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What matters most?

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Title:
What matters most?
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Predictors of quality of life and life satisfaction among young breast cancer survivors
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Martens, Kellie ( author )
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English
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Breast -- Cancer -- Psychological aspects ( lcsh )
Breast -- Cancer -- Patients -- Attitudes ( lcsh )
Breast -- Cancer -- Patients -- Attitudes ( fast )
Breast -- Cancer -- Psychological aspects ( fast )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

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Includes bibliographical references.
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System requirements: Adobe Reader.
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by Kellie Martens.

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University of Florida
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ocn953416790
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Full Text
WHAT MATTERS MOST?
PREDICTORS OF QUALITY OF LIFE AND LIFE SATISFACTION AMONG
YOUNG BREAST CANCER SURVIVORS
by
KELLIE MARTENS
B.A., University of Michigan, 2007
M.A., University of Colorado Denver, 2014
A thesis submitted to the
Faculty of the Graduate School of the
University of Colorado in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
Clinical Health Psychology
2016


This thesis for the Doctor of Philosophy degree by
Kellie Martens
has been approved for the
Clinical Health Psychology Program
by
Krista Ranby, Chair
Kristin Kilboum, Advisor
Evelinn Borrayo
James Grigsby
Jana Bolduan Lomax


Kellie Martens (Ph.D., Clinical Health Psychology)
What Mahers Most?
Predictors of Quality of Life and Life Satisfaction among Young Breast Cancer Survivors
Thesis directed by Assistant Professor Krista Ranby
ABSTRACT
This study tested a literature-based model of psychosocial adjustment among
young breast cancer (BC) survivors. The model included biological factors (BRCA
positive, stage of cancer, premature menopause, medical comorbidities, time in
remission), psychological factors (psychological diagnoses, cognitive functioning), and
social/practical factors (social support, parenting, finances, fertility). Factors were
hypothesized to impact distressing reactions (depression, anxiety, fear of recurrence, and
traumatic distress), and adaptive reactions (hope, benefit finding) to survivorship.
Reactions to survivorship were hypothesized to impact quality of life (QoL) and
satisfaction with life (SWL). Young BC survivors (N = 284) were recruited via social
media to complete a web-based survey. The self-report items in the survey assessed
demographic and biopsychosocial factors, and self-report measures including the
Functional Assessment of Cancer Therapy for breast cancer (FACT-B) and Satisfaction
with Life Scale (SWLS). Latent variables were created for Adaptive and Distressing
Reactions. Structural Equation Modeling (SEM) was performed in MPlus to test the
hypothesized relationships between biopsychosocial factors, Adaptive and Distressing
Reactions, and the outcomes of QoL and SWL. The hypothesized model fit the observed
data adequately well: x2 (100) = 332.92,p < .001, CFI = .86, RMSEA = .09, SRMR =
.05. The final model accounted for 86% and 62% of the variance in QoL and SWL,
respectively. Support, parenting, and fertility concerns were the only significant
m


predictors of adjustment. Adaptive Reactions was associated with SWLS (Beta = .58, p <
.0001), but not QoL. Distressing Reactions was associated with SWL (Beta = .26, p =
.01) QoL (Beta = .81,p <001). QoL and SWL were significantly associated (Beta =
19 ,p < .05). The biopsychosocial factors that predicted Adaptive and Distressing
Reactions to survivorship were social support, parenting concerns, and fertility concerns.
Stage of cancer, time in remission, comorbidities, premature menopause, psychiatric
diagnoses, and cognitive functioning were not related to psychosocial adjustment.
Depression, anxiety, fear of recurrence, and traumatic distress (Distressing Reactions)
were strongly associated with lower levels of QoL and SWL whereas hope and benefit
finding (Adaptive Reactions) were only associated with higher levels of SWL.
The form and content of this abstract are approved. I recommend its publication.
Approved: Krista Ranby


DEDICATION
I dedicate this work to my husband and my daughters. Thank you for your support
and encouragement every step of the way.


ACKNOWLEDGMENTS
I would like to thank my mentor, Kristin Kilboum, for all of her guidance throughout
this project. I thank Krista Ranby, Evelinn Borrayo, Jana Bolduan Lomax, and James
Grigsby for their support and input as my committee members. I thank Megan Grigsby,
Stephanie Hooker, and Ryan Asherin for their support and help; they are not only my
colleagues, but they are also my friends. Young Survival Coalition was an invaluable part of
my recruitment process and deserves recognition for the amazing work that they do with
young breast cancer survivors. Finally, I would like to acknowledge all of the many
individuals and organizations who helped me reach eligible participants: my mother, Bethany
Aronrow, Rocky Mountain Cancer Centers, Laurri Jones, Dr. Rachel Rabinovitch, Dr.
Virginia Borges, StupidCancer, Komen, Young Womens Breast Cancer Program at Siteman
Cancer Center, Young Survivors Network Inc., The Young Breast Cancer Survivorship
Program at UCLA, Ashley at 3 Little Birds 4 Life, Young Breast Cancer Survivors of SW
Michigan/Kalamazoo area, Young Survivors United Against Breast Cancer, Beyond the Pink
Moon, Jo Vogeli, Lacey Clement, Samantha Saxton, The Leukemia and Lymphoma Society,
University of Colorado Hospital Cancer Center, Kelly Adams, Dr. Robert Fisher, and more.
Due to the large outpouring of support I received, it is likely that I forgot someone; your
efforts are appreciated more than you know.
vi


TABLE OF CONTENTS
CHAPTER
I. INTRODUCTION............................................................... 1
Biological Concerns: BRCA, Stage, Menopause, Comorbidities, and Time since
Treatment................................................................4
Social and Practical Concerns: Infertility, Social Support, Parenting,
Finances.................................................................6
Psychological Concerns: Longstanding Diagnoses and Cognitive
Decline..................................................................7
Psychosocial Adjustment: Distressing Reactions to Survivorship...........8
Psychosocial Adjustment: Adaptive Reactions to Survivorship..............9
Quality of Life and Life Satisfaction...................................10
II. STUDY AIMS AM) HYPOTHESES................................................. 12
III. METHOD................................................................... 14
Sample..................................................................14
Procedure...............................................................14
Measures................................................................15
Quality of Life................................................... 15
Satisfaction with Life............................................ 16
Benefit Finding................................................... 16
Hope.............................................................. 17
Positive Impact of Children........................................17
Distress.......................................................... 17
Fear of Recurrence.................................................18
vii


Anxiety and Depression................................................. 18
Psychological Diagnosis................................................ 19
Financial Concerns..................................................... 19
Social Support......................................................... 19
Parenting Concerns......................................................20
Fertility Concerns......................................................20
Comorbidities...........................................................20
BRCA Positive...........................................................21
Stage...................................................................21
Menopause...............................................................21
Time since treatment....................................................21
Cognitive decline.......................................................22
Data Analysis.................................................................22
Descriptive Statistics..................................................22
Reliability Analysis....................................................22
Correlations...........................................................23
Structural Equation Modeling............................................23
IV. RESULTS.........................................................................26
Sample Description............................................................26
Reliability of the Psychosocial Scales........................................29
Correlations..................................................................32
Measurement Models............................................................35
viii


Structural Equation Model.................................36
V. DISCUSSION...................................................40
Biopsychosocial Factors...................................40
Distressing and Adaptive Reactions........................45
Quality of Life and Life Satisfaction: What matters most?.48
REFERENCES......................................................54
APPENDIX
A: FUNCTIONAL ASSESSMENT OF CANCER THERAPY-BREAST.........62
B: SATISFACTION WITH FIFE SCALE...........................65
C: BENEFIT FINDING MEASURE................................66
D: HOPE SCALE.............................................67
E: IMPACT OF EVENTS SCALE-REVISED.........................68
F: CONCERNS ABOUT RECURRENCE SCALE........................70
G: HOSPITAL ANXIETY AND DEPRESSION SCALE..................71
H: FINANCIAL PROBLEMS SUBSCALE OF THE QLACS...............75
I: REPRODUCTIVE CONCERNS SCALE............................76
J: MULTIDIMENSIONAL SCALE OF PERCEIVED SOCIAL SUPPORT.....77
K: CHARLESON COMORBIDITY INDEX............................78
L: FUNCTIONAL ASSESSMENT OF CANCER THERAPY-COGNITIVE
FUNCTION...........................................79
IX


LIST OF TABLES
Table
1. Demographic and Medical Characteristics of Participants...........................28
2. Psychometric Properties of Psychosocial Scales...................................31
3. Exploratory Factor Analysis of the PICS..........................................32
4. Bivariate Correlations between Predictor Variables................................33
5. Bivariate Correlations between Modeled Variables and Outcomes of Quality of Life and
Life Satisfaction....................................................................34
6. Unstandardized, Standardized, and Significance Levels for Model in Figure 2......37
x


LIST OF FIGURES
Figure
1. Hypothesized conceptual model of psychosocial adjustment among young breast cancer
survivors........................................................................ 13
2. Results for hypothesized structural equation model of psychosocial adjustment.36
xi


CHAPTER I
INTRODUCTION
Breast cancer is the most common non-cutaneous malignancy among women
worldwide, accounting for nearly twenty-five percent of all female cancers (Ferlay et al.,
2013). In the United States, approximately one in eight women will be diagnosed with breast
cancer at some point in their lives (National Cancer Institute [NCI], 2011). Breast cancer is
more common in women over the age of 50 years (American Cancer Society [ACS], 2012),
and therefore research has historically focused on older breast cancer survivors. Although
less common in women under the age of 50 years, breast cancer is also the most prevalent
type of cancer diagnosed in younger women. Furthermore, breast cancer incidence rates in
women under the age of 50 years have remained stable whereas rates in older women have
steadily decreased since the 1980s (ACS, 2013). Combined with advances in detection and
treatment, the steady rates of younger women diagnosed with breast cancer have resulted in
more young women who identify as breast cancer survivors (NCI, 2011). Current research
suggests that the growing population of young breast cancer survivors faces unique
challenges when compared to their older counterparts (Gabriel & Domchek, 2010).
Before discussing the specific challenges of young breast cancer survivors, it may
prove helpful to define the terminology incorporated throughout this paper. First, young as
it relates to cancer survivorship has been defined differently throughout the literature. Some
researchers define young as under the age of 35 years (Knobf, 2006), others as under the
age of 40 years (Gabriel & Domchek, 2010), and others go so far as 55 years and under
(Howard-Anderson, Ganz, Bower, & Stanton, 2012). Ultimately, the age considered young
relates to the purpose and hypotheses of the given study. In this study, young breast cancer
1


survivors are defined as women who were diagnosed between the ages of 19 and 45 years.
This age range was chosen in order to represent women who are no longer adolescents, but
are also most likely pre-menopausal. The average age of menopause onset in the United
States is 50 years (National Institute of Health, 2012); because this study seeks to understand
the specific challenges of pre-menopausal women who are diagnosed with breast cancer, the
upper limit of 45 years old was chosen.
In addition to differing classifications of young, there is some variability in terms of
defining a cancer survivor (Khan, Rose, & Evans, 2012). The National Coalition for
Cancer Survivorship (NCCS) asserts that cancer survivorship starts at the time of diagnosis
and continues after treatment is complete (NCCS, 2011). However, Khan et al. (2012) note
that others use the end of active treatment as a cut-off, and that those who have completed
treatment may be considered survivors whereas those who are in active treatment may be
considered patients. For the purpose of this study, the latter definition will be used when
describing cancer survivorship, and the term patient will be reserved for those who are in
active treatment.
Another phrase that is often used in psychosocial oncology and will be utilized
throughout this paper is adjustment. Brennan (2001) defines adjustment in the context of
chronic illness as the psychological processes that occur over time as the individual, and
those in their social world, manage, learn from and adapt to the multitude of changes which
have been precipitated by the illness and its treatment (p. 2). By this definition, adjustment
is an on-going process and may consist of both negative and positive reactions to
survivorship. Therefore, this study will examine both negative (distressing) and positive
(adaptive) sequelae of surviving breast cancer at a young age.
2


Despite the varying terminology used among breast cancer researchers, current
literature indicates that women who are younger report unique concerns and psychosocial
challenges when compared to older women. These findings can be interpreted from the life-
stage perspective in that younger women are in a different stage of life than older women and
therefore have distinct psychosocial concerns following a cancer diagnosis. The life-stage
perspective states that the shared goals of a group depend on the stage of life (Rowland,
1989). As a simplistic example, a child may have the goal of pleasing her parents, while an
adult may have the goal of pleasing her boss. Cimprich, Ronis, and Martinez-Ramos (2012)
applied the life-stage theoretical model to breast cancer survivorship by describing the
different goals that accompany the life-stage of younger women. From this viewpoint,
younger women likely have different goals and aspirations than older women, and these
goals may be interrupted by a cancer diagnosis, thereby resulting in a unique set of
survivorship concerns.
Specifically, compared to older women, young women may be more focused on
forming a romantic relationship, having children, or starting a career; thus, it follows that a
breast cancer diagnosis may interfere with these goals and lead to concerns about
relationships, fertility, and employment. Furthermore, these concerns may not be as
prominent for older women, and therefore warrant additional research among young women.
This study will organize the unique factors that influence young breast cancer survivorship
into biological, psychological, and social variables. In other words, this study will utilize a
biopsychosocial framework (Engel, 1980) to study psychosocial adjustment among young
breast cancer survivors.
3


Biological Concerns: BRCA, Stage, Menopause, Comorbidities, and Time since
Treatment
Physiologically, young women who are diagnosed with breast cancer may have more
complications related to the disease and treatment. Young women are more likely to be
diagnosed with an aggressive grade of breast cancer (Azim et al., 2012), triple negative
breast cancer (Partridge et al., 2013), and to have both local and distant metastases
(Purushotham et al., 2014). Young survivors are also more likely to possess a BRCA
mutation, which often leads them to pursue more aggressive treatments and to additional
concern about the increased risk of cancer in family members (De Sanjose et al., 2003; Kwon
et al., 2010; Robertson et al., 2012). Considering these complications, young women
diagnosed with breast cancer are unfortunately more likely to have a recurrence and to die
than older women (ACS, 2013).
In the first international consensus guidelines for breast cancer in young women,
Partridge et al. (2014) describe the recommended course of treatment for young women with
early stage versus those with later stage breast cancer. Of course, patients who are diagnosed
with a more advanced stage of breast cancer are also more likely to undergo aggressive
treatment and to have an increased risk of mortality and recurrence. Therefore, women who
are diagnosed with later stage breast cancer are more likely to experience ongoing physical
and psychosocial complications following treatment completion. For example, women with
later stage cancer are more likely to undergo adjuvant therapies such as hormone therapy or
chemotherapy, which may lead to lower overall quality of life (Hopwood, Haviland, Mills,
Sumo, & Bliss, 2008).
4


Young breast cancer survivors may have to cope with premature menopause as a
result of ovarian damage after chemotherapy or hormone therapies such as tamoxifen
(Nystedt et al., 2000). Early onset of menopause may lead to increased symptom burden such
as severe radiation-induced dermatitis, hot flashes, vaginal dryness and dyspareunia, fatigue,
chemotherapy-induced peripheral neuropathy, bone-loss, and cognitive decline (Loprinzi,
Wolf, Barton, & Laack, 2008). Young survivors who prematurely enter menopause may also
experience more distress due to sexual dysfunction and poor body image (Ruddy et al.,
2011).
Because young age is typically associated with better health and fewer chronic
medical conditions, young women are expected to have fewer comorbid diagnoses than older
women. Nonetheless, comorbidities are negatively associated with treatment outcome and
quality of life in all breast cancer survivors, including young survivors (Land, Dalton, Jensen,
& Ewertz, 2010; Land, Dalton, Jorgensen, & Ewertz, 2012). Comorbid conditions may
complicate breast cancer treatment, overall health, and adjustment to survivorship post-
treatment.
Because of the advancements in modem medicine, cancer survivors as a whole are
living longer than in the past (DeSantis et al., 2014). Among breast cancer survivors, distress
often increases in the year post-treatment due to the uncertainty that comes with stopping the
active process of treating cancer but decreases into long-term survivorship (Ganz et al., 2004;
Deshields et al., 2005). Even so, some women continue to report emotional distress, physical
symptoms, and fear of recurrence into long-term survivorship (Harrington, Hansen,
Moskowitz, Todd, & Feuerstein, 2010). Therefore, time since completing treatment is an
5


important factor to consider when comparing distress levels among women who are at
different time-points in the post-treatment trajectory.
Social and Practical Concerns: Infertility, Social Support, Parenting, Finances
With the theoretical underpinnings of the life-stage perspective in mind, social
concerns of a women diagnosed in her 20s or 30s are certainly unique, and they likely
surround the development of a career and a family. Recent literature has focused more on
reproductive concerns of young women. Both women who already have biologic children as
well as those who are not currently parents report fear of infertility due to cancer treatment
(Gorman, Usita, Madlensky, & Pierce, 2011; Camp-Sorrell, 2009). As discussed previously,
infertility is most common among patients who undergo chemotherapy, a common course of
treatment among young women (Arndt et al., 2004). Even women who remain
premenopausal after completing treatment express significant concerns about fertility (Ruddy
et al., 2011). Surprisingly, doctors may be reluctant to discuss fertility preservation or the
potential for pregnancy during treatment (Goncalves, Tarrier, & Quinn, 2014), even though
reproductive concerns often impact treatment decisions, and quality of life (Andersen,
Bowen, Morea, Stein, & Baker, 2009).
Related to fertility concerns, young breast cancer survivors also have concerns about
their children. Because more women are having children into their forties than in previous
decades, (Matthews & Hamilton, 2009) the upper limit of the age range for this study reflects
survivors who are more likely to have dependent children than older survivors. Concerns
may relate to the question, What will happen to my children if I am no longer able to take
care of them? Or, considering the increased likelihood of genetic heritability in families
with young cancer survivors, they may also be concerned about the possibility of their
6


children getting cancer (Barnes et al., 2000). Mothers diagnosed with early stage breast
cancer may express concerns in regard to feelings of guilt, their childrens ability to cope,
role confusion, a perceived lack of social support from healthcare professionals, and
struggles maintaining a household with dependent children (Semple & McCance, 2010).
Mothers may experience higher levels of perceived stress and depression than patients
without children (Schlegal, 2012; Schmitt, 2008). On the other hand, some qualitative
research suggests that motherhood in breast cancer patients may influence a sense of
increased social support and making meaning in life (Billhult & Sergesten, 2003; Semple &
McMcance, 2010).
Young survivors may also feel less supported by friends and family, perhaps because
they still look young and healthy or because their support network has had less
experience with caretaking and chronic illness. Some literature, however, suggests that older
survivors have less social support than young survivors (Sammarco, 2009). Regardless of
age, perceived social support is associated with better psychosocial outcomes including
quality of life, as well as decreased pain (Bloom, Stewart, Chang, & Banks, 2004;
Sammarco, 2001). Although perceived social support typically declines post-treatment, many
breast cancer survivors may still report the need for social support (Arora, Rutten, Gustafson,
Moser, & Hawkins, 2007).
Psychological Concerns: Longstanding Diagnoses and Cognitive Decline
Previous psychological diagnoses, including anxiety and depression, have been found
to decrease breast cancer survivors quality of life (Reich, Lesur, & Perdrizet-Chevallier,
2008). Although a previous diagnosis alone does not mean that a patient will exhibit
problematic psychological symptoms during treatment and survivorship, it does suggest an
7


increased risk for negative affect including depressive thoughts, fear of recurrence, or overall
psychological distress. This study hypothesizes that self-report of treatment for mental health
difficulties will be associated with more distressing reactions and less adaptive reactions.
With regard to cognitive decline after breast cancer, some women report cognitive
difficulties following chemotherapy, a syndrome commonly known as chemo-brain or
chemo-fog (Raffa & Tallarida, 2010). Chemotherapy related cognitive changes may
include a variety of complaints about cognitive function, such as: memory problems,
inattention, and slowed processing speed (Hess & Insel, 2007). Psychological research has
illustrated that depression and anxiety may also affect cognitive decline post-treatment for
breast cancer (Shilling & Jenkins, 2006; Vardy et al., 2008; Hermelink et al., 2010).
Although this study will not examine objective neuropsychological data, it may prove more
useful to understand participants perceived cognitive decline and its impact on their
adjustment to survivorship.
Psychosocial Adjustment: Distressing Reactions to Survivorship
All of the aforementioned biopsychosocial concerns that are unique to young breast
cancer survivors are expected to impact psychosocial adjustment to survivorship. A
systematic review by Howard-Andersen, Ganz, Bower, & Stanton (2011) examined the
psychosocial adjustment of young breast cancer survivors compared to their older
counterparts. Howard-Andersen and colleagues found that young survivors typically report
higher levels of depressive symptoms and are more likely to have clinical depression.
Furthermore, young survivors also report higher levels of general distress, anxiety, and fear
of recurrence (Howard-Anderson, Ganz, Bower, & Stanton, 2011; Liu et al., 2011; Koch et
al., 2014). Some evidence also suggests that young survivors report fewer positive
8


psychosocial effects (such as benefit finding) after breast cancer treatment (Costanzo, Ryff,
& Singer, 2009).
Psychosocial Adjustment: Adaptive Reactions to Survivorship
Despite the challenges associated with having breast cancer, some survivors do find
positive in their experience. Finding benefit in the breast cancer experience may improve
adjustment to survivorship, especially in those who perceive breast cancer as a moderate
threat (i.e., not a minimal or a severe threat) to their life (Lechner, Carver, Antoni, Weaver,
& Phillips, 2006; Carver & Antoni, 2004); therefore, benefit finding might be expected to
relate to the biopsychosocial concerns of young survivors. In addition to mitigating distress
and depression, benefit finding may even improve physical health across diverse medical
populations, including breast cancer patients (Bower, Low, Moskowitz, Sepah, & Epel,
2008).
Contrary to findings that suggest finding benefit helps adjustment to survivorship,
some studies have shown that benefit finding in general does not relate to well-being, quality
of life, or psychological adjustment (Fromm, Andrykowski, & Hunt, 1996; Cordova,
Cunningham, Carlson, & Andrykowski, 2001; Lehman et al., 1993; Tomich & Helgeson,
2004). Benefit finding may correspond with higher levels of distress, perhaps related to the
idea of post-traumatic growth and the aforementioned need to perceive breast cancer as a
threat in order to find meaning in the threat itself.
Hope is a construct that is not commonly studied in breast cancer survivorship,
especially when compared with the more negative construct of hopelessness. In fact, there
are no known studies examining hope in young survivors specifically. Stanton, Danoff-Burg,
and Huggins (2002) examined hope as a predictor of adjustment in breast cancer survivors
9


(not limited to young women) and found that it was associated with lower levels of distress.
Even when compared to constructs such as dispositional optimism, hope is considered more
stable than many of the other constructs in this study (Snyder et al., 2001). It is considered a
primarily cognitive construct, consisting of two parts: 1. Having a sense of being effective at
setting and meeting goals, known as agency in the Hope Scale and 2. Having a sense of
being able to generate plans toward meeting ones goals, known as pathways in the Hope
Scale (Snyder et al., 2001). Hopelessness is strongly associated with depression and suicide
attempt, which may be especially important considering the increased risk for suicide
completion among cancer patients (Anguiano, Mayer, Piven, & Rosenstein, 2012).
As mentioned previously, young breast cancer survivors are more likely to have
dependent children as they go through treatment and survivorship. Although there are
challenges associated with having dependent children and experiencing cancer, some
mothers also report positive effects of parenthood. Parenting may help survivors find
meaning in their cancer experience or increase perceived social support (Billhult &
Sergesten, 2003; Semple & McMcance, 2010). Mothers increased meaning making in life
may also relate to lower levels of distress (Bauer-Wu & Farran, 2013).
Quality of Life and Life Satisfaction
The outcomes of this study include quality of life and life satisfaction. Current
research overwhelmingly states that young breast cancer survivors have poorer quality of life
than older survivors, both in terms of health-related and global quality of life (Howard-
Anderson, Ganz, Bower, & Stanton, 2011; Avis, Crawford, & Manuel, 2005; Kroenke et al.,
2004; Wenzel et al., 1999). Quality of life has been studied in relation to many of the
aforementioned concerns of young breast cancer survivors. However, with the exception of
10


reviews, few studies report the relationship between all of the aforementioned variables; this
study therefore seeks to understand the interrelationship between the most commonly studied
biopsychosocial factors impacting psychosocial adjustment among young breast cancer
survivors.
Although quality of life is a more traditional outcome measure in research with
cancer survivors, this study also includes a more broad measure of life satisfaction. To date,
no other studies have specifically examined young breast cancer survivors satisfaction with
life. Satisfaction with life is a subjective evaluation of ones life, based only on ones own
standards of what is important (Diener, Emmons, Larsem, & Griffin, 1985). Whereas many
quality of life measures examine specific constructs such as health, social life, and finances,
life satisfaction does not relate to any specific constructs. Thus, although it should be
expected that quality of life and life satisfaction are related to one another, satisfaction with
life may provide a more general sense of how happy an individual is with their life. This
study sought to understand predictors of both quality of life and life satisfaction, based on a
conceptually-driven structural equation modeling incorporating each of the aforementioned
biopsychosocial variables.
11


CHAPTER II
STUDY AIMS AND HYPOTHESES
The specific aims were as follows:
1. To describe young breast cancer survivors adjustment to survivorship,
based on the current literature.
2. To examine the utility of the included assessment measures in a sample of
young breast cancer survivors who will complete the measures as part of
a web-based survey.
3. To test the fit of a structural equation model that is based on the most
recent research describing young breast cancer survivors adjustment to
survivorship.
4. To determine which variables in the hypothesized structural model are
most predictive of quality of life and life satisfaction.
The hypothesized structural model is illustrated in Figure 1. Circles indicate latent
(i.e., unmeasured) variables, whereas rectangles indicate measured variables. The absence of
a connecting line between two variables implies that there is no direct effect hypothesized
between those two variables. All variables in the model were allowed to correlate with one
another. The model incorporates each of the aforementioned biospychosocial factors that
were hypothesized to impact adjustment to survivorship among young breast cancer
survivors, as well as the relationship between the adaptive and distressing reactions to
survivorship and the outcomes of quality of life and life satisfaction.
Adaptive and distressing reactions to survivorship were hypothesized to predict
quality of life and life satisfaction, and to mediate the influence of the biopsychosocial
12


factors on quality of life and life satisfaction. Adaptive Reaction is a latent variable
consisting of three indicators: benefit finding, the positive impact of children during cancer,
and hope. Distressing Reaction is a latent variable consisting of five indicators: decision
regret, anxiety, depression, fear of recurrence, and general distress. The biopsychosocial
concerns are all individually measured variables, and are expected to predict adaptive and
distressing reactions to survivorship; they are grouped together in Figure 1 for ease of
understanding the conceptual framework.
Figure 1. Hypothesized conceptual model of psychosocial adjustment
13


CHAPTER III
METHOD
Sample
This study utilized a cross-sectional research design, a design in which participants
are selected and assessed in relation to current characteristics (Kazdin, 2003). In this case, the
characteristics chosen for inclusion criteria were used to determine if participants were
eligible to complete the survey. The inclusion criteria were:
1. Female
2. Survivors of non-recurrent breast cancer
3. Age 19-45 years at the time of diagnosis
4. Premenopausal at the time of diagnosis
5. Post-treatment, with the exception of hormone or antibody therapies
6. English speaking
7. Access to the internet to complete the web-based survey
Adequate sample size is an important consideration when performing a structural
equation model (SEM) analysis (Tabachnick & Fidell, 2012). MacCallum, Browne, and
Sugawara (1996) provide guidelines for the minimum sample size necessary for different
levels of power and to ensure goodness-of-fit. The authors suggest a sample size between
200 and 300 for adequate power when conducting an SEM analysis.
Procedure
The appropriate permissions were obtained from institutional and hospital review
boards prior to beginning recruitment. The sample was recruited from cancer centers
nationwide, as well as online support groups, email blasts, listservs, message boards and
social media sites. Participants were informed of the link for the web-based survey, either by
means of the various internet sources or by flyers. Those who wished to participate in the
survey entered the survey link into their web-browser or clicked on the link, and were then
14


taken to a Qualtrics online survey. Permissions were obtained from the moderators of online
organizations before requesting to share the survey link via flyer or online. If the investigator
was granted permission to recruit, moderators of online groups posted a standardized
recruitment script that briefly introduced the purpose and requirements of participation for
the study. For local hospitals or support groups, flyers were distributed in waiting rooms, by
staff, or by group leaders.
The first page of the survey consisted of a consent page with language to the standard
of the Colorado Multiple Institutional Review Board (COMIRB). The consent page informed
participants about the purpose of the study, stated that there is no compensation given for
participation, and discussed the potential risks of participation. Potential risks were noted to
include the possibility for emotional upset or distress after answering some of the questions,
which is considered minimal by the standards of the COMIRB. Even so, participants
answered comprehension questions after completing the consent page of the study in order to
ensure understanding of the consent and potential risks of participation. Furthermore, both
the consent and final page of the survey included contact information for the primary
investigator, local and national cancer support resources, and a licensed clinical psychologist
with specialized training in psychosocial oncology.
Measures
Quality of Life
The Functional Assessment of Cancer Therapy for breast cancer (FACT-B) consists
of 37 items assessed on a 5-point likert scale: 0 = Not at all, 1 = A little bit, 2 = Somewhat, 2
= Quite a bit, 4 = Very much. There are five sub scales, which are added together to create a
global measure of health-related quality of life (see Appendix A). These subscales measure
15


more discrete parts of quality of life, including physical well-being, social/family well-being,
functional well-being, and additional concerns. Participants were asked how much each
statement applied to them over the past seven days. The FACT-B was designed specifically
for use with breast cancer patients and has high internal consistency; Cronbachs Alpha (a) =
.90 for the total FACT-B measure, and the subscales have internal consistencies ranging from
a = .63 -.86 (Brady et al., 1997).
Satisfaction with Life
The Satisfaction With Life Scale (SWLS) is a five item measure assessing overall
perceived life satisfaction (see Appendix B). It is a positively framed measure, and
participants answer on a seven point likert scale which gives scores ranging from low
satisfaction to high satisfaction. The scale has a reported a = .87 and has been found to
correlate highly with other measures of subjective well-being (Diener, Emmons, Larsem, &
Griffin, 1985).
Benefit Finding
Tomich and Helgeson (2004) refer to benefit finding as positive changes that result
from the otherwise distressing nature of being diagnosed with cancer. They created a 15-item
measure (see Appendix C) to assess benefit finding after having breast cancer. The items
focus on diverse potential benefits ranging from family and social relationships, life
priorities, sense of spirituality, career goals, self-control, and the ability to accept
circumstances. Response options are: 0 = I disagree a lot, 1 = I disagree a little, 2 = I agree
a little, and 3 = I agree a lot. The scale has been used with breast cancer survivors, including
young survivors of early stage cancer of the breast (Lechner et al., 2003). Previous studies
have demonstrated that the scale has an internal consistency of a =.91.
16


Hope
Snyder et al. (1991) designed a measure of the construct of hope (See Appendix D).
The measure consists of 11 items total; seven items assessing hope and four filler items. It
has been found to have convergent validity with other measures of related constructs, as well
as high internal consistency (a = .80). It has previously been used with survivors of breast
cancer (Stanton, Danoff-Burg, & Huggins, 2002).
Positive Impact of Children
The Positive Impact of Children Scale (PICS) was developed by the primary
investigator. It was designed to measure the positive effects that may be reported by women
who have dependent children during a chronic illness such as cancer. The items were written
based on themes within the qualitative literature about motherhood during breast cancer. The
PICS contains five items such as my children gave me a reason to fight the cancer and
having children helped me focus on the positive. Items are scored on a 5-point likert scale
ranging from not at all to almost always.
Distress
The Impact of Events Scale- Revised (IES-R; Weiss & Marmar, 1996) will be used as
a measure of traumatic distress (see Appendix E). It consists of 22 items, each assessed on a
five-point likert scale: 0 = Not at all, 1 = A little bit, 2 = Moderately, 2 = Quite a bit, 4 =
Extremely. The IES-R, although often used to assess PTSD symptomology, has also been
used as a measure of stress/distress with breast cancer patients in randomized controlled trials
(Stanton et al., 2005). It consists of three subscales which assess intrusion, avoidance, and
hyperarousal. The intrusion subscale assesses intrusive thoughts, nightmares, intrusive
feelings and imagery, and dissociative-like re-experiencing. The avoidance scale focuses on
17


numbing of responsiveness, avoidance of feelings, situations, and ideas. The hyperarousal
subscale assesses anger, irritability, hypervigilance, difficulty concentrating, and a
heightened startle response (Christianson & Marren, 2008). The IES-R has an internal
consistency of a = .96 and has demonstrated convergent validity with the PTSD checklist and
the PTSD Coping Inventory (Creamer et al., 2003).
Fear of Recurrence
The Concerns About Recurrence Scale (CARS) has 30 items total (see Appendix F),
although only the first four items will be utilized in this study. The first four items are an
overall fear of recurrence subscale assessing frequency, potential for upset, consistency, and
intensity of fears. The overall fear of recurrence subscale has an internal consistency of a =
.86 and also correlates with the Intrusive Thoughts (r = .64, p < .001) and Avoidance (r =
.50, p < .001) subscales of the Impact of Events Scale and the Distress (r = .54 ,p < .001) and
Well-Being (r = -.44, p < .001) subscales of the Mental Health Index (Vickburg, 2003).
Anxiety and Depression
The Hospital Anxiety and Depression Scale (HADS; Zigmond & Snaith, 1983) is a
14-item self-report questionnaire consisting of an anxiety subscale and a depression subscale
(see Appendix G). Each subscale has an equal number of items, and is designed to assess
symptomology over the past week using a four-point likert scale. The HADS is appropriate
for medical populations because the fatigue and insomnia criteria have been omitted due to
the potential confounds of treatment or disease symptoms. The anxiety has demonstrated an
internal consistency of a = .80-.93., while the depression scale has a = .81-.90 (Herrmann,
1996). Additionally, when using a cut-off score of 10, both scales show convergent validity
18


with the anxiety and depression portions of the Structured Clinical Interview for the
Diagnostic and Statistical Manual of mental disorders (Alexander, Palmer, & Stone, 2010).
Psychological Diagnoses
Participants answered the question: Do you have a history of any of the following
(select all that apply): a. Depression, b. Anxiety, c. Eating disorders, e. Bipolar disorder,
f. Schizophrenia, g. Panic Disorder, h. Obsessive-Compulsive Disorder, g. Other. Those
who answered other specified what other mental health problems they have had. All yes
responses were added together.
Financial Concerns
Financial concerns were measured with the Financial Problems subscale of the
Quality of Life in Adult Cancer Survivors (QLACS) instrument (see Appendix H). The
subscale consists of four items assessed on a seven-point likert scale that measure financial
concerns specific to the cancer diagnosis and/or treatment. The subscale shows convergent
validity with a measure of economic strain and has good internal consistency, with a = .82
(Avis et al., 2005).
Social Support
The Multidimensional Scale of Perceived Social Support (see Appendix J) consists of
12 items scored on a seven-point likert scale, assessing three domains of perceived social
support; it breaks into the subscales of friends, family, and significant other (Zimet, Dahlem,
Zimet & Farley, 1988). Unlike the social subscale of the FACT-B, which relates more
directly to having breast cancer, this measure is a more general measure of social support.
The overall scale has good internal consistency, with a ranging from .84 to .92.
19


Parenting Concerns
Participants who reported that they had dependent children at the time of their cancer
diagnosis answered the Parenting Concerns Questionnaire (PCQ; see Appendix M). The PCQ
is a 15-item measure of distress specifically related to parenting during cancer. It was
developed for and tested with outpatient oncology patients with children under the age of 18
years and demonstrated a =.83 (Muriel et al., 2012). Participants answered how concerned
they are about their children in the domains of practical and emotional concerns about the
impact of an illness on their child(ren) and, if they have a co-parent, about the co-parents
ability to care for the child(ren). Items are rated on a 5-point likert scale ranging from Not at
all concerned to extremely concerned.
Fertility Concerns
The Reproductive Concerns Scale (RCS) is a 14-item measure designed specifically
for use with young cancer survivors (see Appendix I) and it has been used with young breast
cancer survivors in other studies (Gorman, Malcarne, Roesch, Madlensky, & Pierce, 2009). It
asks questions about how the cancer diagnosis affects the ability to have children, the
importance of having children, and loss of control of ones reproductive future. It has good
internal consistency, with a ranging from .81 in controls to .91 in young female cancer
patients (Wenzel et al., 2005). Unlike the PCQ and PICS, the RCS is designed for women
without children as well as mothers.
Comorbidities
The Charlson Comorbidity Index (CCI) assesses for the presence of 19 pre-existing
medical conditions, giving different weights to the conditions based on severity and risk of
mortality (see Appendix K). The index has been validated for use with breast cancer patients,
20


and it has been shown that breast cancer patients generally have a low score on the CCI
(Charlson, Pompei, Ales, & MacKenzie, 1987). For this reason, it was hypothesized that
comorbidities may be particularly salient in this study; those participants with a higher score
on the CCI may exhibit significantly higher biological concerns than those with fewer
comorbidities.
BRCA Positive
One item will ask participants if they are carriers of BRCA1 or BRCA2 mutations,
with the option of answering Yes, BRCA1 only, Yes, BRCA2 only, Yes, BRCA1 and
BRCA2 No, or Im not sure/I don 7 know This item will be scored dichotomously.
Stage
Participants will select which stage of breast cancer they were treated for, with the
stages ranging from O/DCIS to IV. The option lam unsure was also be provided for those
who are unsure of their stage.
Menopause
Participants will first answer the question, Were you pre-menopausal at the time of
your breast cancer diagnosis? to ensure that they meet the inclusion criteria. A follow-up
question will ask, Did you begin menopause as a result of cancer treatment? The latter
question will be scored dichotomously.
Time Since Treatment
Time since treatment will reflect a question that asks how long it has been since
participants completed treatment, with the exception of hormone and endocrine therapies.
Time since treatment, for the purpose of these correlation-based analyses, was scored
continuously in years.
21


Cognitive Decline
The Functional Assessment of Cancer Therapy-Cognitive function (FACT-Cog)
Version 3 is a 37-item measure of perceived cognitive functioning (see Appendix L). It
consists of four different subscales: 1. Perceived cognitive impairments, 2. Comments from
others about cognitive functioning, 3. Perceived cognitive abilities, and 4. Impact of
cognitive function on quality of life. For the purposes of this study, only the first 3 subscales
(30 items) will be used, since the FACT-B is a measure of quality of life included in the
model. The FACT-Cog has strong psychometric properties in breast cancer patients, with
each scale having an a > .77 (Cheung et al., 2013). Items are assessed on a four-point likert
scale.
Data Analysis
Descriptive Statistics
Descriptive statistics were analyzed using IBM SPSS software. The investigator used
descriptives to understand the data and to correct any errors in data entry from the Qualtrics
software to SPSS. Means, standard deviations, ranges, skew, and kurtosis were examined to
understand the distribution of the individual scales. Variables were examined for outliers,
excluding data points only if they were deemed erroneous or highly unrepresentative of the
sample. Scales were scored as either means or sums, depending on the suggested scoring or
commonly utilized scoring procedures so that the results may be easily compared to other
studies.
Reliability Analysis
Internal consistency of the scales in the sample was confirmed through factor
analysis. Only measures with adequate internal consistency (Cronbachs Alpha > .70) were
22


utilized. The Positive Impact of Children Scale was re-examined to determine how the
originally written 5-item measure compared to the measure without the one item (my
children distracted me from the cancer) with a previously low factor loading.
Correlations
Correlations were examined at multiple points in the data analyses. Bivariate
correlations between all measured variables were examined to understand the relationships
between variables before importing the data into MPlus. This technique is helpful in
identifying errors in data entry as well as confirming that the final model results are plausible
given the bivariate relationships. After the latent variables were tested using confirmatory
factor analysis, the latent variables and remaining measured variables were correlated with
one another; these correlations also help interpret the overall structural model and guide
potential future iterations of the model.
Structural Equation Modeling
Before performing structural equation modeling in MPlus to test the fit of the data
with the hypothesized model, the following assumptions for structural equation modeling
were examined:
1. Sample size and missing data: This sample (N = 284) was sufficiently large to
conduct a structural equation analysis with the number of specific paths in the model.
Individual responses were only excluded from the analyses if participants dropped out
of the survey prior to completing any outcome measures. Otherwise, there was no
apparent pattern to missing data and no outliers were deleted from the analyses.
Missing data were estimated using the full information maximum likelihood
23


estimation method so that missing means and variances were estimated using an
expectation-maximization algorithm.
2. Normality of sampling distribution: Each measured variable was examined to ensure
that there were not highly skewed or kurtotic variables. The Positive Impact of
Children Scale (PICS) and Charleson Comorbidity Index (CCI) demonstrated non-
normal distributions. Transforming the PICS using a LoglO transformation, as
suggested by Tabachnick & Fidell (2012) for negative skew, did not improve the
normality or change the model fit, and it was therefore left untransformed in
subsequent analyses. The CCI was dichotomized for the structural model analyses
because so few participants reported any comorbidities other than their cancer
diagnosis; participants were either grouped into some other or no other
comorbidities groups.
3. Linearity: Measured variables were examined and confirmed to show a linear
relationship with one another.
4. Absence of multicollinearity and singularity: In structural equation modeling, if any
variables are too highly correlated or perfectly correlated with one another, this
suggests that the constructs in the model are not discrete constructs and should not be
examined as such because the model is highly influenced by inflated correlations.
This assumption was met in that none of the measured variables were correlated
higher than .72 (anxiety and traumatic distress).
Once these assumptions were met, a confirmatory factor analysis was run to
determine the fit of the data with the hypothesized latent variables. Upon confirming the
24


hypothesized latent variables fit the data well, also known as testing the measurement model,
the entire hypothesized structural model was analyzed.
25


CHAPTER IV
RESULTS
Sample Description
Recruitment took place in two phases, with the first phase between June 2013 and
November 2013 (n= 153) and the second phase between January 2015 and April 2015 (n =
131). A total of 425 people clicked agree on the consent page, but it was not possible to
monitor the number of people who were shown the consent page and did not click agree.
Following the consent page, participants were then shown comprehension questions,
demographic and disease characteristic questions, and finally the measures of the outcomes
in the model. Between the consent page and the outcome measures, 141 participants were
lost. Thirty eight did not meet inclusion/exclusion criteria for the following reasons: seven
were pregnant, 16 had recurrent breast cancer, and 15 were in active cancer treatment
(chemotherapy and/or radiation). The remaining 103 participants lost between the consent
and outcome measures dropped out for unknown reasons, with the majority dropping out in
the first third of the demographic questions. Thus, the final sample included 284 participants,
with 247 completing the entire survey and 37 dropping out at various, seemingly random
points in the outcome measure portion of the survey. Facebook was the primary method of
recruitment (60.9%), with other methods including online support groups (15.5%). Young
Survival Coalition (YSC), a national non-profit organization specifically focused on young
womens breast cancer (Young Survival Coalition, 2015), was the primary organization
involved in recruitment; 39% of participants who reported where they learned about the
survey named YSCs Facebook page or YSC support groups.
26


In the final sample of 284 non-recurrent breast cancer survivors diagnosed between
the ages of 19 and 45, the majority of participants were Caucasian (88.7%), partnered
(73.6%), working full or part time (79.9%), and college educated (73.6%). Participants were
from 39 different states, with the largest proportions being from Colorado (14.4%) and
California (9.2%). The vast majority of participants were diagnosed with non-metastatic
disease (94.7%), which is to be expected considering the eligibility criterion that prohibits
women who are in active treatment from participating. Participants completed the survey
after a mean of 5.4 years since diagnosis, with 58.9% having completed treatment within 3
years or less. In terms of treatments undergone, there were a multitude of treatment
combinations reported, with the majority of women undergoing chemotherapy (80.3%),
mastectomy (76.1%), and radiation (55.6%). Only 3.85% of women were still undergoing
hormone and/or antibody therapy. Demographic and medical characteristics of participants
are presented in Table 1.
27


Table 1
Demographic and Medical Characteristics of Participants (N = 284)
Variable Total Variable Total
Ra.ce/Ethnicity N (%) Time since treatment N (%)
Caucasian 252 (88.7) < 6 months 41 (15.8)
Hispanic 14 (4.9) 7-11 months 26 (8.6)
Asian/Pacific Island 6(2.1) 12-23 months 50(17.6)
African American 5 (1.8) 2 years 29 (10.2)
Other 7 (2.5) 3 years 19 (6.7)
4 years 28 (9.9)
Age Mean (SD) > 5 years 68 (23.9)
Time of survey 40.0 (6.7)
Time of diagnosis 35.5 (5.3) Years since diagnosis Mean (SD)
Years 5.4 (4.6)
Relationship Status N (%)
Partnered 209 (73.6) Current treatment N (%)
Single 75 (26.4) None 273 (96.1)
Antibody therapy 6 (2.1)
Education N (%) Hormone therapy 4 (1.4)
College degree 209 (73.6) Antibody & 1 (.35)
No college degree 75 (26.4) Hormone
Employment status N (%) Past Treatment** N (%)
Employed full-time 183 (64.4) Mastectomy 216 (76.1)
Employed part-time 44 (15.5) Chemotherapy 223 (80.3)
Unemployed 38(13.3) Lumpectomy 97 (34.2)
Unable to work 19 (6.7) Radiation 158 (55.6)
Antibody therapy 57 (20.1)
Stage N (%) Hormone therapy 112 (39.4)
DCIS/0 19 (6.7) Reconstruction 196 (69.0)
I 64 (22.5) Prophylactic 66 (22.2)
n 135 (47.5) Surgery
in 51 (18)
IV 9 (3.2) BRCA N (%)
Unsure 6(2.1) No mutation 197 (69.4)
Unsure 34 (12)
Menopause onset* N (%) BRCA 1 mutation 20 (7.0)
Due to tx 70 (24.6) BRCA 2 mutation 20 (7.0)
Not due to tx 60 (21.1)
* The question to assess menopause onset as a result of cancer treatment was only shown to participants in
second phase of the study (n= 130).
** Participants selected any treatments they underwent; these totals will not equal 100%.


Reliability of the Psychosocial Scales
Each of the psychosocial scales demonstrated adequate internal consistency, each
scale demonstrating a Cronbachs alpha value greater than .70. These values, along with
other relevant psychometric properties, are presented along with descriptive statistics in
Table 2. Of note, between the first and second phase of recruitment, the FACT-Cog and a
question asking if participants began menopause as a result of treatment were added based on
participant feedback; these measures therefore have a lower sample size than the other
measures. Another discrepancy in sample size can be seen in measures that were shown only
to women with dependent children at the time of their diagnosis, the Positive Impact of
Children Scale and the Parenting Concerns Questionnaire. Other discrepancies in the total n
are attributable to drop-out throughout the course of the survey. Participants were only given
a total score if they answered at least 75% of the items on a given scale.
The PICS, which was subjected to additional analyses because it was designed
specifically for this study, also demonstrated adequate internal consistency. The PICS was
analyzed for psychometric soundness using an empirical cut-off of eigenvalues greater than 1
atp<.001. Bartletts test of sphericity was significant, x2(10) = 343.92, p < .001, and the
Kaiser-Meyer Olkin measure of sampling adequacy was .78. Both indices suggest that the
sample responses were factorable. The exploratory factor analysis suggested a one-factor
solution using all five of the items, which explained 62.03% of the variance in the PICS item
scores. All factor loadings were above .40, which can be considered adequately high for
loadings in an exploratory analysis. The factor loadings are presented in Table 3. Cronbachs
alpha of the PICS = .83, suggesting adequate internal consistency of this new scale. The
PICS was significantly correlated with Tomich and Helgesons measure of benefit finding (r
29


= 39 ,p < .01). The positive impact of children was not significantly associated with
parenting concerns. These results suggest that the PICS is measuring a unique construct from
more general benefit findings and that the positive impact of children is not associated with
parenting concerns; that is, a mother may have significant concerns about her children
throughout the cancer experience and may still report benefit from being a mother while
going through cancer.
30


Table 2
Psychometric Properties of Psychosocial Scales
Range
Variable
n M SD a Potential Actual Skew Kurtosis
Financial Concerns 248 3.12 2.05 .89 1-7 1-7 .56 -1.11
Social Support 248 65.45 17.87 .95 12-84 12-84 -1.22 1.00
Parenting Concerns* 145 2.10 .92 .91 1-5 1-5 .92 .06
Reproductive Concerns 247 16.25 13.63 .90 0-56 0-49 .60 -.82
Co morbidities 284 0.52 1.27 N/A 0-25 0-6 2.99 9.26
Hope 261 24.52 4.35 .88 8-32 8-32 -.88 .30
Benefit Finding 261 42.38 7.64 .86 15-60 18-60 -.28 .08
Positive Impact of Children* 145 4.39 .76 .83 1-5 1-5 - 1.77 3.56
Cognitive Concerns** 112 47.91 34.59 .90 0-128 0 121 .34 - 1.13
Anxiety 250 8.48 5.05 .90 0-21 0-21 .29 -.60
Depression 250 4.41 4.00 .86 0-21 0-20 1.08 .95
Fear of Recurrence 250 4.00 1.34 .94 1-6 1-6 -.29 -.91
Distress 251 2.22 .86 .94 1-5 1-5 .61 -.44
Quality of Life 284 98.75 25.10 .94 0-148 22 144 -.65 -.02
Life Satisfaction 282 22.84 7.91 .92 5-35 5-35 -.58 -.65
*The Parenting Concerns (PCQ) and Positive Impact of Children (PICS) measures were shown only to
participants with dependent children.
**Cognitive Concerns (FACT-Cog) was shown only to participants recruited in the second phase of this study,
as it was added following feedback from participants in the first phase.
31


Table 3
Exploratory Factor Analysis of the Positive Impact of Children Scale (n = 145)
Item Factor Loading
Having children helped me focus on the positive. .85
My children gave me an appreciation for life. .87
My children gave me a reason to fight the cancer. .83
My children distracted me from the cancer. .65
Being a parent helped me identify as more than a cancer patient. .71
Bartletts r(10) = 343.92, p < .001; KMO = .78.
Correlations
Correlations between all measured variables were examined prior to analyzing the
structural model. Bivariate correlations between all measured variables are presented in
Table 4. After determining the latent variables were supported by the data using
Confirmatory Factor Analysis, correlations between latent variables, the measured predictors
in the model, and the outcomes of quality of life and life satisfaction were examined.
Correlations between all predictor variables and the outcomes of Quality of Life and Life
Satisfaction are presented in Table 5. Initial observations of correlations revealed that all
significant relationships were in the hypothesized direction.
32


Table 4
Bivariate Correlations between Predictor Variables
Variable Financial Social Support Parenting Concerns Fertility pjwgm Psvch Dx Cognitive Sx BRCA Began Meno- pause Stage ca Time Since Tx Hope Pol Impact Children Benefit Depression Anxiety Distress FoR
Financial 1.00
Social Support -.23** 1.00
Parenting Concern 51** -i7** 1.00
II .09 -55* 54** 1.00
Psych Dx .19** -.17** 50** .08 1.00
Cognitive Sx 51** -il* 57** .08 .16 1.00
BRCA -.00 .01 -.07 -58** .08 -.01 1.00
Began Menopause i7** .17 -.18 55** .04 57** .17 1.00
Stage .18** -.03 58** .06 .08 .18* .05 58* 1.00
ca is** -.05 58** .14* .07 .06 -.09 .19* 54** 1.00
Time Since Tx -i3** .10 -.10 -58** .01 -.30 .12* .13 .06 -.03 1.00
Hope -55** 54** -.47** -54** .19** -57** .03 .14 -50** -.19** .06 1.00
Pos. Impact Children -.02 i8** -.05 -.08 -.09 .17 .01 .09 .04 .01 -.05 .16 1.00
Benefit -.09 i7** -.12 -57** -.06 -.02 54* -.09 -.01 -.11 .08 .49** 59** 1.00
Depression 55** -.45** 59** 56** 51** .61** -.04 54* .15* 51** -52 -.70** -.17* -.45** 1.00
Anxiety j** -52** .63** 57** .33** .45** .03 .19* .10* 51** -.14* -.50** -.17* -.23** .65** 1.00
Distress .48** -56** .66** 53** .30** .47** -.05 55* .12 51** -.29* -.39** -.06 -.15* .53** .72** 1.00
Fear of Recurrence 51** -55* 51** 50** .14* .21* -.01 52 .12 50** -is** -.31** .00 -.07 .29** .59** .58** 1.00
££!
* p < .05, **p < .01, ***p < .001
33


Table 5
Bivariate Correlations between Modeled Variables and Outcomes of Quality of Life and Life
Satisfaction
Variable Quality of Life Life Satisfaction
Quality of Life 1.00
Life Satisfaction .70** 1.00
Adaptive Reaction .63***
Hope .63** .66**
Benefit Finding .40** .47**
Positive Impact of Children .18* .25**
Distressing Reaction 9Q*** - .01
Depression -.81** -.64**
Anxiety -.71** -.50**
Fear of Recurrence _ 49** - .24**
Traumatic Distress -.65** - .42**
Financial Concerns _ 49** - .38**
Social Support .46** .37**
Parenting Concerns -.71** -.64**
Fertility _ 29** -.34**
Psychological Diagnoses - .34** - .28**
Cognitive Decline -.64** - .32**
Stage -.17** _ 18**
CCI - .22** _ 19**
Time Since Diagnosis .22** .11
BRCA positive .07 .06
*p < .05, **p < .01, ***p < .001
34


Measurement Models
Maximum likelihood estimation was employed to estimate all models in MPlus. The
first step was to perform a Confirmatory Factor Analysis (CFA) of the latent variables
Distressing and Adaptive Reactions to confirm the hypothesized measurement model within
the overall structural model. The Adaptive Reactions latent variable, consisting of Hope,
Benefit Finding, and the Positive Impact of Children, is just-identified in its number of paths
estimated; therefore, model fit statistics are not available. All path estimates were significant
at/> < .001. The standardized factor loadings for the indicators of Adaptive Reactions were as
follows: Hope = .72, Benefit Finding = .59, and the Positive Impact of Children = .35. These
loadings suggest that Hope is the strongest predictor of Adaptive Reactions. All indicators
demonstrated a sufficiently high loading considering a cut-off of .30 to .40 for factor loadings
(Tabachnik & Fidell, 2012; Bowen & Guo, 2012), noting that the Positive Impact of Children
Scale (PICS) has a borderline low loading suggesting that it may not be as conceptually
related to the other two indicators of Adaptive Reactions.
The Distressing Reaction measurement model fit the data well: %2 (2) = 25.70,/) <
.001, CFI = .94, RMSEA = .23, SRMR = .047. Although many researchers suggest that good
model fit is indicated with RMSEA below .05 and an insignificant chi-square, this model was
retained based on new literature suggesting that RMSEA can be artificially inflated in models
with low degrees of freedom (Kenny, Kaniskan, & McCoach, 2014). Furthermore, chi-square
has long been known to reach significance in large sample sizes participants (Marsh, Balia, &
McDonald, 1988). All path estimates were significant at p < .001, Standardized factor
loadings for the indicators of Distressing Reactions were as follows: Anxiety = .93,
Traumatic Distress = .79, Depression = .68, and Fear of Recurrence = .62.
35


Structural Equation Model
Following the confirmation of the hypothesized measurement model, the overall
structural model was analyzed. The hypothesized structural model fit the observed data
adequately well: x2 (100) = 332.92,p < .001, CFI = .86, RMSEA = .09, SRMR = .05. The
final model accounted for 86% and 62% of the variance in Quality of Life and Life
Satisfaction, respectively. The standardized results of the hypothesized model are shown in
Figure 2. Standardized and unstandardized parameter estimates, as well as all significance
levels, are provided in Table 6.
Figure 2. Results for hypothesized structural equation model of psychosocial adjustment
among young breast cancer survivors, both significant and non-significant path estimates
included (significant paths in bold)
36


Table 6
Unstandardized, Standardized, and Significance Levels for Model in Figure 2 (Standard
Errors in Parentheses; N = 284)
Parameter Estimate Standardized Unstandardized (SE) P
Measurement Model
Adaptive Reactions Benefit Finding .57 1.00 .000
Adaptive Reactions Hope .85 .85 (.10) .000
Adaptive Reactions Positive Impact of Children .29 .05 (.02) .001
Distressing Reactions Traumatic Distress .73 1.00 .000
Distressing Reactions Concerns about Recurrence .53 1.18 (.14) .000
Distressing Reactions Anxiety .80 6.47 (.51) .000
Distressing Reactions Depression .84 5.44 (.45) .000
Covariance Adaptive and Distressing Reactions -.67 - .70 (.16) .000
Structural Model
BRCA positive Adaptive Reactions .06 .733 (.80) .358
BRCA positive Distressing Reactions .00 - .00 (.08) .992
Later Stage Adaptive Reactions -.11 - .55 (.37) .130
Later Stage Distressing Reactions .00 .00 (.00) .926
Menopause Adaptive Reactions .06 .56 (.99) .575
Menopause Distressing Reactions .07 .09 (.10) .368
Comorbidities Adaptive Reactions -.04 - .14 (.22) .511
Comorbidities Distressing Reactions .08 .04 (.02) .088
Time since treatment Adaptive Reactions -.01 - .01 (.07) .843
Time since treatment Distressing Reactions -.08 - .01 (.01) .109
Psychological Diagnoses Adaptive Reactions -.03 - .12 (.26) .631
Psychological Diagnoses Distressing Reactions .08 .04 (.03) .091
Cognitive Difficulty Adaptive Reactions .11 .01 (.02) .385
Cognitive DifficultyDistresssing Reactions .12 .00 (.00) .183
Financial ConcernsAdaptive Reactions .00 - .00 (.18) .998
Financial ConcernsDistressing Reactions .01 .00 (.02) .818
Social Support Adaptive Reactions .30 .07 (.02) .000
Social Support Distressing Reactions -.25 - .00 (.00) .000
Parenting Concerns Adaptive Reactions -.36 - 1.70 (.56) .002
Parenting Concerns Distressing Reactions .58 .39 (.05) .000
Fertility' Concerns Adaptive Reactions -.19 - .06 (.02) .004
Fertility' Concerns Distressing Reactions .17 .01 (.00) .001
Adaptive ReactionsQuality' of Life .07 .43 (.51) .402
Adaptive ReactionsLife Satisfaction .58 1.04 (.22) .000
Distressing Reactions Quality of Life -.87 -35.14 (4.20) .000
Distressing ReactionsLife Satisfaction -.26 -3.31 (1.33) .010
Covariance Quality of Life and Life Satisfaction .19 8.42 (4.60) 044
37


Relatively few of the hypothesized direct effects from the biopsychosocial factors to
the latent variables were significant. In fact, none of the biological or psychological factors
were significant predictors of either Adaptive Reactions or Distressing Reactions. That is,
having a BRCA mutation, later stage of cancer, onset of menopause due to treatment, greater
medical comorbidity, and length of time since completing treatment were not significant
predictors of the latent variables; nor were greater cognitive decline or psychological
diagnoses.
Three of the four social factors significantly predicted both Adaptive and Distressing
Reactions. Higher levels of social support (standardized path estimate = .30,p < .0001),
fewer parenting concerns (standardized path estimate = -.36, p < .01), and fewer concerns
about fertility (standardized path estimate = .19,p < 01) all significantly predicted more
Adaptive Reactions. These same three variables significantly predicted Distressing
Reactions; lower levels of social support (standardized path estimate = .25, p <.0001), more
concerns about parenting (standardized path estimate = .58,/) <.0001), and more concerns
about fertility (standardized path estimate = .17,p < 01). Financial concerns and the
remaining variables conceptually grouped with social factors, did not significantly predict
either latent variable. Adaptive and Distressing Reactions were significantly negatively
associated with one another (standardized path estimate = .67, p <.0001).
Adaptive and Distressing Reactions, examined as part of the entire structural model,
were similarly represented by their indicators compared to when they were examined
individually as a measurement model. All factor loadings were significant at/) < .0001 with
the exception of the Positive Impact of Children Scale which was significant at/) < .01.
Standardized and unstandardized factor loadings are shown in Table 6. Figure 2 provides
38


standardized factor loadings only. Of note, the loading for the Positive Impact of Children
from Adaptive Reactions was below the range of what is typically considered acceptable for
a factor loading (Tabachnik & Fidell, 2012; Bowen & Guo, 2012). Therefore, the final
structural model was examined both with the PICS and without it to determine if excluding it
from the analyses would improve model fit or change path estimates. Because excluding the
PICS did not change different model fit or the associations between Adaptive Reactions and
the outcomes of Quality of Life and Life Satisfaction, it was retained in the final model.
Adaptive Reactions was positively associated with Satisfaction with Life
(standardized path estimate = .58, p < .0001), but it was not significantly related to health-
related Quality of Life when considered as a predictor with Distressing Reactions.
Distressing Reactions was negatively related to both Satisfaction with Life (standardized path
estimate = .26, p = .01) and health-related Quality of Life (standardized path estimate = -
87 ,p <.001). The two outcomes of Quality of Life and Satisfaction with Life were
significantly associated with one another, even after controlling for the variance accounted
for by their shared predictors in the model (standardized path estimate = 19, p < .05).
39


CHAPTER V
DISCUSSION
This study aimed to test a theoretically-driven structural equation model of
psychosocial adjustment among young breast cancer survivors, and to use the results to
understand which predictors have the greatest influence on quality of life and life
satisfaction. The structural model hypothesized that biological variables such as cancer stage
and medical comorbidity, psychological variables such as mental health diagnoses and
cognitive decline, and social variables such as social support and concerned about children,
would indirectly impact quality of life and life satisfaction. That is, the effect of the
biopsychosocial variables was hypothesized to be mediated by adaptive (or positive) and
distressing (or negative) reactions to survivorship. The hypothesized model was partially
supported by the data.
Biopsychosocial Factors
Individually measured variables, grouped conceptually as biological, psychological
and social variables, were hypothesized to predict Adaptive and Distressing Reactions to
survivorship. The only biopsychosocial variables that were significantly related to
psychosocial adjustment and the outcomes of quality of life and life satisfaction were social
support, parenting concerns, and concerns about fertility. Lower social support was related to
lower adaptive reactions and higher distressing reactions, and was therefore related to lower
health-related quality of life and life satisfaction. This is consistent with other studies that
suggest perceived social support is associated with quality of life (Bloom, Stewart, Chang, &
Banks, 2004; Sammarco, 2001), and also that the need for social support persists well past
completing treatment (Arora, Rutten, Gustafson, Moser, & Hawkins, 2007). Although social
40


support is commonly studied among cancer survivors, it may be especially pertinent for
younger women as they may perceive less social support due to the fact that breast cancer is
much more common in older women. Alternatively, social support is highly predictive of
better quality of life and fewer mental health problems in the general population as well
(Williams, Ware, & Donald, 1981), and this finding may be unrelated to participants cancer
experience.
Parenting concerns also indirectly influenced life satisfaction and satisfaction with
life by means of reactions to survivorship. Having more concerns about dependent children
was related to lower adaptive reactions and higher distressing reactions, and therefore
indirectly related to lower quality of life and life satisfaction. Concerns about children is a
relatively new and understudied construct in cancer survivorship, and one that likely plays an
especially important role in adjustment with younger women who are more likely to have
dependent children under their care during treatment. More resources are becoming available
to help women learn how to speak with their children about cancer, which may help with
concern about talking to children. These findings suggest that mothers may also benefit from
discussing their wishes for their children if their prognosis worsens. Research exploring the
impact of parenting concerns might also compare breast cancer survivors to the general
population, as it would be expected that all parents have a certain degree of anxiety related to
taking care of their children but that cancer survivors and patients concerns may be more
distressing or otherwise different from the day-to-day concerns of non-cancer controls.
In addition to parenting concerns, fertility concerns were related to lower levels of
Adaptive Reactions and higher levels of Distressing Reactions, thereby negatively impacting
both quality of life and life satisfaction. Infertility and premature menopause can be caused
41


by chemotherapy or other hormonal therapies (Arndt et al., 2004), and can be concerning for
women with and without biologic children at the time of diagnosis (Gorman, Usita,
Madlensky, & Pierce, 2011; Camp-Sorrell, 2009). Knowledge about infertility and fertility
preservation is growing, but practitioners may still be more focused on conversations about
cancer treatment than conversations about fertility preservation (Goncalves, Tarrier, &
Quinn, 2014). This study aligns with the findings of others in that reproductive concerns
were significantly related to health-related quality of life and life satisfaction (Andersen,
Bowen, Morea, Stein, & Baker, 2009), thereby suggesting that fertility should be addressed
prior to starting treatment and that grief over loss of fertility might be addressed if a survivor
was unable to preserve her ability to have biological children.
Interestingly, the biomedical variables of disease stage, having a BRCA mutation,
comorbidities, onset of menopause due to treatment and length of time since treatment, were
not significant predictors of Adaptive or Distressing Reactions. More progressed disease,
known as later stage, is known to be related to poorer psychosocial outcomes in young breast
cancer survivors (Hopwood, Haviland, Mills, Sumo, & Bliss, 2008), and was hypothesized to
negatively impact adjustment in this sample. One explanation for the lack of a significant
finding related to disease stage and the outcomes is that the psychosocial variables are more
predictive of adjustment than stage of disease. If psychosocial variables are indeed more
predictive of adjustment than aggressiveness of treatment, then this finding would argue for
psychosocial interventions for all young breast cancer survivors. Alternatively, because so
few women in this sample were later stage, it is possible that the sample lacked power to
detect an effect of later stage on adjustment. Because the inclusion criteria required that
participants be complete with active treatment, fewer women with stage IV disease would be
42


expected to participate because they are more likely to be in ongoing treatment. That is,
because stage IV breast cancer is metastatic and the intent of treatment is generally more
focused on prolonging life and decreasing symptom burden, women with stage IV cancer are
often in and out of active treatment indefinitely. Perhaps women who completed the survey
with stage IV breast cancer perceived as though they were complete with treatment because
they were without evidence of disease at the time of the study.
Similar to sparse number of women with stage IV disease, few women were aware of
having a BRCA mutation, few women had other medical comorbidities, and approximately
half of women knowingly began menopause due to treatment. These findings may suggest
that having a genetic mutation or beginning menopause alone may be less important for long-
term psychosocial adjustment than psychosocial variables or perceptions of these biological
factors. Alternatively, because having a BRCA mutation and beginning menopause were
dichotomized for analyses, they may have had limited power to influence psychosocial
adjustment. The same might be true for medical comorbidities, in that the majority of the
sample reported no other major medical illnesses.
With regard to length of time since treatment, it was hypothesized that women who
completed treatment more recently would be more distressed and report lower adaptive
adjustment than those who were further from completing treatment. This hypothesis was not
supported, which is inconsistent with the findings of some researchers (Ganz et al., 2004;
Deshields et al., 2005) but consistent with others (Harrington, Hansen, Moskowitz, Todd, &
Feuerstein, 2010). Long-term survivorship is often described as five years post-diagnosis, a
time when the rate of recurrence drops significantly for most cancers if there has not been a
recurrence since completing initial treatment. As such, it makes sense that survivors might
43


feel less cancer-related distress after five years, either because they feel as though they are at
less risk of developing cancer or simply because time heals. This effect may not have been
present in this sample because the majority of women were still within five years of
completing treatment. It is also possible that this self-selected sample of women who are
mostly active in the online breast cancer community, are more focused on their cancer
experience and are generally more similar than different from one another, despite their
different places in the treatment trajectory.
Psychological diagnoses prior to cancer and cognitive decline were considered
conceptually similar, and were grouped together as psychological variables. Both were
hypothesized to negatively impact adjustment to survivorship, quality of life, and life
satisfaction. With regard to psychological diagnoses, it was hypothesized that having
previous psychological difficulties or diagnoses would predispose survivors to have cancer-
related distress. The lack of a significant relationship between previous psychological
diagnoses and distress following cancer could be explained in a number of ways. First,
perhaps having previous treatment for mental health issues could be viewed as a protective
rather than predisposing factor in that participants may have learned coping strategies in
treatment. Second, few participants reported any diagnoses and those that did largely
reported only having one diagnosis; therefore, there may not have been sufficient power to
detect an effect. Third, participants who denied having any previous diagnoses may have
diagnoses that they are unaware of because they have never seen a mental health
professional. Finally, it is possible that previous psychological diagnoses truly does not
impact adjustment to survivorship and that patients with and without a mental health history
should be given equal attention when screening for distress and implementing interventions.
44


In terms of cognitive decline, having cognitive problems was related to Adaptive and
Distressing reactions at a bivariate level but were no longer significantly related after
controlling for the other biopsychosocial variables. Cognitive decline was also related to the
outcomes of quality of life and life satisfaction at a bivariate level, but was not significantly
related when a direct path from cognitive decline to the outcomes was tested; thus, it was no
longer significant when controlling for Adaptive and Distressing Reactions either. Cognitive
difficulties following cancer treatment, often referred to as chemo brain, can also be
impacted by constructs subsumed within Distressing Reactions. Namely, depression and
anxiety are known to produce symptoms of cognitive decline (Shilling & Jenkins, 2006;
Vardy et al., 2008; Hermelink et al., 2010). Because cognitive decline was most highly
correlated with the measures of depression and anxiety in this sample, the psychological
variables within Distressing Reactions may be responsible for decreased cognitive abilities
and are therefore more important to examine as predictors of quality of life and life
satisfaction than are cognitive problems alone.
Distressing and Adaptive Reactions
Both latent variables were supported by the data. Depression, anxiety, fear of
recurrence, and traumatic distress were all highly related to one another and can be
considered as one construct; for the purposes of interpretation, the construct was named
Distressing Reactions. The support for the Distressing Reactions latent factor suggests that
screening could attempt to incorporate items from each of those different factors in order to
explain a larger proportion of the variance in overall distress. Further, Distressing Reactions
was predictive of both outcomes, with an especially strong relationship between Distressing
Reactions and health-related quality of life. This relationship, considering the factors used to
45


estimate Distressing Reactions, was consistent with the current literature (Reich, Lesur, &
Perdrizet-Chevallier, 2008). The relationship between Distressing Reactions and life
satisfaction is new to the literature in that satisfaction with life is not a commonly studied
outcome in young breast cancer survivorship. Perhaps the relationship between Distressing
Reactions and life satisfaction is lower than that between Distressing Reactions and health-
related quality of life because some women are generally satisfied with their life despite
having lower health-related quality of life than before cancer treatment. In other words, they
may be thankful for their life despite its given challenges, perhaps considering that the
alternative would have been to lose their battle with cancer. Furthermore, quality of life
measures assess emotional adjustment, so it would be expected that quality of life might be
more related to Distressing Reactions than life satisfaction. Life satisfaction does not include
constructs related to emotional distress. Certainly, it might be helpful to further investigate
other outcomes besides the commonly studied health-related quality of life as we consider
psychosocial adjustment to cancer survivorship so that we can better understand how life
satisfaction differs from quality of life at a more individual level.
With regard to the Adaptive Reactions latent variable, hope, benefit finding, and the
positive impact of children were considered one construct. Adaptive Reactions, when
considered within the overall structural model with all three hypothesized factors, was only
marginally supported by the data in that the Positive Impact of Children Scale (PICS)
demonstrated a lower loading than what is typically considered adequate. Although the
majority of women reported a positive impact of having dependent children during cancer
survivorship, this scale was largely unrelated to the outcomes in the model, suggesting it may
not be related to psychosocial adjustment. Hope and benefit finding were also more strongly
46


related to one another than to the PICS, and were more highly associated with life
satisfaction. Therefore, when considering an underlying construct such as Adaptive
Reactions, hope and benefit finding account for the majority of the variance and are therefore
responsible for the associations (and lack thereof) with the outcomes. Hope accounted for the
majority of the variance in Adaptive Reactions. Of note, hope is known to be negatively
associated with depression as it was in this sample, but the relationship between hope and the
outcomes should not be considered entirely attributable to depression. Hope is considered a
separate construct related to using a more active coping style (Stanton, Danoff-Burg, &
Huggins, 2002). High levels of hope are supposedly related to: 1. Having a sense of being
effective at setting and meeting goals, known as agency in the Hope Scale and 2. Having a
sense of being able to generate plans toward meeting ones goals, known as pathways in
the Hope Scale (Snyder et al., 2001). Hope is considered relatively stable, perhaps even
more so than dispositional optimism, and may therefore be examined as a protective factor
even before starting breast cancer treatment (Snyder et al., 2001; Stanton, Danoff-Burg, &
Huggins, 2002).
Adaptive Reactions predicted life satisfaction but was not significantly related to
health-related quality of life. This finding was contrary to the hypothesis and to some
literature stating that positive outcomes such as benefit finding are related to better quality of
life (Lechner, Carver, Antoni, Weaver, & Phillips, 2006; Carver & Antoni, 2004); however,
it is consistent with other literature that benefit finding is unrelated to quality of life (Fromm,
Andrykowski, & Hunt, 1996; Cordova, Cunningham, Carlson, & Andrykowski, 2001;
Lehman et al., 1993; Tomich & Helgeson, 2004). Perhaps the two related constructs of
benefit finding and hope were unrelated to health-related quality of life because the sample
47


reported overall higher distress scores (and lower quality of life scores) than other studies of
breast cancer survivors, and there is some evidence that positive factors such as benefit
finding only improves quality of life when survivors perceive their cancer as a moderate (i.e.,
not a minimal or severe) threat (Lechner, Carver, Antoni, Weaver, & Phillips, 2006; Carver
& Antoni, 2004).
Quality of Life and Life Satisfaction: What matters most?
Overall, the young survivors in this sample demonstrated lower levels of global
quality of life on the FACT-B than young survivors in other studies using the same
instrument. Other studies have found a mean of approximately M= 111 (Avis, Crawford, &
Manuel, 2005; Wenzel et al., 1999), whereas the mean in this sample was M= 98.75 (SD =
25.10), which is a qualitatively large difference in global quality of life scores. The
discrepancy may be accounted for by the age of this sample in comparison to the age of
samples in previous works. Others using the FACT-B included women who were 50 years
old or younger, so women diagnosed with breast cancer at an older age were eligible for the
other studies. Additionally, it is notable that the sample in this study included women any
length of time post-treatment whereas the other studies had a narrower window for inclusion
(3 years and 2 months, respectively). It might be expected that the current sample would have
higher self-reported quality of life because they are further from treatment. It is possible that
the young breast cancer survivors in this sample demonstrated lower quality of life because
of the self-selected group of participants; that is, the majority of participants were recruited
via the online cancer community and may therefore reflect survivors who are more distressed
than those who are no longer in need of the online support community.
48


In terms of life satisfaction, no studies to date have used the Satisfaction with Life
Scale with young survivors of breast cancer. Tate and Forchheimer (2002) found that breast
cancer survivors, without restraints on age, demonstrated slightly higher satisfaction with life
scores when compared to the present sample of young survivors. Both Tate et al.s sample
and the current sample of young women had satisfaction with life scores lower than the
general population (Diener et al., 1985). The findings that quality of life and life satisfaction
were both relatively low in this sample suggest that these are important outcomes for further
investigation, perhaps to understand the influence of self-selection on similar samples.
In sum, the current study aimed to test a structural equation model which
hypothesized that several biopsychosocial factors would impact quality of life and life
satisfaction through the mediation of both Distressing and Adaptive Reactions to
survivorship. Furthermore, the researcher sought to understand which variables matter
most when it comes to young breast cancer survivors psychosocial adjustment. Only social
support, parenting concerns, and reproductive concerns were significant predictors of
reactions to survivorship after controlling for the other biopsychosocial factors, suggesting
that these are domains that warrant further investigation. Low levels of social support
predicted higher levels of distress and lower levels of adaptive reactions. Parenting and
fertility concerns had the same effect. Clinically, practitioners might consider screening early
in treatment for concerns related to social support, parenting, and fertility with hopes of
preventing distress related to these areas. Distressing Reactions and Adaptive Reactions
mediated the impact of social support, parenting concerns, and fertility concerns on the
outcomes of quality of life and life satisfaction.
49


Depression, anxiety, fear of recurrence and traumatic distress, were highly correlated
and considered one underlying construct named Distressing Reactions. Distressing Reactions
was the most important predictor of quality of life and life satisfaction when controlling for
all the other predictors in the structural model, with the majority of the variance accounted
for by depression. As such, screening and interventions should also focus on depression,
anxiety, fear of recurrence, and traumatic distress well into survivorship.
Hope, and benefit findings were supported as one underlying construct named
Adaptive Reactions; the positive impact of children was not as highly correlated and is
therefore not considered as predictive of Adaptive Reactions or the outcome variables.
Higher levels of Adaptive Reactions related to higher satisfaction with life, but were not
significantly related to quality of life, perhaps because of a theory that has been applied to
benefit finding as a potential protective factor in other cancer survivorship research; that is,
perhaps benefit finding and hope were not significantly related to health related quality of life
because this sample demonstrated lower than expected quality of life (and relatively high
levels of distress) and protective effects may only occur with moderate levels of distress.
Alternatively, the Adaptive Reactions may simply be independent of quality of life. The
relationship between satisfaction with life and health-related quality of life may warrant
further exploration, as does the meaning of life satisfaction in a population who is often
examined primarily in terms of health-related quality of life. If life satisfaction is an outcome
of interest, then it may prove helpful to understand if hope and benefit finding are more
dispositional traits or if they can be encouraged through psychological interventions.
There are several limitations to the findings of this study. As with many cohort
studies, selection bias presents a potential threat to internal validity. Participants self-selected
50


to participate in the study and may therefore be qualitatively different from those who opted
not to participate. The majority of survivors were recruited from support groups and
advocacy websites; the type of women who are actively seeking support and participating in
studies may be a unique subgroup of young survivors. They may be faring better than other
survivors in that they are actively involved in a support community. Considering the
relatively low levels of quality of life when compared to other studies of breast cancer
survivors, it could also be argued that these women were more distressed on average than
those who did not participate in the study.
Because this study utilized a web-based survey and recruitment from numerous sites,
the accrual rate of participants is unknown. It is likely that the response rate was low, as
indicated by the accrual rate of other web-based studies (Cook, Heath, & Thompson, 2000).
It was also impossible to guarantee that each woman completed the study only once,
although only one survey was allowed per IP address.
Women were asked about their cancer experience in a retrospective manner, and their
responses may be different than they would have been soon after completing treatment,
especially for those who were further from completing treatment. The individual scales were
kept in their original formatted time frames; some asked women to reflect on the past week,
others on the past month, and so on. Therefore, there may have been some error associated
with the different time-frames given for the questionnaires.
Any findings from this study are limited in terms of generalizability. These findings
should not be generalized to patients undergoing active treatment. Additionally, participants
were almost entirely Caucasian and of relatively high socioeconomic status (SES); the results
may not generalize to minority breast cancer survivors or those of a lower SES. The findings
51


may not be generalizable to young women with other cancer types or young cancer survivors
with different cancer types.
This study did not utilize a comparison group, such as a group of older survivors to
understand how this sample is truly unique from others. Thus, it is not possible to draw
conclusions about this group of young survivors in comparison to other groups. Further,
because this study includes women of any stage and any length of time after completing
treatment, it may not be possible to compare this sample to a different subgroup with a
specific stage or length of time in remission.
With respect to the analyses run, although structural equation modeling is sometimes
referred to as causal modeling, it is important to remember that these paths are not
demonstrating cause and effect. Rather, the paths in the model are based largely on
correlations. Therefore, interpreting these results as the only model or the reality would
be an error in that there are likely many other variables that could also be taken together to
explain variance in these same outcomes. Furthermore, although this model was designed
based on a conceptual theory, another model with different path structure might also fit the
data well.
Despite these limitations, the findings from this study have several implications for
researchers and practitioners. Considering the most important predictors of quality of life and
life satisfaction, it might be helpful for practitioners to inquire early on about young breast
cancer patients level of social support, concerns about fertility, and parenting concerns.
Screening and psychological interventions might include these domains as well as
depression, anxiety, fear of recurrence, traumatic distress, hope, and benefit finding. Medical
interventions might put more emphasis on fertility preservation to reduce distress as a result
52


of loss of fertility, and psychological interventions should explore grief for those who do lose
the ability to have biological children. Future studies might benefit from using a longitudinal
design to understand how these biopsychosocial factors impact adjustment over the course of
treatment and survivorship, as well as testing the efficacy of test the efficacy of well-known
interventions in psychosocial oncology (e.g., cognitive behavioral therapy) in young breast
cancer survivors specifically.
53


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61


APPENDIX A
FUNCTIONAL ASSESSMENT OF CANCER THERAPY- BREAST (FACT-B)
FACT-B (VersioD 4)
Below is a list of statements that other people with your illness have said are important. Please circle
or mark one number per line to indicate your response as it applies to the past 7 days.
PHYSICAL WELL-BEING Not at all A little bit Some- what Quite a bit Very much
I have a lack of enerev 0 i 2 3 4
I have nausea 0 i 2 3 4
Because of my physical condition, I have trouble meeting the needs of my family 0 i 2 3 4
CJM I have pain 0 i 2 3 4
G*.' I am bothered bv side effects of treatment 0 i 2 3 4
I feel ill 0 i 2 3 4
C#7 I am forced to spend time in bed 0 i 2 3 4
SOCIAL/FAMILY WELL-BEING Not at all A little bit Some- what Quite a bit Very much
C&l I feel close to mv friends 0 i 2 3 4
I get emotional support from my family 0 i 2 3 4
Gi3 I get support from mv friends 0 i 2 3 4
Q54 My family has accepted my illness 0 i 2 3 4
(£5 I am satisfied with family communication about my illness 0 i 2 3 4
GS6 I feel close to my partner (or the person who is my main support) 0 i 2 3 4
Regardless of your current level of sexual activity, please
answer the following question If you prefer not to answer it,
please mark this box and go to the next section.
I am satisfied with mv sex life...........................
62


FACT-B (VersioD 4)
Please circle or mark one number per line to indicate your response as it applies to the past 7
days.
EMOTIONAL WELL-BEING Not at all A little bit Some what Quite a bit Very much
GEI I feel sad .... 0 1 2 3 4
GE2 I am satisfied with how I am coping with my illness.... .... 0 1 2 3 4
C£J I am losing hope in the fight against mv illness .... 0 1 2 3 4
OTA I feel nervous .... 0 1 2 3 4
C£l I worry about dying .... 0 1 2 3 4
CJE* I worry that mv condition will get worse .... 0 1 2 3 4
FUNCTIONAL WELL-BEING Not at all A little bit Some- what Quite a bit Very much
GFI I am able to work (include work at home) .... 0 i 2 3 4
UF2 Mv work (include work at home) is fulfilling .... 0 i 2 3 4
tiFJ I am able to enjov life .... 0 i 2 3 4
UFA I have accepted my illness .... 0 i 2 3 4
CFJ: I am sleeping well .... 0 i 2 3 4
GF6 I am enjoying the things I usually do for fun .... 0 i 2 3 4
OF7 I am content with the quality of my life right now .... 0 i 2 3 4
63


FACT-B (Version 4)
Please circle or mark one number per line to Indicate your response as It applies to tbe past 7
days.
ADDITIONAL CONCERNS Not at all A little bit Some what Quite a bit Very much
B1 I have been short of breath .. 0 i 2 3 4
B2 I am self-conscious about the wav I dress .. 0 i 2 3 4
BJ One or both of mv arms are swollen or tender .. 0 i 2 3 4
114 I fed sexually attractive .. 0 i 2 3 4
115 I am bothered bv hair loss .. 0 i 2 3 4
Ik. I worry that other members of my family might someday set the same illness I have .. 0 i 2 3 4
ft? I worry about the effect of stress on my illness .. 0 i 2 3 4
lid I am bothered bv a change in weisht .. 0 i 2 3 4
IW I am able to fteel like a woman .. 0 i 2 3 4
n I have certain parts of my body where I experience pain. .. 0 i 2 3 4
64


APPENDIX B
SATISFACTION WITH LIFE SCALE (SWLS)
Below are five statements that you may agree or disagree with. Using the 1 7 scale below,
indicate your agreement with each item by placing the appropriate number on the line
preceding that item. Please be open and honest in your responding.
7 Strongly agree
6 Agree
5 Slightly agree
4 Neither agree nor disagree
3 Slightly disagree
2 Disagree
1 Strongly disagree
_____In most ways my life is close to my ideal.
_____The conditions of my life are excellent.
_____I am satisfied with my life.
So far I have gotten the important things I want in life.
If I could live my life over, I would change almost nothing.
31-35 Extremely satisfied
26 30 Satisfied
21-25 Slightly satisfied
20 Neutral
15-19 Slightly dissatisfied
10 14 Dissatisfied
5-9 Extremely dissatisfied
65


APPENDIX C
BENEFIT FINDING MEASURE (Tomich & Helgeson, 2004)
Having had breast cancer:
1. has made me more sensitive to family issues.
2. has led me to be more accepting of things.
3. has taught me how to adjust to things I cannot change.
4. has given my family a sense of continuity, a sense of history.
5. has made me a more responsible person.
6. has made me realize the importance of planning for my familys future.
7. has brought my family closer together.
8. has made me more productive.
9. has helped me take things as they come.
10. has helped me to budget my time better.
11. has made me more grateful for each day.
12. has taught me to be patient.
13. has taught me to control my temper.
14. has renewed my interest in participating in different activities.
15. has led me to cope better with stress and problems.
66


APPENDIX D
HOPE SCALE
Directions: Read each item carefully. Using the scale shown below, please select the number
that best describes YOU and put that number in the blank provided.
1 = Definitely False 2 = Mostly False 3 = Mostly True 4 = Definitely True
1. I can think of many ways to get out of a jam. (Pathways)
2. I energetically pursue my goals. (Agency)
3.1 feel tired most of the time. (Filler)
4. There are lots of ways around any problem. (Pathways)
5. I am easily downed in an argument. (Filler)
6. I can think of many ways to get the things in life that are
most important to me. (Pathways)
7. I worry about my health. (Filler)
8. Even when others get discouraged, I know I can find a way
to solve the problem. (Pathways)
9. My past experiences have prepared me well for my future.
(Agency)
10. Ive been pretty successful in life. (Agency)
11.1 usually find myself worrying about something. (Filler)
12.1 meet the goals that I set for myself. (Agency)
67


APPENDIX E
IMPACT OF EVENTS SCALE- REVISED (IES-R)
INSTRUCTIONS: Below is a list of difficulties people sometimes have after stressful life
events. Please read each item, and then indicate how distressing each difficulty has been for
you DURING THE PAST SEVEN DAYS with respect to______________________________________
which occurred on_________________. How much were you distressed or bothered by these
difficulties?
Item Response Anchors are 0 = Not at all; 1 = A little bit; 2 = Moderately; 3 = Quite a bit; 4
= Extremely.
The Intrusion subscale is the MEAN item response of items 1, 2, 3, 6, 9, 14, 16, 20. Thus,
scores can range from 0 through 4.
The Avoidance subscale is the MEAN item response of items 5, 7, 8, 11, 12, 13, 17, 22.
Thus, scores can range from 0 through 4.
The Hyperarousal subscale is the MEAN item response of items 4, 10, 15, 18, 19, 21. Thus,
scores can range from 0 through 4.
I. Any reminder brought back feelings about it.
2.1 had trouble staying asleep.
3. Other things kept making me think about it.
4. I felt irritable and angry.
5. I avoided letting myself get upset when I thought about it or was reminded of it.
6. I thought about it when I didnt mean to.
7.1 felt as if it hadnt happened or wasnt real.
8. I stayed away from reminders of it.
9. Pictures about it popped into my mind.
10. I was jumpy and easily startled.
II. I tried not to think about it.
68


12.1 was aware that I still had a lot of feelings about it, but I didnt deal with them.
13. My feelings about it were kind of numb.
14. I found myself acting or feeling like I was back at that time.
15.1 had trouble falling asleep.
16.1 had waves of strong feelings about it.
17. I tried to remove it from my memory.
18. I had trouble concentrating.
19. Reminders of it caused me to have physical reactions, such as sweating, trouble
breathing, nausea, or a pounding heart.
20.1 had dreams about it.
21.1 felt watchful and on-guard.
22.1 tried not to talk about it.
Total IES-R score:
69


APPENDIX F
CONCERNS ABOUT RECURRENCE SCALE (CARS)
The following questions ask you to tell us about any worries you may have about the possibility of
breast cancer recurrence. By recurrence we mean the breast cancer coming back in the same breast or
another area of the body, or a new breast cancer in either breast.
Although most women who have been diagnosed with early stage breast cancer will never have
another problem with the cancer, we are aware that many women do worry about this possibility.
Other women may not worry about recurrence at all. Either way, your answers to these questions are
very important to us. We understand that it may be upsetting to think about or answer questions about
the possibility of recurrence. However, we need your help to understand how women think about this
possibility.
1. How much time do you spend thinking about the possibility that your breast cancer could recur?
1
I Dont Think
About It At All
I Think About
It All The Time
2. How much does the possibility that your breast cancer could recur upset you?
1 2 3 4 5 6
It Does Not Upset Me At All
It Makes Me Extremely Upset
3. How often do you worry about the possibility that your breast cancer could recur?
1 2 3 4 5 6
I Never Worry About It
I Worry About It All The Time
4. How afraid are you that your breast cancer may recur?
1 2 3 4 5 6
Not At All Afraid Very Afraid
70


APPENDIX G
HOSPITAL ANXIETY AND DEPRESSION SCALE (HADS)
Instruct the patient to answer how it currently describes their feelings.
(1) I feel tense or wound up:
Most of the time 3
A lot of the time 2
From time to time, occasionally 1
Not at all 0
(D) I still enjoy the things I used to enjoy:
Definitely as much 0
Not quite so much 1
Only a little 2
Hardly at all 3
(1) I get a sort of frightened feeling as if something awful is about to happen
Very definitely and quite badly 3
Yes, but not too badly 2
A little, but it doesnt worry me 1
Not at all 0
(D) I can laugh and see the funny side of things:
As much as I always could 0


Not quite so much now 1
Definitely not so much now 2
Not at all 3
(1) Worrying thoughts go through my mind
A great deal of the time 3
A lot of the time 2
From time to time, but not too often 1
Only occasionally 0
(D) I feel cheerful:
Not at all 3
Not often 2
Sometimes 1
Most of the time 0
(1) I can sit at ease and feel relaxed:
Definitely 0
Usually 1
Not Often 2
Not at all 3
(D) I feel as if I am slowed down:


Nearly all the time 3
Very often 2
Sometimes 1
Not at all 0
(1) I get a sort of frightened feeling like butterflies in the stomach:
Not at all 0
Occasionally 1
Quite Often 2
Very Often 3
(D) I have lost interest in my appearance:
Definitely 3
I dont take as much care as I should 2
I may not take quite as much care 1
I take just as much care as ever 0
(1) I feel restless as I have to be on the move:
Very much indeed 3
Quite a lot 2
Not very much 1
Not at all 0
73


(D) I look forward with enjoyment to things:
As much as I ever did 0
Rather less than I used to 1
Definitely less than I used to 2
Hardly at all 3
(1) I get sudden feelings of panic:
Very often indeed 3
Quite often 2
Not very often 1
Not at all 0
(D) I can enjoy a good book or radio or TV program:
Often 0
Sometimes 1
Not often 2
Very seldom 3
Scoring (add the As = Anxiety. Add the Ds = Depression). The norms below will give you an
idea of the level of Anxiety and Depression.
0-7 = Normal
8-10 = Borderline abnormal
11-21 = Abnormal
74


APPENDIX H
FINANCIAL PROBLEMS SUBSCALE OF THE QLACS
The next set of questions asks specifically about the effects of your cancer or its treatment.
Again, for each statement, indicate how often each of these statements has been true for you
in the past four weeks.
1. You had financial problems because of the cost of cancer surgery or treatment.
2. You had problems with insurance because of cancer.
3. You had money problems that arose because you had cancer.
4. You had financial problems due to a loss of income as a result of cancer.
75


APPENDIX I
REPRODUCTIVE CONCERNS SCALE
REPRODUCTIVE CONCERNS SCALE
The next statements reflect possible feelings or thoughts about pregnancy, fertility (ability to
get pregnant), & reproduction (having children). Please rate how true each one has been for
you during the past month. If you do not feel that the statement is relevant to you, please
answer Not at all
During the past month:
Not at all
A little bit
Some-what
Quite a bit
Very much
1. I have concerns about my ability to have children.
2. I am content with the number of children that I have.
3.1 feel less of a woman because of reproductive problems.
4. An illness/disease has affected my ability to have children.
5. I am angry that my ability to have children has been affected.
6. I am able to talk openly about fertility or reproductive concerns.
7. Others are to blame for my reproductive problems.
8.1 am sad that my ability to have children has been affected.
9. I have had control over my reproductive future.
10. I feel guilt about my reproductive problems.
11. I have mourned the loss of my ability to have children.
12. I blame myself for my reproductive problems.
13. Iam frustrated that my ability to have children has been affected.
14.1 am less satisfied with my life because of reproductive problems.
76


APPENDIX J
MULTIDIMENSIONAL SCALE OF PERCEIVED SOCIAL SUPPORT
Instructions: We are interested in how you feel about the following statements. Read each
statement carefully. Indicate how you feel about each statement.
Circle the 1 if you Very Strongly Disagree
Circle the 2 if you Strongly Disagree
Circle the 3 if you Mildly Disagree
Circle the 4 if you are Neutral
Circle the 5 if you Mildly Agree
Circle the 6 if you Strongly Agree
Circle the 7 if you Very Strongly Agree
1. There is a special person who is around when I am in need. (SO)
2. There is a special person with whom I can share my joys and sorrows. (SO)
3. My family really tries to help me. (Fam)
4. I get the emotional help and support I need from my family. (Fam)
5. I have a special person who is a real source of comfort to me. (SO)
6. My friends really try to help me. (Fri)
7. I can count on my friends when things go wrong. (Fri)
8. I can talk about my problems with my family. (Fam)
9. I have friends with whom I can share my joys and sorrows. (Fri)
10. There is a special person in my life who cares about my feelings. (SO)
11. My family is willing to help me make decisions. (Fam)
12. I can talk about my problems with my friends. (Fri)
The items tended to divide into factor groups relating to the source of the social support,
namely family (Fam), friends (Fri) or significant other (SO).
77


APPENDIX K
CHARLSON COMORBIDITY INDEX
Table 1. Charlson Comorbidity Index Scoring System
Score
Condition
1 Myocardial infarction (history, not ECG changes only)
Congestive heart failure
Peripheral vascular disease (includes aortic aneurysm &6 cm)
Cerebrovascular disease: CVA with mild or no residua or TIA
Dementia
Chronic pulmonary disease
Connective tissue disease
Peptic ulcer disease
Mild liver disease (without portal hypertension, includes chronic hepatitis)
Diabetes without end-organ damage (excludes diet-controlled alone)
2 Hemiplegia
Moderate or severe renal disease
Diabetes with end-organ damage (retinopathy, neuropathy, nephropathy, or brittle diabetes)
Tumor without metastases (exclude if >5 y from diagnosis)
Leukemia (acute or chronic)
Lymphoma
3 Moderate or severe liver disease
6 Metastatic solid tumor
AIDS (not just HIV positive)
NOTE. For each decade > 40 years of age, a score of 1 is added to the above score.
Abbreviations: ECG, electrocardiogram; CVA, cerebrovascular accident; TIA, transient ischemic attack; AIDS, acquired
immunodeficiency syndrome; HIV, human immunodeficiency virus.
78


APPENDIX L
FACT-COG
Below is a list of statements that other people with your condition have said are important. Please circle
or mark one number per line to indicate your response as it applies to the past 7 days.
coiA i
CajiAi
C'ogC?
CogM9
CctMIO
toy.Mll
Co(jVl5
CnftVIS
CogVIh
CogV 17b
e C.£F2?
CtijjF2d
PERCEIVED COGNITIVE IMPAIRMENTS Never About once a week Two to three times a week Nearly every day Several times a day
I have had trouble forming thoughts 0 1 2 3 4
My thinking has been slow 0 1 2 3 4
I have had trouble concentrating 0 1 2 3 4
I have had trouble finding my way to a familiar place 0 1 2 3 4
I have had trouble remembering where I put things, like mv kevs or mv wallet 0 1 2 3 4
I have had trouble remembering new information, like phone numbers or simple instructions 0 1 2 3 4
I have had trouble recalling the name of an object while talking to someone 0 1 2 3 4
I have had trouble finding the right word(s) to express myself 0 1 2 3 4
I have used the wrong word when I referred to an object 0 1 2 3 4
I have had trouble saying what I mean in conversations with others 0 1 2 3 4
I have walked into a room and forgotten what I meant to get or do there 0 1 2 3 4
I have had to work really hard to pay attention or I would make a mistake 0 1 2 3 4
I have forgotten names of people soon after being introduced 0 1 2 3 4
79


Please circle or mark one number per line to indicate your response as it applies to
the past 7 days.
Never About Two to Nearly Several
once a three every times a
week times a day day
week
£'c*F25 My reactions in everyday situations have been
slow 0 1 2 3 4
CobCOI I have had to work harder than usual to keep track
of what I was dome 0 1 2 3 4
CQ0C32 Mv thinking has been slower than usual .. 0 1 2 3 4
C*C33i I have had to work harder than usual to express
myself clearly 0 1 2 3 4
t'Rt'33c I have had to use written lists more often than
usual so I would not forget things .. 0 1 2 3 4
CpjMT! I have trouble keeping track of what I am doing if I
am interrupted 0 1 2 3 4
Ct|MT3 I have trouble shifting back and forth between
different activities that require thinking 0 1 2 3 4
Please circle or mark one number per line to indicate your response as it applies to
the past 7 davs.
Never About Two to Nearly Several
once a three every times a
COMMENTS FROM OTHERS week times a day day
week
CocOl Other people have told me I seemed to have trouble
remembering information .. 0 1 2 3 4
ObOJ Other people have told me I seemed to have trouble
speaking clearly 0 1 2 3 4
CUlp3 Other people have told me I seemed to have trouble
thinking clearly .. 0 1 2 3 4
CoeCM Other people have told me I seemed confused .. 0 1 2 3 4
80


Please circle or mark one number per line to indicate your response as it applies to
the past 7 davs.
PERCEIVED COGNITIVE ABILITIES Not at all A little lit Some- what Quite a bit Very much
Co* ft! I have been able to concentrate 0 1 2 3 4
Cos rvi I have been able to bring to mind words that I wanted to use while talking to someone 0 1 2 3 4
Cos PMI I have been able to remember things, like where I left my keys or wallet 0 1 2 3 4
Cog pm; I have been able to remember to do things, like take medicine or buy something I needed 0 1 2 3 4
Cos PHI I am able to pay attention and keep track of what I am doing without extra effort 0 1 2 3 4
Cos PCH 1 My mind is as sharp as it has ahvavs been 0 1 2 3 4
Ci>u KH j My memorv is as good as it has ahvavs been 0 1 2 3 4
Cos rwr i I am able to shift back and forth between two activities that require thinking 0 1 2 3 4
Cos mi I am able to keep track of what I am doing, even if I am interrupted 0 1 2 3 4
Please circle or mark one number per line to indicate your response as it applies to the past 7 davs. Not A little Some- Quite IMPACT ON QUALITY OF LIFE ataU bit what abit Very much
Co*QJ3 I have been upset about these problems 0 1 2 3 4
CosOJ-? These problems have interfered with my ability' to work 0 1 2 3 4
CosQJS These problems have interfered with my ability' to do things I enjoy 0 1 2 3 4
CosQ4l These problems have interfered with the quality' of my life 0 1 2 3 4
81


Appendix M
Parenting Concerns Questionnaire
Rated from:
0 = Not at all concerned
1 = A little bit concerned
2= Somewhat concerned
3= Very concerned
4= Extremely concerned
Factor 1: Practical impact of illness on child
My own mood, worries or emotions are affecting my children
My physical limits or low energy are affecting my children
I am not able to spend as much time with my children as Id like
My illness is changing my childrens routine
Changes in my memory or attention are affecting my children
Factor 2: Emotional impact of illness on child
My children are emotionally upset by my illness
My children are worried that I am going to die
My children get upset when we talk about the illness
My children might be in need of professional mental health care
My children get confused or upset by what others say about my illness
Factor 3: Concerns about co-parent
My childrens other parent would not be able to meet their emotional needs if I die
There is no one to take good care of my children if I die
My partner is not providing me with enough practical support
My partner is not providing me with enough emotional support
My childrens other parent would not be a responsible caregiver if I die
82


Full Text

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WHAT MATTERS MOST? PREDICTORS OF QUALITY OF LIFE AND LIFE SATISFACTION AMONG YOUNG BREAST CANCER SURVIVORS by KELLIE MARTENS B.A., University of Michigan, 2007 M.A., University of Colorado Denver, 2014 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Philosophy Clinical Health Psychology 2016

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ii This thesis for the Doctor of Philosophy degree by Kellie Martens has been approved for the Clinical Health Psychology Program by Krista Ranby Chair Kristin Kilbourn Advisor Evelinn Borrayo James Grigsby Jana Bolduan Lomax April 6 2016

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iii Kellie Martens (Ph.D. Clinical Health Psychology) What Matters M ost? Predictors of Quality of Life and Life Satisfaction among Young Breast Cancer Survivors Thesis directed by Assistant Professor Krista Ranby ABSTRACT This study tested a literature based model of psychosocial adjustment among young breast cancer (BC) survivors. The model included biological factors (BRCA positive, stage of cancer, premature menopause, medical comorbidities, time in remission), psychological factors (psychological diagnoses, cognitive functioning), and social/practical factors (social support, parenting, fi nances, fertility). Factors were hypothesized to impact distressing reactions (depression, anxiety, fear of recurrence, and traumatic distress), and adaptive reactions (hope, benefit finding) to survivorship. Reactions to survivorship were hypothesized to impact quality of life (QoL) and satisfaction with life (SWL). Young BC survivors (N = 284) were recruited via social media to complete a web based survey. The self report items in the survey assessed demographic and biopsychosocial factors, and self repor t measures including the Functional Assessment of Cancer Therapy for breast cancer (FACT B) and Satisfaction with Life Scale (SWLS). Latent variables were created for Adaptive and Distressing Reactions. Structural Equation Modeling (SEM) was performed in M Plus to test the hypothesized relationships between biopsychosocial factors, Adaptive and Distressing Reactions, and the outcomes of QoL and SWL. The hypothesized model fit the observed data adequately 2 (100) = 332.92, p < .001, CFI = .86, RMSEA = .09, SRMR = .05. The final model accounted for 86% and 62% of the variance in QoL and SWL respectively. Support, parenting, and fertility concerns were the only significant

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iv predictors of adjustment. Adaptive Reactions was associated with SWLS (Beta = .58, p < .0001), but not QoL. Distressing Reactions was associated with SWL (Beta = .26, p = .01) QoL (Beta = .87, p <.001). QoL and SWL were significantly associated (Beta = .19, p < .05). The biopsychosocial factors that predicted Adaptive and Distressing Reactions to survivorship were social support, parenting concerns, and fertility concerns. Stage of cancer, time in remission, comorbidities, premature menopause, psychiatric diagno ses, and cognitive functioning were not related to psychosocial adjustment. Depression, anxiety, fear of recurrence, and traumatic distress (Distressing Reactions) were strongly associated with lower levels of QoL and SWL whereas hope and benefit finding ( Adaptive Reactions) were only associated with higher levels of SWL. The form and content of this abstract are approved. I recommend its publication. Approved: Krista Ranby

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v DEDICATION I dedicate this work to my husband and my daughters Thank you for your support and encouragement every step of the way.

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vi ACKNOWLEDGMENTS I would like to thank my mentor, Kristin Kilbour n, for all of her guidance throughout this project I thank Krista Ranby, Evelinn Borrayo Jana Bolduan Lomax, and James Grigsby for their support and input as my committee members. I thank Megan Grigsby, Stephanie Hooker, and Ryan Asherin for their support and help; they are not only my colleagues but they are also my friends. Young Survival Coalition was an invaluable part of my recruitment process and deserves recognition for the amazing work that they do with young breast cancer surviv ors. Finally, I would like to acknowledge all of the many individuals and organizations who helped me reach eligible participants: my mother, Bethany Aronrow, Rocky Mountain Cancer Centers, Laurri Jones, Dr. Rachel Rabinovitch, Dr. Virginia Borges, StupidC Cancer Center, Young Survivors Network Inc., The Young Breast Cancer Survivorship Program at UCLA, Ashley at 3 Little Birds 4 Life, Young Breast Cancer Survivors of SW Michigan/Kalamazoo area Yo ung Survivors United Against Breast Cancer, Beyond the Pink Moon Jo Vogeli, Lacey Clement, Samantha Saxton, The Leukemia and Lymphoma Society, University of Colorado Hospital Cancer Center, Kelly Adams, Dr. Robert Fisher, and more. Due to the large outpou ring of support I received, it is likely that I forgot someone; your efforts are appreciated more than you know.

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vii TABLE OF CONTENTS CHAPTER I. INTRODUCTION ................................ ................................ ................................ .......... 1 Biological Concerns: BRCA, Stage, Menopause, Comorbidities, and Time since Treatment ...... ............................................................................................................. 4 Social and Practical Concerns: Infertil ity, Social Support, Parenting, Finances ................................................................. .................................... ................. 6 Psychological Concerns: Longs tanding Diagnoses an d Cognitive Decline ............................................................ ........................................................ .. 7 Psychosocial Adjustment: Distressing Reactions to Survivorship 8 Psychosocial Adjustment: Adaptive Reactions to Survivorship .............................. ...9 Quality of Life and Life Satisfaction ................................................................ .........10 II. STUDY AIMS AND HYPOTHESE S ................................ ................................ ........... 12 III. METHOD ................................ ................................ ................................ ...................... 1 4 Sample ................................ ................................ ................................ ....................... 1 4 Procedure ................................ ................................ ................................ .................. 1 4 Measures ................................ ................................ ................................ .................. 15 Quality of Life. ................................ ................................ ................................ 15 Satisfaction with Life. ................................ ................................ ...................... 1 6 Benefit Finding. ................................ ................................ ............................... 1 6 Hope. ................................ ................................ ................................ ................ 1 7 ...... 1 7 Distress. ................................ ................................ ................................ ............ 1 7 Fear of Recurrence. ................................ ................................ .......................... 1 8

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viii Anxiety and Depression. ................................ ................................ .................. 1 8 Psychological Diagnosis. ................................ ................................ ................. 1 9 Financial Concerns. ................................ ................................ .......................... 1 9 Social Support. ................................ ................................ ................................ 1 9 Parenting Concerns ................................ ................................ ......................... 20 Fertility Concerns. ................................ ................................ ............................ 20 Comorbidities. ................................ ................................ ................................ .. 20 BRCA Positive ................................ ................................ ................................ 21 Stage ................................ ................................ ................................ ................ 21 Menopause ................................ ................................ ................................ ...... 21 T ime since treatment ................................ ................................ ....................... 21 Cognitive decline ................................ ................................ ............................ 22 Data Analysis ................................ ................................ ................................ ........... 2 2 Descriptive Statistics. ................................ ................................ ....................... 2 2 Reliability Analysis ........................................................................................... 2 2 .... 2 3 Structural Equation M odeling ................................ ................................ .......... 2 3 ..... 2 6 Sample Description ..................... ..................................... .26 Reliability of the Psych ................ 2 9 Correlations ........................................................................................ ............. ........ ... 32 Measurement Models ................................................................... ............................ ...35

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ix Structural Equation Model ................................................................... ....................36 V. ...................................... 40 Biopsychosocial Factors ........................................................ ..... .............................40 Distressing and Adaptive Reactions ........................ .................. ..............................45 Quality of Life and Life Satisfaction: What matters most? ....... ..............................48 REFERENCES ................................ ................................ ................................ ................... 54 APPENDI X A: FUNCTIONAL ASS ESSMENT OF CANCER THERAPY BREAST ............ 62 B : SATISFACTION WITH LIFE SCALE ................................ ............................ 65 C: BENEFIT FINDING MEASURE ................................ ................................ ..... 66 D: HOPE SCALE ................................ ................................ ................................ .. 67 E: IMPACT OF EVENTS SCALE REVISED ................................ ..................... 68 F: CONCERNS ABOUT RECURRENCE SCALE ................................ .............. 7 0 G: HOSPITAL ANXIETY AND DEPRESS ION SCALE ................................ .... 7 1 H : FINANCIAL PROBLEMS SUBSCALE OF THE QLACS ............................ 75 I : REPRODUCTIVE CONCERNS SCALE ................................ .......................... 76 J : MULTIDIMENSIONAL SCALE OF PERCEIVED SOCIAL SUPPORT ........ 77 K : CHARLESON COMORBIDITY INDEX ......................................................... 78 L: FUNCTIONAL ASSESSMENT OF CANCER THERAPY COGNITIVE FUNCTION................................................................................ ..............................79

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x LIST OF TABLES Table 1 Demographic and Medical Characteristics of Participants ................................ ......... ... 28 2 Psychometric Properties of Psychosocial Scales ................................................... ........ 31 3 Exploratory Factor Analysis of the PICS .............................................................. .......... 32 4 Bivariate Correlations between Predictor Variables ......................................... .......... ....33 5 Bivariate Correlations between Modeled Variables and Outcomes of Quality of Life and Life Satisfaction ................................ ................................ ................................ ............... ... 34 6. Unstandardized, Standardized, and Significance Levels for Model in Figure 2 .............37

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xi LIST O F FIGURES Figure 1 Hypothesized conceptual model of psychosocial adjustment among young breast cancer survivors ....................................................................................... ............................. .. 13 2 Results for hypothesized structural equation model of psychosocial adjustment ....... ... 3 6

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1 CHAPTER I INTRODUCTION Breast cancer is the most common non cutaneous malignancy among women worldwide, accounting for nearly twenty five percent of all female cancers (Ferlay et al., 2013). In the United States, approximately one in eight women will be diagnosed with breast cancer at some point in their lives (National Cancer Institute [NCI], 2011). Breast cancer is more common in women over the age of 50 years (American Cance r Society [ACS], 2012), and therefore research has historically focused on older breast cancer survivors. Although less common in women under the age of 50 years, breast cancer is also the most prevalent type of cancer diagnosed in younger women. Furthermo re, breast cancer incidence rates in women under the age of 50 years have remained stable whereas rates in older women have steadily decreased since the 1980s (ACS, 2013). Combined with advances in detection and treatment, the steady rates of younger women diagnosed with breast cancer have resulted in more young women who identify as breast cancer survivors (NCI, 2011). Current research suggests that the growing population of young breast cancer survivors faces unique challenges when compared to their older counterparts (Gabriel & Domchek, 2010). Before discussing the specific challenges of young breast cancer survivors, it may it relates to cancer survivorship has been defined differently throughout the literature. Some age of 40 years (Gabriel & Domchek, 2010), and others go so far as 55 years and under ( Howard Anderson, Gan relates to the purpose and hypotheses of the given study. In this study, young breast cancer

PAGE 13

2 survivors are defined as women who were diagnosed between the ages of 19 and 45 years. This age range was chosen in order to represent women who are no longer adolescents, but are also most likely pre menopausal. The average age of menopause onset in the United States is 50 years (National Institute of Health, 2012); because this study seeks to under stand the specific challenges of pre menopausal women who are diagnosed with breast cancer, the upper limit of 45 years old was chosen. (Khan, Rose, & Evans, 2012). The National Coalition for Cancer Survivorship (NCCS) asserts that cancer survivorship starts at the time of diagnosis and continues after treatment is complete (NCCS, 2011). However, Khan et al. (2012) note that others use th e end of active treatment as a cut off, and that those who have completed active treatment. Another phrase that is often used in psychosocial oncology and will be utilized justment in the context of those in their social world, manage, learn from and adapt to the multitude of changes which have been precipitated by the illness and its is an on going process and may consist of both negative and positive reactions to survivorship. Therefore, this study will examine both negative (distressing) and positive (adaptive) sequelae of surviving breast cancer at a young age.

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3 Despite the varying terminology used among breast cancer researchers, current literature indicates that women who are younger report unique concerns and psychosocial challenges when compared to older women. These findings can be interpreted from the life stage perspective in that younger women are in a different stage of life than older women and therefore have distinct psychosocial concerns following a cancer diagnosis. The life stage perspective states that the shared goals of a group depend on the stage of life (Rowland, 1989). As a simplistic example, a child may have the goal of pleasing her parents, while an adult may have the goal of pleasing her boss. Cimprich, Ronis, and Martinez Ramos (2012) applied the life stage the oretical model to breast cancer survivorship by describing the different goals that accompany the life stage of younger women. From this viewpoint, younger women likely have different goals and aspirations than older women, and these goals may be interrupt ed by a cancer diagnosis, thereby resulting in a unique set of survivorship concerns. Specifically, compared to older women, young women may be more focused on forming a romantic relationship, having children, or starting a career; thus, it follows that a breast cancer diagnosis may interfere with these goals and lead to concerns about relationships, fertility, and employment. Furthermore, these concerns may not be as prominent for older women, and therefore warrant additional research among young women. T his study will organize the unique factors that influence young breast cancer survivorship into biological, psychological, and social variables. In other words, this study will utilize a biopsychosocial framework (Engel, 1980) to study psychosocial adjustm ent among young breast cancer survivors.

PAGE 15

4 Biological Concerns: BRCA, Stage, Menopause, Comorbidities, and Time since Treatment Physiologically, young women who are diagnosed with breast cancer may have more complications related to the disease and treatmen t. Young women are more likely to be diagnosed with an aggressive grade of breast cancer (Azim et al., 2012), triple negative breast cancer (Partridge et al., 2013), and to have both local and distant metastases (Purushotham et al., 2014). Young survivors are also more likely to possess a BRCA mutation, which often leads them to pursue more aggressive treatments and to additional concern about the increased risk of cancer in family members (De Sanjose et al., 2003; Kwon et al., 2010; Robertson et al., 2012) Considering these complications, young women diagnosed with breast cancer are unfortunately more likely to have a recurrence and to die than older women (ACS, 2013). In the first international consensus guidelines for breast cancer in young women, Partri dge et al. (2014) describe the recommended course of treatment for young women with early stage versus those with later stage breast cancer. Of course, patients who are diagnosed with a more advanced stage of breast cancer are also more likely to undergo a ggressive treatment and to have an increased risk of mortality and recurrence. Therefore, women who are diagnosed with later stage breast cancer are more likely to experience ongoing physical and psychosocial complications following treatment completion. F or example, women with later stage cancer are more likely to undergo adjuvant therapies such as hormone therapy or chemotherapy, which may lead to lower overall quality of life (Hopwood, Haviland, Mills, Sumo, & Bliss, 2008).

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5 Young breast cancer survivors may have to cope with premature menopause as a result of ovarian damage after chemotherapy or hormone therapies such as tamoxifen (Nystedt et al., 2000). Early onset of menopause may lead to increased symptom burden such as severe radiation induced dermat itis, hot flashes, vaginal dryness and dyspareunia, fatigue, chemotherapy induced peripheral neuropathy, bone loss, and cognitive decline (Loprinzi, Wolf, Barton, & Laack, 2008). Young survivors who prematurely enter menopause may also experience more dist ress due to sexual dysfunction and poor body image (Ruddy et al., 2011). Because young age is typically associated with better health and fewer chronic medical conditions, young women are expected to have fewer comorbid diagnoses than older women. Nonethe less, comorbidities are negatively associated with treatment outcome and quality of life in all breast cancer survivors, including young survivors (Land, Dalton, Jensen, & Ewertz, 2010; Land, Dalton, Jorgensen, & Ewertz, 2012). Comorbid conditions may comp licate breast cancer treatment, overall health, and adjustment to survivorship post treatment. Because of the advancements in modern medicine, cancer survivors as a whole are living longer than in the past (DeSantis et al., 2014). Among breast cancer surv ivors, distress often increases in the year post treatment due to the uncertainty that comes with stopping the active process of treating cancer but decreases into long term survivorship (Ganz et al., 2004; Deshields et al., 2005). Even so, some women cont inue to report emotional distress, physical symptoms, and fear of recurrence into long term survivorship (Harrington, Hansen, Moskowitz, Todd, & Feuerstein, 2010). Therefore, time since completing treatment is an

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6 important factor to consider when comparing distress levels among women who are at different time points in the post treatment trajectory. Social and Practical Concerns: Infertility, Social Support, Parenting, Finances With the theoretical underpinnings of the life stage perspective in mind, socia l concerns of a women diagnosed in her 20s or 30s are certainly unique, and they likely surround the development of a career and a family. Recent literature has focused more on reproductive concerns of young women. Both women who already have biologic chil dren as well as those who are not currently parents report fear of infertility due to cancer treatment (Gorman, Usita, Madlensky, & Pierce, 2011; Camp Sorrell, 2009). As discussed previously, infertility is most common among patients who undergo chemothera py, a common course of treatment among young women (Arndt et al., 2004). Even women who remain premenopausal after completing treatment express significant concerns about fertility (Ruddy et al., 2011). Surprisingly, doctors may be reluctant to discuss fer tility preservation or the potential for pregnancy during treatment (Goncalves, Tarrier, & Quinn, 2014), even though reproductive concerns often impact treatment decisions, and quality of life (Andersen, Bowen, Morea, Stein, & Baker, 2009). Related to fert ility concerns, young breast cancer survivors also have concerns about their children. Because more women are having children into their forties than in previous decades, (Matthews & Hamilton, 2009) the upper limit of the age range for this study reflects survivors who are more likely to have dependent children than older survivors. Concerns ity in families with young cancer survivors, they may also be concerned about the possibility of their

PAGE 18

7 children getting cancer (Barnes et al., 2000). Mothers diagnosed with early stage breast cancer may express concerns in regard to feelings of guilt, thei role confusion, a perceived lack of social support from healthcare professionals, and struggles maintaining a household with dependent children (Semple & McCance, 2010). Mothers may experience higher levels of perceived stress and depression than patients without children (Schlegal, 2012; Schmitt, 2008). On the other hand, some qualitative research suggests that motherhood in breast cancer patients may influence a sense of increased social support and making meaning in life (Bi llhult & Sergesten, 2003; Semple & McMcance, 2010). Young survivors may also feel less supported by friends and family, perhaps because experience with caretaking and chr onic illness. Some literature, however, suggests that older survivors have less social support than young survivors (Sammarco, 2009). Regardless of age, perceived social support is associated with better psychosocial outcomes including quality of life, as well as decreased pain (Bloom, Stewart, Chang, & Banks, 2004; Sammarco, 2001). Although perceived social support typically declines post treatment, many breast cancer survivors may still report the need for social support (Arora, Rutten, Gustafson, Moser, & Hawkins, 2007). Psychological Concerns: Longstanding Diagnoses and Cognitive Decline Previous psychological diagnoses, including anxiety and depression, have been found Perdrizet Chevallier, 2008). Although a previous diagnosis alone does not mean that a patient will exhibit problematic psychological symptoms during treatment and survivorship, it does suggest an

PAGE 19

8 increased risk for negative affect including depressive thou ghts, fear of recurrence, or overall psychological distress. This study hypothesizes that self report of treatment for mental health difficulties will be associated with more distressing reactions and less adaptive reactions. With regard to cognitive dec line after breast cancer, some women report cognitive Raffa & Tallarida, 2010). Chemotherapy related cognitive changes may include a variety of complaints about cognitive function, such as: memory problems, inattention, and slowed processing speed (Hess & Insel, 2007). Psychological research has illustrated that depression and anxiety may also affect cognitive decline post treatment for breast cancer (Shilling & Jenkins, 2006; Vardy et al., 2008; Hermelink et al., 2010). Although this study will not examine objective neuropsychological data, it may prove more adjustment to survi vorship. Psychosocial Adjustment: Distressing Reactions to Survivorship All of the aforementioned biopsychosocial concerns that are unique to young breast cancer survivors are expected to impact psychosocial adjustment to survivorship. A systematic review by Howard Andersen, Ganz, Bower, & Stanton (2011) examined the psychosoc ial adjustment of young breast cancer survivors compared to their older counterparts. Howard Andersen and colleagues found that young survivors typically report higher levels of depressive symptoms and are more likely to have clinical depression. Furthermo re, young survivors also report higher levels of general distress, anxiety, and fear of recurrence (Howard Anderson, Ganz, Bower, & Stanton, 2011; Liu et al., 2011; Koch et al., 2014). Some evidence also suggests that young survivors report fewer positive

PAGE 20

9 psychosocial effects (such as benefit finding) after breast cancer treatment (Costanzo, Ryff, & Singer, 2009). Psychosocial Adjustment: Adaptive Reactions to Survivorship Despite the challenges associated with having breast cancer, some survivors do find positive in their experience. Finding benefit in the breast cancer experience may improve adjustment to survivorship, especially in those who perceive breast cancer as a moderate threat (i.e., not a minimal or a severe threat) to their life (Lechner, Carv er, Antoni, Weaver, & Phillips, 2006; Carver & Antoni, 2004); therefore, benefit finding might be expected to relate to the biopsychosocial concerns of young survivors. In addition to mitigating distress and depression, benefit finding may even improve phy sical health across diverse medical populations, including breast cancer patients (Bower, Low, Moskowitz, Sepah, & Epel, 2008). Contrary to findings that suggest finding benefit helps adjustment to survivorship, some studies have shown that benefit findi ng in general does not relate to well being, quality of life, or psychological adjustment (Fromm, Andrykowski, & Hunt, 1996; Cordova, Cunningham, Carlson, & Andrykowski, 2001; Lehman et al., 1993; Tomich & Helgeson, 2004). Benefit finding may correspond wi th higher levels of distress, perhaps related to the idea of post traumatic growth and the aforementioned need to perceive breast cancer as a threat in order to find meaning in the threat itself. Hope is a construct that is not commonly studied in breast cancer survivorship, especially when compared with the more negative construct of hopelessness In fact, there are no known studies examining hope in young survivors specifically. Stanton, Danoff Burg, and Huggins (2002) examined hope as a predictor of ad justment in breast cancer survivors

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10 (not limited to young women) and found that it was associated with lower levels of distress. Even when compared to constructs such as dispositional optimism, hope is considered more stable than many of the other construc ts in this study (Snyder et al., 2001). It is considered a primarily cognitive construct, consisting of two parts: 1. Having a sense of being effective at being able to Scale (Snyder et al., 2001). Hopelessness is strongly associated with depression and suicide attempt, which may be especially important considering the increased risk for suicide c ompletion among cancer patients (Anguiano, Mayer, Piven & Rosenstein, 2012). As mentioned previously, young breast cancer survivors are more likely to have dependent children as they go through treatment and survivorship. Although there are challenges associated with having dependent children and experiencing cancer, some mothers also report positive effects of parenthood. Parenting may help survivors find meaning in their cancer experience or increase perceived social support ( Billhult & Sergesten, 200 may also relate to lower levels of distress (Bauer Wu & Farran, 2013). Quality of Life and Life Satisfaction The outcomes of this study include quality of life and life satisfaction. Current research overwhelmingly states that young breast cancer survivors have poorer quality of life than older survivors, both in terms of health related and global quality of life (Howard Anderson, Ganz, Bower, & Stanton, 2011; Avis, Crawford, & Manuel, 2005; Kroenke et al., 2004; Wenzel et al., 1999). Quality of life has been studied in relation to many of the aforementioned concerns of young breast cancer survivors. However, with the exception of

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11 reviews, few studies report the relationship between all of the aforementioned variables; this study therefore seeks to understand the interrelationship between the most commonly studied biopsychosocial factors impacting psychosocial adjustment among young breast cancer survivors. Although quality of life is a more traditional outcome measure in research with cancer survivors, this study also includes a more broad measure of life satisfaction. To date, life. Satisfacti standards of what is important (Diener, Emmons, Larsem, & Griffin, 1985). Whereas many quality of life measures examine specific constructs such as health, social life, and fina nces, life satisfaction does not relate to any specific constructs. Thus, although it should be expected that quality of life and life satisfaction are related to one another, satisfaction with life may provide a more general sense of how happy an individu al is with their life. This study sought to understand predictors of both quality of life and life satisfaction, based on a conceptually driven structural equation modeling incorporating each of the aforementioned biopsychosocial variables.

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12 CHAPTER II STUDY AIMS AND HYPOTHESES The specific aims were as follows: 1. based on the current literature. 2. To examine the utility of the included assessment measures in a sample of young breast cancer survivors who will complete the measures as part of a web based survey. 3. To test the fit of a structural equation model that is based on the most survivorship. 4. To determine which variables in the hypothesized structural model are most predictive of quality of life and life satisfaction. The hypothesized structural model is illustrated in Figure 1. Circles indicate latent (i.e., unmeasured) variables, whereas rectangles indicate measured variables. The absence of a connecting line between two variables implies that there is no direct effec t hypothesized between those two variables. All variables in the model were allowed to correlate with one another. The model incorporates each of the aforementioned biospychosocial factors that were hypothesized to impact adjustment to survivorship among y oung breast cancer survivors, as well as the relationship between the adaptive and distressing reactions to survivorship and the outcomes of quality of life and life satisfaction. Adaptive and distressing reactions to survivorship were hypothesized to pre dict quality of life and life satisfaction, and to mediate the influence of the biopsychosocial

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13 factors on quality of life and life satisfaction Adaptive Reaction is a latent variable consisting of three indicators: benefit finding, the positive impact of children during cancer, and hope. Distressing Reaction is a latent variable consisting of five indicators: decision regret, anxiety, depression fear of recurrence, and general distress. The biopsychosocial concerns are all individually measured variables and are expected to predict adaptive and distressing reactions to survivorship; they are grouped together in Figure 1 for ease of understanding the conceptual framework. Figure 1. Hypothesized conceptual model of psychosocial adjustment

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14 CHAPTER III METHOD Sample This study utilized a cross sectional research design, a design in which participants are selected and assessed in relation to current characteristi cs (Kazdin, 2003). In this case, the characteristics chosen for inclusion criteria were used to determine if participants were eligible to complete the survey. The inclusion criteria were: 1. Female 2. Survivors of non recurrent breast cancer 3. Age 19 45 years at the time of diagnosis 4. Premenopausal at the time of diagnosis 5. Post treatment, with the exception of hormone or antibody therapies 6. English speaking 7. Access to the internet to complete the web based survey Adequate sample siz e is an important consideration when performing a structural equation model (SEM) analysis (Tabachnick & Fidell, 2012). MacCallum, Browne, and Sugawara (1996) provide guidelines for the minimum sample size necessary for different levels of power and to ens ure goodness of fit. The authors suggest a sample size between 200 and 300 for adequate power when conducting an SEM analysis. Procedure The appropriate permissions were obtained from institutional and hospital review boards prior to beginning recruitment. The sample was recruited from cancer centers nationwide, as well as online support groups, email blasts, listservs, message boards and social media sites. Participants were informed of the link for the web based survey, either by means of the various inte rnet sources or by flyers. Those who wish ed to participate in the survey enter ed the survey link into their web browser or click ed on the link, and were then

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15 taken to a Qualtrics online survey. Permissions were obtained from the moderators of online organi zations before requesting to share the survey link via flyer or online. If the investigator was granted permission to recruit, moderators of online groups posted a standardized recruitment script that briefly introduced the purpose and requirements of part icipation for the study. For local hospitals or support groups, flyers were distributed in waiting rooms, by staff, or by group leaders. The first page of the survey consisted of a consent page with language to the standard of the Colorado Multiple Institu tional Review Board (COMIRB). The consent page informed participants about the purpose of the study, stated that there is no compensation given for participation, and discussed the potential risks of participation. Potential risks were noted to include the possibility for emotional upset or distress after answering some of the questions, which is considered minimal by the standards of the COMIRB. Even so, participants answered comprehension questions after completing the consent page of the study in order t o ensure understanding of the consent and potential risks of participation. Furthermore, both the consent and final page of the survey included contact information for the primary investigator, local and national cancer support resources, and a licensed cl inical psychologist with specialized training in psychosocial oncology. Measures Quality of Life The Functional Assessment of Cancer Therapy for breast cancer (FACT B) consists of 37 items assessed on a 5 point likert scale: 0 = Not at all, 1 = A little bit, 2 = Somewhat, 3 = Quite a bit, 4 = Very much There are five subscales, which are added together to create a global measure of health related quality of life (see Appendix A). These subscales measure

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16 more discrete parts of quality of life, including physical well being, social/family well being, functional well being, and additional concerns. Participants were asked how much each statement applie d to them over the past seven days. The FACT B was designed specifically for us .90 for the total FACT B measure and the subscales have internal consistencies ranging from .86 (Brady et al., 1997). Satisfaction with Life The Satisfaction With Life Scale (SWLS) is a five item measure assessing overall perceived life satisfaction (see Appendix B). It is a positively framed measure, and participants answer on a seven point likert scale which gives scores ranging from low satisfaction to high satisfaction correlate highly with other measures of subjective well being (Diener, Emmons, Larsem, & Griffin, 1985). Benefit Finding Tomich and Helgeson (2004) refer to benefit finding as positive changes that result from the otherwise distressing nature of being diagnosed with cancer. They created a 15 item measure (see Appendix C) to assess benefit finding after having breast cancer. The items focus on diverse potential benefits ranging from family and social relationships, life priorities, sense of spirituality, career goals, self control, and the ability to accept circumstances. Response options are: 0 = I disagree a lot, 1 = I disagree a little 2 = I agree a little and 3 = I agree a lot The scale has been used with breast cancer survivors, including young survivors of early stage cancer of the breast (Lechner et al., 2003). Previous studies have demonstrated that the scale has an internal consisten

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17 Hope Snyder et al. (1991) designed a measure of the construct of hope (See Appendix D). The measure consists of 11 items total; seven items assessing hope and four filler items. It has been found to have convergent validity with other measu res of related constructs, as well cancer (Stanton, Danoff Burg, & Huggins, 2002). Positive Impact of Children The Positive Impact of Children Scale (PICS) was de veloped by the primary investigator. It was designed to measure the positive effects that may be reported by women who have dependent children during a chronic illness such as cancer. The items were written based on themes within the qualitative literatur e about motherhood during breast cancer. The point likert scale ranging from not at all to al most always. Distress The Impact of Events Scale Revised (IES R ; Weiss & Marmar, 1996 ) will be used as a measure of traumatic distress (see Appendix E). It consists of 22 items, each assessed on a f ive point likert scale: 0 = Not at all, 1 = A little bi t, 2 = Moderately, 3 = Quite a bit, 4 = Extremely. The IES R, although often used to assess PTSD symptomology, has also been used as a measure of stress/distress with breast cancer patients in randomized controlled trials (Stanton et al., 2005). It consist s of three subscales which assess intrusion, avoidance, and hyperarousal. The intrusion subscale assesses intrusive thoughts, nightmares, intrusive feelings and imagery, and dissociative like re experiencing. The avoidance scale focuses on

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18 numbing of respo nsiveness, avoidance of feelings, situations, and ideas. The hyperarousal subscale assesses anger, irritability, hypervigilance, difficulty concentrating, and a heightened startle response (Christianson & Marren, 2008). The IES R has an internal consistenc the PTSD Coping Inventory (Creamer et al., 2003). Fear of Recurrence The Concerns About Recurrence Scale (CARS) has 30 items total (see Appendix F), although only the first four items will be utilized in this study. The first four items are an overall fear of recurrence subscale assessing frequency, potential for upset, consistency, and intensity of fears. The overall fear of recurrence subscale has an internal consistency of .86 and also correlates with the Intrusive Thoughts ( r = .64, p < .001) and Avoidance ( r = .50, p < .001) subscales of the Impact of Events Scale and the Distress ( r = .54, p < .001) and Well Being ( r = .44, p < .001) subscales of the Mental Health Index (Vickburg, 2003). Anxiety and Depression The Hospital Anxiety and Depression Scale (HADS ; Zigmond & Snaith, 1983 ) is a 14 item self report questionnaire consisting of an anxiety subscale and a depression subscale (see Appendix G). Each subscale has an equal number of items, and is designed to assess symptomology over the past week using a four point likert scale. The HADS is appropriate for medical populations because the fatigue and insomnia criteria have been omitted due to the potential confounds of treatment or disease symptoms. The anxiety has demonstrated an .90 (Herrmann, 1996). Additionally, when using a cut off score of 10, both scales show convergent validity

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19 with the anxiety and depression portions of the Structured Clinical Interview for the Diagnostic and Statistical Manual of mental disorders (Alexander, Palmer, & Stone, 2010). Psychological Diagnoses Participants answer ed of any of the following (select all that apply): a. Depression, b. Anxiety, c. Eating disorders, e. Bipolar disorder, f. Schizophrenia, g. Panic Disorder, h. Obsessive responses were added together. Financial Concerns Financial concerns were measured with the Financial Problems subscale of the Quality of Life in Adult Cancer Survivors (QLACS) instrument (see Appendix H). The su bscale consists of four items assessed on a seven point likert scale that measure financial concerns specific to the cancer diagnosis and/or treatment. The subscale shows convergent validity with a measure of economic strain and has good internal consisten (Avis et al., 2005). Social Support The Multidimensional Scale of Perceived Social Support (see Appendix J) consists of 12 items scored on a seven point likert scale, assessing three domains of perceived social support; it breaks into the subscales of friends, family, and significant other (Zimet, Dahlem, Zimet & Farley, 1988). Unlike the social subscale of the FACT B which relates more directly to having breast cancer, this measure is a more general measure of social support. The overall

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20 Parenting Concerns Participants who reported that they had dependent children at the time of their cancer diagnosis answered the Parenting Concerns Questionnaire (PCQ ; see Appendix M ). The PCQ is a 15 item measure of distress specifically related to parenting during cancer. It was developed for and tested with outpatient oncology patients with children under the age of 18 Participan ts answered how concerned they are about their children in the domains of practical and emotional concerns about the impact of an illness on their child(ren) and, if they have a co parent, about the co ability to care for the child(ren). Items are rated on a 5 point likert scale ranging from Fertility Concerns The Reproductive Concerns Scale (RCS) is a 14 item measure designed specifically for use with young cancer survivors (see Appendix I) and it has been used with young breast cancer survivors in other studies (Gorman, Malcarne, Roesch, Madlensky, & Pierce, 2009). It asks questions about how the cancer diagnosis affects the ability to have children, the importance of having children, and loss of c patients (Wenzel et al., 2005). Unlike the PCQ and PICS, the RCS is designed for women without children as well as moth ers. Comorbidities The Charlson Comorbidity Index (CCI) assesses for the presence of 19 pre existing medical conditions, giving different weights to the conditions based on severity and risk of mortality (see Appendix K). The index has been validated for u se with breast cancer patients,

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21 and it has been shown that breast cancer patients generally have a low score on the CCI (Charlson, Pompei, Ales, & MacKenzie, 1987). For this reason, it was hypothesized that comorbidities may be particularly salient in this study; those participants with a higher score on the CCI may exhibit significantly higher biological concerns than those with fewer comorbidities. BRCA P ositive One item will ask participants if they are carriers of BRCA1 or BRCA2 mutations, with the op Stage Participants will select which stage of breast cancer they were treated for, with the I am unsure was also be provided for those who are unsure of their stage. Menopause menopausal at the time of re that they meet the inclusion criteria. A follow up question will be scored dichotomously. Time Since T reatment Time since treatment will reflect a question that as ks how long it has been since participants completed treatment, with the exception of hormone and endocrine therapies. Time since treatment, for the purpose of these correlation based analyses, was scored continuously in years.

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22 Cognitive D ecline The Functional Assessment of Cancer Therapy C ognitive function (FACT Cog) Version 3 is a 37 item mea sure of perceived cognitive functioning (see Appendix L). It consists of four different subscales: 1. Perceived cognitive impairments, 2. Comments from others about cognitive functioning, 3. Perceived cognitive abilities, and 4. Impact of cognitive functio n on quality of life. For the purposes of this study, only the first 3 subscales (30 items) will be used, since the FACT B is a measure of quality of life included in the model. The FACT Cog has strong psychometric properties in breast cancer patients, wit h point likert scale. Data Analysis Descriptive Statistics Descriptive statistics were analyzed using IBM SPSS software. The investigator used descriptives to understand the data and to correct any errors in data entry from the Qualtrics software to SPSS. Means, standard deviations, ranges, skew, and kurtosis were examined to understand the distribution of the individual scales. Variables were examined for outliers, excluding data points only if they were deemed erroneous or highly unrepresentative of the sample. Scales were scored as either means or sums, depending on the suggested scoring or commonly utilized scoring procedures so that the results may be easily compared to ot her studies. Reliability Analysis Internal consistency of the scales in the sample w as confirmed through factor

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23 utilized. The Positive Impact of Children Scale was re examined to determine how the originally written 5 Correl ations Correlations were examined at multiple points in the data analyses. Bivariate correlations between all measured variables were examined to understand the relationships between variables before importing the data into MPlus This technique is helpfu l in identifying errors in data entry as well as confirming that the final model results are plausible given the bivariate relationships. After the latent variables were tested using confirmatory factor analysis, the latent variables and remaining measured variables were correlated with one another; these correlations also help interpret the overall structural model and guide potential future iterations of the model. Structural Equation Modeling Before performing structural equation modeling in MPlus to t est the fit of the data with the hypothesized model, the following assumptions for structural equation modeling were examined: 1. Sample size and missing data: This sample (N = 284) was sufficiently large to conduct a structural equation analysis with the number of specific paths in the model. Individual responses were only excluded from the analyses if participants dropped out of the survey prior to completing any outcome measures. Otherwise, there was no ap parent pattern to missing data and no outliers were deleted from the analyses. Missing data were estimated using the full information maximum likelihood

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24 estimation method so that missing means and variances were estimated using an expectation maximizati on algorithm. 2. Normality of sampling distribution: Each measured variable was examined to ensure that there were not highly skewed or kurtotic variable s. The Positive Impact of Children Scale (PICS) and Charleson Comorbidity Index (CCI) demonstrated non n ormal distributions. Transforming the PICS using a Log10 transformat ion, as suggested by Tabachnick & Fidell (2012) for negative skew, did not improve the normality or change the model fit, and it was therefore left untransformed in subsequent analyses. Th e CCI was dichotomized for the structural model analyses because so few participants reported any comorbidities other than their cancer comorbidities groups. 3. Linearity: Measured v ariables were examined and confirmed to show a linear relationship with one another. 4. Absence of m ulticollinearity and s ingularity: In structural equation modeling, if any variables are too highly correlated or perfectly correlated with one another, this s uggests that the constructs in the model are not discrete constructs and should not be examined as such because the model is highly influenced by inflated correlations. This assumption was met in that none of the measured varia bles were correlated higher t han .72 (anxiety and traumatic distress). Once these assumptions were met, a confirmatory factor analysis was run to determine the fit of the data with the hypothesized latent variables. Upon confirming the

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25 hypothesized latent variables fit the data well, also known as testing the measurement model, the entire hypothesized structural model was analyzed.

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26 CHAPTER IV RESULTS Sample Description Recruitment took place in two phases, with the first phase between June 2013 and November 2013 ( n = 153) and the second phase between January 2015 and April 2015 ( n = 131). A total of 425 people consent page, but it was not possible to Follow ing the consent page, participants were then shown comprehension questions, demographic and disease characteristic questions, and finally the measures of the outcomes in the model. Between the consent page and the outcome measures, 141 participants were lo st. Thirty eight did not meet inclusion/exclusion criteria for the following reasons: seven were pregnant, 16 had recurrent breast cancer, and 15 were in active cancer treatment (chemotherapy and/or radiation). The remaining 103 participants lost between t he consent and outcome measures dropped out for unknown reasons, with the majority dropping out in the first third of the demographic questions. Thus, the final sample included 284 participants, with 247 completing the entire survey and 37 dropping out at various, seemingly random points in the outcome measure portion of the survey. Facebook was the primary method of recruitment (60.9%), with other methods including online support groups (15.5%). Young Survival Coalition (YSC), a national non profit organiz ation specifically focused on young involved in recruitment; 39% of participants who reported where they learned about the groups.

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27 In the final sample of 284 non recurrent breast cancer survivors diagnosed between the ages of 19 and 45, the majority of participants were Caucasian (88.7%), partnered (73.6%), working full or part time (79.9%), and college educated (73.6%). Part icipants were from 39 different states, with the largest proportions being from Colorado (14.4%) and California (9.2%). The vast majority of participants were diagnosed with non metastatic disease (94.7%), which is to be expected considering the eligibilit y criterion that prohibits women who are in active treatment from participating. Participants completed the survey after a mean of 5.4 years since diagnosis, with 58.9% having completed treatment within 3 years or less. In terms of treatments undergone, th ere were a multitude of treatment combinations reported, with the majority of women undergoing chemotherapy (80.3%), mastectomy (76.1%), and radiation (55.6%). Only 3.85% of women were still undergoing hormone and/or antibody therapy. Demographic and medic al characteristics of participants are presented in Table 1.

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28 Table 1 Demographic and Medical Characteristics of Participants (N = 284) The question to assess menopause onset as a result of cancer treatment was only shown to participants in the second phase of the study ( n = 130). ** Participants selected any treatments they underwent; these totals will not equal 100%.

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29 Reliability of the Psychosocial Scales Each of the psychosocial scales demonstrated adequate internal consistency, each along with other relevant psychometric properties, are presented along with descriptive statistics in Table 2. Of note, b et ween the first and second phase of recruitment, the FACT Cog and a question asking if participants began menopause as a result of treatment were added based on participant feedback; these measures therefore have a lower sample size than the other measures. Another discrepancy in sample size can be seen in measures that were shown only to women with dependent children at the time of their diagnosis, the Positive Impact of Children Scale and the Parenting Concerns Questionnaire. Other discrepancies in the total n are attributable to drop out throughout the course of the survey. Participants were only given a total score if they answered at least 75% of the items on a given scale. The PICS, which was subjected to additional analyses be cause it was designed specifically for this study, also demonstrate d adequate internal consistency. The PICS was analyzed for psychometric soundness using an empirical cut off of eigenvalues greater than 1 at p 2 (10) = 343.92, p < .001, and the Kaiser Meyer Olkin measure of sampling adequacy was .78. Both indices suggest that the sample responses were factorable. The exploratory factor analysis suggested a one factor solution using all five of the items, which explained 62.03% of the variance in the PICS item scores. All factor loadings were above .40, which can be considered adequately high for alpha of the PICS = .83, suggesting adequate internal consistency of this new scale. The r

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30 = .39, p < .01). The positive impact of children was not significantly associated with parenting concerns. These results suggest that the PICS is measuring a unique construct from more general benefit findings and that the positive impact of children is not associat ed with parenting concerns; that is, a mother may have significant concerns about her children throughout the cancer experience and may still report benefit from being a mother while going through cancer.

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31 Table 2 Psychometric Properties of Psychosocial Sc ales The Parenting Concerns (PCQ) and Positive Impact of Children (PICS) measures were shown only to participants with dependent children. **Cognitive Concerns (FACT Cog) was shown only to participants recruited in the second phase of this study, as it was added following feedback from participants in the first phase. Range Variable n M SD Potential Actual Skew Kurtosis Financial Concerns 248 3.12 2.05 .89 1 7 1 7 .56 1.11 Social Support 248 65.45 17.87 .95 12 84 12 84 1.22 1.00 Parenting Concerns* 145 2.10 .92 .91 1 5 1 5 .92 .06 Reproductive Concerns 247 16.25 13.63 .9 0 0 56 0 49 .60 .82 Comorbidities 284 0.52 1.27 N/A 0 25 0 6 2.99 9.26 Hope 261 24.52 4.35 .88 8 32 8 32 .88 .30 Benefit Finding 261 42.38 7.64 .86 15 60 18 60 .28 .08 Positive Impact of Children* 145 4.39 .76 .83 1 5 1 5 1.77 3.56 Cognitive Concerns** 112 47.91 34.59 .90 0 128 0 121 .34 1.13 Anxiety 250 8.48 5.05 .90 0 21 0 21 .29 .60 Depression 250 4.41 4.00 .86 0 21 0 20 1.08 .95 Fear of Recurrence 250 4.00 1.34 .94 1 6 1 6 .29 .91 Distress 251 2.22 .86 .94 1 5 1 5 .61 .44 Quality of Life 284 98.75 25.10 94 0 148 22 1 44 .65 .02 Life Satisfaction 282 22.84 7.91 92 5 35 5 35 58 .65

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32 Table 3 Explora tory Factor Analysis of the Positive Impact of Children Scale ( n = 145) Item Factor Loading Having children helped me focus on the positive. .85 My children gave me an appreciation for life. .87 My children gave me a reason to fight the cancer. .83 My children distracted me from the cancer. .65 Being a parent helped me identify as more than a cancer patient. .71 2 (10) = 343.92 p < .001; KMO = .78. C orrelations Correlations between all measured variables were examined prior to analyzing the structural model. Bivariate correlations between all measured variables are presented in Table 4. After determining the latent variables were supported by the dat a using Confirmatory Factor Analysis, correlations between latent variables, the measured predictors in the model, and the outcomes of quality of life and life satisfaction were examined. Correlations between all predictor variables and the outcomes of Qua lity of Life and Life Satisfaction are presented in Table 5. Initial observations of correlations revealed that all significant relationships were in the hypothesized direction.

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33 Table 4 Bivariate Correlations between Predictor Variables

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34 Table 5 Bivariate Correlations between Modeled Variables and Outco mes of Quality of Life and Life Satisfaction Variable Quality of Life Life Satisfaction Quality of Life 1.00 Life Satisfaction .70** 1.00 Adaptive Reaction .43*** 63*** Hope Benefit Finding Positive Impact of Children .63** .40** .18* .66** .47** .25** Distressing Reaction .90*** .01 Depression Anxiety Fear of Recurrence Traumatic Distress .81** .71** .49** .65** .64** .50** .24** .42** Financial Concerns .49** .38** Social Support .46** .37** Parenting Concerns .71** .64** Fertility 29** .34** Psychological Diagnoses .34** .28** Cognitive Decline .64** .32** Stage .17** .18** CCI .22** .19** Time Since Diagnosis BRCA positive .22** .07 .11 .06 p < .05, ** p < .01, *** p < .001

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35 Measurement Models Maximum likelihood estimation was employed to estimate all models in MPlus The first step was to perform a Confirmatory Factor Analysis (CFA) of the latent variables Distressing and Adaptive Reactions to confirm the hypothesized measurement model within the overall structural model. The Adaptive Reactions latent variable, consi sting of Hope, Benefit Finding, and the Positive Impact of Children, is just identified in its number of paths estimated; therefore, model fit statistics are not available. All path estimates were significant at p < .001. The standardized factor loadings f or the indicators of Adaptive Reactions were as follows: Hope = .72, Benefit Finding = .59, and the Positive Impact of Children = .35. These loadings suggest that Hope is the strongest predictor of Adaptive Reactions. All indicators demonstrated a sufficie ntly high loading considering a cut off of .30 to .40 for factor loadings ( Tabachnik & Fidell, 2012; Bowen & Guo, 2012), noting that the Positive Impact of Children Scale (PICS) has a borderline low loading suggesting that it may not be as conceptually rel ated to the other two indicators of Adaptive Reactions. The Distressing Reaction measurement model fit the data well : 2 ( 2 ) = 25.70, p < .001, CFI = .94, RMSEA = .23, SRMR = .047. Although many researchers suggest that good model fit is indicated with RMSEA below .05 and an insignificant chi square, this model was retained based on new literature suggesting that RMSEA can be artificially i nflated in models with low degrees of freedom (Kenny, Kaniskan, & McCoach, 2014). Furthermore, chi square has long been known to reach significance in large sample sizes participants ( Marsh, Balla, & McDonald 1988 ). All path estimates were significant at p < .001, Standardized factor loadings for the indicators of Distressing Reactions were as follows: Anxiety = .93, Traumatic Distress = .79, Depression = .68, and Fear of Recurrence = .62.

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36 Structural Equation Model Following the confirmation of the hypothesized measurement model, the overall structural model was analyzed. The hypothesized structural model fit the observed data 2 (100) = 332.92, p < .001, CFI = .86, RMSEA = .09, SRMR = .05. The fin al model accounted for 86% and 62% of the variance in Quality of Life and Life Satisfaction, respectively. The standardized results of the hypothesized model are shown in Figure 2. Standardized and unstandardized parameter estimates, as well as all signifi cance levels, are provided in Table 6. p < .05, ** p < .01, *** p < .001 Figure 2. Results for hypothesized structural equation model of psychosocial adjustment among young breast cancer survivors, both significant and non significant path estimates included (significant paths in bold)

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37 Table 6 Unstandardized, Standardized, and Significance Levels for Model in Figure 2 (Standard Errors in Parentheses; N = 284)

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38 Relatively few of the hypothesized direct effects from the biopsychosocial factors to the latent variables were significant. In fact, none of the biological or psychological factors were significant predictors of either Adaptive Reactions or Distressing Reactions. That is, having a BRCA mutation, later stage of cancer, onset of m enopause due to treatment, greater medical comorbidity, and length of time since completing treatment were not significant predictors of the latent variables; nor were greater cognitive decline or psychological diagnoses. Three of the four social factors significantly predicted both Adaptive and Distressing Reactions. Higher levels of social support (standardized path estimate = .30, p < .0001), fewer parenting concerns (standardized path estimate = .36, p < .01), and fewer concerns about fertility (stan dardized path estimate = .19, p <.01) all significantly predicted more Adaptive Reactions. These same three variables significantly predicted Distressing Reactions; lower levels of social support (standardized path estimate = .25, p <.0001), more conce rns about parenting (standardized path estimate = .58, p <.0001), and more concerns about fertility (standardized path estimate = .17, p <.01). Financial concerns and the remaining variables conceptually grouped with social factors, did not significantly predict either latent variable. Adaptive and Distressing Reactions were significantly negatively associated with one another (standardized path estimate = .67, p <.0001). Adaptive and Distressing Reactions, examined as part of the entire structural model, were similarly represented by their indicators compared to when they were examined individually as a measurement model. All factor loadings were significant at p < .0001 with the exception of the Positive Impact of Children Scale which was significant at p < .01. Standardized and unstandardized factor loadings are shown in Table 6. Figure 2 provides

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39 standardized factor loadings only. Of note, the loading for the Positive Impact of Children from Adaptive Reactions was below the range of what is typically considered acceptable for a factor loading ( Tabachnik & Fidell 2012; Bowen & Guo, 2012). Therefore, the final structural model was examined both with the PICS an d without it to determine if excluding it from the analyses would improve model fit or change path estimates. Because excluding the PICS did not change different model fit or the associations between Adaptive Reactions and the outcomes of Quality of Life a nd Life Satisfaction, it was retained in the final model. Adaptive Reactions was positively associated with Satisfaction with Life (standardized path estimate = .58, p < .0001), but it was not significantly related to health related Quality of Life when c onsidered as a predictor with Distressing Reactions. Distressing Reactions was negatively related to both Satisfaction with Life (standardized path estimate = .26, p = .01) and health related Quality of Life (standardized path estimate = .87, p <.001). The two outcomes of Quality of Life and Satisfaction with Life were significantly associated with one another, even after controlling for the variance accounted for by their shared predictors in the model (standardized path estimate = .19, p < .05).

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40 CHAPTER V DISCUSSION This study aimed to test a theoretically driven structural equation model of psychosocial adjustment among young breast cancer survivors and to use the results to understand which predictors have the greatest influence on quality of life and life satisfaction The structural model hypothesized that biological variables such as cancer stage and medical comorbidity, psychological variables such as mental health diagnoses and cognitive decline, and social variables such as social sup port and concerned about children, would indirectly impact quality of life and life satisfaction. That is, the effect of the biopsychosocial variables was hypothesized to be mediated by adaptive (or positive) and distressing (or negative) reactions to surv ivorship. The hypothesized model was partially supported by the data. Biopsychosocial Factors Individually measured variables, grouped conceptually as biological, psychological and social variables, were hypothesized to predict Adaptive and Distressing Reactions to survivorship. The only biopsychosocial variables that were significantly related to psychosocial adjustment and the outcomes of quality of life and life satisfaction were social support, parenting concerns, and concerns about fertility. Lower social support was related to lower adaptive reactions and higher distressing reactions, and was therefore related to lower health related quality of life and life satisfaction. This is consistent with other studies that suggest perceived social support is associated with quality of life ( Bloom, Stewart, Chang, & Banks, 2004; Sammarco, 2001 ), and also that the need for social support persists well past completing treatment (Arora, Rutten, Gustafson, Moser, & Hawkins, 2007). Although social

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41 support is common ly studied among cancer survivors, it may be especially pertinent for younger women as they may perceive less social support due to the fact that breast cancer is much more common in older women. Alternatively, social support is highly predictive of better quality of life and fewer mental health problems in the general population as well experience. Parenting concerns also indirectly influenced life satisfaction an d satisfaction with life by means of reactions to survivorship. Having more concerns about dependent children was related to lower adaptive reactions and higher distressing reactions, and therefore indirectly related to lower quality of life and life satis faction. Concerns about children is a relatively new and understudied construct in cancer survivorship, and one that likely plays an especially important role in adjustment with younger women who are more likely to have dependent children under their care during treatment. More resources are becoming available to help women learn how to speak with their children about cancer, which may help with concern about talking to children. These findings suggest that mothers may also benefit from discussing their wis hes for their children if their prognosis worsens. Research exploring the impact of parenting concerns might also compare breast cancer survivors to the general population, as it would be expected that all parents have a certain degree of anxiety related t o distressing or otherwise different from the day to day concerns of non cancer controls. In addition to parenting concerns, fertility concerns were related to low er levels of Adaptive Reactions and higher levels of Distressing Reactions, thereby negatively impacting both quality of life and life satisfaction. Infertility and premature menopause can be caused

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42 by chemotherapy or other hormonal therapies ( Arndt et al. 2004 ), and can be concerning for women with and without biologic children at the time of diagnosis ( Gorman, Usita, Madlensky, & Pierce, 2011; Camp Sorrell, 2009 ). Knowledge about infertility and fertility preservation is growing, but practitioners may st ill be more focused on conversations about cancer treatment than conversations about fertility preservation ( Goncalves, Tarrier, & Quinn, 2014 ). This study aligns with the findings of others in that reproductive concerns were significantly related to healt h related quality of life and life satisfaction ( Andersen, Bowen, Morea, Stein, & Baker, 2009 ), thereby suggesting that fertility should be addressed prior to starting treatment and that grief over loss of fertility might be addressed if a survivor was unable to preserve her ability to have biological children. Interestingly, the biomedical vari ables of disease stage, having a BRCA mutation, comorbidities, onset of menopause due to treatment and length of time since treatment, were not significant predictors of Adaptive or Distressing Reactions. More progressed disease, known as later stage, is k nown to be related to poorer psychosocial outcomes in young breast cancer survivors ( Hopwood, Haviland, Mills, Sumo, & Bliss, 2008 ), and was hypothesized to negatively impact adjustment in this sample. One explanation for the lack of a significant finding related to disease stage and the outcomes is that the psychosocial variables are more predictive of adjustment than stage of disease. If psychosocial variables are indeed more predictive of adjustment than aggressiveness of treatment, then this finding wou ld argue for psychosocial interventions for all young breast cancer survivors. Alternatively, because so few women in this sample were later stage, it is possible that the sample lacked power to detect an effect of later stage on adjustment. Because the in clusion criteria required that participants be complete with active treatment, fewer women with stage IV disease would be

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43 expected to participate because they are more likely to be in ongoing treatment. That is, because stage IV breast cancer is metastatic and the intent of treatment is generally more focused on prolonging life and decreasing symptom burden, women with stage IV cancer are often in and out of active treatment indefinitely Perhaps women who completed the survey with stage IV breast cancer pe rceived as though they were complete with treatment because they were without evidence of disease at the time of the study. Similar to sparse number of women with stage IV disease, few women were aware of having a BRCA mutation, few women had other medic al comorbidities, and approximately half of women knowingly began menopause due to treatment. These findings may suggest that having a genetic mutation or beginning menopause alone may be less important for long term psychosocial adjustment than psychosoci al variables or perceptions of these biological factors. Alternatively, because having a BRCA mutation and beginning menopause were dichotomized for analyses, they may have had limited power to influence psychosocial adjustment. The same might be true for medical comorbidities, in that the majority of the sample reported no other major medical illnesses. With regard to length of time since treatment, it was hypothesized that women who completed treatment more recently would be more distressed and report l ower adaptive adjustment than those who were further from completing treatment. This hypothesis was not supported, which is inconsistent with the findings of some researchers ( Ganz et al., 2004; Deshields et al., 2005 ) but consistent with others ( Harringto n, Hansen, Moskowitz, Todd, & Feuerstein, 2010 ). Long term survivorship is often described as five years post diagnosis, a time when the rate of recurrence drops significantly for most cancers if there has not been a recurrence since completing initial tre atment. As such, it makes sense that survivors might

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44 feel less cancer related distress after five years, either because they feel as though they are at present in this sample because the majority of women were still within five years of completing treatment. It is also possible that this self selected sample of women who are mostly active in the online breast cancer community, are more focused on their cancer experi ence and are generally more similar than different from one another, despite their different places in the treatment trajectory. Psychological diagnoses prior to cancer and cognitive decline were considered conceptually similar, and were grouped together hypothesized to negatively impact adjustment to survivorship, quality of life, and life satisfaction. With regard to psychological diagnoses, it was hypothesized that having previous psychological difficulties or dia gnoses would predispose survivors to have cancer related distress. The lack of a significant relationship between previous psychological diagnoses and distress following cancer could be explained in a number of ways. First, perhaps having previous treatmen t for mental health issues could be viewed as a protective rather than predisposing factor in that participants may have learned coping strategies in treatment. Second, few participants reported any diagnoses and those that did largely reported only having one diagnosis; therefore, there may not have been sufficient power to detect an effect. Third, participants who denied having any previous diagnoses may have diagnoses that they are unaware of because they have never seen a mental health professional. Fin ally, it is possible that previous psychological diagnoses truly does not impact adjustment to survivorship and that patients with and without a mental health history should be given equal attention when screening for distress and implementing intervention s.

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45 In terms of cognitive decline, having cognitive problems was related to Adaptive and Distressing reactions at a bivariate level but were no longer significantly related after controlling for the other biopsychosocial variables. Cognitive decline was a lso related to the outcomes of quality of life and life satisfaction at a bivariate level, but was not significantly related when a direct path from cognitive decline to the outcomes was tested; thus, it was no longer significant when controlling for Adapt ive and Distressing Reactions either. Cognitive impacted by constructs subsumed within Distressing Reactions. Namely, depression and anxiety are known to produce sympt oms of cognitive decline ( Shilling & Jenkins, 2006; Vardy et al., 2008; Hermelink et al., 2010 ). Because cognitive decline was most highly correlated with the measures of depression and anxiety in this sample, the psychological variables within Distressing Reactions may be responsible for decreased cognitive abilities and are therefore more important to examine as predictors of quality of life and life satisfaction than are cognitive problems alone. Distressing and Adaptive Reactions Both latent variable s were supported by the data. Depression, anxiety, fear of recurrence, and traumatic distress were all highly related to one another and can be considered as one construct; for the purposes of interpretation, the construct was named Distressing Reactions. The support for the Distressing Reactions latent factor suggests that screening could attempt to incorporate items from each of those different factors in order to explain a larger proportion of the variance in overall distress. Furt her, Distressing Reactions was predictive of both outcomes, with an especially strong relationship between Distressing Reactions and health related quality of life. This relationship, considering the factors used to

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46 estimate Distressing Reactions, was cons istent with the current literature ( Reich, Lesur, & Perdrizet Chevallier, 2008 ). The relationship between Distressing Reactions and life satisfaction is new to the literature in that satisfaction with life is not a commonly studied outcome in young breast cancer survivorship. Perhaps the relationship between Distressing Reactions and life satisfaction is lower than that between Distressing Reactions and health related quality of life because some women are generally satisfied with their life despite having lower health related quality of life than before cancer treatment. In other words, they may be thankful for their life despite its given challenges, perhaps considering that the alternative would have been to lose their battle with cancer. Furthermore, qua lity of life measures assess emotional adjustment, so it would be expected that quality of life might be more related to Distressing Reactions than life satisfaction. Life satisfaction does not include constructs related to emotional distress. Certainly, i t might be helpful to further investigate other outcomes besides the commonly studied health related quality of life as we consider psychosocial adjustment to cancer survivorship so that we can better understand how life satisfaction differs from quality o f life at a more individual level. With regard to the Adaptive Reactions latent variable, hope, benefit finding, and the positive impact of children were considered one construct. Adaptive Reactions, when considered within the overall structural model wi th all three hypothesized factors, was only marginally supported by the data in that the Positive Impact of Children Scale (PICS) demonstrated a lower loading than what is typically considered adequate. Although the majority of women reported a positive im pact of having dependent children during cancer survivorship, this scale was largely unrelated to the outcomes in the model, suggesting it may not be related to psychosocial adjustment. Hope and benefit finding were also more strongly

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47 related to one anothe r than to the PICS, and were more highly associated with life satisfaction. Therefore, when considering an underlying construct such as Adaptive Reactions, hope and benefit finding account for the majority of the variance and are therefore responsible for the associations (and lack thereof) with the outcomes. Hope accounted for the majority of the variance in Adaptive Reactions. Of note, hope is known to be negatively associated with depression as it was in this sample, but the relationship between hope and the outcomes should not be considered entirely attributable to depression. Hope is considered a separate construct related to using a more active coping style ( Stanton, Danoff Burg, & Huggins, 2002 ). High levels of hope are supposedly related to: 1. Havin g a sense of being the Hope Scale (Snyder et al., 2001). Hope is considere d relatively stable, perhaps even more so than dispositional optimism, and may therefore be examined as a protective factor even before starting breast cancer treatment (Snyder et al., 2001; Stanton, Danoff Burg, & Huggins, 2002). Adaptive Reactions predi cted life satisfaction but was not significantly related to health related quality of life. This finding was contrary to the hypothesis and to some literature stating that positive outcomes such as benefit finding are related to better quality of life ( Lec hner, Carver, Antoni, Weaver, & Phillips, 2006; Carver & Antoni, 2004 ); however, it is consistent with other literature that benefit finding is unrelated to quality of life ( Fromm, Andrykowski, & Hunt, 1996; Cordova, Cunningham, Carlson, & Andrykowski, 200 1; Lehman et al., 1993; Tomich & Helgeson, 2004 ). Perhaps the two related constructs of benefit finding and hope were unrelated to health related quality of life because the sample

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48 reported overall higher distress scores (and lower quality of life scores) than other studies of breast cancer survivors, and there is some evidence that positive factors such as benefit finding only improves quality of life when survivors perceive their cancer as a moderate (i.e., not a minimal or severe) threat ( Lechner, Carve r, Antoni, Weaver, & Phillips, 2006; Carver & Antoni, 2004 ). Quality of Life and Life Satisfaction: What matters most? Overall, the young survivors in this sample demonstrated lower levels of global quality of life on the FACT B than young survivors in other studies using the same instrument. Other studies have found a mean of approximately M = 111 (Avis, Crawford, & Manuel, 2005; Wenzel et al., 1999), whereas the m ean in this sample was M = 98.75 ( SD = 25.10), which is a qualitatively large difference in global quality of life scores. The discrepancy may be accounted for by the age of this sample in comparison to the age of samples in previous works. Others using the FACT B included women who were 50 years old or younger, so women diagnosed with breast cancer at an older age were eligible for the other studies. Additionally, it is notable that the sample in this study included women any length of time post treatment whereas the other studies had a narr ower window for inclusion (3 years and 2 months, respectively). It might be expected that the current sample would have higher self reported quality of life because they are further from treatment. It is possible that the young breast cancer survivors in t his sample demonstrated lower quality of life because of the self selected group of participants; that is, the majority of participants were recruited via the online cancer community and may therefore reflect survivors who are more distressed than those wh o are no longer in need of the online support community.

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49 In terms of life satisfaction, no studies to date have used the Satisfaction with Life Scale with young survivors of breast cancer. Tate and Forchheimer (2002) found that breast cancer survivors, wi thout restraints on age, demonstrated slightly higher satisfaction with life and the current sample of young women had satisfaction with life scores lower than the gen eral population (Diener et al., 1985). The findings that quality of life and life satisfaction were both relatively low in this sample suggest that these are important outcomes for further investigation, perhaps to understand the influence of self selectio n on similar samples. In sum, the current study aimed to test a structural equation model which hypothesized that several biopsychosocial factors would impact quality of life and life satisfaction through the mediation of both Distressing and Adaptive Rea ctions to support, parenting concerns, and reproductive concerns were signific ant predictors of reactions to survivorship after controlling for the other biopsychosocial factors, suggesting that these are domains that warrant further investigation. Low levels of social support predicted higher levels of distress and lower levels of adaptive reactions. Parenting and fertility concerns had the same effect. Clinically, practitioners might consider screening early in treatment for concerns related to social support, parenting, and fertility with hopes of preventing distress related to th ese areas. Distressing Reactions and Adaptive Reactions mediated the impact of social support, parenting concerns, and fertility concerns on the outcomes of quality of life and life satisfaction.

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50 Depression, anxiety, fear of recurrence and traumatic distr ess, were highly correlated and considered one underlying construct named Distressing Reactions. Distressing Reactions was the most important predictor of quality of life and life satisfaction when controlling for all the other predictors in the structural model, with the majority of the variance accounted for by depression. As such, screening and interventions should also focus on depression, anxiety, fear of recurrence, and traumatic distress well into survivorship. Hope, and benefit findings were suppor ted as one underlying construct named Adaptive Reactions; the positive impact of children was not as highly correlated and is therefore not considered as predictive of Adaptive Reactions or the outcome variables. Higher levels of Adaptive Reactions related to higher satisfaction with life, but were not significantly related to quality of life, perhaps because of a theory that has been applied to benefit finding as a potential protective factor in other cancer survivorship research; that is, perhaps benefit finding and hope were not significantly related to health related quality of life because this sample demonstrated lower than expected quality of life (and relatively high levels of distress) and protective effects may only occur with moderate levels of di stress. Alternatively, the Adaptive Reactions may simply be independent of quality of life. The relationship between satisfaction with life and health related quality of life may warrant further exploration, as does the meaning of life satisfaction in a po pulation who is often examined primarily in terms of health related quality of life. If life satisfaction is an outcome of interest, then it may prove helpful to understand if hope and benefit finding are more dispositional traits or if they can be encoura ged through psychological interventions. There are several limitations to the findings of this study. As with many cohort studies, selection bias presents a potential threat to internal validity. Participants self selected

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51 to participate in the study and m ay therefore be qualitatively different from those who opted not to participate. The majority of survivors were recruited from support groups and advocacy websites; the type of women who are actively seeking support and participating in studies may be a un ique subgroup of young survivors. They may be faring better than other survivors in that they are actively involved in a support community. Considering the relatively low levels of quality of life when compared to other studies of breast cancer survivors, it could also be argued that these women were more distressed on average than those who did not participate in the study. Because this study utilized a web based survey and recruitment from numerous sites, the accrual rate of participants is unknown. It i s likely that the response rate was low, as indicated by the accrual rate of other web based studies (Cook, Heath, & Thompson, 2000). It was also impossible to guarantee that each woman completed the study only once, although only one survey was allowed pe r IP address. Women were asked about their cancer experience in a retrospective manner, and their responses may be different than they would have been soon after completing treatment, especially for those who were further from completing treatment. The ind ividual scales were kept in their original formatted time frames; some asked women to reflect on the past week, others on the past month, and so on. Therefore, there may have been some error associated with the different time frames given for the questionn aires. Any findings from this study are limited in terms of generalizability These findings should not be generalized to patients undergoing active treatment. Additionally, participants were almost entirely Caucasian and of relatively high socioeconomic status (SES); the results may not generalize to minority breast cancer survivors or those of a lower SES. The findings

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52 may not be generalizable to young women with other cancer types or young cancer survivors with different cancer types. This study did no t utilize a comparison group, such as a group of older survivors to understand how this sample is truly unique from others. Thus, it is not possible to draw conclusions about this group of young survivors in comparison to other groups. Further, because thi s study includes women of any stage and any length of time after completing treatment, it may not be possible to compare this sample to a different subgroup with a specific stage or length of time in remission. With respect to the analyses run, although structural equation modeling is sometimes demonstrating cause and effect. Rather, the paths in the model are based largely on correlations. Therefore, interpreting these only be an error in that there are likely many other variables that could also be taken together to explain variance in these same outcomes. Furthermore, although this model was designed based on a conceptual theory, another model with different path structure might also fit the data well. Despite these limitations, the findings from this study have several implications for researchers and practitioners. Considering the most important predictors of quality of l ife and life satisfaction, it might be helpful for practitioners to inquire early on about young breast Screening and psychological interventions might include thes e domains as well as depression, anxiety, fear of recurrence, traumatic distress, hope, and benefit finding. Medical interventions might put more emphasis on fertility preservation to reduce distress as a result

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53 of loss of fertility, and psychological inte rventions should explore grief for those who do lose the ability to have biological children. Future studies might benefit from using a longitudinal design to understand how these biopsychosocial factors impact adjustment over the course of treatment and s urvivorship, as well as testing the efficacy of test the efficacy of well known interventions in psychosocial oncology (e.g., cognitive behavioral therapy) in young breast cancer survivors specifically.

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58 Kwon, J.S., Gutierrez Barrera, A.M., Young, D., Sun, C.C., Daniels, M.S., Lu, K.H., & Arun, B. (2010). Expanding the criteria for BRCA mutation testing in breast cancer survivors. Jou rnal of Clinical Oncology, 28 (27), 4214 20. Land, L.H., Dalton, S.O., Jensen, M., & Ewertz, M. (2010). Comorbidities' impact on survival after treatment for early breast cancer. Journal of Clinical Oncology, 28, 15. Land, L.H., Dalton, S.O., Jorgensen, T .L., & Ewertz, M. (2012). Comorbidity and survival after early breast cancer. A review Critical Reviews in Oncology/Hematology, 81 (2), 196 205. Lechner, S. C., Zakowski, S. G., Antoni, M. H., Greenhawt, M., Block, K., & Block, P. (2003). Do sociodemographic and disease related variables influence benefit finding in cancer patients? Psycho Oncology, 12, 491 499. Lechner, S.C., Carver, C.S., Antoni, M.H., Weaver, K.E., & Phillips, K.M. (2006). Curvilinear associations between benefit findin g and psychosocial adjustment to breast cancer. Journal of Consulting and Clinical Psychology, 74 (5), 828 840. Lehman, D. R., Davis, C. G., Delongis, A., Wortman, C. B., Bluck, S., Mandel, D. R., & Ellard, J. H. (1993). Positive and negative life changes following bereavement and their relations to adjustment. Journal of Social and Clinical Psychology, 12, 90 112. Liu, Y., Perez, M., Schootman, M., Aft, R.L., Gillanders, W.E., Jeffe, D.B. (2011). Correlates of fear of cancer recurrence in women with ducta l carcinoma in situ and early invasive breast cancer. Breast Cancer Research & Treatment, 130 165 173. Loprinzi, C.L., Wolf, S.L., Barton, D.L., Laack, N.N. (2008). Symptom management in premenopausal patients with breast cancer. Lancet Oncology, 9, 993 1001. Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness of fit indexes in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 103, 391 410. Matthews, T.J. & Hamilton, B.E. (2009). Delayed childbearing: More women are having their first child later in life. National Center for Health Statistics, 21 Retrieved from http://www.cdc.gov/nchs/data/dat abriefs/db21.pdf McLaughlin K A Hatzenbuehler M L & Keyes K M. (2010). Responses to discrimination and psychiatric disorders among black, Hispanic, female, and lesbian, gay, and bisexual individuals. Am J Public Health 100, 1477 14 84. Muriel, A. C., Moore, C.W., Baer, L., Park, E.R., Kornblith, A.B., Pirl, W., Rauch, P.K. (2012). Measuring psychosocial distress and parenting concerns among adults with cancer. Cancer, 5671 5678. doi: 10.1002/cncr.27572,

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59 National Cancer Institute, Surveillance Epidemiology and End Results. (2011). SEER Stat Fact Sheets: Breast Retrieved from http://seer.cancer.gov/statfacts/html/breast.html National Cancer Institute. (2012). Breast Cancer Treatment. Retrieved from http://www.cancer.gov/cancertopics/pdq/treatment/breast/Patient/page5 National Coalition for C ancer Survivorship (2011). Retrieved from http://tb4cz3en3e.scholar.serialssolutions.com/?sid=google&auinit=NF&aulast=Khan &atitle=Defining+cancer+survivorship:+a+more+transparent+approach+is+needed&i d=doi:10.1007/s11764 011 0194 6&title=Journal+of+cancer+s urvivorship&volume=6&issue=1&date=2012&spage=3 3&issn=1932 2259 National Institute of Health (2012). Health and Aging. Retrieved from http://www.nia.nih.gov/health/publication/menopause N ystedt, M., Berglund, G., Bolund, C., Brandberg, Y., Fornander, T., Rutqvist, L.E. (2000). Randomized trial of adjuvant tamoxifen and/or goserelin in premenopausal breast cancer self rated physiological effects and symptoms. Acta Oncologica, 39, 959 968. Partridge, A.H., Gelber, S., Piccart Gebhart, M.J., Focant, F., Scullion, M., Holmes, E., Gelber, R.D. (2013). Effect of age on breast cancer outcomes in women with human epidermal growth factor receptor 2 positive breast cancer: results from a herce ptin adjuvant trial. Journal of Clinical Oncology, 31 (21), 2692 2908. Doi: 10.1200/JCO.2012.44.1956. Peate, M., Meiser, B., Friedlander, M., Zorbas, H., Rovelli, S., Sansom Daly, U., related knowledge decision making preferences, and treatment intentions in young women with breast cancer An Australian Fertility Decision Aid Collaborative Group Study. Journal of Clinical Oncology, 29 (13), 1670 1677. Purushotham, A., Shamil, E., Cariati, M., Agbaje, O. Muhidin, A., Gillett, C., ...Holmberg, L. (2014). Age at diagnosis and distant metastasis in breast cancer A surprising inverse relationship. European Journal of Cancer, 50, 1697 1705. Raffa, R.B. & Tallarida, R.J. (2010). Chemo fog. Advances in Experim ental Medicine and Biology, 678. Reich, M., Lesur, A., Perdrizet Chevallier, C. (2008). Depression, quality of life and breast cancer: A review of the literature. Breast Cancer Research and Treatment, 110, 9 17. Robertson, L., Hanson, H., Seal, S., Warren Perry, M., Hughes, D., Howell, I.,. .Rahman, N. (2012). BRCA1 testing should be offered to individuals with triple negative breast cancer diagnosed below 50 years. British Journal of Cancer, 106 (6), 1234 1238.

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60 Rowland, J.H. (1989). Developmental stage and adaptation: adult model. Handbook of Psychooncology, 25 43. Ruddy, K.J., Gelber, S., Ginsburg, E.S., Schapira, L., Abusief, M.E., Meyer, M.E., & Partridge, A.H. (2011). Menopausal symptoms and fertility concerns in premenopausal breast cancer survivors: a comparison to age and gravidity matched controls. Menopause: The Journal of The North American Menopause Society, 18 (1), 105 108. Sammarco A. (2001). Perceived social support, uncertainty, and quality of life of younger breast cancer survivors. Cancer Nursing, 24 (3), 212 219. Sammarco, A. (2009). Quality of life of breast cancer survivors: A comparative study of age cohorts. Cancer Nursing 32 (5), 347 356. Schlegel, R.J., Manning, M.A., Molix, L.A., Talley, A.E., & Bettencourt, B.A. (2012). Predictors of depressive symptoms among breast cancer patients during the first year post diagnosis. Psychology and Health, 27, 277 293. Schmitt, F., Piha, J., Helenius, H., Baldus, C., Kienbacher, C., Steck, B.,. Romer, G. (2008). Multinational study of cancer patients and their children: Factors associated with family functioning. Journal of Clinical Oncology, 26 (36), 5877 5883. Semple, C.J. & Mc Cancer Nursing, 33 (2), 110 118. Shilling, V., & Jenkins, V. (2006). Self reported cognitive problems in women receiving adjuvant therapy for breast cancer. European Journal of Oncology Nursing, 11 6 15. Snyder, C.R., Harris, C., Anderson, J.R., Holleran, S.A., Irving, L.M., Harney, P. (1991). The will and the ways: Development and validation of an individual differences measure of hope. Journal of Personality and Social Psychology, 60 (4), 570 585. Stanton, A.L., Danoff Burg, S., Huggins, M.E. (2002). The first year after breast cancer diagnosis: Hope and coping strategies as predictors of adjustment. Psycho oncology, 11 (2), 93 102. Stanton, A.L., Ganz, P.A., Kwan, L., Meyerowitz, B.E., Bower, J.E., Krupnick, J.L., Belin, T.R. (2005). Outcomes from the moving beyond cancer psychoeducational, randomized, controlled trial with breast cancer patients. Journal of Clinical Oncology, 23 (25), 6009 60 18. Tabachnik, B.G. & Fidell, L.S. (2012). Using Multivariate Statistics. New Jersey: Pearson Education, Inc.

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62 APPENDIX A FUNCTIONAL ASSESSMENT OF CANCER THERAPY BREAST (FACT B)

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65 APPENDIX B SATISFACTION WITH LIFE SCALE (SWLS) Below are five statements that you may agree or disagree with. Using the 1 7 scale below, indicate your agreement with each item by placing the appropriate number on the line preceding that item. Please be open and honest in your responding. 7 Strongly agree 6 Agree 5 Slightly agree 4 Neither agree nor disagree 3 Slightly disagree 2 Disagree 1 Strongly disagree ____ In most ways my life is close to my ideal. ____ The conditions of my life ar e excellent. ____ I am satisfied with my life. ____ So far I have gotten the important things I want in life. ____ If I could live my life over, I would change almost nothing. 31 35 Extremely satisfied 26 30 Satisfied 21 25 Slightly satisfied 20 Neutral 15 19 Slightly dissatisfied 10 14 Dissatisfied 5 9 Extremely dissatisfied

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66 APPENDIX C BENEFIT FINDING MEASURE (Tomich & Helgeson, 2004) Having had breast cancer: 1. has made me more sensitive to family issues. 2. has led me to be more accepting of things. 3. has taught me how to adjust to things I cannot change. 4. has given my family a sense of continuity, a sense of history. 5. has made me a more responsible person. 6. has made me realize the impor 7. has brought my family closer together. 8. has made me more productive. 9. has helped me take things as they come. 10. has helped me to budget my time better. 11. has made me more gratef ul for each day. 12. has taught me to be patient. 13. has taught me to control my temper. 14. has renewed my interest in participating in different activities. 15. has led me to cope better with stress and problems.

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67 APPENDIX D HOPE SCALE Directions: Read each item carefully. Using the scale shown below, please select the number that best describes YOU and put tha t number in the blank provided. 1 = Definitely False 2 = Mostly False 3 = Mostly True 4 = Definitel y True 1. I can think of many ways to get out of a jam. (Pathways) 2. I energetically pursue my goals. (Agency) 3. I feel tired most of the time. (Filler) 4. There are lots of ways around any problem. (Pathways) 5. I am easily downed in an argument. (Fille r) 6. I can think of many ways to get the things in life that are most important to me. (Pathways) 7. I worry about my health. (Filler) 8. Even when others get discouraged, I know I can find a way to solve the problem. (Pathways) 9. My past experiences hav e prepared me well for my future. (Agency) I I. I usually find myself worrying about something. (Filler) 12. I meet the goals that I set for myself. (Agency)

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68 APPENDIX E IMPACT OF EVENTS SCALE REVISED (I ES R) INSTRUCTIONS: Below is a list of difficulties people sometimes have after stressful life events. Please read each item, and then indicate how distressing each difficulty has been for you DURING THE PAST SEVEN DAYS with respect to ___________________________, which occurred on ______________. How much were you distressed or bothered by these difficulties? Item Response Anchors are 0 = Not at all; 1 = A little bit; 2 = Moderately; 3 = Quite a bit; 4 = Extremely. The Intrusion subscale is the MEAN item response of items 1, 2, 3, 6, 9, 14, 16, 20. Thus, scores can range from 0 through 4. The Avoidance subscale is the MEAN item response of items 5, 7, 8, 11, 12, 13, 17, 22. Thus, scores can range from 0 through 4. The Hyperarous al subscale is the MEAN item response of items 4, 10, 15, 18, 19, 21. Thus, scores can range from 0 through 4. 1. Any reminder brought back feelings about it. 2. I had trouble staying asleep. 3. Other things kept making me think about it. 4. I felt irrita ble and angry. 5. I avoided letting myself get upset when I thought about it or was reminded of it. 8. I stayed away from reminders of it. 9. Pictures about it popped into my mind. 10. I was jumpy and easily startled. 11. I tried not to think about it.

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69 13. My feelings about it were kind of numb. 14. I found myself acting or feeling like I was back at that time. 15. I had trouble falling asleep. 16. I had waves of strong feelings about it. 17. I tried to remove it from my memory. 18. I had trouble concentrating. 19. Reminders of it caused me to have physical reactions, such as sweating, trouble breathing, nausea, or a pounding heart. 20. I had dreams about it. 21. I felt watchful and on guard. 22. I tried not to talk about it. Total IES R score : ____________

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70 APPENDIX F CONCERNS ABOUT RECURRENCE SCALE (CARS) The follo wing questions ask you to tell us about any worries you may have about the possibility of breast cancer recurrence. By recurrence we mean the breast cancer coming back in the same breast or another area of the body, or a new breast cancer in either breast. Although most women who have been diagnosed with early stage breast cancer will never have another problem with the cancer, we are aware that many women do worry about this possibility. Other women may not worry about recurrence at all. Either way, your answers to these questions are very important to us. We understand that it may be upsetting to think about or answer questions about the possibility of recurrence. However, we need your help to understand how women think about this possibility. 1. How much time do you spend thinking about the possibility that your breast cancer could recur? 1 About It At All 2 3 4 5 6 I Think About It All The Time 2. How much does the possibility that your breast cancer could recur upset you? 1 It Does Not Upset Me At All 2 3 4 5 6 It Makes Me Extremely Upset 3. How often do you worry about the possibility that your breast cancer could recur? 1 I Never Worry About It 2 3 4 5 6 I Worry About It All The Time 4. How afraid are you that your breast cancer may recur? 1 Not At All Afraid 2 3 4 5 6 Very Afraid

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71 APPENDIX G HOSPITAL ANXI ETY AND DEPRESSION SCALE (HADS) Instruct the patient to answer how it curr ently describes their feelings. (1) Most of the time 3 A lot of the time 2 From time to time, occasionally 1 Not at all 0 (D) I still enjoy the things I used to enjoy: Definitely as much 0 Not quite so much 1 Only a little 2 Hardly at all 3 (1) I get a sort of frightened feeling as if something awful is about to happen: Very definitely and quite badly 3 Yes, but not too badly 2 Not at all 0 (D) I can laugh and see the f unny side of things: As much as I always could 0

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72 Not quite so much now 1 Definitely not so much now 2 Not at all 3 (1) Worrying thoughts go through my mind: A great deal of the time 3 A lot of the time 2 From time to time, but not too often 1 Only occasionally 0 (D) I feel cheerful: Not at all 3 Not often 2 Sometimes 1 Most of the time 0 (1) I can sit at ease and feel relaxed: Definitely 0 Usually 1 Not Often 2 Not at all 3 (D) I feel as if I am slowed down:

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73 Nearly all the time 3 Very often 2 Sometimes 1 Not at all 0 (1) Not at all 0 Occasionally 1 Quite Often 2 Very Often 3 (D) I have lost interest in my appearance: Definitely 3 I may no t take quite as much care 1 I take just as much care as ever 0 (1) I feel restless as I have to be on the move: Very much indeed 3 Quite a lot 2 Not very much 1 Not at all 0

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74 (D) I look forward with enjoyment to things: As much as I ever did 0 Rather less than I used to 1 Definitely less than I used to 2 Hardly at all 3 (1) I get sudden feelings of panic: Very often indeed 3 Quite often 2 Not very often 1 Not at all 0 (D) I can enjoy a good book or radio or TV program: Often 0 Sometimes 1 Not often 2 Very sel dom 3 Scoring (add the As = Anxiety. Add the Ds = Depression). The norms below will give you an idea of the level of Anxiety and Depression. 0 7 = Normal 8 10 = Borderline abnormal 11 21 = Abnormal

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7 5 APPENDIX H FINANCIAL PROBLEMS SUBSCALE OF THE QLACS The next set of questions asks specifically about the effects of your cancer or its treatment. Again, for each statement, indicate how often each of these statements has been true for you in the past four weeks. 1. You had financial problems because of the cost of cancer surgery or treatment. 2. You had problems with insurance because of cancer. 3. You had money problems that arose because you had cancer. 4. You had financial problems due to a loss of income as a result of cancer.

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76 APPENDIX I REPRODUCTIVE CONCERNS SCALE REPRODUCTIVE CONCERNS SCALE The next statements reflect possible feelings or thoughts about pregnancy, fertility (ability to get pregnant), & reproduction (having children). Please rate how true each one has been for you during the past mo nth. If you do not feel that the statement is relevant to you, please During the past month: Not at all A little bit Some what Quite a bit Very much 1. I have concerns about my ability to have children. 2. I am content with the number of children that I have. 3. I feel less of a woman because of reproductive problems. 4. An illness/disease has affected my ability to have children. 5. I am angry that my ability to have children has been affected. 6. I am able to talk openly about fertil ity or reproductive concerns. 7. Others are to blame for my reproductive problems. 8. I am sad that my ability to have children has been affected. 9. I have had control over my reproductive future. 10. I feel guilt about my reproductive problems. 11. I have mourned the loss of my ability to have children. 12. I blame myself for my reproductive problems. 13. I am frustrated that my ability to have children has been affected. 14. I am less satisfied with my life because of reproductive problems.

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77 APPENDIX J MULTIDIMENSIONAL SCALE OF PERCEIVED SOCIAL SUPPORT Instructions: We are interested in how you feel about the following statements. Read each statement carefully. Indicate how you feel about each statement. Ci Circle the 1. There is a special person who is around when I am in need. (SO) 2. There is a special person with whom I can share my joys and sorrows. (SO) 3. My family really tries to help me. (Fam) 4. I get the emotional help and support I need from my family. (Fam) 5. I have a special person who is a real sou rce of comfort to me. (SO) 6. My friends really try to help me. (Fri) 7. I can count on my friends when things go wrong. (Fri) 8. I can talk about my problems with my family. (Fam) 9. I have friends with whom I can share my joys and sorrows. (Fri) 10. There is a speci al person in my life who cares about my feelings. (SO) 11. My family is willing to help me make decisions. (Fam) 12. I can talk about my problems with my friends. (Fri) The items tended to divide into factor groups relating to the source of the social sup port, namely family (Fam), friends (Fri) or significant other (SO).

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78 APPENDIX K CHARLSON COMORBIDITY INDEX

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79 APPENDIX L FACT COG

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82 Appendix M Parenting Concerns Questionnaire Rated from: 0 = Not at all concerned 1= A little bit concerned 2= Somewhat concerned 3= Very concerned 4= Extremely concerned Factor 1: Practical impact of illness on child My own mood, worries or emotions are affecting my children My physical limits or low energy are affecting my children I am not able to Changes in my memory or attention are affecting my children Factor 2: Emotional impact of illness on child My children are emotionally upset by my illness My children are worried that I am going to die My children get upset when we talk about the illness My children might be in need of professional mental health care My children get confused or upset by what others say about my illness Factor 3: Concerns about co parent There is no one to take good care of my children if I die My partner is not providing me with enough practical support My partner is not providing me with enough emotional support