
adult responsibility. The ASQ consists of 56 statements such as, “Disagreements between your
parents” and “Peers hassling you about the way you look,” which subjects responded to using a
5-point Likert scale where 1 reflects “Not stressful at all.” and 5 indicates “Very stressful”.
Alpha reliability coefficients for the dimensions of stress (stress of home life, school
performance, school attendance, romantic relationships, peer pressure, teacher interactions,
future uncertainty school and leisure time conflict, financial pressure, and emerging adult
responsibility) have been demonstrated as α= 0.92, 0.88, 0.69, 0.86, 0.88, 0.87, 0.82, 0.86, 0.83,
and 0.62, respectively for students aged 13-18 years in schools from diverse socio-economic
conditions (196). The psychometric properties of the ASQ have been supported through
concurrent validity analyses with anxiety, depression, and self-esteem as well as test-retest
reliabilities for each of the dimensions of stress, ranging between 0.68 to 0.88 (196). Data can be

39

analyzed as individual scores for each subscale or totaled as an indicator of overall stress. The
latter strategy was chosen for the present analysis and scores could range from 58-290 (196).
Chronbach’s alpha reliability coefficient was calculated for the total ASQ, because we chose to
use the sum of all scales to represent stress in this sample (α= 0.96).
Lazarus suggests that upon recognition of a stimulus, there is an appraisal process which
individualizes the stress response and subsequently influences the coping response (21). To
better explain the appraisal and coping processes of our subjects, we included the Ways of
Coping Questionnaire (WCQ) (197, 198). A modified version has been used in this age group
(199). The WCQ assesses five coping processes: problem-focused, wishful thinking, seeks
social support, blamed self, and avoidance (alpha reliability coefficients are α= 0.82, 0.85, 0.75,
0.78, and 0.74, respectively in adults) (198). The WCQ requires the participant to recall his/her
most stressful encounter in the previous week and consider that event as s/he answers a total of
sixty-six questions such as “I criticized or lectured myself” and “I just concentrated on what I
had to do next – the next step”. Participants responded by recalling the stressful event and then
indicating to what extent the statement was used by selecting “Does not apply or not used”,
“Used somewhat”, “Used quite a bit”, “Used a great deal” which corresponds with a 0-3 Likert
scale. For present investigation, we chose to focus on the problem-focused scale as it has been
the focus of similar investigations on coping strategies and health in this age group (200, 201).
Chronbach’s alpha reliability coefficient was only calculated for problem focused coping (α=
0.82) in the present sample as it was the aspect of coping relevant to the aims of this study.
Assessment of additional metabolic syndrome variables: Subjects who consented to
having additional metabolic syndrome variables assessed (n=123) via blood analysis were asked
to abstain from eating breakfast on the day of assessment and reminder notes were sent home to

40

ensure a fasting sample. These variables included total cholesterol (TC, mg/dL), high density
lipoprotein cholesterol (HDL-C, mg/dL), triglycerides (TG, mg/dL), and glucose (GLU, mg/dL).
MH met students before school to collect a single finger stick blood draw and provide breakfast
for participants. Blood sampling by finger stick (35 uL) was chosen for reasons of compliance
and avoidance of undue stress for the study participant. Blood sample collection was conducted
in accordance with the guidelines provided by the Michigan State University Office of Radiation,
Chemical & Biological Safety (ORCBS) in order to minimize risk associated with blood borne
pathogens. Upon collection, samples were analyzed using a portable analyzer according to the
protocol of the manufacturer (Cholestech LDX System, Hayward, CA).
Derivation of the metabolic syndrome score. A composite risk factor, or metabolic
syndrome score was derived by summing the age-standardized residuals (Z-scores) for GLU,
MAP, HDL-C, TG and WC. These variables were chosen because they represent the same ones
used in the adult clinical criteria and this variable has been used in recent work from our
laboratory (17, 126, 202). Because the metabolic syndrome typically does not manifest until
later in life and is a dichotomous variable, the use of a composite score is advantageous as it
allows each subject to have a continuous value that is comparable to others in the study. A lower
score is indicative of a better metabolic risk factor profile relative to the study sample.
Data Analysis: To assess Aim One, descriptive statistics were calculated for each variable
for boys, girls, and the total sample. To assess Aim Two, linear regression analysis was used to
determine the relationships between stress (as determined by the sum of the ten sub-scales of the
Adolescent Stress Questionnaire) and metabolic syndrome related variables, controlling for
chronological age, and gender. To assess Aim Three, linear regression analysis was used to
determine the relationships between (problem-focused) coping resource and metabolic

41

syndrome-related variables, controlling for chronological age, and gender. Regression analyses
were used to assess Aims Four and Five. Interaction terms between stress and physical activity
and between coping resources and physical activity were created to determine the moderating
influence of physical activity, controlling for maturity status, and gender. Regression analyses
were used to assess Aims Six and Seven. Interaction terms between stress and heath related
fitness, and (problem-focused) coping resources and heath related fitness were created to
determine the moderating influence of health-related fitness, controlling for maturity status and
gender.
Statistical power and sample size analyses. The ability to detect effect modification is
considered the most power limited analysis (203). Therefore, power calculations were based on
being able to evaluate Aims Four through Seven. With α = 0.05 and power = 0.80, we assumed
2

a minimum detectible effect size (MDES) of F =0.067. MDES was determined using previous
research on this topic (17) and a statistical power and sample size calculator (204). For a
2

medium effect size of F =0.15, 55 students are required for power = 0.80, α = 0.05. For a small2
to-moderate effect size of F =0.07, 115 students are required for power = 0.80, α = 0.05.

42

CHAPTER 4
RESULTS

Participants
Consent was obtained from 136 of 200 (68%) middle school students. However, two
students declined participation after consenting, and one moved prior to completion of data
collection, reducing participant number to 133. In an effort to maintain a homogeneous sample,
participants with preexisting conditions that might skew the results (e.g., insulin-dependent
diabetes, Down Syndrome, etc.) were excluded from analyses (n= 5). Statistical criteria for
outliers further reduced the sample, the details of which are discussed prior to discussion of the
regression models. Final sample size was reduced to 126, which exceeded the minimum desired
sample size according to power and sample size calculations sufficient to detect a smallmoderate effect, determined a priori.
Key characteristics were selected to categorize those with incomplete data sets and then
compared by category to assess the degree of attrition bias. Key characteristics included the
presence of a metabolic syndrome score (n= 11 cases with missing data) and the presence of a
physical activity assessment via SWA (n= 14 cases with missing data) (each coded yes or no).
Independent variables assessed in each of these analyses were chosen because they are key
variables that were collected in nearly all the sample (i.e., PAQ-A score, PACER laps, BMI, and
stress). No statistically significant group differences between those with complete or incomplete
metabolic syndrome score data sets were found when examining PAQ-A score (t= 0.22, p=
0.82), PACER laps (t= -0.54, p= 0.59), BMI (t= 0.653, p=0.52), and stress (t= 0.14 , p= 0.89).
Likewise, no statistically significant group differences between those with complete or
incomplete physical activity assessment via SWA data sets were found when examining PAQ-A
43

score (t= -1.29, p= 0.20), PACER laps (t= 1.72, p= 0.09), BMI (t= 0.20, p= 0.98), and stress (t=
0.96, p= 0.34). These results suggest there was no statistically significant attrition bias for these
variables.
Participant Characteristics
th

th

Participants were 7 and 8 grade students (n= 126, 55% male) from a local middle
school. All study participants were enrolled in physical education class in the Fall 2010 and/or
Spring 2011 semesters. The specific school was chosen for the study was based on its proximity
to the investigators and a previously established collaborative relationship with the investigators.
It should be noted that physical education is an elective course in this school district. The
students at this school are primarily Caucasian (70%), with approximately 20% AfricanAmerican, 5% Hispanic, and 5% Asian comprising the other ethnicities at the school. The
middle school age range was chosen because this is a critical time of physical and emotional
development that can lead to inappropriate behaviors and increased health risks. Participants in
this study ranged in age between 12.2 and 15.5 years. Mean ages for boys and girls in this study
were 13.5 and 13.3 years, respectively.
To address Aim 1, descriptive characteristics were calculated for all variables for boys,
girls, and the total sample (Table 1a-c). Table 1a shows the results for anthropometric and
th

metabolic data. Both boys and girls approximated the 50 percentile for height and the 75

th

percentiles for weight and BMI, respectively according to the 2000 CDC growth charts (205).
Descriptive characteristics for boys and girls were similar, except boys were slightly older (t= 2.08, p<.05, d= 0.62) and taller (t= -2.27, p<.05, d= 7.49).

44

The majority of participants were average maturers and maturity status did not differ by
gender (Table 1a). Likewise few variables of interest differed between early and late maturers.
Late maturers completed more PACER laps compared to early maturers (65.8 vs. 39.7 laps,
respectively) and did not perform as well on the sit-and-reach assessment (11.0 vs. 13.4 inches,
respectively). Late maturers reported more physical activity via the PAQ-A (2.14 vs. 1.63,
respectively). Additionally, late maturers exhibited a more favorable metabolic syndrome
composite score than early maturers (-2.11 vs. 2.44, respectively), relative to the study sample.
There was very little gender difference among metabolic variables. Boys exhibited
higher SBP compared to girls (113 mmHg vs. 109 mmHg, respectively; t= -2.04, d= 12.32
p<0.05); however, no other gender differences were observed among the other metabolic
variables. Mean values for blood pressure and blood lipids approximated the 50th to 75th
percentiles, according to age- and gender-specific norms (188, 206). In a simple, exploratory
search for potential outliers, we found four participants with triglyceride levels and two
participants with SBP values greater than three standard deviations above the mean for each of
those variables. However, when factored in as a component of the metabolic syndrome
composite score, which was a primary outcome variable for this study, none of these participants
met any criteria for outlier classification and, therefore, were not excluded.
Descriptive results for physical activity and health related fitness are shown in Table 1b.
Analysis of PAQ-A data showed boys reported higher physical activity levels compared to girls
(2.6 vs. 1.7, [out of 4] respectively; t= -3.34, d= 0.72, p<0.05). Mean minutes of MVPA
determined by the armband (SWA) exceeded the minimum recommendations (85) for both boys
and girls and were nearly twice the national average (194), which may be reflective of our
sample being drawn from students enrolled in physical education class. Gender differences were
45

not observed in MVPA as determined by the armband. However, when considering the
components of MVPA, boys accumulated more VPA compared to girls (16.3 vs. 11.1 minutes
respectively, t= -2.03, d= 13.47, p<0.05).
Gender differences were present in some health-related components of fitness.
Compared to girls, boys completed more PACER laps (67 vs. 44, respectively, t= -6.46, d=
23.08) and curl-ups (62 vs. 53, respectively; t= -2.37, d= 22.51, p<0.05). However, girls
performed better on the sit-and-reach test (13 vs. 11 inches, respectively; t= 4.27, d= 3.06,
p<0.05). On average, FitnessGram testing results were in the HFZ for all assessments. Likewise,
the proportion of boys and girls in the present sample meeting the HFZ exceeded recently
reported prevalences of Texas data in all variables except BMI in both boys and girls and pushups in boys (207). Comparison of our results to those of Texas children is relevant, given that
they are the only descriptive health related fitness data available which utilize the recently
revised FitnessGram standards (207). Our subjects appear to be more fit compared to students
from Texas, which may be reflective of our sample being drawn from students who elected to
enroll in physical education class. When examining the psychosocial variables assessed in this
study (Table 1c), girls reported higher stress (t= 3.02, d= 40.38, p<0.05), but there was no
difference in participant reported problem-focused coping scores.

Outlier screening
Before conducting any further analyses, data were screened for possible outliers. In
accordance with procedures by Tabachnick and Fidell (203) one subject was excluded because
the standardized residual for the primary outcome variables exceeded the acceptable range. After
examination of Mahalanobis distances (203), one additional potential outlier was identified.
46

Outlier status was supported after additional examination of the Leverage value, which exceeded
the acceptable level as determined by procedures according to Belsley, Kuh, and Welsch (208).
To calculate the critical Leverage value, the following equation was used: Leverage (h): if h >
2(k+1)/n (where k = # predictors). In all, these procedures resulted in the exclusion of two
additional subjects, resulting in the final sample size of 126 participants.

Regression analyses
(Aims 2-3: Main Effects)
Multiple regression analysis was used to examine the relationship between stress and
metabolic syndrome related variables (i.e., metabolic syndrome composite score, Table 2a, and
BMI, Table 2b). Independent variables in these models included gender, APHV, and stress.
Both models were significant (p <0.05) with AVPH emerging as a significant predictor in both
models (β = 0.65, t= 9.28, p <0.05; and β = 0.31, t= 3.56, p <0.05), for the metabolic syndrome
composite score and BMI, respectively). Additionally, stress also emerged as a significant
predictor of BMI (β = 0.19, t= 209, p <0.05). Similarly, analyses examining problem-focused
coping as a predictor of metabolic syndrome composite score or BMI were also both significant
models with APHV emerging as a significant predictor (β = 0.65, t= 9.26, p <0.05; and β = 0.30,
t= 3.37, p <0.05, for metabolic syndrome and BMI, respectively) (Tables 3a and b, respectively).
Independent variables in these models included gender, APHV, and problem-focused coping.

Regression analyses
(Aims 4-5: Physical Activity as a Moderator)

47

Tables 4a and 4b show the results of regression analyses of the PAQ-A score and MVPA,
respectively, and stress, on the metabolic syndrome composite score. Independent variables in
these models included gender, APHV, physical activity (PAQ-A score and MVPA, respectively),
stress, and a physical activity-stress interaction term. Both models significantly predicted
metabolic syndrome composite score; however, the only significant predictor in either model
was APHV (β = 0.64, t= 8.97, p <0.05; and β = 0.67, t= 9.01, p <0.05, for PAQ-A score and
MVPA, respectively). Similar results were observed when examining the same independent
variables (gender, APHV, physical activity via PAQ-A and MVPA, and stress) and BMI as the
outcome variable (Tables 5a and 5b). Likewise, when including gender, APHV, physical
activity (PAQ-A score and MVPA, respectively) and problem-focused coping as independent
variables, significant relationships were observed when examining the metabolic syndrome
composite score (Tables 6a and 6b) and BMI (Tables 7a and 7b) as outcome variables. In both
of these models, APHV was the only significant predictor. (β = 0.30, t= 3.44, p <0.05; and β =
0.31, t= 3.34, p <0.05, for PAQ-A score and MVPA, respectively)

Regression analyses
(Aims 6-7: Health-Related Fitness as a Moderator)
Regression analysis was used to examine gender, APHV, aerobic fitness, and stress as
predictors of the metabolic syndrome composite score yielded a significant model (p< 0.05).
However, the only significant predictor was APHV (β= 0.66, t= 9.37, p< 0.05) (Table 8a).
Likewise, multiple regression analysis of health-related fitness and stress on the metabolic
syndrome composite score also yielded a significant model with APHV emerging as the only
significant predictor (β= 0.68, t= 9.45, p< 0.05p< 0.05) (Table 8b). Table 9 shows the results of

48

regression analysis of aerobic fitness and stress on BMI. Gender, APHV, aerobic fitness, stress,
and an interaction between aerobic fitness and stress were entered as independent variables in the
model. This model was statistically significant (p< 0.05), however, the only significant
predictors of BMI were gender (β= 0.20, t= 2.16, p< 0.05) and APHV (β= 0.22, t= 2.77, p<
0.05). In these models, neither stress nor fitness (aerobic and health-related) significantly
influenced the metabolic syndrome composite score.
Regression analysis of aerobic fitness and problem-focused coping on the metabolic
syndrome composite score was significant (p< 0.05) (Table 10a). In this model, gender, APHV,
aerobic fitness, and problem-focused coping were examined as independent variables. The only
significant predictor in this model was APHV (β= 0.66, t= 9.28, p< 0.05). Similarly, regression
analysis of health-related fitness and problem-focused coping on the metabolic syndrome
composite score also produced a significant model (p< 0.05) (Table 10b). Only APHV emerged
as a significant predictor in this model (β= 0.66, t= 9.28, p< 0.05). Table 11 shows significant
results of regression analysis examining the independent variables gender, APHV, aerobic
fitness, problem-focused coping, and an interaction between aerobic fitness and problem-focused
on BMI (p< 0.05). APHV (β= 0.22, t= 2.64, p< 0.05) and aerobic fitness as determined by
PACER laps (β= -0.53, t= -2.86, p< 0.05) were significant predictors in this model. These results
suggest that maturity status imparts a larger and more consistent influence metabolic syndrome
related variables compared to the other variables examined.

Summary of Results
Our results suggest physical activity, stress, and problem-focused coping have little
influence on the metabolic syndrome composite score or BMI in this sample. Maturity status

49

(i.e., APHV) was consistently observed as a significant predictor of metabolic syndrome related
variables. The statistically significant influence of maturity status on our outcome variables is
not surprising. Metabolic syndrome is a progressive condition development of which begins in
early adolescence (69-71). Likewise, the influence of maturity status on BMI is also not
surprising as increases in BMI are typical of normal growth and maturation. However, one main
effects model showed stress to be a significant predictor of BMI in this sample (Table 2b).
Likewise, individual models showed a main effect of physical activity (Table 7b) and aerobic
fitness as determined by pacer laps (Table 11).

50

CHAPTER 5
DISCUSSION
Metabolic syndrome is a comprehensive indicator of health and, given the increasing
prevalence in children and adolescents (45), is viewed as a serious public health concern.
Considerable attention has focused primarily on two behavioral factors associated with metabolic
syndrome, diet and physical activity energy expenditure. Because these two variables leave a
considerable portion of the variance in the metabolic syndrome phenotype unexplained,
investigation of the pathogenic potential of factors that extend beyond the traditional concept of
energy imbalance has intensified. One intriguing line of research implicates perturbations in the
stress response system and the putative role that dysregulation may have on the development of
obesity and metabolic syndrome. This relationship has been well established in the adult
literature (6-9), and is receiving increased attention in pediatric work (12-15). An increased
focus on mental health determinants such as stress is warranted given the marked increase of
psychotropic medication prescription and physicians' office visits for treatment of emotional and
behavioral problems in youth (23, 24), particularly when considering the favorable relationship
between physical activity and/or fitness and stress. Researchers have generally demonstrated an
inverse relationship between physical activity and/or fitness and stress-related measures (27-30).
Likewise, the relationship between stress and metabolic syndrome may be influenced by physical
activity and/or fitness; however, literature addressing this potential effect modification is sparse
but promising (17, 20). Physical activity and fitness may improve metabolic health by directly
influencing risk factors associated with the metabolic syndrome, as well as providing a healthy
coping resource that may serve to moderate the relationship between stress and poor metabolic
health. Identifying these relationships is critical as we continue to refine and develop new

51

strategies for addressing childhood obesity by providing effective coping skills through positive
health habits.
This dissertation had three major foci, 1) to describe metabolic syndrome-related
variables, physical activity, health-related fitness, psychosocial stress, and problem-focused
coping resources in a sample of middle-school students, 2) to examine the relationship between
psychosocial health and metabolic syndrome related variables, and 3) to investigate the possible
moderating effect of physical activity and health-related fitness on this relationship.

Participant Characteristics (Aim 1)
th

th

Participants were 7 and 8 grade students (n= 126) from a local middle school. All
were enrolled in physical education class in the Fall 2010 and/or Spring 2011 semesters. The
middle school age range was chosen because this is a critical time of physical and emotional
development that can lead to inappropriate behaviors and increased health risks. The specific
school chosen for the study was based on its proximity to the investigators and a previously
established collaborative relationship with the investigators. Because of the close partnership
between the physical education instructor and the investigators, the research project was carried
out in a way that augmented many of the lessons taught during a normal school day. This unique
collaboration allowed for ease of facilitation of the project and a longstanding welcome at the
school.
th

Participants were of average height (50 percentile) and slightly above average weight
th

and BMI (75 percentile) according to the CDC growth charts (205). Likewise, mean values of
physiologic and metabolic characteristics (e.g., blood pressure, cholesterol, etc.) approximated

52

th

th

the 50 to 75 percentiles, according to age- and gender-specific norms (188, 206). Mean
minutes of MVPA determined by the armband exceeded the minimum daily recommendations
(85) and were nearly twice the national average (194). On average, FitnessGram testing results
were in the HFZ for all assessments. Likewise, the proportion of boys and girls in the present
sample meeting the HFZ exceeded recently reported prevalences of Texas youth in all variables
except BMI in both boys and girls and push-ups in boys (207). Comparison of our results to
those of Texas children is relevant, given that they are the only descriptive health-related fitness
data available which utilize the recently revised FitnessGram standards (207). Our subjects
appear to be more active compared to national samples and more fit compared to students from
Texas. Overall, our study participants were metabolically healthy, active, and fit which may be
reflective of our sample being drawn from students enrolled in physical education class.
Given the lack of consensus in methodology linking stress measures with poor metabolic
health in youth, we chose to build upon previous work from our lab (17) that incorporated a
broad-based approach to operationalize stress by assessing variables known to be related to the
appraisal of the demands of daily life (e.g., trait-anxiety, depression, etc.) as well as variables
that are known to affect the well-being of youth (e.g., self-esteem, appearance-related teasing,
etc.). We sought to accomplish a similar broad-based approach, but do so using only two, more
comprehensive assessment tools. The psychosocial variables assessed in this study were stress
and problem-focus coping. We chose to measure stress as the sum of ten subscales (stress of
home life, school performance, school attendance, romantic relationships, peer pressure, teacher
interactions, future uncertainty, school and leisure time conflict, financial pressure, and emerging
adult responsibility) that comprise the ASQ (196). The ten subscales meet the recommended
criteria found in the psychology literature which suggests the assessment should capture stress
53

associated with family life and the participants’ interaction with the environment in which they
live (209). Similarly, the etiological significance of the stress appraisal process on health
outcomes in children has also been noted as deserving additional attention (209). To address this
knowledge gap, we used Lazarus and Folkman’s Ways of Coping Questionnaire (WCQ) and we
focused specifically on problem-focused coping (197-199). This focus was prompted by
previous studies that have examined the relationship between problem-focused coping and health
(200, 201) .
Results from the present study showed greater reported stress in girls than boys which is
typical of adolescents (210, 211) and adults (212, 213). Two studies to date have independently
examined the validity of the ASQ (196, 214). The original validation study examined 1039
Australian boys and girls and used measures of anxiety, depression, and self-esteem as indicators
of construct validity. ASQ scores were positively associated with anxiety and depression and
negatively associated with self-esteem, suggesting the ASQ is a valid measure of stress (196).
Likewise, demographic correlates of the ASQ showed reported stress was greater in girls than
boys (196). The ASQ underwent a second validation when it was translated into a Norwegian
version (ASQ-N) (214). Measures of anxiety and depression were positively associated with the
ASQ-N and negatively associated with self-esteem in the Norwegian version (214). Further, the
Norwegian version demonstrated similar gender differences as were observed in the original
survey (214). Although there is no clear rationale for the gender differences, they are well
recognized in the literature (196, 210-213). Further, these gender differences align with
psychotropic medication prescription trends and physician office visits for the treatment of
emotional problems in boys and girls (23, 24, 215). The similar gender patterning responses of

54

the current study provides some evidence of construct validity that the ASQ captured meaningful
dimensions of stress in this sample.
Although gender differences in coping behaviors are not well-defined, the tendency to
employ problem-focused coping strategies traditionally has been more apparent in males (216),
which is in line with the notion that males are socialized to deal with adversities in a more
action-oriented way (217). The role of socialization in the development of coping behaviors can
be explained as social constraints differentially being presented to both genders that predisposes
each gender do perceive and deal with adversity in a certain way (217). However, more recent
investigations suggest that gender differences in coping strategies are becoming less significant
as social roles continue to evolve (218, 219). In the present study, gender differences were not
observed when examining problem-focused coping. Therefore, the results of the present study
support literature suggesting gender differences may not be apparent in more contemporary
assessments.
This dissertation was part of a larger research project in which resiliency was measured.
In a separate analysis of these data, Holmes et al. (220) also did not observe gender differences
when examining resiliency in this group. Resiliency is a measure of stress and coping ability
(221) and is reflective of characteristics of those who thrive in the face of adversity. Resilient
individuals are characterized as viewing stress as a challenge or an opportunity for improvement,
having a greater tolerance to negative affect, and greater reliance on action-oriented approaches
to problem solving (221). In the same report, Holmes et al. (220) note a significant positive
correlation between problem-focused coping and resiliency. This relationship is consistent with
literature regarding the link between coping strategies and resiliency (222) and provides some
evidence of construct validity of the WCQ in the present analysis.

55

Psychosocial health and metabolic syndrome related variables (Aims 2-3)
As mentioned previously, it has been difficult to operationalize stress in a way that is
methodologically feasible to examine relationships with health outcomes (10, 223). Stress is a
ubiquitous term that includes identification of stressors, analysis of appraisal and subsequent
employment of coping resources and reappraisal. Generally, the relationship between stress and
health is examined using indicators of chronic stress or stress reactivity (see Holmes et al., for a
review (10)). While the duration and frequency as well as the physiologic responses to a stressor
are clearly important aspects of research examining the relationship between stress and metabolic
health, this limited perspective does not capture the ubiquity of the stress concept. Further, the
methodology used to examine these aspects varies across studies (see Holmes et al., for a review
(10)). Investigations in youth provide an additional challenge when considering normal growth
and maturation are recognized stressors (11). That is, when the speed and magnitude of these
changes exceeds the adolescent’s ability to cope, growth maturation and development can be an
exacerbating influence that may confound the relationship between stress and metabolic health
(11). Additional research is needed to elucidate the relationship between the stress and coping
response and metabolic health in this age group. Particular attention should be directed towards
the influence of growth and maturation on this relationship given its association with both
variables.
Adult literature firmly establishes a relationship between stress, broadly defined as the
adaptive responses that are the result of a disharmonious state when the threat to homeostasis
exceeds a threshold (162), and poor metabolic health (6, 8, 10, 162). When this liberal definition

56

is applied to the pediatric literature, we can acknowledge an increased focus over the last decade
when examining ‘stress-related’ variables such as physiologic markers (e.g., adrenocortical
activity and cortisol levels (12-15, 224, 225) and cardiovascular reactivity (226)), chronic stress
(20, 227), quality of life (18), and stress associated with home life (223, 228), depression (19,
225, 229-231), anxiety (17, 231), self-esteem (17, 230), and teasing (16, 17). The majority of
literature in children and adolescents has been limited to examination of the relationship between
these stress-related variables and BMI and has shown that the two are directly related. Although
the majority of the evidence is restricted to BMI, the significance of examination of stress in
youth is clear given the firmly established link between childhood BMI and adult obesity (5659), hypertension (60-63), dyslipidemia (61-63), insulin resistance (64, 65), poor vascular
health,(66, 67) and CVD and all-cause mortality (65, 68).
In the present study we examined the stress-metabolic health relationship using a multidimensional indicator of perceived stress (i.e., ASQ). To examine metabolic health, we chose to
look at the most common indicator, BMI, and a more robust indicator of metabolic health by
using a metabolic syndrome composite score. The metabolic syndrome composite score allows
each participant to have a continuous value that is comparable to the health of the rest of the
sample. We hypothesized a positive relationship between reported stress and BMI or the
metabolic syndrome composite score in our sample which would be consistent with research to
date (12-20, 223-231). Our results indicated that none of our models demonstrated a significant
relationship between any measure of psychosocial health with the metabolic syndrome
composite score and only one model showed a significant relationship between stress and BMI,
which is inconsistent with previous studies. The departure of our findings from the current trend
may be due partially to the gender differences in reported stress. Girls in the present study

57

reported significantly more stress than boys. The ASQ is a relatively new assessment tool with
the current version published in 2007 (196). Although the difference in perceived stress is
expected (196, 212), it may be that the ASQ is an effective tool to demonstrate the relationship
between stress and health in girls and not boys. Unfortunately, this sample is not powered to
examine the research questions in this way. Additional research with sample sizes sufficient to
examine gender differences is warranted to determine the efficacy of the ASQ in both genders.
The degree to which stress influences health in boys may be better captured by examining
other stress-related variables rather than perceived stress. For example, personality traits such as
trait-anxiety have previously been associated with higher BMI or adverse metabolic health (17,
231) and is commonly used as a measure of construct validity for stress survey tools, as was the
case with the instrument used in this present study (196). Anxiety is a negative emotional state
characterized by nervousness, worry, and apprehension and is often considered in two main
facets, state- and trait-anxiety (232). State-anxiety refers to the transient, emotional state of
nervousness, worry, and apprehension, whereas trait-anxiety refers to a behavioral disposition to
perceive situations that are objectively not dangerous as threatening and respond with a
disproportionate state-anxiety (232, 233). Individuals with high trait-anxiety are more likely to
have a chronically activated stress response system, and thus may be more susceptible to the
adverse effects of stress on certain diseases. Although trait-anxiety was related to the ASQ tool
used in this study, it may be that examination of a propensity to be anxious may be more relevant
to health-related research.
Aggression is another aspect of personality that deserves additional attention, particularly
in boys. Trait aggressiveness is described as a propensity to engage in acts of aggression, a
proneness to anger as well as to hold hostile beliefs about other people across situations (234,

58

235). American boys (aged 2-17) spend an average of thirteen hours per week playing video
games (236). The bulk of video games being played by children and adolescents contain violent
content (237) which is of particular concern given that the playing of violent video games has
been shown to increase aggressive behaviors, thoughts, emotions and decrease prosocial
behavior (238, 239). Aggression is linked with stress and the stress response in that the HPA
axis plays a key regulatory role of aggressive behavior (240). Briefly, greater HPA activity,
which is associated with greater arousal, is thought to be the underlying mechanism of sudden
outbursts of aggression (241). Alternatively, lower HPA activity is also associated with
aggression in that it is associated with hypoarousal that may result in more permanent changes in
brain functions that are associated with violence (241). This perspective of the stress response
and allostasis only partially describes possible adaptations as described by McEwen (161).
McEwen suggests that the normal course of events in the process of "allostasis" (i.e., adapting to
changing demands) consists of an appropriately sized stress response of the SAM and HPA axes
to help us deal with the demand, immediately followed by a rapid deactivation and return to
baseline (161). McEwen also describes other scenarios that could exacerbate the allostatic load
including (a) repeated activations with excessive frequency, (b) failure to habituate (i.e., show a
gradually attenuating response to a familiar stressor), (c) delayed and slow recovery and return to
baseline, and (d) failure of a system to respond, resulting in compensatory or unregulated
activation of other systems. Investigation into the role of aggression in these other allostatic
scenarios could be a viable avenue for exploration as we continue to refine stress assessment
methodology in children and adolescents.
The second indicator of psychosocial health assessed in this study was problem-focused
coping which can be described as behavior aimed at solving the problem associated with a

59

stressor. We hypothesized that problem-focused coping would be associated with more
favorable metabolic health in the present sample, as it has been consistently associated with
better metabolic control in adolescents with Type I diabetes (209, 242). In the present study, we
did not observe a significant relationship between problem-focused coping and metabolic heath
or BMI. This finding is consistent with findings from The Amsterdam Growth and Health Study
(200). Participants in The Amsterdam Growth and Health Study were surveyed on their coping
strategies and type A behavior twice in early adulthood (mean ages 21and 27 years). Body fat
(distribution via subscapular-triceps skinfold ratio and sum of four skinfolds) and a number of
personality traits (e.g., inadequacy, dominance, etc.) were assessed six times between the ages of
13 and 27 years. Coping strategies (problem-focused or otherwise) were not related to fatness at
any time point. However, associations between central fat distribution with type A behavior and
some personality traits associated with type A behavior (e.g., dominance and rigidity) were
observed (200).
The current study only examined one aspect of coping, problem-focused. Additional
investigation is warranted to identify if any other coping strategies (i.e., distancing, selfcontrolling, seeking social support, accepting responsibility, escape or avoidance, planful
problem-solving, and positive reappraisal) may influence metabolic health. One perspective in
the literature describes these coping strategies as serving discrete functions and, in a given
stressful situation, the behaviors of an individual serves multiple coping functions to attenuate
distress (243). Because adolescence is a time when individuals experience many novel stressful
situations, without established coping behaviors, future research endeavors may find it beneficial
to examine coping from this more complex perspective. The survey tool designed to capture this

60

perspective is the A-COPE (243). The A-COPE integrates individual coping theory and family
stress theory in a single tool to assess coping behaviors and style in adolescents (243).
Perhaps the method by which we cope with stress is not as influential on our metabolic
health as is the overall product of the interaction between stress and our coping, or our resiliency.
As previously mentioned, resiliency is a measure of stress and coping ability and is reflective of
characteristics of those who thrive in the face of adversity (221). Little is known about the
relationship between resiliency and metabolic health beyond what can be inferred from stress
and coping literature. Examination of resiliency in future studies may provide a unique
perspective as represents the outcome of the interaction between stress and coping and it
warrants further investigation.

Moderating effect of physical activity and health-related fitness (Aims 4-7)
The evidence supporting the favorable relationship between physical activity or fitness
and metabolic health is well established in youth (97-104). As mentioned previously, evidence
linking ‘stress-related’ and poor metabolic health in youth has been increasing (12-20, 223-231).
Further, the favorable relationship between psychosocial factors and physical activity is accepted
by the scientific community as evidenced by the section devoted to this relationship in the 2005
Evidence Based Physical Activity Recommendations (30). These apparent univariate
relationships have prompted some researchers to investigate how these three variables may be
related within a system of complex interactions.
Literature addressing the moderating potential of physical activity and health-related
fitness is sparse but promising (17, 20, 34). In 2005, Yin and colleagues (20) examined the
relationship of personal and community stress and physical activity with adiposity in 303

61

individuals, aged 12 and 24 years. Physical activity was assessed via self-report as the number of
days per week during which physical activity was sufficient to work up a sweat, and stress was
assessed using the Adolescent Resource Challenge Scale. Adiposity was assessed as waist
circumference, sum of three skinfolds, and BMI. After controlling for possible confounders,
personal stress was associated with the body mass index but not with physical activity. Further,
the interaction of both personal and community stress with physical activity significantly
predicted adiposity measures. These interaction terms accounted for 2-3% of the variance in
adiposity measures, with the total models accounting for 22% of the variance. The age range of
this study includes the ages examined in this dissertation. However, the mean age of subjects in
the Yin et al. study was 16.6 years (20), which is more than three years older than the mean age
of students who participated in this dissertation. Perhaps the beneficial influence of physical
activity and fitness may impart on the deleterious association between stress and metabolic
health is better observed later in adolescence.
Additional evidence for the positive influence of physical activity is observed in the study
by Holmes et al. (17). This study pursued a considerably different conceptual and
methodological approach than the Yin study that allows evaluation of this issue from a different
but complementary perspective. Rather than focusing only on adiposity, the authors chose to
study the metabolic syndrome in the form of a composite score. Because it reflects a broader
spectrum of risk factors, a metabolic syndrome composite score is presumably a more robust
indicator of overall metabolic and cardiovascular health than any single measure of adiposity.
Additionally, the authors acknowledge the issues of operationalizing stress in this age group by
utilizing a broad-based approach to measuring stress. Key variables known to be related to the
appraisal of the demands of daily life (i.e., perceived stress, anxiety, depression, self-esteem), as

62

well as variables known to influence the well-being of school-age youth (i.e., appearance-related
teasing) were assessed using a number of self-report indices. In this study, physical activity was
assessed via accelerometry as minutes per day of moderate-to-vigorous physical activity. In the
Holmes et al. study, school- and sports-related self-esteem (negatively), as well as trait-anxiety
(positively) were significantly associated with the metabolic risk score (r = -0.64, -0.53, 0.53,
respectively) in the low physical activity group. Conversely, none of the stress variables were
associated with the metabolic risk score in the high physical activity group, suggesting a
moderating influence of increased physical activity (17). Although the sample in the Holmes et
al. study was small (n= 37), it was apparently more unhealthy compared to the present sample.
The present sample had a lower prevalence of overweight (38% vs. 43%), lower blood pressure
(MAP, 77 mmHg vs. 87 mmHg), higher HDL cholesterol (50.8 vs. 44.4 mg/dL), and more active
(85 vs. 78 minutes of MVPA/day) compared to the Holmes et. al study. One plausible
explanation for the absence of significant associations between psychosocial and metabolic
health as well as the absence of any moderating influence of physical activity and fitness in the
present study could be participants’ health. Study participants may have lacked sufficient
variance in our indices of metabolic health such that a relationship with psychosocial variables
could not be observed.
Fitness has also demonstrated some viability as a moderator on the stress-metabolic heath
relationship (34). Guszkowska (34) examined health-related fitness in a group of Polish
adolescents using the International Test of Physical Fitness and stress using an inventory
developed by the author to quantify the number of stressful events experienced by the
participants in the previous two weeks. Likewise, health status and major life events and daily
hassles as a source of stress perceived by adolescents were also assessed by taking an inventory

63

of the somatic complaints reported in the previous two weeks and an overall rating of health.
When examining this health rating as an outcome variable, a significant main effect for stress
level (F = 8.39, p<0.0001) and gender (F = 9.97, p<0.0001) was observed, suggesting self-rated
health was better in those who were less stressed and in boys compared to girls. Also in this
model, an interaction between physical fitness and gender was reported by the authors (F = 4.88,
p=0.03), where boys with higher fitness exhibited higher ratings of health. No differences were
observed in girls. The authors interpreted this significant interaction as physical fitness acting as
a resource in boys that improves mood, psychological well-being, and subjective health (34).
The present study failed to show a relationship between psychosocial stress and coping
and metabolic health and was also unable to observe an influence of physical activity or healthrelated fitness on the psychosocial-metabolic health relationship. We feel these results were not
likely due to measurement error by the study technicians given their substantial training prior to
data collection. However, any small amount of measurement error that may have occurred
during data collection was not likely to differentially bias the results.
Although we did not observe our hypothesized relationships, the line of inquiry
examining a moderating influence of physical activity and fitness on this relationship still holds
merit and should not be abandoned. Additional work should focus on identifying indices of
stress that might be most relevant to metabolic health among youth with particular attention to
gender differences. Girls and boys report perception of stress at varying levels (196, 212) and it
is likely tailoring assessment tools to address gender differences in stress may better illustrate the
relationship between stress and health. Future researchers examining gender differences may
find it beneficial to examine biomarkers such as cortisol as biological differences in gender may
be more apparent when examining endocrinologic functions. However, obtaining a

64

comprehensive indicator of dysgregualtion of the stress as described by McEwen (161) requires
multiple sampling over a specified time period, the methods and caveats of which are discussed
in the Holmes et. al. (10).

Summary and Conclusions
This dissertation sought to expand the current body of knowledge regarding child and
adolescent heath by examining metabolic syndrome-related variables, physical activity, healthrelated fitness, psychosocial stress, and problem-focused coping resources and investigate the
relationships that may exist between these variables in a sample of middle-school students.
Results from the present investigation suggest physical activity and psychosocial variables (i.e.,
stress and problem-focused coping) imparted little influence on the metabolic syndrome
composite score or BMI in this sample. The consistent, statistically significant influence of
maturity status (i.e., APHV) on metabolic syndrome related variables yields little novel clinical
significance. As noted earlier, metabolic syndrome is a progressive condition, the origins of
which begin in adolescence and continue to adulthood (69-71). Similarly, increases in BMI are a
part of normal growth and maturation, thus an influence of maturity status on BMI is not
surprising. Given that maturity status was consistently a significant predictor of metabolic
syndrome related variables in all of our models and the metabolic syndrome score was
significantly different between early, average, and late maturers, it may advantageous for
researchers to examine possible differences in the strength of association between psychosocial
and metabolic syndrome variables between maturity statuses.

Although few significant

relationships were observed, this study was an important step in our understanding of the
complex system of interactions that relates psychosocial, physical, and metabolic health. Our

65

results suggest the need for continued methodological refinement, particularly regarding stress
assessment in this age group. A critical step in elucidating the relationship between stress and
health in youth requires identification and concise descriptions of indices of stress that are most
relevant to metabolic health among youth. As previously mentioned, adolescence is a tumultuous
time in life throughout all areas of development. Indicators of greater volumes of stress or
ineffective practices in dealing with stress that have potential to be pathogenic over time must be
examined through the course of adolescence and into adulthood to better understand their
influence on metabolic health. Indicators of greater volumes of stress may be enhanced by
indicators of personality traits that may be unique in certain groups (e.g., aggression in boys) as
they may also identify opportunities for intervention (e.g., violence in video games). Likewise,
indicators of ineffective practices in dealing with stress, or coping, may benefit from further
examination of resiliency. Given that resiliency is the product of the interaction between stress
and coping, examination of this variable may provide a more comprehensive indication of the
influence stress has on health.
As we continue to investigate the etiological sequelae of obesity and metabolic
syndrome, a multi-factorial perspective is essential to develop better-formulated prevention and
treatment strategies. The possibility of improving metabolic health through physical activity
may be twofold: 1) physical activity can directly influence the risk factors associated with the
metabolic syndrome, and 2) physical activity may provide effective coping skills through
positive healthy habits. This is particularly important in adolescents where behaviors and
attitudes are still developing and timely interventions could translate into long-term, positive
health outcomes in adulthood.

66

Table 1a. Anthropometric and metabolic descriptive characteristics of the sample.
Boys (n=69)

Girls (n=57)

Total (n=126)

Anthropometric
Variables
Age (yrs)

13.5 (0.7)*

13.3 (0.6)

Ht (cm)

162.5 (8.3)*

159.6 (6.3)

Age at PHV (yrs)

12.4 (1.3)

12.1 (0.9)

Body mass (kg)

57.5 (14.7)

55.8 (13.2)

BMI (kg/m )

21.6 (4.3)

21.8 (4.5)

WC (cm)

73.5 (11.9)

73.5 (11.7)

37.7%

38.6%

13.4 (0.6)
12.2-15.5
161.2 (7.6)
139.2-184.2
12.3 (1.2)
8.7-15.33
56.8 (14.0)
33.5-100.3
21.7 (4.4)
14.1-39.3
73.5 (11.8)
55.2-113.1
38.1%

113.6 (13.3)*

109.1 (11.0)

DBP (mmHg)

59.8 (6.7)

60.4 (5.3)

MAP (mmHg)

77.7 (8.0)

76.7 (6.1)

Glucose (mg/dL)

93.0 (9.0)

90.1 (9.6)

143.2 (24.4)

143.5 (25.2)

51.3 (15.1)

50.3 (11.9)

92.6 (72.2)

101.8 (52.9)

2

Overweight
Metabolic Variables
SBP (mmHg)

Total Cholesterol
(mg/dL)
HDL Cholesterol
(mg/dL)
Triglycerides (mg/dL)

Metabolic Syndrome
0.39 (3.3)
Composite Score
*p<0.05 for gender difference

-0.46 (2.6)

111.6 (12.5)
78-156
60.1 (6.1)
46-76
77.2 (7.2)
57-102
91.7 (9.3)
72-124
143.3 (24.7)
97-206
50.8 (13.7)
22-97
96.9 (63.9)
45-412
0.01 (3.0)
-6.0-6.2

Values are mean (SD) and range values for boys, girls, and total sample. Due to nonparticipation of some subjects in the finger stick portion of the study, glucose, total cholesterol,
HDL Cholesterol, Triglycerides, and the metabolic syndrome composite score have a slightly
smaller sample size (n= 115, total; 62, boys; and 53, girls)
Ht, height; PHV, peak height velocity; BMI, body mass index; WC, waist circumference;
SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure;
HDL-C, high density lipoprotein cholesterol
67

Table 1b. Physical activity and health related fitness descriptive characteristics of the sample.
Boys (n=69)

Girls (n=57)

Total (n=126)

Physical Activity
PAQ-A score (0-4)
MVPA (min/hr)

2.6 (0.8)*
11.4 (5.2)*

1.7 (0.7)
8.9 (4.4)

Moderate PA (min/hr)

9.3 (4.1)*

7.5 (3.3)

Vigorous PA (min/hr)

2.1 (1.6)*

1.4 (1.7)

2.0 (0.7)
10.3 (5.0)
0-265
8.5 (3.9)
1.5-19.9
1.8 (1.7)
0.01-10.1

Health Related Fitness
PACER (laps)

67 (27)*

44 (18)

Curl-ups

62 (24)*

53 (21)

Push-ups

16 (9)

14 (5)

Sit-and-Reach (in)

11 (3)*

13 (3)

26.9 (12.5)

27.4 (8.3)

Body Fat (%)

55 (26)
6-118
58 (23)
2-81
15 (8)
0-50
12 (3)
4-22
27.2 (10.8)
5.0-56.0

*p<0.05 for gender difference
Values are mean (SD) and range values for boys, girls, and total sample. Due to nonparticipation of some subjects in portions of physical activity and fitness assessments sample size
varies (n= 112-126 for the total sample; 62-69 for boys; and 50-57 for girls)
MVPA, moderate to vigorous physical activity

68

Table 1c. Psychosocial descriptive characteristics of the sample.
Boys (n=69)

Girls (n=57)

Total (n=126)

121.7 (34.2)*

144.1 (46.8)

18.5 (9.3)

17.7 (7.4)

131.8 (41.8)
58-258
18.1 (8.5)
0-45

Psychosocial Variables
Total stress
(58-290)
Problem-focus coping
(0-45)

*p<0.05 for gender difference
Values are mean (SD) and range values for boys, girls, and total sample.

69

Table 2a. Multiple Regression of Stress on the Metabolic Syndrome Composite Score.

Gender

B
0.20

β
0.03

p
0.64

APHV

2.97

0.65

0.0001*

-0.001

-0.02

0.82

Stress
2

F= 30.09*, R = 0.41
*p<0.05
APHV, years away from peak height velocity

Table 2b. Multiple Regression of Stress on BMI.

Gender

B
-0.22

β
-0.03

p
0.78

APHV

2.05

0.31

0.001*

Stress

0.20

0.19

0.04*

2

F= 5.45*, R = 0.10
*p<0.05
APHV, years away from peak height velocity

Table 3a. Multiple Regression of Problem-focused Coping on the Metabolic Syndrome
Composite Score.

Gender

B
0.22

β
0.04

p
0.59

APHV

2.96

0.65

0.0001*

-0.002

-0.01

0.95

Problem-focused
Coping
2
F= 30.07*, R = 0.41
*p<0.05

APHV, years away from peak height velocity

70

Table 3b. Multiple Regression of Problem-focused coping on BMI.

Gender

B
-0.64

β
-0.07

p
0.41

APHV

1.98

0.30

0.001*

-0.003

-0.06

0.95

Problem-focused
Coping
2
F= 3.85*, R = 0.06

*p<0.05
APHV, years away from peak height velocity

Table 4a. Multiple Regression of Physical Activity Questionnaire Score and Stress on the
Metabolic Syndrome Composite Score.

Gender

B
0.15

β
0.03

p
0.74

APHV

2.92

0.64

0.0001*

PAQ-A Score

-0.16

-0.04

0.86

Stress

-0.01

-0.09

0.67

PAQ-A Score x Stress
2
F= 17.14*, R = 0.39

0.02

0.10

0.73

*p<0.05
PAQ-A, Physical Activity Questionnaire for Adolescents; APHV, years away from peak
height velocity

71

Table 4b. Multiple Regression of Moderate-to-Vigorous Physical Activity and Stress on the
Metabolic Syndrome Composite Score.

Gender

B
0.08

β
0.01

p
0.86

APHV

3.07

0.67

0.0001*

MVPA

0.13

0.22

0.17

Stress

0.01

0.09

0.41

-0.001

-0.20

0.20

MVPA x Stress
2
F= 18.49*, R = 0.45

*p<0.05
MVPA, Moderate-to-Vigorous Physical Activity; APHV, years away from peak height
velocity

Table 5a. Multiple Regression of Physical Activity Questionnaire Score and Stress on Body
Mass Index.

Gender

B
-0.20

β
-0.02

p
0.81

APHV

2.03

0.30

0.001*

PAQ-A Score

2.05

0.35

0.22

Stress

0.05

0.45

0.08

PAQ-A Score x Stress
2
F= 3.39*, R = 0.09

-0.01

-0.40

0.27

*p<0.05
APHV, years away from peak height velocity

72

Table 5b. Multiple Regression of Moderate-to-Vigorous Physical Activity and Stress on Body
Mass Index.

Gender

B
0.11

β
0.01

p
0.90

APHV

2.12

0.31

0.001*

MVPA

-0.04

-0.04

0.85

Stress

0.03

0.24

0.08

-0.001

-0.15

0.46

MVPA x Stress
2

F= 3.78*, R = 0.11

*p<0.05
MVPA, Moderate-to-Vigorous Physical Activity; APHV, years away from peak height
velocity

Table 6a. Multiple Regression of Physical Activity Questionnaire Score and Problem-focused
Coping on Metabolic Syndrome Composite Score

Gender

B
0.12

β
0.03

p
0.32

APHV

2.98

0.64

0.0001*

PAQ-A Score

-0.40

-0.10

0.52

Problem-focused
Coping
PAQ-A Score x Problemfocused Coping
2
F= 17.39*, R = 0.40

-0.06

-0.17

0.38

0.03

0.24

0.34

*p<0.05
PAQ-A, Physical Activity Questionnaire for Adolescents; APHV, years away from peak
height velocity

73

Table 6b. Multiple Regression of Moderate-to-Vigorous Physical Activity and Problem-focused
Coping on Metabolic Syndrome Composite Score.

Gender

B
0.10

β
0.02

p
0.83

APHV

3.09

0.67

0.0001*

MVPA

0.02

0.04

0.78

-0.004

-0.01

0.92

0.00003

0.01

0.99

Problem-focused
Coping
MVPA x Problemfocused Coping
2
F= 17.88*, R = 0.44

*p<0.05
MVPA, Moderate-to-Vigorous Physical Activity; APHV, years away from peak height
velocity

Table 7a. Multiple Regression of Physical Activity Questionnaire Score and Problem-focused
Coping on Body Mass Index.

Gender

B
-0.74

β
-0.09

p
0.37

APHV

1.97

0.29

0.002*

PAQ-A Score

0.23

0.04

0.85

Problem-focused
Coping
PAQ-A Score x Problemfocused Coping
2
F= 2.20, R = 0.05

-0.01

-0.02

0.94

0.004

0.02

0.95

*p<0.05
PAQ-A, Physical Activity Questionnaire for Adolescents; APHV, years away from peak
height velocity
74

Table 7b. Multiple Regression of Moderate-to-Vigorous Physical Activity and Problem-focused
Coping on Body Mass Index.

Gender

B
-0.32

β
-0.04

p
0.71

APHV

2.11

0.31

0.001*

MVPA

-0.36

-0.40

0.02*

Problem-focused
Coping
MVPA x Problemfocused Coping
2
F= 3.37, R = 0.10

-0.09

-0.17

0.23

0.01

0.25

0.19

*p<0.05
MVPA, Moderate-to-Vigorous Physical Activity; APHV, years away from peak height
velocity

Table 8a. Multiple Regression of Aerobic Fitness and Stress on the Metabolic Syndrome
Composite Score.

Gender

B
-0.20

β
-0.03

p
0.69

APHV

3.06

0.66

0.0001*

PACER laps

0.03

0.30

0.20

Stress

0.01

0.10

0.54

0.0001

-0.17

0.50

PACER laps x Stress
2
F= 4.02*, R = 0.12

*p<0.05
APHV, years away from peak height velocity

75

Table 8b. Multiple Regression of Health Related Fitness and Stress on the Metabolic Syndrome
Composite Score.

Gender

B
0.14

β
0.02

p
0.76

APHV

3.17

0.68

0.0001*

Health Related
Fitness
Stress

2.23

0.37

0.12

0.004

0.06

0.50

Health Related
-0.01
Fitness x Stress
2
F= 18.71*, R = 0.42

-0.24

0.31

*p<0.05
APHV, years away from peak height velocity
Table 9. Multiple Regression of Aerobic Fitness and Stress on Body Mass Index.

Gender

B
1.76

β
0.20

p
0.03*

APHV

1.50

0.22

0.01*

PACER laps

-0.07

-0.44

0.11

Stress

0.02

0.15

0.40

0.0001

-0.04

0.88

PACER laps x Stress
2

F= 9.13*, R = 0.25

*p<0.05
APHV, years away from peak height velocity

76

Table 10a. Multiple Regression of Aerobic Fitness and Problem-focused Coping on Metabolic
Syndrome Composite Score

Gender

B
-0.24

β
-0.04

p
0.62

APHV

3.06

0.66

0.0001*

PACER laps

0.02

0.18

0.27

Problem-focused
Coping
PACER laps x Problemfocused Coping

0.01

0.02

0.90

0.0001

0.001

0.84

2

F= 18.65*, R = 0.42

*p<0.05
APHV, years away from peak height velocity
Table 10b. Multiple Regression of Health Related Fitness and Problem-focused Coping on
Metabolic Syndrome Composite Score.

Gender

B
0.19

β
0.03

p
0.65

APHV

3.13

0.67

0.0001*

Health Related Fitness

1.15

0.19

0.25

Problem-focused
Coping
Health Related Fitness
x Problem-focused
Coping
2
F= 18.38*, R = 0.42

0.01

0.02

0.81

-0.02

-0.06

0.72

*p<0.05
APHV, years away from peak height velocity

77

Table 11. Multiple Regression of Aerobic Fitness and Problem-focused Coping on Body Mass
Index.

Gender

B
1.54

β
0.18

p
0.06

APHV

1.46

0.22

0.01*

PACER laps

-0.09

-0.53*

0.01*

Problem-focused
Coping
PACER laps x Problemfocused Coping

-0.02

-0.03

0.86

0.0001

0.05

0.83

2

F= 8.46*, R = 0.23

*p<0.05
APHV, years away from peak height velocity

78

APPENDICES

79

APPENDIX A
Permission

80

81

APPENDIX B
Consent Forms

82

83

84

85

86

APPENDIX C
Adolescent Stress Questionnaire (ASQ)

87

Adolescent Stress Questionnaire
Please read each item below and indicate, by using the following rating scale, to what extent
each stressor has affected you in the past year.
1= Not at all stressful
2= A little stressful
3= Moderately stressful
4= Quite stressful
5=Very Stressful
_____ 1. Arguments at home.
_____ 2. Difficulty with some subjects.
_____ 3. Going to school.
_____ 4. Abiding by petty rules at home.
_____ 5. Being ignored or rejected by the person you want to go out with.
_____ 6. Breaking up with your boy/girl-friend.
_____ 7. Lack of trust from adults.
_____ 8. Pressure of study.
_____ 9. Getting along with your boy/girl-friend.
_____ 10. Not enough time for activities outside of school hours.
_____ 11. Disagreements between you and your teachers.
_____ 12. Having to take on new family responsibilities with growing older.
_____ 13. Not enough money to buy the things you want.
_____ 14. Concern about your future.
_____ 15. Little or no control over your life.
_____ 16. Making the relationship with your boy/girl-friend work.
_____ 17. Work interfering with school and social activities.
_____ 18. Having to make decisions about future work or education.
_____ 19. Being judged by your friends.
_____ 20. Changes in your physical appearance with growing up.
_____ 21. Living at home.
_____ 22. Having to concentrate too long during school hours.
_____ 23. Having too much homework.
_____ 24. Lack of respect from teachers.
_____ 25. Employers expecting too much from you.
_____ 26. Abiding by petty rules at school.
_____ 27. Parents hassling you about the way you look.
_____ 28. Getting up early in the morning to go to school.
_____ 29. Disagreements between your parents.
_____ 30. Having to take on new financial responsibilities with growing older.
_____ 31. Not being listened to by teachers.
_____ 32. Being hassled for not fitting in.
_____ 33. Not being taken seriously by your parents.
_____ 34. Not having enough time for your boy/girl-friend.
_____ 35. Satisfaction with how you look.
_____ 36. Teachers hassling you about the way you look.
_____ 37. Pressure to make more money.
_____ 38. Having to study things you are not interested in.
_____ 39. Disagreements between you and your peers.
_____ 40. Compulsory school attendance.
88

1= Not at all stressful
2= A little stressful
3= Moderately stressful
4= Quite stressful
5=Very Stressful
_____ 41. Keeping up with your schoolwork.
_____ 42. Lack of freedom.
_____ 43. Getting along with your teachers.
_____ 44. Not enough money to buy the things you need.
_____ 45. Peers hassling you about the way you look.
_____ 46. Disagreements between you and your mother.
_____ 47. Having to study things you do not understand.
_____ 48. Not having enough time for fun.
_____ 49. Putting pressure on yourself to meet your future goals.
_____ 50. Lack of understanding by your parents.
_____ 51. Teachers expecting too much from you.
_____ 52. Pressure to fit in with peers.
_____ 53. Parents expecting too much from you.
_____ 54. Not getting enough timely feedback on schoolwork.
_____ 55. Not getting enough time for leisure.
_____ 56. Disagreements between you and your father.
_____ 57. Lack of school resources.
_____ 58. Disagreements between you and your brothers and sisters.

89

APPENDIX D
Way of Coping Questionnaire (WCQ)

90

Ways of Coping
To respond to the statements in this questionnaire, you must have a specific stressful situation in
mind. By stressful, we mean a situation that was difficult or troubling for you, either because you
felt distressed about what happened, or because you had to use considerable effort to deal with
the situation. Take a few moments and think about the most stressful situation you have
experienced in the last week.
Was the situation about (Mark all that apply):
______ School
______ Family
______ Social Relationships
______ Work
______ Recreational Activity
______ Health
______ Financial
______ Other
Did the situation involve (Mark all that apply):
______ Just You
______ Parents
______ Siblings
______ Other Relatives
______ Peers ______ Friends
______ Boyfriend/Girlfriend
______ Teacher
______ Coach
______ Boss ______ Adult Supervisor
______ Other
Please read each item below and indicate, by using the following rating scale, to what extent you
used it in the situation you have just thought of and described.
0 = Not used
1 = Used somewhat
2 = Used quite a bit
3 = Used a
great deal
_____ 1. Just concentrated on what I had to do next – the next step.
_____ 2. I tried to analyze the problem in order to understand it better.
_____ 3. Turned to work or substitute activity to take my mind off things.
_____ 4. I felt that time would make a difference – the only thing to do was to wait.
_____ 5. Bargained or compromised to get something positive from the situation.
_____ 6. I did something which I didn’t think would work, but at least I was doing something.
_____ 7. Tried to get the person responsible to change his or her mind.
_____ 8. Talked to someone to find out more about the situation.
_____ 9. Criticized or lectured myself.
_____ 10. Tried not to burn my bridges, but leave things open somewhat.
_____ 11. Hoped a miracle would happen.
_____ 12. Went along with fate; sometimes I just have bad luck.
_____ 13. Went on as if nothing had happened.
_____ 14. I tried to keep my feelings to myself.
_____ 15. Looked for the silver lining, so to speak; tried to look on the bright side of things.
_____ 16. Slept more than usual.
_____ 17. I expressed anger to the person(s) who caused the problem.
_____ 18. Accepted sympathy and understanding from someone.
_____ 19. I told myself things that helped me to feel better.
_____ 20. I was inspired to do something creative.
_____ 21. Tried to forget the whole thing.
_____ 22. I got professional help.
_____ 23. Changed or grew as a person in a good way.
_____ 24. I waited to see what would happen.
_____ 25. I apologized or did something to make up.
91

0 = Not used
1 = Used somewhat 2 = Used quite a bit
3 = Used a great deal
_____ 26. I made a plan of action and followed it.
_____ 27. I accepted the next best thing to what I wanted.
_____ 28. I let my feelings out somehow.
_____ 29. Realized I brought the problem on myself.
_____ 30. I came out of the experience better than when I went in.
_____ 31. Talked to someone who could do something concrete about the problem.
_____ 32. Got away from it for a while; tried to rest or take a vacation.
_____ 33. Tried to make myself feel better by eating, drinking, smoking, using drugs or
medication, etc.
_____ 34. Took a big chance or did something very risky.
_____ 35. I tried not to act too hastily or follow my first hunch.
_____ 36. Found new faith.
_____ 37. Maintained my pride and kept a stiff upper lip.
_____ 38. Rediscovered what is important in life.
_____ 39. Changed something so things would turn out all right.
_____ 40. Avoided being with people in general.
_____ 41. Didn’t let it get to me; refused to think too much about it.
_____ 42. I asked a relative or friend I respected for advice.
_____ 43. Kept others from knowing how bad things were.
_____ 44. Made light of the situation; refused to get too serious about it.
_____ 45. Talked to someone about how I was feeling.
_____ 46. Stood my ground and fought for what I wanted.
_____ 47. Took it out on other people.
_____ 48. Drew on my past experiences; I was in a similar situation before.
_____ 49. I knew what had to be done, so I doubled my efforts to make things work.
_____ 50. Refused to believe that it had happened.
_____ 51. I made a promise to myself that things would be different next time.
_____ 52. Came up with a couple of different solutions to the problem.
_____ 53. Accepted it, since nothing could be done.
_____ 54. I tried to keep my feelings from interfering with other things too much.
_____ 55. Wished that I could change what had happened or how I felt.
_____ 56. I changed something about myself.
_____ 57. I daydreamed or imagined a better time or place than the one I was in.
_____ 58. Wished that the situation would go away or somehow be over with.
_____ 59. Had fantasies or wishes about how things might turn out.
_____ 60. I prayed.
_____ 61. I prepared myself for the worst.
_____ 62. I went over in my mind what I would say or do.
_____ 63. I thought about how a person I admire would handle this situation and used that as a
model.
_____ 64. I tried to see things from the other person’s point of view.
_____ 65. I reminded myself how much worse things could be.
_____ 66. I jogged or exercised.

92

APPENDIX E
Physical Activity Questionnaire for Adolescents (PAQ-A)

93

Physical Activity Questionnaire
Now, we would like to find out about your level of physical activity in the past week (last 7
days). Physical activity refers to sports or dance that make you sweat or make your legs feel
tired, or games that make you breathe hard like tag, running, climbing, etc…There are no right or
wrong answers. Please answer all the questions honestly and accurately.
1. Physical activity in your spare time: have you done any of the following activities in the
past 7 days (last week)? If yes, how many times? Circle the appropriate number of times
you have completed each activity. Circle only one in each row.
Skipping
Rowing/canoeing
Rollerblading
Tag
Walking for exercise
Bicycling
Jogging or running
Ice hockey
Swimming
Baseball, softball
Dance
Football
Badminton
Skateboarding
Soccer
Wrestling
Volleyball
Floor hockey
Basketball
Ice skating
Cross-country skiing
Other:____________

No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No

1-2
1-2
1-2
1-2
1-2
1-2
1-2
1-2
1-2
1-2
1-2
1-2
1-2
1-2
1-2
1-2
1-2
1-2
1-2
1-2
1-2
1-2

3-4
3-4
3-4
3-4
3-4
3-4
3-4
3-4
3-4
3-4
3-4
3-4
3-4
3-4
3-4
3-4
3-4
3-4
3-4
3-4
3-4
3-4

5-6
5-6
5-6
5-6
5-6
5-6
5-6
5-6
5-6
5-6
5-6
5-6
5-6
5-6
5-6
5-6
5-6
5-6
5-6
5-6
5-6
5-6

7+
7+
7+
7+
7+
7+
7+
7+
7+
7+
7+
7+
7+
7+
7+
7+
7+
7+
7+
7+
7+
7+

2. In the last 7 days, during physical education (PE) classes, how often were you very active
(playing hard, running, jumping, throwing)? (check one only.)
I didn’t do PE_____
Hardly ever_____
Sometimes_____
Quite often_____
Always_____

94

3. In the last 7 days, what did you normally do at lunch (besides eating)? (check one only.)
Sat down (talking, reading, doing school work)_____
Stood around or walked around_____
Ran or played a little bit_____
Ran around and played quite a bit_____
Ran and played hard most of the time_____
4. In the last 7 days, on how many days right after school, did you do sports, dance or play
games in which you were very active? (check only one).
None_____
1 time last week_____
2 or 3 times last week_____
4 times last week_____
5 times last week_____
5. In the last 7 days, on how many evenings did you do sports, dance, or play games in
which you were active? (check only one.)
None_____
1 time last week_____
2 or 3 times last week_____
4 times last week_____
5 times last week_____
6. During the last weekend, how many times did you do sports, dance, or play games in
which you were very active? (check only one.)
None_____
1 time_____
2-3 times_____
4-5 times_____
6 or more times_____
7. Which one of the following describes you best for the last 7 days? Read all five
statements before deciding on the one answer that best describes you. (check your
answer.
_____ a. All or most of my free time was spent doing things that involve little
physical effort.
_____ b. I sometimes (1-2 times last week) did physical things in my free time
(such as played sports, went running, swimming, bike riding, did aerobics).
_____ c. I often (3-4 times last week) did physical things in my free time.
_____ d. I quite often (5-6 times last week) did physical things in my free time.
_____ e. I very often (7 or more times last week) did physical things in my free
time.

95

8. Mark how often you did physical activity (like playing sports, games, doing dance, or any
other physical activity) for each day last week. (circle only one for each day.)
Monday
None
Little bit
Medium
Often
Very Often
Tuesday
None
Little bit
Medium
Often
Very Often
Wednesday None
Little bit
Medium
Often
Very Often
Thursday
None
Little bit
Medium
Often
Very Often
Friday
None
Little bit
Medium
Often
Very Often
Saturday
None
Little bit
Medium
Often
Very Often
Sunday
None
Little bit
Medium
Often
Very Often
9. Were you sick last week, or did anything prevent you from doing your normal physical
activities? (check only one.)
Yes_____
No_____
If yes, what prevented you? _______________________________________________
10. On a typical school day, how many total hours outside of school do you watch TV, view
videos, work/play on the computer? (Check only one.)
_____ a. I do not watch TV, view videos or use the computer on a typical day
_____ b. less than 1 hour per day
_____ c. 1 hour per day
_____ d. 2 hours per day
_____ e. 3 hours per day
_____ f. 4 or more hours per day

96

APPENDIX F
Correlation Matrix

97

Correlation Matrix of Selected Variables of Interest

PAQ-A
MVPA
Stress
Coping
MetScore
BMI
*p<0.05

PAQ-A
+
0.10
-0.06
0.07
0.06
0.03

MVPA

Stress

Coping

MetScore

BMI

+
-0.26*
-0.13
0.14
-0.17

+
0.10
-0.08
0.16

+
-0.05
-0.03

+
0.04

+

98

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99

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