University of Iowa
Iowa Research Online
Theses and Dissertations
Fall 2015
Predicting reasons for living among chronically ill
and depressed middle aged and older adults
enrolled in a randomized clinical trial
Domonique Renee Casper
University of Iowa
Copyright 2015 Domonique Casper
This dissertation is available at Iowa Research Online: http://ir.uiowa.edu/etd/1957
Recommended Citation
Casper, Domonique Renee. "Predicting reasons for living among chronically ill and depressed middle aged and older adults enrolled in
a randomized clinical trial." PhD (Doctor of Philosophy) thesis, University of Iowa, 2015.
http://ir.uiowa.edu/etd/1957.
Follow this and additional works at: http://ir.uiowa.edu/etd
Part of the Educational Psychology Commons
PREDICTING REASONS FOR LIVING AMONG CHRONICALLY ILL AND DEPRESSED
MIDDLE AGED AND OLDER ADULTS ENROLLED IN A RANDOMIZED CLINICAL
TRIAL
by
Domonique Renee Casper
A thesis submitted in partial fulfillment
of the requirements for the Doctor of Philosophy
degree in Psychological and Quantitative Foundations (Counseling Psychology) in the
Graduate College of
The University of Iowa
December 2015
Thesis Supervisor: Professor Elizabeth Altmaier
Graduate College
The University of Iowa
Iowa City, Iowa
CERTIFICATE OF APPROVAL
____________________________
PH.D. THESIS
_________________
This is to certify that the Ph.D. thesis of
Domonique Renee Casper
has been approved by the Examining Committee for
the thesis requirement for the Doctor of Philosophy degree
in Psychological and Quantitative Foundations (Counseling Psychology) at the December 2015
graduation.
Thesis Committee:
____________________________________________
Elizabeth Altmaier, Thesis Supervisor
____________________________________________
Carolyn Turvey
____________________________________________
John Westefeldd
____________________________________________
Timothy Ansley
____________________________________________
Howard Butcher
ACKNOWLEDGMENTS
First, I would like to thank the study participants. I hope your participation in the study
provided additional support and facilitated growth. I cherish the opportunity to listen to your
experiences of resiliency and triumph, as well as challenge and strain.
It has been a privilege to work under the guidance and mentorship of my academic
advisor and dissertation chair, Dr. Elizabeth Altmaier, since the summer of 2008 when I
participated as an undergraduate summer researcher. Dr. Altmaier has served as a strong model
of professional and personal perseverance, strength, and determination. She has provided me
with unwavering support, motivation, and encouragement throughout my graduate training.
Words cannot describe how grateful I am for the opportunity to work with her.
Additionally, I would like to express my appreciation to Drs. Carolyn Turvey, John
Westefeld, Timothy Ansley, and Howard Butcher for their time, recommendations, and
encouragement throughout the dissertation process. I would also like to extend thanks to the
entire Turvey COPE research team for their dedication to the clinical intervention and research
conducted to serve older adults diagnosed with chronic illness, as well as their support and
assistance with dissertation data collection. Also, thank you to my undergraduate research
professor, Dr. Walter Davis, for setting a strong foundation and springboard for my academic
and research success.
Last, but certainly not least, I would like to express gratitude to my family and friends.
My grandmother, Shirley (Mimi) Bolden, encouraged and inspired me to set high goals and
become a first-generation college student and ultimately seek and attain graduate education. I
appreciate the patience and motivation provided by my mother, Norma Casper, brother, Michael
ii
Casper, and aunt, Wanda Casper, as well as many other family members. Thank you for keeping
me grounded and reminding me to laugh and enjoy the ride. To my near and dear graduate
school friends and colleagues, Christopher Nguyen, Angel Cheng, and Lauren Levy, this journey
would not have been the same without you, I am so blessed and honored to call you friends,
colleagues, and family.
iii
ABSTRACT
The American older adult population is rapidly growing and projected to double by 2030.
There is high proportion of completed suicides in later life subsequent to several biopsychosocial
variables. Medical populations, particularly Chronic Obstructive Pulmonary Disease (COPD)
and Heart Failure (HF), have greater risk of depression, suicidality, and lower quality of life.
Current geropsychology suicide research tends to focus on risk factors, a deficit approach.
In contrast, the present study utilized a strength-based approach to study late life suicide by
predicting life sustaining cognitions, reasons for living (RFL). This study explored the influence
of physical health related quality of life (HRQOL) and depression on RFL in a sample of 75
depressed and chronically ill middle aged and older adults enrolled in a randomized clinical trial.
The results indicate insignificant relationships between demographic variables (e.g. age,
gender, and illness type) and reasons for living. Despite the statistically significant negative
correlations between depression and physical and mental HRQOL at baseline and week five,
only week five depression significantly predicted RFL (β = -1.369, ∆R2 = .063, p= .034), after
the effects of age and baseline depression were held constant.
In conclusion, the present study supports integrative primary care modalities of treatment
for late life depression and highlights the importance of incorporating protective factors in
suicide risk assessments. Future research should consider utilizing population specific
instruments and alternative medical and social variables.
iv
PUBLIC ABSTRACT
Late life suicide is a growing issue secondary to the high proportion of completed
suicides in later life and projected increase in the American older adult population. Current late
life suicide research focuses on suicide risk factors, a deficit approach. In contrast, the present
study aims to explore suicide protective factors through reasons for living, a strength-based
construct pertaining to cognitive deterrents to considering suicide as an option.
This study is exploratory in nature and sought to predict reasons for living among
depressed and chronically ill (chronic obstructive pulmonary disease and heart failure) middle
aged and older adults (55 years and older) enrolled in five weeks of a randomized clinical trial.
Depression significantly predicted reasons for living; week five depression severity
accounted for reasons for living (β = -1.369, ∆R2 = .063, p= .034), after the effects of age and
baseline depression severity were held constant. Conversely, physical health related quality of
life did not predict reasons or living; however, depression and health related quality of life were
significantly correlated at both time points (baseline and week five). Based on this
study’s findings, clinical intervention should target depression; reduction in depression severity
may serve to protect against late life suicide.
v
TABLE OF CONTENTS
LIST OF TABLES .................................................................................................................. viii
LIST OF FIGURES .................................................................................................................. ix
CHAPTER I: INTRODUCTION.................................................................................................1
Late Life, Depression, Chronic Illness, and Suicide .................................................................1
Purpose and Objectives of the Present Study............................................................................6
Summary .................................................................................................................................8
CHAPTER II: LITERATURE REVIEW ................................................................................... 10
Geropsychology..................................................................................................................... 10
Chronic Illnesses in Late Life ................................................................................................ 15
Chronic Obstructive Pulmonary Disease ............................................................................ 15
Heart Failure ...................................................................................................................... 19
Psychosocial Factors Associated with COPD and HF ............................................................ 23
Quality of Life of Patients with COPD or HF ........................................................................ 28
Suicide among Older Adults .................................................................................................. 33
Models ............................................................................................................................... 33
Prevalence.......................................................................................................................... 36
Risk and Protective Factors ................................................................................................ 38
COPD and HF .................................................................................................................... 41
Reasons for Living ................................................................................................................ 43
Depression ......................................................................................................................... 43
Older Adult Population ...................................................................................................... 46
Summary and Conclusion ...................................................................................................... 51
Purpose of Study ................................................................................................................... 52
CHAPTER III: METHODS....................................................................................................... 54
Participants ............................................................................................................................ 54
Procedures ............................................................................................................................. 56
Interventions ...................................................................................................................... 58
Clinician Information ......................................................................................................... 61
Potential Risk ..................................................................................................................... 62
vi
Data Collection .................................................................................................................. 63
Compensation .................................................................................................................... 63
Instruments ............................................................................................................................ 64
Reasons for Living ............................................................................................................. 64
Depression ......................................................................................................................... 65
Health-Related Quality of Life ........................................................................................... 67
CHAPTER IV: RESULTS ........................................................................................................ 70
Preliminary Analysis ............................................................................................................. 70
Research Questions ............................................................................................................... 76
Additional Analyses .............................................................................................................. 80
CHAPTER V: DISSCUSSION ................................................................................................. 84
Demographics ....................................................................................................................... 84
Depression............................................................................................................................. 87
Health Related Quality of Life ............................................................................................... 91
Study Limitations .................................................................................................................. 92
Implications for Future Practice ............................................................................................. 93
Implications for Future Research ........................................................................................... 97
Conclusions ........................................................................................................................... 98
REFERENCES ......................................................................................................................... 99
vii
LIST OF TABLES
Table 1: COPD Classification ................................................................................................... 18
Table 2: Heart Failure NYHA Classification ............................................................................. 22
Table 3: Demographic Characteristics of the Patient Sample ..................................................... 71
Table 4: Medical Characteristics of the Patient Sample ............................................................. 73
Table 5: Psychological Variables............................................................................................... 76
Table 6: Gender and Reasons for Living.................................................................................... 77
Table 7: Illness Type and Reasons for Living ............................................................................ 77
Table 8: Inter-correlation Matrix between Study Variables ........................................................ 78
Table 9: Hierarchical Regression of Physical HRQOL Predicting Reasons for Living ............... 79
Table 10: Hierarchical Regression of Depression Predicting Reasons for Living ....................... 80
Table 11: Hierarchical Regression of Age and Depression Predicting Reasons for Living ......... 81
Table 12: Hierarchical Regression of Mental HRQOL Predicting Reasons for Living ............... 83
Table 13: Hierarchical Regression of Age and Mental HRQOL Predicting Reasons for Living
................................................................................................................................................. 83
viii
LIST OF FIGURES
Figure 1.Relationship between the present study and the larger randomized control trial
procedures..................................................................................................................... 57
Figure 2. Present study’s data collection procedures. ..................................................... 58
Figure 3. Change in physical HRQOL from baseline to week five. ................................ 79
Figure 4. Change in depression severity from baseline to week five. ............................. 82
ix
CHAPTER I: INTRODUCTION
Late life suicide is a growing concern within psychology, particularly geropsychology,
the study of cognition, emotions, personality, interpersonal relationships, and well-being of aging
adults. There is an extensive body of literature addressing the risk or precipitant factors, incident
rates, and prevention or interventions of late life suicide; however, there is little research that
describes protective factors among older adults. Research documents that depressed and
chronically ill older adults are at greater risk of suicide. Moreover, there is no literature that
addresses protective factors against suicide among chronically ill and depressed older adults.
Focus on suicide protective factors vis á vis reasons for living is a novel approach to suicidology,
through assessing strengths rather than focusing on deficits.
Research on protective factors is relevant because the geriatric population is projected to
double in size over the next two decades; older adults currently comprise 13% of the U.S.
population (U.S. Census Bureau, 2011). Additionally, older adults are likely to develop chronic
illness due to age-related physical declines or lifestyle choices, which may result in the
development of mental health problems or functional impairments among older adults.
Investigating protective factors will inform the practice of mental health and health care
providers working with the older adults. It is imperative that mental health and health care
providers are equipped with knowledge pertaining to variables which may influence protective
factors or reasons for living (RFL), among the depressed and chronically ill patients they treat.
Late Life, Depression, Chronic Illness, and Suicide
The “baby-boomer” generation, those born between 1946 and 1964, are aging; therefore,
there are projections of drastic increases in the older adult population (U.S. Census Bureau,
2011). As a multicultural group, older adults exhibit distinct psychological and physiological
1
characteristics. Included in some of the life-span considerations for older adults are depression
and chronic illness. First, statistics indicate that older adults are among the lowest reporters of
depression by age group (NIMH, 2005); however, depression is the most common mental health
condition diagnosed in later life (Hooyman & Kiyak, 2011). It has been hypothesized that older
adults tend to display depressive symptoms following an aggregate of loss (Hooyman & Kiyak,
2011). Older adults manifest depression in unique ways as well. For example, older adults report
somatic complaints, feelings of emptiness, low self-care, and loss of interest in environment that
are often conceptualized by older adults or untrained professionals as consequences of normal
aging, leading to under- or misdiagnosis (Hooyman & Kiyak, 2011).
Secondly, older adults are inflicted by age-related physiological changes that are
exacerbated by lifestyle choices, potentially resulting in the onset of chronic illness such as heart
failure (HF) or chronic obstructive pulmonary disease (COPD).
These conditions are similar in
several aspects. First, both conditions are chronic and progressive, meaning that there is no cure
and patients experience increased intensity of symptoms over time. Additionally, COPD and HF
have several common symptoms such as dyspnea, difficulty breathing, and intense coughing.
Lastly, COPD and HF share similar etiologies, for instance, they both may result from lifestyle
choices. In addition to these disease related commonalities, COPD and HF patients also
experience similar risk for depression and report lower well-being and life satisfaction.
Prevalence of major depression in the U.S. is approximately 7% for 12-month prevalence
and 17% for lifetime prevalence among adults (NIMH, 2005). Older adult rates of depression
vary by site of residency or data collection; however, rates of major depression are
approximately 1-24% (Blazer, 2002; Hybels & Blazer, 2003; Jones, Marcantonio, Rabinowitz,
2003; Kessler, et al., 2003; Zalaquett & Stens, 2006; Zarit, 2009), and is highest in hospital
2
inpatient settings, 6-44% (Gellis, 2006). COPD patients have been reported to endorse
depressive symptoms at a much higher rate, 30-58% (Hynninen, Breitve, Wiborg, Pallesen, &
Nordhus, 2005). Similar reports have been made for HF patients, who report depressive
symptoms at rates between 21-78% (Delville & McDougall, 2008).
Several factors have been postulated to explain this increased risk of depression among
COPD and HF patients. Research suggests that disease severity and physical conditions relate to
endorsed depression (e.g., Angermann et al., 2009 Paukert, LeMaire, & Cully, 2009; Turvey,
Klein, & Pies 2006; Moussas et al., 2008; Schane, Woodruff, Dinno, Covinsky, & Walter, 2008;
Shen et al., 2011; Williams et al., 2002). Particularly, physical limitation and functioning has
been correlated with reported depression. Likewise, researchers have found psychological
functioning is related to depressive symptoms (Angermann et al., 2009; Moussas et al., 2008;
Paukert et al., 2009; Turvey et al., 2006; Yohannes, Baldwin, & Connolly, 2000).
Age and gender have also been correlated with increased depression in samples of COPD
and HF patients. For example, one study found that women with COPD were more likely to
report depression than men (Schane et al., 2008). In similar studies on HF patients, women were
also found to report higher rates of depression (Angermann et al., 2009; Cully, Johnson, Moffett,
Khan, & Deswal, 2009). Age differences are inconsistent across COPD and HF patients. One
study found younger HF patients were more like to be depressed than older patients (Cully et al.,
2009). However another study of COPD patients, found that older age predicted depression
(Schane et al., 2008). Regardless of the direction, age and gender exhibit some direct effect on
depression among COPD and HF patients.
Quality of life, life satisfaction, well-being, or health related quality of life (HRQOL) is
generally lower among chronically ill patients, potentially due to their experience with
3
depression. One study found depressed HF patients endorsed lower HRQOL (Faller et al., 2009).
Another study reported similar results for COPD patients (Cully et al. 2006). Symptom severity
also accounts for HRQOL for COPD and HF patients (Cully et al., 2006; Faller et al., 2007;
Faller et al., 2009; Rustᴓen et al., 2008).
Depression and low quality of life contribute to increased risk of suicide; however, it is
unclear to what degree among COPD and HF patients. Depression is a one of the most common
risk factors of suicide across all age groups. Lower satisfaction with life may cause difficulty
identifying positive life experiences or individual strengths and may consume the individual’s
thought processes, leading to suicidal ideation. Older adults compose the highest proportion of
committed suicides in the U.S. (AAS, 2010). More specifically, Caucasian older adult men aged
85 or older, are at the greatest risk (AAS, 2010). Older adults use more lethal means and tend to
be more successful than younger adults during attempts of suicide (Conwell, Van Orden, &
Caine, 2011; Gallagher-Thompson & Osgood, 1997; Szanto, Prigerson, & Reynolds, 2001).
Medical conditions contribute to rational suicide—a normalized act or thought related to taking
one’s life as a result of the diagnosis, treatment, and coping with chronic illness- among older
adults (Gallagher-Thompson & Osgood, 1997). Reviews of late life suicide research state that
illness severity, number of comorbidities, length since diagnosis, and hospitalization history have
significant impact on suicide risk (Conwell et al., 2011; Gallagher-Thompson & Osgood, 1997;
Heisel, 2006; Szanto et al., 2001).
Additionally, COPD and HF patients are at greater risk for suicide. One study reported,
even after the effects of depression were held constant, older COPD patients were at a higher risk
for suicide (Fiske, O’Riley, & Widoe, 2008). Another study found that compared to healthy
controls, COPD patients reported higher suicidal ideation and suicide attempts (Goodwin, 2011).
4
HF patients similarly have been found to report greater risk of suicidal ideation, even after
controlling for depression.
Reasons that prevent older adults from committing suicide despite their high risk for
suicide is an area of research that is limited, particularly research on subsets of older adults who
are at exponentially greater risk. Reasons for living (RFL), cognitive deterrents to suicide, have
been widely explored among younger adults, college students, and adolescents, as well as those
with psychological complaints such as depression. The Reasons for Living Inventory (RFLI;
Linehan, Goodstein, Nielsen, & Chiles, 1983) is a unique approach to exploring adaptive lifemaintaining characteristics or suicide protective factors. According to research utilizing the
RFLI among depressed primarily college student populations, there are distinct differences in
reported RFL across racial and nationality groups (Oquendo et al., 2005; Richardson-Vejlgaard,
Sher, Oquendo, Lizardi, & Stanley, 2009). Depressed adults who report lower RFL have also
been found to exhibit greater suicidal behavior (e.g., Lizardi et al., 2007).
Research on older adults is limited and consequently inconclusive. One study found that
older participants reported higher moral objections and child related concerns as reasons not to
consider suicide (Miller, Segal, & Coolidge, 2001). Other studies reported that older women
endorsed higher total RFL than older men (Range & Stringer, 1996; Kissane & McLaren, 2006).
Two studies reported no significant differences by gender (Segal & Needham, 2007; June, Segal,
Coolidge, & Klebe, 2010). The sheer number of men and women sampled in these studies may
explain the inconsistencies. Only one study to date has investigated the influence of perceived
health on RFL among older adults (Segal, Shelly, and Frederwick, 2008). This study found
lower health perceptions correlated with fewer total RFL (Segal et al., 2008). Health also
predicted RFL above and beyond life stress, age, depression, and optimism (Segal et al., 2008).
5
Few studies have directly studied the influence of depression among older adults and reported
RFL.
As demonstrated by this brief review of the literature, there are several intersections of
related research topics i.e., between life-span issues such as late life depression and physical
decline, COPD and HF related with depression and lower quality of life, and older adults and
depressed individuals report of lower RFL and suicidal behaviors. However, no literature has
investigated the interrelationships between all of these variables and how the former variables
predict RFL. Several common themes arise in the literature such as the salient role of
depression and physical decline or impairment among chronically ill patients.
Purpose and Objectives of the Present Study
The current study is exploratory in nature with an overall aim to explore RFL among
chronically ill depressed middle aged and older adults. This group is particularly at increased
risk of suicide, as a result of their functional, health, and mental health statuses, as well as their
developmental or life-span phase. This study aims to explore how demographic (e.g., age and
gender) and psychological (e.g., depression and physical health related quality of life) variables
relate to protective factors against suicide. There are three specific purposes of the study. The
primary objective of the study is to examine the influence of depression on reported total RFL.
Depression is a common condition among older adults, COPD and HF patients, and suicidal
adults, thus it is important to explore how depression severity relates to RFL protective strengthbased factors. Research in this area provides further rationale for the use of psychotherapy in
working with older patients who have chronic illness to reduce depression and risk of suicide.
Secondly, this study aims to likewise explore the influence of physical health related
quality of life (HRQOL), such as physical functioning, role limitations, pain, and health status,
6
have on total RFL. Research suggests that COPD and HF patients report lower physical
HRQOL, which is strongly correlated with depression. However, it is unclear what influence
physical HRQOL has on suicide or other related variables such as RFL among COPD and HF
patients. Addressing the relationship between physical functioning and physical HRQOL helps
to inform rehabilitation and health care practices of older adults with chronic illness.
Another objective of the study is to explore the associations between depression,
HRQOL (physical and mental domains), demographic variables (age, gender, and disease type),
and RFL (total and subscales). Research suggests that there are strong associations between
subsets of these variables. As stated previously, this is the first study to investigate these
variables within a sample of COPD and HF patients; therefore, it is important to explore how all
of these variables correlate with one another. Correlational data will build a greater
conceptualization of the experience of COPD and HF patients.
Lastly, this study aims to explore differences in endorsed total RFL based on gender and
age. The literature suggests that women and younger older adults report greater RFL, but is
slightly equivocal. There are no studies which have explored these differences among
chronically ill patients. Exploring differences in reported RFL facilitates treatment triaging and
adaptions to interventions.
The present study is a subset of a larger, National Institutes of Health Research Project
Grant (NIH R01) funded clinical trial, the “Combined Illness Management and Psychotherapy in
Treating Depressed Elders,” through the University of Iowa Hospital and Clinics, Department of
Psychiatry. The principal investigator on this study is Dr. Carolyn Turvey. This NIH funded
clinical trial is an intervention study which aims to reduce depression in clinically depressed
COPD and HF patients through a 10-week semi-manualized psychotherapy intervention
7
combining behavioral activation and interpersonal psychotherapy with an illness management
only control group. The larger study is ongoing and began collecting data in January of 2012.
A total of 75 participants from the larger study also participated in the present study between
December 2012 and January 2015. Data were collected at baseline and week five. The present
study will answer the following research questions:
1) Are their significant total reasons for living group differences based on gender, age, and
illness type?
2) What factors (e.g., baseline and week-five health related quality of life and baseline and
week-five depression scores are associated with total RFL?
3) What influence does endorsed physical HRQOL (baseline to week-five) have on total
RFL, following five weeks of illness management only (IMO) or psychotherapy and
illness management (COMBO) treatment?
4) How does endorsed depression severity (baseline to week-five) effect total RFL,
following five weeks of IMO or COMBO treatment?
Summary
In summary, the purpose of this study was to explore the influence of depression,
physical health related quality of life, and demographic variables on depressed COPD and HF
patients’ reasons for living. Greater understanding related to how these variables are associated
and predict RFL is important for two reasons: 1) the older adult population is projected to grow
rapidly over the next two decades and 2) the aging population is the highest aged group of
suicide completions which may increase as a result of the growing older adult population.
Preventive or protective factors specific to this population, particularly chronically ill older adult
patients, are important to understand and incorporate in work with the clientele.
8
Chapter II provides an in-depth review of the literature that is foundational for the study.
The first section discusses the field of geropsychology, the psychological study of aging, and
provides demographic and salient life-span considerations of the older adult population. Section
two describes COPD and HF etiology, symptomology, prevalence, and other disease related
characteristics. The third section reviews the literature on psychosocial factors, particularly
depression, among COPD and HF patients, followed by a review of the data on quality of life
(section four). The fifth section presents the issue of suicide in later life. Specifically, a
discussion of models explaining suicide, prevalence rates, risk and protective factors, and suicide
among COPD and HF patients is presented. Finally, chapter two concludes with a section on the
RFL research on depressed populations and older adults.
Chapter III provides the methodology for the current study. First, a description of the
larger study through which the study was conducted is reviewed. Next, the participant pool and
selection criteria are described. A description of the study’s procedures such as the
interventions, data collection, compensation, clinician characteristics, and training are provided.
Chapter IV addresses the present study’s research questions. The hypotheses pertaining
to each research question are reviewed. Tables and figures are included to illustrate statistical
and demographic information. Significant and insignificant statistical findings are described.
Additional analyses are also explained and reviewed.
Finally, Chapter V summarizes the present study’s results and offers rationale based on
previous literature and theory. Implications for future clinical practice, with a special emphasis
on geropsychology and counseling psychology, are provided. Additionally, recommendations
for future research are offered. Lastly, limitations of the current study are overviewed.
9
CHAPTER II: LITERATURE REVIEW
This literature review begins by providing a brief overview of the study of
geropsychology, demographics, and common issues. Next, the chapter will present the literature
on chronic illnesses in late life, their definition and common forms, placing emphasis on chronic
obstructive pulmonary disease (COPD) and heart failure (HF). The chapter will then examine
the psychosocial consequences of COPD and HF in late life. Additionally, COPD and HF
patients’ presentation of quality of life and health related quality of life is reviewed. Further
discussed are models, prevalence rates, and risks and protective factors of suicide among the
older adult population. Finally, this chapter provides a review of the literature on reasons for
living, a protective factor against suicide. Specifically, a summary of the existing literature on
reasons for living among depressed populations and older adults are summarized.
Geropsychology
Gerontology, the study of aging, takes a holistic and multidisciplinary approach in
investigating various aspects of aging (Hooyman & Kiyak, 2011). An individual’s
psychological, biological, social, spiritual, and cultural experiences are taken into account.
Geropsychology specifically explores psychological factors, such as intelligence, cognition,
personality, mood, and interpersonal relationships, as they present in an older adult’s life and
impact their well-being (Whitbourne & Whitbourne, 2011). The study of aging and the
psychology of aging are particularly relevant due to the growing older adult population in the
U.S., specifically of the “baby-boomer” population, those born between 1946 and 1964
(Hooyman & Kiyak, 2011; Whitbourne & Whitbourne, 2011).
According to the U.S. Census Bureau 2011 data (U.S. Census Bureau, 2011), adults aged
65 and over, comprise 12.8% of the total population. There are a greater number of older adult
10
women than older adult men, 22 million versus 17 million, respectively (U.S. Census Bureau,
2011). There are also variations in population size across different age groups of older adults.
Particularly, the young old, those aged 65-74, comprise 6.9% of the population; the old-old,
those 75-84 years old, make up 4.1%; and the oldest-old, adults 85 years old and over, form
1.6% of the U.S. population (U.S. Census Bureau, 2011). The older adult population is also
expected to greatly increase over the next few decades. Specifically, the geriatric population is
projected to be 20% of the U.S. population by 2030, 71 million.
Salient life-span considerations regarding older adults include loss, role transitions, death
and dying, depression, and physiological changes (Hooyman & Kiyak, 2011). Older adulthood
has been conceptualized as a “season of losses.” Loss during this life phase may involve the loss
of significant relationships of a partner, family members, friends; autonomy; physical
functioning; or social or financial status (Hooyman & Kiyak, 2011; Whitbourne & Whitbourne,
2011). These individual losses or the aggregate of losses over a short period of time may
negatively impact older adults’ quality of life (Whitbourne & Whitbourne, 2011). Loss may also
bring about the onset of role transitions. For instance, following the loss of a partner, older
adults enter into the widowhood life transition and may face role shifts related to family
dynamics. Likewise, the loss of occupation, either planned or forced, results in a transition into
retirement. As people age, especially as their health declines, death becomes more inevitable.
Issues related to death and dying may occur such as the need to make advance directives, a living
will, or other arrangements. Additionally, older adults may face having those close to them
silence the inevitability of death by avoiding discussions or plans related to end-of-life care.
Additionally, due to the aging process there are several physiological declines. Of particular
11
interest to this study are the declines of the respiratory and cardiovascular systems, as well as
depression among older adults.
Of all mood disorders, depression is the most common condition faced in later life.
Specifically, unipolar depression is present. Depression is commonly viewed as mild and
reactive among older adults (Hooyman &.Kiyak, 2011). The prevalence rate of depression in the
population varies in regard to site of residency or data collection (e.g., community dwelling,
hospital, or nursing home). 10-30% of community dwelling older adults have minor or
subsyndromal depression, 1-4% have major depression, and 2% have dysthymia, longstanding
depression (Blazer, 2002; Hybels & Blazer, 2003; Kessler, et al., 2003; Zalaquett & Stens, 2006,
Zarit, 2009). Rates within nursing homes range between 20-50% for minor depression or
dysthymia and 6-24% for major depression (Blazer, 2002; Jones, Marcantonio, Rabinowitz,
2003). Inpatient older adult facilities have been reported to range between 6-44% in endorsed
major depression (Gellis, 2006).
There are several concerns related to misdiagnosis or underdiagnosis of depression in late
life. The Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR; APA, 2000)
diagnostic criteria state that depressed individuals are those who have experienced at least five of
the following: depressed mood, loss of pleasure, weight gain or loss, sleep disturbance, lack of
activity, low energy, guilt, poor concentration, or thoughts of death of suicide for most of the
past two weeks. Only minor changes were made to the depression diagnostic criteria in the DSM
fifth edition (DSM-V; APA, 2013). Specifically, depression is categorized as the presence of
sad, empty, or irritable mood, accompanied by somatic (i.e., fatigue, psychomotor agitation,
weight loss or gain, and hypersomnia or insomnia) and cognitive (i.e., guilt or worthlessness)
changes that significantly affect the individual’s capacity to function. Symptoms more specific to
12
older adults; however, include: somatic complaints (Balsis & Cully, 2008); sadness, feelings of
emptiness, anxiety or panic; loss of interest in environment; low self-care; and changes in eating
or sleeping habits, which may be overlooked by care providers (Hooyman &.Kiyak, 2011).
There are several challenges to appropriate diagnosis of depression among older adults,
for example, training, treatment seeking, masking, stigma, and perceptions of natural aging
processes. The number of trained geropsychologist is low; as a result professionals may lack
awareness of the unique antecedents and expressions of depression within the older adult
population. When older adults seek treatment or recommendations for depressive symptoms,
they often visit their primary care physician (Hooyman &.Kiyak, 2011) who may lack diagnostic
skills for appropriate diagnosis of depression. Older adults less frequently seek therapy or
endorse depression, due partially to perceptions of depression as a normal part of the aging
process, depression and psychotherapy as stigmatizing, and the necessity to mask depression, all
of which may create issues for appropriate diagnosis.
There are several potential causes of depression among older adults. The most common
seems to be the aggregate experience of loss and inability to cope with multiple stressors
(Hooyman &.Kiyak, 2011). If an individual is unable to activate healthy coping strategies
during times of loss, he or she may present with depressive symptoms. Due to the debilitating
nature of depression, older adults may become isolated and disengaged. They may also become
disinterested or unmotivated to complete activities of daily living (Hooyman &.Kiyak, 2011).
In addition to the unique differences in mood disorders in the aging population, there are
specific physiological changes due to aging. The respiratory organ system and functions of the
human body exhibit complicated forms of decline, due to the integration of multiple organs. The
structure of the human body leads to noticeable changes of the respiratory system with age
13
(Hooyman & Kiyak, 2011). Some respiratory system decline might also be caused by lifestyle
choices (i.e., smoking) or environmental issues (i.e., pollutants and infections; Hooyman &
Kiyak, 2011). These precipitants are unrelated to normal aging (Hooyman & Kiyak, 2011). The
interrelatedness of potential causes to respiratory decline make it difficult to parcel out and
determine the etiology of decline in the respiratory system. Some of the notable changes
exhibited in functioning include: inability to sustain physical activity, increase in fatigue or
exhaustion, decline in the maximum amount of oxygen brought into the body with each breath,
and increased difficulty breathing after exercise (Hooyman & Kiyak, 2011).
There seems to be clearer age-related structural changes in the cardiovascular organ
systems, particularly the heart and surrounding blood vessels (Hooyman & Kiyak, 2011). More
specifically, there is “a reduction in bulk, a replacement of heart muscle with fat, a loss of elastic
tissue, and an increase in collagen” (p. 85, Hooyman & Kiyak, 2011). Some of the physiological
changes occur in the muscle fibers, the vessels, the arteries, and the veins. These changes cause
blood circulation difficulty throughout the body, issues with blood pressure, increased heart rate,
and lowered efficiency in utilizing oxygen. A portion of these changes are likely related to
normal aging, while another portion is due to diet choices.
In sum, the U.S. population and worldwide population is becoming older with increased
projection of the aging population specifically of the baby boomers. With age there are unique
characteristic shifts and changes in mood and physiology, as well as other salient areas of life
such as the experience of loss, death and dying, and life-transitions. Specifically of interest in
this study are the age-related physiological declines that have influence on the occurrence of
chronic illnesses among older adults. The next section will review the general characteristics,
etiology, symptoms, and treatments of COPD and HF.
14
Chronic Illnesses in Late Life
As one ages, there is increased risk for diseases and physical impairment (Hooyman &
Kiyak, 2011). The rate of acute illness, illnesses that are time limited such as cold or infections,
tend to decrease with age; however, the recovery or response to treatment increases, particularly
among older women (Hooyman & Kiyak, 2011). Chronic health conditions are defined as “long
term (more than 3 months), often permanent, leaving a residual disability that may require longterm management or care rather than a cure” (p. 122, Hooyman & Kiyak, 2011) and affect over
80% of individuals 70 years old and over. More than 50% of the older population has comorbid
chronic illnesses. The most common chronic illnesses reported among men and women aged 65
years and older include: hypertension, arthritis, heart disease, diabetes, respiratory diseases (e.g.
emphysema, asthma, and chronic bronchitis), stroke, and cancer (Hooyman & Kiyak, 2011).
This study will focus on two specific forms of chronic illness among older adults: chronic
obstructive pulmonary disease (COPD) and heart failure (HF).
Chronic Obstructive Pulmonary Disease
Chronic obstructive pulmonary disease (COPD) is a progressive (i.e. disease negatively
changes over time) pulmonary disease characterized by inflammation of the airways, which
prevent normal airflow throughout the body. Prevalence rates of COPD are high and are
predicted to increase, especially in developing countries (Stucky & Greenblatt, 2008). Between
2007 and 2009, 11.8 million adults living in the U.S. reported having a diagnosis of COPD,
which at that time was 5.1% of the total population (Akinbami & Liu, 2011). COPD is currently
the fourth leading cause of death worldwide (Akinbami & Liu, 2011).
According to statistics attained between 1998 and 2009, COPD is more prevalent among
women than men at any point across the lifespan, except among the old-old group from 75-84
15
(Akinbami & Liu, 2011). Additionally, older adults, as a whole, are more likely to develop
COPD than other age groups. In regard to race, COPD is more common among Latino’s than any
other racial backgrounds. The prevalence rate of COPD among those below the poverty line is
twice as high as those above poverty level (Akinbami & Liu, 2011).
There are two forms of COPD: emphysema or chronic bronchitis (Lorig, Sobel, &
Laurent, 2000; Stucky & Greenblatt, 2008). Emphysema is likely to occur due to the wearing
down of lung tissue, specifically the air sacs or alveoli (Lorig et al., 2000; Stucky & Greenblatt,
2008). During this process, the air sacs lose elasticity, narrow, and collapse which make it
difficult for the lungs to bring oxygen into the bloodstream (Lorig et al., 2000). In contrast,
airway inflammation, an identifying characteristic of chronic bronchitis, is often due to exposure
to particles or gaseous substances (Stucky & Greenblatt, 2008; Lorig et al., 2000). Chronic
damage to the lungs or bronchi may result (Stucky & Greenblatt, 2008; Lorig et al., 2000).
Furthermore, the inflammation causes the airways to constrict, resulting in obstructions of the
airways (Lorig et al., 2000). Obstruction of the airflow directed in and out of the lungs is present
in both emphysema and chronic bronchitis (Lorig et al., 2000).
A full recovery of pulmonary function is not expected for patients with COPD. The
worsening trajectory of the disease has consequences on patients’ psychological and social
experiences following diagnosis (Stucky & Greenblatt, 2008). Patients with COPD are at greater
risk for psychological conditions such as depression and anxiety, and may also experience lower
quality of life, defined as satisfaction in various areas of life, which is discussed further later in
this chapter (Lorig et al., 2000; Stucky & Greenblatt, 2008).
Symptoms related to COPD include difficulty breathing, breathlessness or dyspnea,
coughing, fatigue, and sputum production (i.e. coughing up a mixture of saliva and mucus;
16
(Lorig et al., 2000; Stucky & Greenblatt, 2008). These symptoms, especially coughing, can be
detected several years prior to the diagnosis of COPD. Greatest benefit, such as slowing the
progression of the illness, is found when COPD is diagnosed early; an early intervention such as
smoking cessation can be helpful. However, the diagnosis of COPD often occurs during a
patient’s first hospitalization related to symptoms congruent with COPD. A spirometry is used
to measure lung function and assists in the diagnosis of COPD diagnosis. The spirometry
measures the forced vital capacity (FVC), which is a measure of the amount of air one can
breathe out; forced expiratory volume in one second (FEV1), the total amount of air the patient
can breathe out in one second; and ratio between FVC and FEV1 (FEV1/FVC) scores. These
measures also aid in classifying COPD in terms of at risk, mild, moderate, severe, and very
severe (see Table 1; Stucky & Greenblatt, 2008).
17
Table 1
COPD Classification
Code
0: At risk
Symptoms and Spirometry results
Normal Spirometry
Chronic symptoms (cough, sputum production)
I: Mild COPD
FEV1/FVC < 70%
FEV1 ≥ 80% predicted
With or without chronic symptoms (cough,
sputum production)
II: Moderate COPD
FEV1/FVC < 70%
50% ≤ FEV1 < 80% predicted
With or without chronic symptoms (cough,
sputum production)
III: Severe COPD
FEV1/FVC < 70%
30% ≤ FEV1 < 50% predicted
With or without chronic symptoms (cough,
sputum production)
IV: VERY Severe COPD
FEV1/FVC < 70%
FEV1 < 30% predicted or FEV1 < 50%
predicted plus chronic respiratory failure
Note. Adapted from “Chronic obstructive pulmonary disease,” by K. Stucky and J.
Greenblatt, 2008, Comprehensive handbook of clinical health psychology, In B. Boyer, &
I. Paharia (Eds.), (pp. 277-297) John Wiley & Sons.
The most common cause of COPD is smoking or exposure to second hand smoke (Lorig
et al., 2000; Stucky & Greenblatt, 2008). Other risk factors include genetic predisposition,
asthma, lung birth deficits, environmental pollutions, childhood lung infections, and other factors
related to lower socioeconomic status (e.g. nutrition, pollution, etc.; Lorig et al., 2000; Stucky &
Greenblatt, 2008). Prolonged exposure to these elements increases one’s risk of COPD (Stucky
& Greenblatt, 2008).
There are various medical treatments that are utilized to reduce symptoms and disease
progression. Smoking cessation is an early intervention that is often helpful in improving the
18
progression of COPD post-diagnosis and minimizing the risk of COPD prior to diagnosis (Lorig
et al., 2000; Stucky & Greenblatt, 2008). Oxygen therapy, bronchodilator medication (e.g.
adrenaline-like medications, theophylline, iprotropium bromide), anti-inflammatory medication
(e.g., cromolyn sodium, inhaled corticosteroids, systemic corticosteroids, expectorants and
mucolytics, and antibiotics), and rehabilitation (e.g., exercise) are also effective in treating
symptoms (Lorig et al., 2000; Stucky & Greenblatt, 2008). Bronchodilator medications are
designed to aid in relaxing the muscles surrounding the airways or to open the airways. Antiinflammatory medications are used to decrease the inflammation and swelling of the airways.
Specific medical intervention goals include: ways to minimize progression, relieve symptoms,
increase ability to exercise, increase overall health status, prevent further decline in respiratory
health, reduce risk for death, and increase QOL (Stucky & Greenblatt, 2008). Treatment foci are
often related to areas or symptoms that are amendable to change (Stucky & Greenblatt, 2008).
Heart Failure
Similar to COPD, heart failure (HF) is also a chronic and progressive condition but of the
cardiovascular system. It “is characterized by the inability to deliver the volume of blood
required by the organism due to a reduction in cardiac pumping performance” (p. 5, Böhm &
Erdmann, 1991). In other words, HF arises when the heart ineffectively pumps blood, thus
insufficiently delivering oxygen and nutrients throughout the body. Approximately 5.7 million
Americans reported having HF between 2005 and 2008 (AHA, 2012a). The American Heart
Association (2012a) reported that 6.6% of all Americans have some form of cardiovascular
disease (CVD); HF, one form of CVD, accounts for 2% of these CVDs.
Prevalence rates of HF increases with age, and older adults are diagnosed with HF at a 10
per 1,000 rate (AHA, 2012a). There is a 20% lifetime risk of HF across the lifespan, even
19
among those aged 80 and above, who have a short life expectancy (AHA, 2012a). HF is a major
cause of mortality. Specifically, 50% of all HF patients die within the first five years following
diagnosis (AHA, 2012a). Fifty percent of severe HF patients die within the first year (AHA,
2012a). Of all deaths in 2008, HF was mentioned in 1 in 9 cases (AHA, 2012a). The primary
cause of approximately 55,000 of deaths per year is HF (CDC, 2012).
Those at greatest risk for developing HF include older adults and African Americans
(AHA, 2012a). The American Heart Association (2012a) reported that African Americans are at
greatest risk of all racial groups, followed by Latino, Caucasian, and Chinese Americans. The
variation in rate of HF is thought explained by the prevalence of heart attack, hypertension,
diabetes, and socioeconomic factors across groups (AHA, 2012a). The rates of HF among older
white males are as follows: 15.2% among those 65-74 years old, 31.7% for those 75-84, and
65.3% for those 85+ (AHA, 2012a). Caucasian women have a similar but lower increased rate
by age, 8.2% 19.8%, and 45.6%, respectively (AHA, 2012a).
There are various types of HF, which include left ventricle, right ventricle, or congestive
HF (AHA, 2012b). The left ventricle (LV) is responsible for pumping oxygenated blood to the
rest of body; it is larger than the other chambers of the heart, and conducts the majority of
pumping for the heart. There are two types of LV HF which include: systolic failure or diastolic
failure (AHA, 2012b). Systolic failure occurs when the LV is unable to contract normally, and
blood does not circulate appropriately. A diastolic failure results when the LV is unable to relax
normally, and blood does not fill the heart during periods of heartbeat rest. The right ventricle
(RV), in contrast, sends deoxygenated blood out to the lungs. RV HF tends to occur as a
consequence of LV failure, and ultimately results in the swelling of the lower extremities. When
the heart experiences difficulty pumping, congestive HF may arise. This process involves a
20
blockage of blood in the veins, leading to congestion in the blood tissues. Similar to RV HF,
swelling occurs in the lower extremities or the lungs, as a result of congestive HF. HF also
affects the kidney’s ability to release sodium causing increased swelling or fluid retention (AHA,
2012b).
At the onset of HF, the body and heart attempt to compensate for the heart’s inability to
pump (AHA, 2012b). The heart enlarges, serving to pump more blood through increased
strength. Another compensation tactic the heart attempts is to develop muscle, as a result blood
pumps stronger. Lastly, the heart may also pump faster to increase output. The body on the other
hand, may narrow blood vessels to maintain a high blood pressure. The body may also channel
blood to more vital organs and away from less vital organs. These bodily attempts to combat
against HF work only temporarily and they may also increase difficulty in early diagnosis. There
is no cure of HF, but patients can generally live a full life, especially when they follow their
physicians’ recommendations and manage their medications appropriately (AHA, 2012b).
The onset of symptoms occurs after a depletion of blood flow to various organs or by
“congestive upstream” (p. 5, Böhm & Erdmann, 1991) of the right or left ventricle of the heart.
Symptoms of HF include fatigue, shortness of breath or dyspnea, fluid retention, difficulty
breathing, wheezing or coughing, increased heart rate, and weight gain (AHA, 2012b; HFSA,
2012). These symptoms negatively influence activities of daily living, particularly ADLs that
require physical exertion (AHA, 2012b; Böhm & Erdmann, 1991). The New York Heart
Association (NYHA) Functional Classification classifies the severity of symptoms based on
patients’ physical limitations and quality of life, which range on a scale of I-IV denoting mild to
severe symptoms (see Table 2).
21
Table 2
Heart Failure NYHA Classification
Class
Class I – Mild
Patients symptoms/ Functional Capacity
No limitation of physical activity. Ordinary
physical activity does not cause undue fatigue,
palpitation, or dyspnea (shortness of breath).
Class II – Mild
Slight limitation of physical activity.
Comfortable at rest, but ordinary physical
activity results in fatigue, palpitation, or
dyspnea.
Class III – Moderate
Marked limitation of physical activity.
Comfortable at rest, but less than ordinary
activity causes fatigue, palpitation, or dyspnea.
Class IV – Severe
Unable to carry out any physical activity
without discomfort. Symptoms of cardiac
insufficiency at rest. If any physical activity is
undertaken, discomfort is increased.
Note. Adapted from “Heart disease and stroke statistics--2012 update: A report from the
American Heart Association.” American Heart Association, 2012, Circulation, pp. 3-218.
Lifestyle choices which increase one’s risk of HF include smoking, drug and alcohol
abuse, overweight or obesity, eating habits, and a lack of physical activity (AHA, 2012b). Other
medical conditions may increase one’s risk of HF, such as a previous heart attack, coronary heart
disease, hypertension, heart birth deficits, lung disease, diabetes, and abnormal heart valves or
heart muscle conditions (AHA, 2012b). About 75% of HF cases are preceded by hypertension
(AHA, 2012a).
Treatment interventions focus on minimizing symptoms and managing the illness (AHA,
2012b). There are three strains of interventions which include lifestyle changes, medication, and
surgery. Controlling one’s substance abuse or use may slow down the disease progress. A
reduction of sodium intake is often recommended to limit swelling. Other lifestyle or diet
changes that may reduce symptoms are losing or maintaining weight, eating a healthy diet,
22
engaging in physical activity, getting rest, and tracking fluid intake (AHA, 2012b). Various
forms of medications may be prescribed for HF; these medications include anticoagulants,
antiplatelet agents, angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor
blockers or inhibitors, beta blockers, calcium channel blockers, diuretics, vasodilators, digitalis
preparations, and statins (AHA, 2012b). The variant forms of medication require attention to
medication management.
There are several similar characteristics of between COPD and HF. Both conditions are
progressive and chronic, there are no cures for the illnesses, and the diseases become more
severe over time. They also share similar etiology; for example, both COPD and HF tend to
occur as a result of negative lifestyle choices such as smoking tobacco, substance abuse, and a
lack of physical activity. Although COPD and HF affect two distinct organ systems of the body,
the respiratory and cardiovascular system, respectively, each results in similar symptomology.
For instance, both COPD and HF patients may experience shortness of breath or dyspnea,
difficulty breathing, and intense coughing. For these reasons, the next section will discuss
collectively the research related to the psychosocial consequences of COPD and HF.
Psychosocial Factors Associated with COPD and HF
The prevalence rates of depression and anxiety disorders are particularly high among
both COPD (e.g., Hynninen et al., 2005; Schane et al., 2008; Zhang, Ho, Cheung, Fu, & Mak,
2011) and HF (Cully et al., 2009; Deville & McDougall, 2008) patients. These rates are
significantly higher than healthy controls. It remains unclear whether the diagnosis of COPD or
HF increases a patient’s risk for the onset of depression and anxiety, or if having depression or
anxiety increases one’s risk for developing COPD or HF. In the following section, the literature
23
on the incidence of psychosocial factors (e.g., depression and anxiety) of COPD and HF, and the
relationships between these variables is reviewed.
According to the National Institute on Mental Health (NIMH; 2005), adults in the U.S.
have an average 12 month prevalence rate of major depression disorder (MDD) of 6.7%.
Additionally, there is a 16.5% lifetime prevalence rate of MDD among U.S. adults (NIMH,
2005). The NIMH also reported a 12 month prevalence rate of 2% of severe depression. Older
adults, according to these statistics those aged 60+, are the least likely age group to report
depression. When all types of anxiety disorders are combined, adults in the U.S. have an 18.1%
12 month or 22.8% life time prevalence rate of anxiety. Similar to depression, older adults are
less likely to report anxiety disorders; 15.3% of 60+ year olds compared to 30.2% of 18-29 year
olds, 35.1% of 30-44 year olds, and 30.8% of 45-59 year olds (NIMH, 2005).
Reports suggest that patients with COPD or HF have significantly higher prevalence of
depression; however, the data on anxiety are equivocal, especially among HF patients. In a
review of the literature on the psychological characteristics of patients with COPD, Hynninen et
al. (2005) reported a 30-58% prevalence rate of clinically significant mental disorders. This rate
was higher than the healthy controls they also reviewed. In a recent meta-analysis of studies
reporting the rate of depression among COPD patients compared with healthy controls, Zhang et
al. (2011) reported a random effect model rate of 24.6% compared to 11.7% of normal controls.
Reports of anxiety prevalence in studies with COPD patients ranged from 10-95.8% (Hynninen
et al., 2005).
Similar results were reported for HF patients. In a review of the literature on depression
and HF, Delville and McDougall (2008) reported an incidence rate ranging between 21-78%.
Within the HF literature, there are variations in reported prevalence rates, due to the type and
24
severity of mood and/or anxiety disorders. Reported rates of minor and major depression vary
between 7.3- 23% and 5.6-27%, respectively (Angermann et al., 2009; Lossnitzer et. al., 2012;
Shen et al., 2011). Cully et al. (2009) found an 18.1% prevalence rate of depression and/or
anxiety (e.g., depression NOS, anxiety NOS, MDD, PTSD, or dysthymia) in the first 12 months
following the diagnosis of HF.
In regard to anxiety alone, Shen et al. (2011) found that 24% of
HF patients reported mild anxiety, while 21% endorsed moderate to severe anxiety, which
remained steady during a 6 month follow-up assessment.
In comparison studies, depressed HF and COPD patients have been found to have
significantly different medical, psychological, and demographic characteristics than nondepressed patients. For instance in regard to medical factors, depressed HF patients are likely to
have greater number of comorbidities (e.g. hypertension, diabetes), greater severity of illness
(e.g. NYHA classification for HF; Angermann et al., 2009), greater impairment (Klein et al.,
2007), and greater mobility limitations (Paukert et al., 2009; Turvey et al., 2006; Williams et al.,
2002). After controlling for demographic, medical classification, and mental health treatment,
greater depression and anxiety significantly correlated with and predicted lower physical
functioning (Shen et al., 2011). Interestingly, a comorbid diagnosis of COPD has been found to
correlate with a higher prevalence of depression among HF patients (Angermann et al., 2009).
Additionally, Angermann et al. (2009) found that a diagnosis of COPD individually accounted
for a significant proportion of the variance in prevalence of depression. Medical characteristics
increasing one’s risk for depression among patients diagnosed with COPD alone include:
respiratory symptoms, difficulty walking, dyspnea (Schane et al., 2008), co-morbid medical
conditions (e.g. cancer, diabetes, or arthritis; Schane et al., 2008), and greater time since
25
diagnosis (Moussas et al., 2008). These medical conditions may also have bearing on the
psychological factors experienced by chronically ill patients.
Cognitions, history of mood disorders, other disorders, and coping strategies further
explain the difference between chronically ill patients with or without mood disorders. In regard
to psychological functioning, depressed HF patients tend to have more negative attitudes toward
their illness and lower perceived social support. These variables significantly differentiate
depressed versus non-depressed groups (Turvey et al., 2006). A history of depression
(Angermann et al., 2009), greater use of maladaptive coping mechanisms, chance locus of
control orientation, and lower self-efficacy (Paukert et al., 2009) are also greater among HF
patients with current depressive symptomology. COPD patients with depression are more likely
to experience anxiety (Yohannes et al., 2000; Moussas et al., 2008). Yohannes et al. (2000)
reported a strong comorbidity between depression and anxiety among COPD patients (37%); this
rate of anxiety is far less frequent among non-depressed COPD patients (5%).
Demographically, depressed HF patients were more likely to be female (Angermann et
al., 2009; Cully et al., 2009), less educated, unmarried (Williams et al., 2002), younger, and
Caucasian (Cully et al., 2009). More specifically, in regard to gender differences, women tend to
endorse more depressive symptoms that meet criteria for major depression; however, there is no
such difference in minor depression (Angermann et al., 2009). Gender differences have been
accounted for by a positive history of depression and severity of HF (Angermann et al., 2009).
Similarly, demographic factors which predicted depression among patients with COPD included:
older age (Moussas et al., 2008), female gender, single marital status, and less than high school
education (Schane et al., 2008).
26
Anxiety as a standalone condition, on the other hand, has been found to exhibit unique
characteristics between COPD and HF patients. In general, anxiety is more closely related to the
experience of patients with COPD than HF. Existing literature related to differences in
characteristics that coincide with the presence of psychological disorders is greater for anxiety
among COPD patients than HF patients. Greater anxiety has been predicted by body mass index
(BMI), social class, gender (Yohannes et al., 2000), and age (de Voogd, Sanderman, Postema,
van Sonderen, & Wempe, 2011) for COPD patients. Additionally, medical factors such as
increased time since diagnosis or duration of illness (Moussas et al., 2008) and increased
physical exertion as a result of dyspnea (de Voogd et al., 2011) increase COPD patients’ risk of
anxiety. Because there is little commonality in the experiences of COPD and HF patients
regarding anxiety, anxiety will play a limited role in the remainder of this paper and current
study.
There is inconsistent evidence regarding severe consequences of depression and/or
anxiety for chronically ill patients. For instance, Lossnitzer et al., (2012) found that mortality
rates were greater among those HF patients who were depressed, which was suspected to result
from contrasting NYHA classification, frequency of hospital visits, history of depression, and
physical functioning. In an earlier study, Cully et al. (2009) found disconfirming evidence, and
concluded that mortality was lower among depressed and anxious HF patients. Williams et al.,
(2002) reported that more severe self-reported depression is associated with a greater risk of
death and hospitalizations, which may help explain the inconsistency of findings. Depressed and
anxious HF patients visited the hospital for mental and health concerns more often than patients
with only depression or anxiety (Cully et al., 2009). However, after controlling for demographic
and health issues, depression and anxiety no longer predicted hospitalization (Cully et al., 2009).
27
Various factors may account for the variation of reported incidence rates across HF and
COPD studies. The measures used to assess symptoms may greatly influence reported rates of
depression and anxiety. Some studies used measures of depressive and anxiety traits or
symptoms (e.g., Beck’s Depression Inventory Second Edition, BDI-II; Beck, Steer, & Brown,
1996), Beck’s Anxiety Inventory (BAI; Beck, 1993), or Geriatric Depression Scale (GDS;
Yesavage et al., 1983]; while others used diagnostic measures (e.g. Structured Clinical Interview
for the DSM-IV-TR, SCID; First, Spitzer, Gibbon, & Williams, 2002). Additionally, the various
sampling methods used may influence rates. Studies which relied on convenience samples may
lack representativeness, particularly the experiences of COPD or HF patients with depression
and anxiety. Lastly, although comorbidity with other medical conditions and other mood
disorders, as well as severity of COPD and HF, are controlled for in studies that use correlation
and regression analysis, studies reporting rates of depression and anxiety do not take into account
the influence of these variables. These variables likely increase the risk of depression or anxiety
among COPD and HF patients.
The literature on depression and anxiety suggests that patients with COPD and HF are at
greater risk for these conditions. Given the unique experiences of older adults with depression,
older patients with COPD and HF are at an even greater risk for these conditions due to age
related concerns such as the experience of loss and physical health and occurrence in these
specific patient populations. These experiences with mood anxiety and disorders may also have a
negative influence on individuals’ perceived life satisfaction and health related quality of life.
Quality of Life of Patients with COPD or HF
Like many other medical conditions, the diagnosis and treatment of COPD and HF often
take a toll on patients’ satisfaction with various areas of life, also known as quality of life (QOL).
28
COPD and HF patients consistently report lower health and psychological related QOL
(HRQOL; Anderson, 1995; Cully et al. 2006; Cully, Phillips, Kunik, Stanley, & Deswal, 2010;
Klein, Turvey, & Pies, 2007; Yohannes et al., 2000). Therefore, it is important to explore what
variables predict or influence QOL and HRQOL among patients with chronic illness.
When compared to individuals without chronic illness or with other forms of chronic
illness, patients with COPD tend to report lower QOL (Anderson, 1995). Using the cognitive
model of stress, coping, and adaptation, which is a person-environment interaction model
asserting that persons’ cognitive response to an event and coping resources mediates the
interaction, Anderson (1995) found as COPD participants aged, a hypothesized person oriented
variable of QOL, they reported higher overall QOL. Functional status and disease severity, also
person oriented, were negatively related to QOL (Anderson, 1995). Psychological variables have
been found to correlate with QOL. Anderson (1995) and Cully et al. (2006) found that selfesteem and social support are positively related to QOL. Based on the previously mentioned
model, these variables are mediating coping resources (Anderson, 1995). As depression
increases among COPD patients, they report lower QOL (Anderson, 1995). Anxiety, a common
psychosocial consequence of COPD, and optimism (i.e., mediating variables), however, were
found insignificantly related to QOL (Anderson, 1995).
Research suggests that these mood disorders also correlate with health related quality of
life (HRQOL), how an individual with an illness perceives their life (i.e., mental and physical
domains). HRQOL has been measured using generic and disease specific measures, such
Medical Outcomes Study 36 Item Short Form Health Survey (SF-36; Ware & Shervourne,
1992), the Chronic Respiratory Disease Questionnaire (CRDQ; Guyatt, Berman, Townsend,
Pugsley, & Chambers, 1987), St. George’s Respiratory Questionnaire (SGRQ; Jones, Quirk, &
29
Baveystock, 1991), Quality of Life for Respiratory Illness Questionnaire (QOLRIQ; Maillé et al.,
1997), Minnesota Living with Heart Failure Questionnaire (MLHFQ; Rector, Kubo, & Cohn,
1987), and the Kansas City Cardiomyopathy Questionnaire (KCCQ; Green, Porter, Brensnahan,
& Spertus, 2000).
As previously mentioned, patients with COPD and HF are at risk for mood disorders, as
well as lower HRQOL. One study found that depressed HF patients report lower HRQOL in both
physical functioning and psychological or mental domains than non-depressed HF patients
(Faller et al., 2009). COPD patients who were depressed also reported lower QOL than nondepressed COPD patients (Yohannes et al., 2000). Faller et al. (2009) reported that depression
also predicted physical and psychological functioning measured on a generic measure of
HRQOL for patients with HF. Psychological components of QOL are better explained by
depression (Cully et al., 2006). This is not surprising considering the overlap of the item content
among measures and constructs. In an attempt to clarify whether depression alters or biases
one’s perception of QOL, Faller et al. (2010) found that psychological HRQOL remained
constant among depressed HF patients regardless of severity, but decreased with increases in
disease severity in non-depressed HF patients. Within a special population of HF patients,
veterans who were depressed have been found to report poorer perceived QOL than nondepressed veterans (Cully et al., 2010). Factors that predicted higher QOL among veterans
included the following: older age, lower NYHA classification, and lower self-reported
depression (Cully et al., 2010).
Depression seems to account for a large portion of HRQOL; however, the role of disease
and symptom severity is also significant. One study found that the HF NYHA class accounts for
physical functioning HRQOL but not psychological HRQOL (Faller et al., 2009). Similarly,
30
another study found that disease severity was significantly associated with physical scales such
as physical symptoms and limitations on a HF disease specific measure (Faller et al., 2010).
Cully et al. (2006) also found that COPD severity, as measured by FEV1, is negatively correlated
with mental and physical HRQOL, but is unrelated to anxiety and depression. Specifically, they
found that FEV1 added to the prediction of physically-oriented HRQOL factors. Pain, one
symptom HF patients may experience, has been found to explain disease specific HRQOL
(Rustᴓen et al., 2008). The severity of chronic illness is related to mortality. Faller et al. (2007)
found that SF-36 scores and a disease specific measure of HRQOL are capable of predicting
survival among HF patients. The psychological domain of the SF-36 and the full disease specific
measure predicted survival, after controlling for variables related to disease prognosis (Faller et
al., 2007).
Factors subsequent to the diagnosis, treatment, and coping with chronic illness, such as
the patient’s perception of their illness, ability status, and utilization of heath care service,
correlate to HRQOL. One study found that COPD patients who were less self-sufficient or
disabled reported lower HRQOL across all domains on the SF-36 (Braido et al., 2011).
Additionally, disabled patients reported lower perceptions of HRQOL than independently
functioning patients (Braido et al., 2011). Another study found that COPD patients who focused
less on their symptoms, had positive thoughts about their illness outcome, and coped emotionally
with their illness reported higher HRQOL on the SF-36 and a disease specific measure of
HRQOL (Scharloo et al., 2007). When the chronicity of the illness was less salient, patients
reported greater physical functioning on the SF-36 (Scharloo et al., 2007).
Regardless of the perception of illness severity, it is important for patients with chronic
illness to be cognizant of the need to seek health care, specifically awareness of the symptoms to
31
monitor. Patient characteristics may cause them to be more or less inclined to seek medical
services. Desikan, Mason, Rupp, and Skehan (2002) conducted a longitudinal study and found
that patients’ physical HRQOL improved across time. This trend in HRQOL corresponded with
decreased health care utilization (Desikan et al., 2002), suggesting that as patients experience
improvements in their physical well-being they may be less inclined to utilize health care
services. This trend is quite concerning because chronic illness is progressive and unknown
factors may exacerbate symptoms. Caregivers play an important role in monitoring symptoms
and helping the patient become more mindful of the need to seek medical care.
At various phases of illness management, COPD and HF patients may find it necessary to
seek caregiving services. Caregiving may be provided from various sources, such as a family
member, friend, neighbor, visiting nurse, or hospice or palliative care. These forms of care have
been found to correlate with HRQOL. One study found that although there were no differences
between COPD patients in regard to their reported HRQOL based on caregiver support (no
support, support by one person, or support by more than one person), patients living with more
than one caregiver had higher reported respiratory HRQOL than patients with one caregiver
(Wakabayashi et al., 2011). In another study investigating the maintenance of psychological
heath and QOL following rehabilitation treatment, Bratås, Espnes, Ranneestad, and Walstad
(2011) found that patients who lived alone were nearly three times more likely to maintain
HRQOL than those who lived with other people. Caregiver support may stunt patients’
improvement in physical impairment. It likely depends on the length of caregiver services, since
patients who have received support for longer periods of times may report greater HRQOL than
those who are transitioning from independent care to caregiver services. However, the current
literature has not analyzed the length of services.
32
Overall, COPD and HF patients have been found to report lower life satisfaction or wellbeing and physical or psychological HRQOL compared to healthy counterparts and counterparts
with other chronic illnesses. These reports are consistent across generic and disease specific
measures of quality of life. Depression, physical functioning, and other health related concerns
have been found to correlate with quality of life; however, there is significant overlap between
these constructs. Moreover, lower life satisfaction may cause chronically ill patients to be more
inclined to have thoughts of suicide or exhibit other suicidal behaviors, due to their frustrations
with life and other mood and physical related concerns. The next section describes the issue of
suicide in late life, specifically presenting models of suicide, rates, risk and protective factors,
and data relevant to COPD and HF patients.
Suicide among Older Adults
A complete review of the literature on suicide among older adults is beyond the scope of
this chapter; however, a brief overview of the issue including models applied to older adult
suicide, the rates, risks and protective factors among older adults is provided, as well as a review
of suicide among COPD and HF patients (see McIntosh, Santos, Hubbard, & Overholser, 1994
for a full review of the topic).
Models
Several types of theories have been developed to explain the precipitants and types or
forms of suicide, many of which have foundations in particular disciplines. More recent theories,
however, have utilized a multidisciplinary approach highlighting biological, social, and
psychological precipitants of suicide (Westefeld, Range, Rogers, Maples, Bromley, & Alcorn,
2000). McIntosh et al. (1994) reviewed these theories and applied them to elder suicide.
Additionally in an overview of suicide, Westefeld et al. (2000) provided a synopsis of four
33
common models of suicide. The following is a summary of a selection of relevant models, and
an application of the models to issues related to later life suicide.
Suicide was once thought of as solely an individualistic act (Bearman, 1991). As one of
the first to theorize about suicide with an emphasis on sociological concepts, Durkheim (1979)
introduced the notion that suicide could be considered a societal act. He theorized that suicide
was dependent on social solidarity within a given society. Durkheim (1979) explained that there
is stability in suicide rates across different categories or groups of individuals. He further
explained that there are structures of social relations which cause variations in suicide rates.
These social relations bind and constrain individuals (Durkheim, 1979).
Durkheim (1979) hypothesized that there are four types of suicide: egoism, altruism,
anomie, and fatalism. Egoistic suicide is characterized by an absence of social relations in a
society (Durkheim, 1979). Altruistic suicide in contrast, is characterized by the total presence of
social relations in a society (Durkheim, 1979). Furthermore, egoistic suicide is that of an
individualistic person, altruistic suicide on the other hand is that of a collectivistic person
(Durkheim, 1979). Anomic suicide is characterized by a normless society in which the social
structure is undefined (Durkheim, 1979). Fatalism is characterized by referring to another
individual for judgment in contrast to an entire society (Durkheim, 1979).
McIntosh et al. (1994) applied Durkheim’s forms of suicide to experiences of older adults
and theories of aging. For example, older adults have a tendency to become socially isolated due
to loss of relationships or negative societal views toward older adults such as ageism, which may
make older adults more likely to engage in an egoistic form of suicide. Similarly, the
disengagement theory of aging states that older adults tend to disengage from society, which is
34
thought of as beneficial for the individual and society as a whole. This prospective on aging
increases the risk of suicide among older adults due to egoistic suicide.
In an article overviewing suicide, Westefeld et al. (2000) described four predominant
models of suicide (Blumenthal & Kupfer, 1986; Jacobs, Brewer, & Klein-Benham, 1999;
Shneidman, 1987; Stillion, McDowell, & May, 1989). Westefeld et al. (2000) described the
Overlap Model (Blumenthal & Kupfer, 1986), composed of five intersecting factors: 1)
psychosocial, 2) biological, 3) psychiatric, 4) personality, and 5) family history or genetics.
Greater risk of suicide is predicted with more overlap of these factors. This model may predict
elder suicide due to the overlapping risks of psychosocial and biological domains. More
specifically, due strictly to age, older adults may be biologically at risk of suicide, which may be
partly due to health concerns within the population. Psychosocially, as described previously,
older adults have a greater tendency of being socially isolated and having poor social support.
The Three Element Model (Jacobs et al., 1999) includes three influential domains for
suicide risk which include the following: 1) predisposing factors (e.g., mental disorders), 2)
potentiating factors (e.g., personality, family history, society, life stress and means), and 3)
suicidal threshold (Westefeld et al., 2000). According to Westefeld’s et al. (2000) review,
presence of the former two factors increases one’s threshold to suicide, or tendency to engage in
suicidal behaviors. Consistent with this model, older adults who are often at risk of aggregate
stressors and societal withdrawal may have a high suicide threshold. In regard to the topic of this
study, older adults with chronic illness, particularly COPD and HF, are at greater risk of mental
health issues (predisposing factors), which may further increase their risk of suicide.
The Social Trajectory Model (Stillion et al., 1989), as described by Westefeld et al.
(2000) focuses on the triggers to suicidal thoughts, often a result of psychological, biological,
35
cognitive, and environmental factors. Older adults may possess environmental triggers as a
result of their experience with loss. Loss during older adulthood may take various shapes; for
example the loss of health, loved ones, physical functioning, occupation, or identity; any one of
these losses or a combination of several losses may predispose an older adult to greater risk of
suicide. Particularly when older adults experience biological or physical ailments, the trigger of
suicidal ideation may take rise at the time of diagnosis, or after a significant time unsuccessfully
coping with the illness.
Lastly, the Cubic Model (Shneidman, 1987) focuses on the psychological components of
suicide. The model posits that an individual may fall on any one of 125 cubelets, which range
from high to low on three core planes: press (i.e., events that influence behavior, cognition, or
affect), pain (i.e., emotional pain resulting from unmet psychological needs), and perturbation
(i.e., state of feeling upset). An individual falls on a scale from 1-5 on each plane, and those
highest on each plane have the greatest risk for suicide.
McIntosh et al. (1994) stated that
demographic variables such as age have little influence on one’s suicidal risk, however, based on
this model age may be related if it correlates with psychological pain. In addition to the
influence of age on psychological pain, age also coincides with press. The events in an elder’s
life such as loss may make them more susceptible to experiencing suicidal thoughts.
Prevalence
As previously mentioned, older adults currently comprise about 13% of the United States
population, a figure estimated to drastically increase over the next 30 years due to the aging of
the “Baby Boomer” cohort. The rate of completed suicide within this population is high.
According to the 2013 suicide statistics, older adult suicides make up 14.1% of all completed
suicides and occur at a rate of 16.1 per 100,000 (approximately 20 per day; American
36
Association of Suicidology [AAS], 2013). Rates of suicide of those aged 85 and over is 18.6 per
100,000, while those between the age of 55 and 64 is 18.1 per 100,000. Older adult men and
women complete suicide at different rates; males commit suicide two times the rate of all older
adult suicide (29 per 100,000 per year; AAS, 2009). As age increases in older adult male
population, so does the rate of completed suicide; however, among females of the same age
range, the rate decreases or remains consistent with increases in age (AAS, 2009). Men over 85
years old are most at risk for completed suicide, more specifically Caucasian males. In 2010,
47.33 Caucasian males per 100,000 committed suicide (AAS, 2009).
There has been a reported rise in completed suicides among the baby boomer generation,
which received recent media attention. Specifically, three articles were published in 2013 in The
Washington Post, The New York Times, and Psychiatric News shedding light upon the increased
rates of suicide in the baby boomer generation through anecdotal reports and interviews with
prominent psychologists such as Conwell, Arias, and Knight (Levin, 2014, Bahrampour, 2013,
Parker-Pope, 2013). These articles posit several possible causes of the increased rate such as the
recession or increased economic worry, change, physical disability, and social isolation or loss of
connectedness. Cohort effects were also discussed as authors suggested that youth baby boomers
had higher rates of suicide than previous generations, difficulty accepting the reality of aging,
and a lack of adversity as youth leading to difficulty coping with stressors later in life (Levin,
2014, Bahrampour, 2013, Parker-Pope, 2013).
Interestingly, although older adults have the highest rate of completed suicide, they
attempt suicide less often than younger age groups. For every one suicide, across all age groups,
there are approximately 100-200 suicide attempts; however, among older adults for every one
suicide there are only four attempted suicides (AAS, 2009). This stark contrast may be a result of
37
the lethality of means and intent of suicidal behavior among older adults (Conwell, Van Orden,
& Caine, 2011; Gallagher-Thompson & Osgood, 1997; Szanto, Prigerson, & Reynolds, 2001).
Older adults tend to use more lethal means and are more successful during attempts of suicide
(Conwell et al., 1997; Szanto et al., 2001). Older adults, particularly older men, most often use
firearms (71.3%) followed by hanging or the use of poisons (Gallagher-Thompson & Osgood,
1997). Older adults who attempt suicide by firearm tend to shoot themselves in the head, in
contrast to younger adults who tend to shoot themselves in less lethal regions of the body, such
as the abdomen or extremities (Szanto et al., 2001). Additionally due to environmental or
demographic factors such as living alone, isolation, or unattended assisted living, older adults are
less likely to receive emergency care following a suicide attempt. The lack of emergency care
received may contribute to or result in an older adult’s death by suicide and explain the high
rates of completed suicides.
Risk and Protective Factors
There have been several published reviews of the literature on older adult suicide
focusing on the risk of suicide among the population (Conwell et al., 2011; Gallagher-Thompson
& Osgood, 1997; Heisel, 2006; Klinger, 1999; Szanto et al., 2001). These articles suggest that
the risk of older adult suicide falls under four broad categories: 1) medical or biological, 2)
demographic, 3) social, and 2) psychological or mental health variables. It is important to note
that no one risk factor or combination of risk factors is an exact prediction of future suicidal
behavior. However, a patient or client presentation of risk is informative for care providers to
have awareness of and assess for.
Reviews of the literature regarding the likelihood of medical illnesses creating additional
risk of suicide have reported positive yet inconsistent correlations. Chronic and acute illness has
38
been described as having influence on risk of suicide, and leading to what is termed “rational
suicide”-- a normalized desire to end one’s life as a result of coping with an intense medical
condition (Gallagher-Thompson & Osgood, 1997). Medical illness also often co-exists with
other common risk factors (Conwell et al., 2011; Gallagher-Thompson & Osgood, 1997; Heisel,
2006; Klinger, 1999; Szanto et al., 2001). However, it remains unclear whether risk of suicide is
a result of these co-occurring illnesses such as depression, disability, and pain (Conwell et al.,
2011; Gallagher-Thompson & Osgood, 1997; Szanto et al., 2001). Other medically related
conditions with risk of suicide that have been supported within the literature include pain,
physical impairment or functioning, and perceived health status (Conwell et al., 2011; GallagherThompson & Osgood, 1997; Heisel, 2006; Szanto et al., 2001). The number of illnesses, severity
of illness, length of hospital stay, time since diagnosis, and prognosis of conditions have all been
reported to have a bearing on the risk of suicide or to differentiate between deaths by suicide and
other forms of death among medically ill older adults (Conwell et al., 2011; GallagherThompson & Osgood, 1997; Heisel, 2006; Szanto et al., 2001). However, some reviews have
stated that medical illness is not influential in suicidal risk (Conwell et al., 2011).
Demographic variables, such as gender, race, and marital status, influence older adults’
risk of suicide. As previously mentioned, males are at greater risk for suicide and complete
suicide two to four times more than women, and up to 10 times greater by age 75 (Szanto et al.,
2001). Several reasons have been posited to explain this gender difference. For example, older
males tend to have untreated or undiagnosed mental health conditions (e.g. depression), and
comorbid substance abuse, and to neglect social relationships more often than females (Szanto et
al., 2001). Widowed males and females are also at greater risk of suicide (Conwell et al., 2011;
39
Szanto et al., 2001). Furthermore, there are differences in the risk of suicide by race; white males
are at greatest risk, while black females are at a significantly lower risk (Conwell et al., 2011).
Several aspects of one’s social well-being, particularly in the older adult population, may
make cause them to consider suicide as an option (Conwell et al., 2011; Gallagher-Thompson &
Osgood, 1997; Klinger, 1999; Szanto et al., 2001). Loneliness and isolation contribute to risk of
suicide among older adults and may be exacerbated by older adults’ life phase and loss of
significant others (e.g. partner, friends, and family members). A lack of belongingness in
interpersonal relationships or within a community has been related to increase risk of suicide
(Conwell et al., 2011). Risk is high for those older adults who are experiencing family discord or
a separation (Conwell et al., 2011). Social factors may also increase the risk of death following a
suicide attempt (Klinger, 1999). For instance, people who are isolated or without close family or
friends may not only be at greater risk of suicide, but may also be less likely found or provided
with appropriate medical treatment necessary to sustain life, following an attempt.
Various cognitions, personality types, and mental health disorders have been reported to
increase risk of suicide within the older adult population. Similar to all age groups,
hopelessness, helplessness, low self-concept, and low self-esteem have been reported to increase
older adults’ risk for suicidal ideation and completed suicide (Gallagher-Thompson & Osgood,
1997; Klinger, 1999; Szanto et al., 2001). Axis II personality disorders is a less common risk
factor than among younger adults (Heisel, 2006).
Mental health conditions such as depression, dementia or delirium, and comorbidity with
substance abuse or anxiety have also been found to relate with increased risk of suicide among
the aging population (Conwell et al., 2011; Gallagher-Thompson & Osgood, 1997; Klinger,
1999; Szanto et al., 2001). Depression is the most common risk factor of suicide among older
40
adults (Conwell et al., 2011; Gallagher-Thompson & Osgood, 1997; Klinger, 1999; Szanto et al.,
2001). The risk of mental health disorders and suicide is particularly relevant for this population
because older adults tend to not seek mental health treatment due to stigma, low access to care,
lack of specialized professionals, and negative attitudes to mental health, especially among older
males (Conwell et al., 2011; Gallagher-Thompson & Osgood, 1997; Klinger, 1999; Szanto et al.,
2001). More often older adults have been found to seek medical treatment prior to an attempted
or completed suicide (Conwell et al., 2011; Gallagher-Thompson & Osgood, 1997; Klinger,
1999; Szanto et al., 2001).
Unfortunately, there is very limited research on the factors that protect older adults from
suicide. In reviews of the literature, protective factors are often cited as the absence of risk
factors (Conwell et al., 2011). One review article also stated that having children or living with
children, close relationships with relatives, having hobbies, or involvement in religious activities
are also protective (Heisel, 2006). It is important to further explore this aspect of risk assessment
because protective factors can be viewed as strengths, which can later be fostered to prevent
suicide.
COPD and HF
Patients with COPD and HF have also been found to be at greater risk for suicide.
However, mixed results have been found in regard to older adults with COPD and risk of suicide,
as reported in a review of the literature on medical illness among older adults and suicide (Fiske
et al., 2008). Some studies have found no additional risk in the population, while others have
found that COPD patients who are older and married have greater risk even when the effects of
depression are controlled for (Fiske et al., 2008). One specific article found that COPD patients
were at greater risk or odds for suicidal ideation and suicide attempts, when compared to patients
41
without COPD (Goodwin, 2011). These results remained significant even after controlling for
demographic differences, major depression, panic disorder, and substance abuse (Goodwin,
2011). However, Goodwin (2011) found that the significant difference between the groups in
regard to suicidal ideation dropped after nicotine dependency was controlled for.
In a study investigating the odds of suicidal ideation among HF patients, 17% of patients
reported suicidal or self-harm thoughts (Lossnitzer et al., 2009). These patients were also more
likely to have a lifetime history of major depression, which significantly increased their risk for
suicidal or self-harm ideation (3.9 odds ratio for first episode vs. 10.8 for lifetime history;
Lossnitzer et. al., 2009). Even after controlling for mental health conditions and other medical
illnesses, Juurlink (2004) found that patients with HF are at greater risk of suicide. Various other
cardiovascular diseases are not associated with risk of suicide (Fiske et al., 2008). Fiske et al.
(2008) suggested this contrast may be a result of the impairment or specific symptoms of the
diseases.
Overall, older adults are at high risk of suicide. Several factors have been posited to
explain this increase risk, of which loss, impairment, depression, and demographic variables
(e.g., gender and race ethnicity) seem to be most significant. Older adults with chronic illness
such as COPD and HF are also at risk for suicide, these conditions have been correlated with
many of the aforementioned risk factors. The current research on older adult suicide or suicide
among chronic ill patients is clear in regard to risk; however, there is a gap in relation to
protective factors. One means by which to capture protective factors against suicide is the use of
the Reasons for Living Inventory (Linehan, Goodstein, Nielsen, & Chiles, 1983), a measure of
cognitive deterrents to suicide. In the following section, a review of the utilization of the
inventory in two key areas, depression and older adults, is provided.
42
Reasons for Living
Extensive research has been conducted on various populations regarding cognitive
deterrents to suicide [e. g. reasons for living (RFL)]. These reasons fall under several categories,
such as fear of suicide (FS), fear of social disapproval (FSD), moral objections (MO), child
related concerns (CRC), responsibility to family (RF), and survival and coping beliefs (SCB).
These factors are regarded as protective against committing suicide. The Reasons for Living
Inventory (RFLI) developed by Linehan et al. (1983) has been found effective in differentiating
between suicidal and non-suicidal individuals. RFL also has predictive value toward future
suicide attempts (Oquendo et al., 2004). Depression is one of the major risk factors for suicide,
and depressed populations tend to exhibit unique characteristics in terms of suicidal behavior,
endorsed RFL, and effects of comorbidity on psychological functioning. To illustrate these
differences the following is a review of the literature on depression and RFL, beginning with a
description of racial and ethnic differences, suicidal behaviors, and comorbidity.
Depression
Rates of suicide and depression have been found to vary insignificantly by race/ethnicity
and nationality; however, reported RFL are significantly different in these populations (Oquendo
et al., 2005; Richardson-Vejlgaard et al., 2009). The current literature on depression and RFL
has primarily been conducted in college populations due to convenience sampling. One study
reported no differences in attempt status, total number of attempts, and suicidal intent between
Latino and non-Latino participants (Oquendo et al., 2005). Non-Latinos and Latinos were found,
however, to differ in suicidal ideation and lethality of attempts (Oquendo et al., 2005). Another
study found no differences between diagnosed mood disorder, suicidal ideation, and previous
43
attempted suicide among Caucasian, African American, and Latino participants (RichardsonVejlgaard et al., 2009).
In regard to RFL, Latinos tend to report greater overall RFL than non-Latinos (Oquendo
et al., 2005). Latinos also differ from non-Latinos in relation to subscales; specifically Latinos
endorse greater SCB, RF, and MO against suicide (Oquendo et al., 2005). African Americans
have been found to report more MO against suicide than Caucasians or Latinos (RichardsonVejlgaard et al., 2009). When compared only to Caucasians, African Americans endorsed more
reasons of not considering suicide on the following scales: SCB, CRC, and total (RichardsonVejlgaard et al., 2009). Latino and Caucasians reported similarly on RFL subscales, specifically
rating CRC the greatest reason against suicide, while African Americans endorsed SCB and CRC
as the greatest reasons (Richardson-Vejlgaard et al., 2009). One study found that Chinese report
greater FSD reasons to not commit suicide than Americans (Chiles et al., 1989); this may reflect
and capture their collectivistic views. In relation to other protective factors, such as discussing
suicidal thoughts and coping ability, Americans possess or utilize more protective resources
(Chiles et al., 1989). Cultural values and experience may account for the reported differenced
based on ethnicity and nationality, as many of the subscales of the RFLI are culturally loaded,
specifically the MO and FSD.
In regard to differentiations of suicidal behavior, those who attempted suicide prior to
undergoing treatment for depression (i.e., baseline attempters), were more likely to have
pessimistic views, aggression/impulsivity traits, childhood abuse, history of TBI, alcoholism or
substance abuse, familial history of suicidal behavior, fewer RFL, and thoughts of suicide in the
context of a depressive episode than non-attempters (Oquendo et al., 2004). Additionally,
baseline attempters have been found to have higher severity of depression, greater hopelessness,
44
and fewer RFL than non-attempters (Lizardi et al., 2007). Before a 2-year follow up, 14% of
depressed patients attempted suicide; these individuals were less likely to report high RFL when
previously assessed (Oquendo et al., 2004). Inpatients with mental disturbances, who are also
religiously affiliated, have been found to have less suicidal behavior and lower aggression
(Strosahl, Chiles, & Linehan, 1992).
The Moral Objection (MO) subscale of the RFLI has been the most informative RFL
variable in exploring suicidal behavior among depressed individuals. It has been found capable
of differentiating between low and high lethality of suicide attempters (Malone, Oquendo, Haas,
Ellis & Mann, 2000), and in conjunction with age (Oquendo et al., 2005). Particularly, older
participants who endorsed few MO to suicide were more likely to use lethal means during
suicide attempts (Oquendo et al., 2005). Those reporting low MO also have a greater likelihood
of attempting suicide; higher self-perceived depression and hopelessness, and less anxiety
(Lizardi et al., 2008). Furthermore, thoughts of suicide correlate with fewer MO to suicide, a
combination of high suicidal ideation and few MO may result in the individual attempting
suicide (Dervic, Grunebaum, Burke, Mann, & Oquendo, 2006). Specific patient characteristics
correlate with reported MO RFL. For example, MO was independently associated with fewer
suicidal acts among adult depressed patients who were abused during childhood (Dervic et al.,
2006). Additionally, individuals with alcohol dependency have fewer MO to suicide
(Richardson-Vehlgaard et al., 2009).
Comorbidity of mental and health related concerns affects RFL. The data on mental
health comorbidities are mixed. One study found that perceived RFL were significantly lower in
past attempters who were experiencing a depressive episode of bipolar disorder (Galfalvy et al.,
2006). However, another study noted regarding alcohol dependency, no differences in the
45
primary reason for not committing suicide among those with or without a history of dependency
(Richardson-Vehlgaard et al., 2009). Richardson-Vehlgaard et al. (2009) also documented that
individuals with depression, regardless of history of alcohol dependency, reported RF as a major
reason for not considering suicide. Among medically ill AIDS patients, greater reported
depressive symptoms are associated with negative perceptions of health and higher levels of pain
(Braden, Overholser, & Silverman, 2011). Depression predicts RFL among patients with AIDS
(Braden et al., 2011). Braden et al. (2011) also found that QOL accounted for a significant
portion of the variance in RFL of patients with AIDS. RFL associated with the following
domains of QOL: achievement, self-expression, environment, and interpersonal relationships
(Braden et al., 2011).
Older Adult Population
Several articles have been recently published exploring common demographic (i.e. age,
gender, and race/ethnicity), psychosocial (i.e., hopelessness, personality, coping, and social
support), cognitive (i.e., suicidal ideation), and physical (i.e., self-reported health status) risk
factors for committing suicide in relation to reasons for living among older adults. Research on
RFL, however, focuses on protective factors against later life suicide, which is often nonexistent
in the suicide literature on older adults. Issues specifically related to older adults are particularly
relevant due to the aging American population and the increased risk of suicide among older
adults.
Regarding demographic factors, there is some controversy over the interpretation of age
differences or changes with age, particularly when age is the dependent variable. However,
current research regarding RFL has explored age as a demographic variable through correlation
and regression methodology. Therefore, these studies do not attempt to explore changes across
46
the life span, or to explicate differences as one matures. Instead, these studies explore how age
relates to the RFL variable. Comparisons between older and younger adults have been most
frequently explored. Older adults have been found to report higher MO and CRC (Miller et al.,
2001). Additionally, in another study with 18-95 year old individuals, older participants reported
higher overall totals in MO, RF, and FS as psychological deterrents to committing suicide
(McLaren, 2011).
Interestingly, many of the factors that are found significantly higher among older adults
are congruent with their phase of life. Child rearing and religiosity are likely more salient for
middle aged adults and older adults, than for youth and young adults leading to the difference in
reported preventive factors against suicide. Even among older adults, who may be well past
child rearing years, such related concerns against suicide transfer to their grandchildren as they
participate in their upbringing.
Coinciding with the stark gender related differences in rate of committed suicide,
research on gender differences in reported RFL have also been significant, although variable.
Older adult women have been found to endorse higher total RFL (Kissane & McLaren, 2006;
Range et al., 1996) and RF than men. In addition, gender has been found to predict RFL,
accounting for 8% of variance in RF when combined with marital status (Kissane et al., 2006).
Gender has two times as much predictive value than marital status, on standardized beta scale
(Kissane et al., 2006). Similarly in an Australian sample of older adults, a researcher reported
that women endorsed higher total RFL, CRC, and FS (McLaren, 2011). In contrast, Segal and
Needham (2007) and June, Segal, Coolidge, and Klebe (2010) found no significant difference
between men and women on total RFL. Furthermore, they found that men and women were
equivalent in their endorsement on subscales, based on a rank ordering of RFL subscale scores.
47
One potential major contributor to the mixed data on gender differences is the disproportionate
distribution of older adult men and women in studies investigating and finding significant
differences across gender on RFL. It is important to note that research on gender difference
among older adults is limited and in its early stages. Although the research on gender differences
is mixed, it seems that in relation to RFL, gender may play a role in explaining gender
differences in committed suicide.
Race/ethnicity and RFL has been an under researched area in the older adult population.
One study has been published to date which explored the role of race/ethnicity as a moderator to
the relationships between reasons for living, social support, and religiosity among older adult
African Americans and Caucasians (Britton et al., 2008). The results from this study suggest that
although race/ethnicity alone did not account for a significant proportion of the variance in
reasons for living, these factors did significantly interact with social support and religiosity.
Specifically, the data suggests that there is a stronger relationship between religiosity and RFL
among African Americans than Caucasians (e.g. 55% and 9% of the variance of RFL,
respectively), and a stronger relationship between social support and RFL among Caucasians
than African Americans (e.g. 20% and 8% of the variance of RFL, respectively).
The
percentages of accounted variance represent contrasts between African Americans and
Caucasians on variables of social support and religiosity.
Social support, a sense of belongingness, and connectedness are important psychosocial
factors investigated among older adults and RFL. These variables are consistent with some of
the subscales on the RFLI such as CRC, FSD, and RF. Research suggests that psychological
components of social belongingness such as feeling valued and needed correlate with total RFL,
MO, FSD, and SCB subscales (Kissane et al., 2006). Antecedent components of social
48
belongingness, such as “energy for involvement, potential and desire for involvement, and
probability of shared or complementary characteristics” (p. 245) correlate with total RFL, CRC,
and SCB (Kissane et al., 2006). Both psychological and antecedent belongingness collectively
account for 17% of the variance in total scores on RFL (Kissane et al., 2006). Social support in
conjunction with religiosity has been shown to predict RFL, but not social support alone, in a
sample of African American and Caucasian older adults (June et al., 2010).
One’s ability to cope with the stresses of daily living has been explored in the suicidology
and RFL line of research; however, there is only minimal research on older adults’ coping
mechanisms as a contributor to greater RFL. Research suggests that older adults who report
higher coping skills as measured by the Coping Orientations to Problems Experienced Scale
(COPE; Carver. Scheier & Weintraub, 1989) also report higher RFL (Range & Stringer, 1996;
Marty, Segal, & Coolidge, 2010). Various forms of coping (e.g., problem focused, emotion
focused, general coping) positively correlate with the SCB subscale on the RFLI, which is
intuitive because this subscale measures one’s beliefs about their coping. In relation to other
subscales on RFL, the aforementioned forms of coping positively correlate with CRC, RF, and
MO (Range et al., 1996: Marty et al., 2010).
Another infrequently investigated variable among the RFL older adult research is mental
health disorders such as personality disorders and personality traits. A recent article by Marty et
al. (2010) found that clusters of personality disorders (PD) related to the RFLI. Cluster B and C
personality disorders had the fewest correlations with RFL subscales. Personality traits,
extroversion, conscientiousness, openness, narcissism, and agreeableness, have also been found
to correlate with RFL (Marty et al., 2010). Older adults who are more extraverted and
49
conscientious possess more RFL. There are also several other relationships between personality
traits and subscales of the RFLI (Marty et al., 2010).
Hopelessness is a commonly cited risk factor for committed suicide and suicidal ideation.
However, like many of the other factors related to suicide, there is a shortage of literature
exploring the relationship between RFL and hopelessness. Of note, one study has explored the
relationship between hopelessness, suicidal ideation and RFL among older adults (Britton et al.,
2008). Significant findings from this research suggest that suicidal ideation is most common for
those with higher reported hopelessness and depression. Participants who endorsed greater RFL
also report lower suicidal ideation. An interesting finding from this research was that RF
strengthened the relationship between hopelessness and suicidal ideation. The researchers
hypothesized that the older adult may feel overwhelmed by the potential burden they may be
placing on others which may then lead to thoughts of suicide (Britton et al., 2008).
Medical and health related issues may greatly impact older adults’ RFL. Existential
issues or fear of death may also be more influential among older adults with health concerns.
Segal, Shelly, and Frederwick (2008) assessed 104 older adult men and women who ranged in
age from 60 to 92 years-old. They investigated perceived health rating and RFL, health measured
on a self-reported scale from 1-100. Participants with lower health scores reported fewer RFL.
Health has the strongest unique effect on RFL, even among other variables such as life stress,
age, depression, and optimism. Greater perceived health was also related to SCB, RF, and FSD
(Segal et al., 2008).
In sum, in the reasons for living literature, depressed individuals have been reported to
exhibit unique suicidal behaviors and psychological concerns. These individuals also endorse
fewer total cognitive deterrents to suicide, as well as report differentially on subscales (e.g.,
50
moral objections). The research on older adult reasons for living is limited and suggested that
older adults display unique life phase report. For example, older adults report greater numbers
of child related concerns and moral objections to suicide. Only one study has been conducted on
older adults’ report of health and reasons for living; this is an important line of research due to
increasing number of older adults, potential for physical ailments among older adults, and risk of
later life suicide. It is also vital to determine what reasons older adults hold against committing
suicide and which they perceive as protective factors.
Summary and Conclusion
COPD and HF are progressive and chronic conditions that affect millions of American
older adults. Consequential to the aging process, the older adult population may experience
many salient life-span related issues such as loss, role transitions, depression, death and dying, or
physiological change or decline. These often become late life stressors faced by older adults.
Additionally, physiological change may make older adults more susceptible to chronic illness
such as COPD or HF. As it relates to the onset of COPD and HF, age related physiological or
stress changes are exacerbated by lifestyle choices such as substance use or abuse, particularly
smoking.
Research suggests that there is a strong and positive relationship between the diagnosis of
COPD or HF and psychosocial factors such as depression and anxiety. Studies have more
inconclusively reported a connection between HF and anxiety than with COPD. There is also
some controversy over whether depression is a precursor for developing COPD or HF, or vice
versa, if the diagnosis of COPD or HF causes one to become depressed. Based on the existing
literature, however, the former is more supported. There are few studies that have investigated
51
the influence of depression on other aspects of COPD and HF patients’ quality of life and
functioning.
Depression, one of the most consistent mood related conditions correlated with COPD
and HF, is also a common risk factor for suicide across the life-span. Some research suggests
that COPD and HF patients are at greater risk of suicide. This risk may be exponentially high
when COPD and HF patients have comorbid depression. There has been little research
investigating the relationship between depression and any components of suicide (i.e. suicidal
ideation, attempt, self-harm, risk factors, and protective factors) among patients with COPD and
HF, especially in later life.
One component of assessing suicidal risk, which is often excluded from clinical and
research practice, is assessing protective factors against suicide. There is inconclusive evidence
to suggest what prevents older adults from committing suicide, and the evidence is essentially
non-existent for older adults with chronic illness such as COPD and HF. This is an important
area of research because older adults with chronic illness are at high risk for suicide, due to
reported high rates of suicide based on age and physical health conditions.
Purpose of Study
The overarching purpose of this study was to explore factors that influence the reasons
for living among depressed middle aged and older adults with chronic illness (COPD and HF).
The first specific purpose of this study was to examine the role of depression, following five
weeks of treatment, in predicting total reasons for living among COPD and HF middle aged and
older adults. Although older adults endorse depression far less than younger adults, depression is
the most common of all mental health related concerns in later life. Additionally, COPD and HF
patients are at greater risk for depression, and depression is a common risk factor for suicide.
52
Directly addressing and reducing depression may decrease risk of suicide and cause rise for
greater endorsed reasons for living.
The second purpose of this study was to determine what influence physical HRQOL,
following five weeks of treatment, has on reasons for living. COPD and HF patients report
lower HRQOL of which physical health is one component. Additionally, there are age related
impairments which older adults may experience. Individuals who have physical impairments
may find life less rewarding and subsequently have fewer reasons for living. Studying the
influence physical impairment has on reasons for living has direct influence on physiological or
rehabilitation interventions for COPD and HF patients.
Thirdly, this study sought to investigate correlates of reasons for living by exploring the
relationship between all HRQOL variables, depression, demographic variables (gender, age, and
illness type), and total reasons for living. No study thus far has explored the relationships
between these variables collectively. However, several studies have determined associations
between subsets of these variables.
Lastly, differences between age groups and men and women in reported total reasons for
living were examined. Literature suggests that men and women report differently regarding
reasons for living, specifically women endorse greater reasons of living. Additionally, older
adults report fewer reasons for living than younger adults. Exploring whether age and gender
affects reasons for living may help mental health and health care providers triage care and
allocate appropriate services such as suicide risk assessment.
53
CHAPTER III: METHODS
This chapter discusses the methods and procedures which were used in the study. It
begins by describing the participants and how they were selected. Next, a description of the
procedures and measures is provided.
Participants
This study was a subset of a larger, National Institutes of Health Research Project Grant
(NIH R01) funded clinical trial, the “Combined Illness Management and Psychotherapy in
Treating Depressed Elders,” conducted through the University of Iowa Hospital and Clinics,
Department of Psychiatry. The principal investigator on this study was Dr. Carolyn Turvey. The
larger clinical trial seeks to explore the effectiveness of psychotherapy and illness management
interventions, designed by Dr. Turvey, for depressed older adults with COPD or HF.
Specifically, the psychotherapy intervention addresses the functional impairment and depressive
symptoms patients may experience coping with their illness (more details below).
Participants for the current study took part in the larger study and clinical trial. Therefore
the procedures of the present were adopted by the clinical trial. Participants include adults over
the age of 55, who have been diagnosed with COPD or HF. Prior to consenting to participate in
the larger clinical trial, these individuals were positively screened for current minor or major
depression. Men and women were recruited equally. The sample was mixed race (e.g.
Caucasian, Latino, Asian, and African American). Due to the demographics of the data
collection sites, participants were primarily of Caucasian background.
There were several phases of inclusion or exclusion for participation in the larger clinical
trial vis á vis the present study: 1) chart screen, 2) phone screen, and 3) consent. Participants
were recruited via patient lists from The University of Iowa Hospital and Clinics (UIHC), the
54
Iowa City Veteran’s Affairs Medical Center (VAMC), and select community clinics which were
part of the Institute for Clinical and Translational Science at The University of Iowa. These lists
were chart screened for inclusion/exclusion criteria, particularly a diagnosis of COPD or HF,
before recruitment letters and brochures were mailed to prospective subjects. One to two weeks
following the mailing, patients received a follow-up phone call. The follow-up phone call
entailed determination of interest and further screening (e.g., patient confirmation of COPD or
HF diagnosis, a depression screen, assessment of physical impairment, and mental health and
substance abuse history). Patients who met inclusion/exclusion criteria were scheduled for an inperson consent visit, during which the final inclusion screening was performed and the consent
form was signed. The consent visit consists of administering two measures for mood, the
Beck’s Depression Inventory Second version (BDI-II; Beck, Steer, & Brown, 1996) and the
Structured Clinical Interview for the DSM-IV-TR (SCID; First, Spitzer, Gibbon, & Williams,
1994), and cognitive impairment with the Mini Mental Status Examination (MMSE; Folstein,
Folstein, & Hugh; 1975). To meet criteria for inclusion, patients must score ≥10 on the BDI-II,
≤24 on the MMSE, and meet criteria for either major or minor depression on the SCID. Patients
also completed additional baseline assessments at this time. Complete eligibility for participation
in the Trial includes the following:
Inclusion Criteria
Age 55+
Diagnosis of HF (systolic or diastolic) OR COPD
Depressive symptoms: ≥10 on BDI-II
Functional impairment: ≤ 70 on the physical impairment subscale of the Rand MOS 36
55
If prescribed an antidepressant, stable dose for 8 weeks
Exclusion Criteria
Diagnosis of psychiatric disorder (e.g. bipolar disorder, any psychotic disorder, medically
serious suicide attempt within the past six months, or current substance abuse disorder)
Cognitive impairment: Documented in medical record or MMSE ≥ 24)
Awaiting transplant
Significant hearing impairment
Current psychotherapy
Residence in long term care facility
Participants of the current study included those involved in five weeks (baseline to week
5 assessment) of the trial anytime between December 19, 2012 and January 2015. An a priori
power analysis was conducted for the regression analysis; an N of 70 was required for a power of
.80 with a moderate effect size.
Procedures
The “Combined Illness Management and Psychotherapy in Treating Depressed Elders” is
a multisite study and participants are recruited from various hospital settings. These sites include
the University of Iowa Hospital and Clinics (UIHC) which serves over 592 patients with COPD
and 630 with HF, the Iowa City Veterans Affairs Medical Center, and other select community
clinics which were part of the Institute for Clinical and Translational Science at the University of
Iowa which serve hundreds of veterans and civilians with COPD and with HF. These sites serve
patients who reside in various areas of the state of Iowa.
After completing three phases of screening, patients completed baseline self-report
assessments. They were then randomized based on illness type (COPD or HF), antidepressant
56
use (positive or negative), and depression severity (minor or major) into one of two intervention
groups and assigned a clinician. Both intervention groups were 10 weeks long, semi-structured,
and semi-manualized. Three of the ten sessions were in-person home visits (visits 1, 5, and 10),
which were between an hour to an hour and a half in length. The remaining seven sessions were
conducted via telephone for approximately a half of an hour. The intention of home and
telephone visits was to increase treatment accessibility due to patient demographics (i.e.
physically impaired older adults). The basis of the study structure is depicted in Figures 1 and 2,
included data collected during baseline and week five assessments.
The Combined Illness Management and Psychotherapy in Treating
Depressed Elders Randomized Clinical Trail
n= 201 (between May 2011 and January 2015)
36 dropped during the
intervention
Present Study
90 participated prior to the
present study
n=75 between December
2012 and January 2015
Figure 1.Relationship between the present study and the larger randomized control trial
procedures.
57
Baseline data collection
Present Study
Sample
n= 75
n= 75
(BDI-II & RAND36)
Week 5 data collection
Intervention
Met criteria for
participation between
December 2012 and
January 2015
n= 75
(RFLI, BDI-II, &
RAND- 36)
Figure 2. Present study’s data collection procedures.
Interventions
The psychotherapy intervention component of the study was an integration of
Interpersonal Psychotherapy (IPT; Klerman, Weissman, & Rounsaville, 1984) and Behavioral
Activation (BA; Martell, Addis, & Jacobson, 2001). The overall aim of the intervention was to
disrupt the depression-disability spiral (Bruce, 2001), described as the experience of winding or
spiraling between depression and impairment. For instance, as chronically ill patients experience
impairment, they become depressed due to their limitations; depression causes them to disengage
from enjoyable activities and social relationships, causing patients to feel even more depressed.
The hypothesis of the larger clinical trial was that as participants began to accept their illness
(particularly their limitations), set realistic expectations and goals, seek support and help from
others, and become active in enjoyable activities, they would experience reductions in depressive
symptoms. This hypothesis is supported theoretically in the use of IPT, which addresses
patients’ role transitions and grief and loss, in this case as it relates to patients’ health. Behavioral
58
activation, on the other hand, focuses on goal oriented behaviors and assesses for barriers or
other factors that may affect successful completion of the goals (i.e. realistic and purposeful
goals).
Combined psychotherapy and illness management (COMBO). Participants who were
randomized into the combined psychotherapy and illness management intervention worked with
a clinician and directly addressed mood related concerns, through following a series of eight
modules of treatment. Modules of treatment included: an introduction, grief and loss, role
transition, asking for help, taking action, rumination, pacing yourself, and termination. The
pacing yourself and rumination modules were supplementary and were addressed as needed.
There was no exact ordering for introducing modules; clinical judgment and patient need were
used to guide timing. However, treatment most often begun with the grief and loss module.
Patients also reviewed and addressed illness management techniques. The flow of the full
clinical trial intervention was as follows (note the current study pertained only to baseline to
week five data and intervention):
Session 1: Introduction- During the introduction of the intervention, COPD and HF
patients were asked about their mood, impairment, and social network, following which the
clinician introduced the aims and process of the intervention. Also during this visit, the clinician
and patient reviewed a booklet on illness management, specific to the client’s condition (either
COPD or HF). Together they explored the patient’s comorbid medical conditions, the cause of
their COPD or HF, symptoms, and current self-care practices. Specific illness management or
self-care techniques were reviewed focused on following the treatment plan provided by their
medical provider or other care providers. Based on these techniques, the client selected an
illness management task and set a goal to work towards over the course of the week. Typically,
59
patients were assigned reading, the grief and loss booklet, which was then discussed during the
second session.
Sessions 2-3: Grief and Loss and Role Transitions- In subsequent sessions, the clinician
reviewed and assessed client’s illness management goals. The grief and loss booklet served as a
guide to discuss issues related to impairment and depression. Role transition was also introduced
as it relates to changes in the patients’ relationships and roles, as a recipient and provider of
support.
Sessions 4-8: Behavioral Activation- The patient and clinician continued to discuss grief
and loss, role transition issues, and asking for help was introduced. Through these modules,
patients focused on acceptance of their current condition, as well as internal and external
expectations. The patient and clinician began to identify enjoyable and rewarding activities that
patients avoided or disengaged in due to depression or impairment. After week five (not
included in the present study data collection or analyses), patients and clinician worked
collaboratively to develop realistic behavioral goals related to these activities. Illness
management goals were also discussed and developed. From week to week, patients developed
both mood and illness related goals, and assessed them the following week with the clinician.
The fifth visit was an in-person home visit, where the patient and clinician continue to discuss
goals. During these middle sessions, rumination and pacing yourself were addressed, on an as
needed basis.
Illness management only (IMO). Patients randomized into the illness management only
(IMO) group of the intervention spent sessions reviewing illness management needs and setting
goals around these areas. The IMO intervention was similar to the COMBO process, with the
60
exception of receiving psychotherapy related to mood. Goals related to illness management or
self-care were set each week and assessed for barriers.
The present study used data from both the IMO and COMBO intervention groups to
increase statistical power and capture holistically the experience of the COPD and HF patients
sampled by including all participants regardless of treatment randomization. Moreover, the
COMBO and IMO groups are considered similar during the first five weeks of the intervention;
both treatment groups primarily focus on illness management goals. Additionally, both groups
received similar structured treatment; for instance, they both received in home and telephone
visits for five weeks. Therefore, the two treatment groups were combined, for the purposes of
data analysis.
Clinician Information
Three trained clinicians provided both COMBO and IMO treatment. Two clinicians were
full-time employees of the larger clinical trial and one was a part-time employee, graduate
student, and is the present study’s dissertation researcher. Two clinicians were Caucasian
women, and one was an African American woman. Two of the clinicians have experience with
psychotherapy, one with her master’s degree in marriage and family therapy and the other was a
current counseling psychology graduate student. The third clinician has a strong work history in
research. Prior to providing treatment to clients, each clinician engaged in several hours of tape
review, shadowing, and one-on-one review of treatment manuals for COPD and HF COMBO
and IMO treatment with Dr. Turvey. I worked direct with 8 patients also involved in the present
study’s data collection and analysis. The study underwent intervention fidelity check, which aims
to determine whether or not the intervention is conducted as manualized and helps minimize
61
implementation differences. This process was thought to minimize researcher contamination
and bias since the current researcher also was involved directly in the intervention.
Potential Risk
There was potential for indirect risk, as a result of participation in the intervention and
data collection portions of this study. For instance, patients might have experienced an
exacerbation of depression or negative reaction to disclosing information pertaining to their
impairment or mood. Regarding the measures of interest of the current study, participants may
have increased thoughts about death or self-harm as a result of responding to questions related to
suicide.
Several steps were set to minimize these risks. For example, during each visit, clinicians
assessed patient’s mood and physical health, by asking participants to rate their mood on a scale
from one (worse possible mood) to ten (best possible mood) over the last week and determine if
their health has been better, worse, or the same as the previous week. Furthermore, patients who
endorsed a score of 1 or higher on the item regarding suicide on the BDI-II or reported suicidal
ideation during the clinician administered SCID, at any assessment were administered a full
suicide risk assessment. If risk was high, the participant was offered a safety plan and contacted
by the study psychiatrist or referred to a suicide prevention counselor at the VA. At the onset of
treatment, patients were also asked to complete an emergency contact information sheet, which
lists a personal contact name and number, as well as a physician name and contact. Participants
were provided with the National Suicide Prevention Hotline number and encouraged to contact
911 in case of emergency.
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Data Collection
There were a total of seven data collection points throughout the larger clinical trial:
baseline, week 3, week 5, week 7, and week 10, 6-months post and 1-year post. Assessments
were both self-report paper and pencil measure or administered by phone. Self-report
assessment packets for week 5 and 10 were delivered to the patient by the clinician, at the
respective home visit and self-report packets were sent via mail for 6 month and 1 year follow
up. These assessments were returned to assessors via a self-addressed pre-paid envelope.
The current study includes assessments gathered at baseline and week 5. Depression
(BDI-II) and health-related quality of life (RAND-36) were assessed at baseline and week-five.
Reasons for living (RFLI) was administered only at week-five, following participation in five
weeks of COMBO or IMO treatment. Baseline and week-five assessment of depression and
HRQOL was collected to determine whether these variables had an effect on reported reasons for
living. These two data points and selection of week five only administration of the RFLI were
selected with consideration of the total number of items and measures, as well as participant
burden of assessments at other data points. Additionally, the two treatment groups were
considered equal at week five secondary to aforementioned similarities in treatment structure and
content.
Compensation
Participants were compensated for their involvement in the trial via check. There were
seven assessments in total, and patients were awarded $10.00 for each. Participants who traveled
to Iowa City for the consent visit were provided a $25.00 gift card for their travel expenses. Of
these seven assessment or data points, only two were utilized for the purposes of the present
study.
63
Instruments
Reasons for Living
The Reasons for Living Inventory (RFLI; Linehan, Goodstein, Nielsen, Chiles, 1983) is a
widely used measure which assesses cognitive deterrents to considering suicide. The RFLI
assesses life oriented beliefs and focuses on strengths rather than deficits, a unique prospective in
suicidology. The RFLI is the first the first and most widely utilized measure to assess suicide
protective factors. Most other measures assess other aspects of suicidality such as intent,
ideation, risk, past history etc. Therefore, this measure was chosen for its fit with the purpose of
the present study.
The RFLI has 48 items and contains six factors (e.g. social and coping beliefs [SCB],
responsibility to family [RF], fear of suicide [FS], fear of social disapproval [FSD], moral
objections [MO], and child related concerns [CRC]). In addition to subscale scores, a total RFL
score can be obtained. The inventory is scored on a 6-point Likert scale, which ranges from “not
at all important” to “extremely important,” scored from 1 to 6. Total scores range on a scale
from 48-288, with higher scores indicating greater reasons to stay alive. Total scores were
obtained by summing all items, while subscale scores were created by summing each subscale
set of items. Items were framed by asking participants to consider: “why killing yourself is not
or would never be an alternative for you.” Examples of item stems include: “I have a
responsibility and commitment to my family,” “I have a desire to live,” or “I am afraid of death.”
Several studies have reported psychometric properties of the RFLI in various
populations; however, most often these have been conducted on college student populations
(Osman, Gilfford, Jones, Liskiss, Osman, & Wenzel, 1993; Osman, Gregg, Osman, & Jones,
1992; Osman, Jones, & Osman, 1991). The total score for the RFLI has moderate to strong
64
internal consistency, Cronbach’s alpha ranging between .70- .89. Subscales of the RFLI also
have moderate to strong reported alphas. The specific alphas reported per subscales were as
follows: SCB range between .91-.92, RF between .79-.82, FS between .79-.85, CRC between
,82-.85, FSD between .80-.84, and MO between .74-.80 (Linehan et al., 1983; Osman et al.,
1993; Osman et al., 1992; Osman, Jones, & Osman, 1991). Test-retest reliability assessed three
weeks apart for the total RFLI was .83 (Osman et al., 1991). Subscale test-retest reliability
coefficients were also moderate to high, ranging from .75-.85 (Osman et al., 1991). The RFLI
effectively differentiates between suicidal and non-suicidal individuals (Linehan et al., 1983).
The RFLI also has predictive value toward future suicide attempts of depressed patients
(Oquendo et al., 2004). Overall, total and subscale alphas range from moderate to high, and
support the use of the RFLI in detecting protective factors against suicide.
Depression
Beck’s Depression Inventory Second Edition (BDI-II; Beck et al., 1996) is a widely
utilized, 21-item self-report measure of severity of depressive symptoms. The BDI-II has been
used in research and in clinical settings with various populations (Quilty, Zhang, & Bagby,
2010). In 1996, the previous version of the measure was revised to match major depression
disorder criteria of the Diagnostic Statistical Manual Forth Edition Text Revision (DSM-IV TR;
BDI-II; Beck et al., 1996). The BDI-II is utilized as a severity measure and not for diagnostic
purposes (Beck et al., 1996). Frequently, the BDI-II has been used as an outcome variable in
treatment studies (Quilty et al., 2010). Item scores range from 0-4, each item has unique ranges
based on severity or intensity pertaining to the particular item. Total scores range from 0-63.
Scoring criteria and cutoff scores are as follows: 0-13 minimal depression, 14-19 mild
depression, 20-28 moderate depression, and 29-63 severe depression (Beck et al., 1996).
65
The BDI-II has strong psychometric properties. Internal consistency for the BDI-II
ranges between .89 and .91 (Dozois, Dobson, & Ahnberg, 1998; Osman, Kopper, Barrios,
Gutierrez, & Bagge, 2004). The BDI-II has a two factor model consisting of CognitiveAffective and Somatic symptoms (Titov, Dear, McMillian, Anderson, Zou, & Sunderland, 2011),
and both of these factors have moderate to strong alpha levels, .90 and .80, respectively (Osman
et al., 2004). Titov et al. (2011) reported that reductions of depressive symptoms have been
effectively detected in pre- and post-treatment assessments. They also found a large effect size
(ES= 1.42) for pre- post- treatment using the BDI-II.
Steer, Ball, Ranieri, and Beck (1999) conducted a factor analysis in a sample of clinically
depressed patients and posited a two-factor model of depression. Steer et al. (1999) model
includes a somatic-affective and cognitive dimension of depression. They found that age
significantly correlated with depression severity, specifically depression increased with age
during young adulthood, but after age 38 it decreased into late adulthood. Steer et al. (1999)
explored whether or not it was necessary to control for age in their factor analysis. Salient
symptoms were similar before and after controlling for age; therefore, they concluded controlling
for age was unnecessary. A previous factor analysis in a psychiatric sample (Beck, Steer, and
Brown, 1996) resulted in similar outcomes, which differed from previous iterations of the BDI.
Steer et al. (1996) also proposed two first order factors: cognitive and non-cognitive. The
cognitive factor is comprised on 8 items, while the non-cognitive factor is comprised of 13 items.
Steer et al. reported good internal consistency for both the cognitive and non-cognitive factors
(coefficient alphas= .81 and .87, respectively). Steer et al. (1999) recommended the use of the
cognitive factor in medical and psychiatric patient populations to reduce the effects of somatic
complaints. However, recent literature suggests there significant correlation between the
66
cognitive and somatic affective factors (Thombs, Ziegelstein, Beck, and Pilote, 2008).
Therefore, for the purposes of the current study the total BDI-II severity score is used.
Previous researchers have suggested that the BDI was not an appropriate measure for
older adults or those with chronic illness due to the potential of over-reported somatic concerns
(Gallagher, 1986). However, Thombs et al. (2010) found patients who had a myocardial infarct
did not significantly differ in reported somatic complaints from matched controls and college
students. Internal consistency of the BDI-II with older adults is moderate to high (α=.86-.90;
Low & Hubley, 2007; Segal, Coolidge, Cahill, & O’Riley, 2008; Steer, Rissmiller, & Beck,
2000), which does not differ from alphas reported in younger populations. Alphas are higher for
older women than among men on the BDI-II, .94 and .81, respectively (Low & Hubley, 2007).
In regard to construct validity, the BDI-II has moderate convergent validity with the Center for
Epidemiologic Studies Depression Scale (CES-D; r=.69) and Coolidge Axis II Inventory for
depression (CATI depression; r=.66), and divergent validity with the Short Psychological WellBeing Scale (SPWB; r= -.60) and health status (r= -.27) among older adults (Segal, et al., 2008).
The BDI-II exhibits strong psychometric properties in both community dwelling (Segal, et al.,
2008) and inpatient (Steer, et al., 2000) older adult samples. In sum, the BDI-II is shown
effective in measuring pre- and post- severity of depression in older adult populations.
Health-Related Quality of Life
The RAND 36-item Health Survey 1.0 (RAND-36; Hays, Sherbourne, & Mazel, 1993) is
a variation of the commonly used Medical Outcomes Study 36 Item Short Form Health Survey
(SF-36; Ware & Sherbourne, 1992). The RAND-36 is a 36-item self-report measure intended to
assess various health factors which resemble the subscales of the SF-36 (Hays et al., 1993). For
instance, the RAND-36 examines eight health concepts: physical functioning (i.e., limitations in
67
activities of daily living), pain (i.e., limitations due to bodily pain), role limitations due to
physical health problems, role limitations due to personal or emotional problems, general mental
health, social functioning (i.e., limitations in social activities), vitality (i.e., energy or fatigue),
and general health perceptions (i.e., subjective evaluation of health), with 10, 4, 3, 4, 5, 2, 2, and
5 items, respectively (Hays et al., 1993). This measure also includes an item on health changes
over the last year, but it does not factor on to one of the previously mentioned scales. Each scale
score loads on one of two summary scores, physical component scale (i.e., physical functioning,
pain, and role limitation related to physical health) and mental health component scale (i.e.,
psychological well-being and role limitations due to mood issues; Hays & Morales, 2001).
Items vary in response answers, participants were asked to answer using yes/no, forced
choice, 3-, 5-, and 6- point Likert scales. Unlike the SF-36, the RAND-36, has a simpler scoring
method (Hays et al., 1993). In such, higher scores denote greater HRQOL. Per item, scores range
between 0 and 100 based on the response format listed above. Items that factor on a particular
scale or summary scale were averaged to determine the scaled/summary scores, which also range
between 0-100 (Hays et al., 1993; Hays et al., 2001).
In the current study, the physical health summary score, derived from averaging scores
on the physical functioning, pain, general health, and role limitations due to physical health
problems, was one of the core variables. These subscale variables were of particular interest due
to the intersections between impairment and depression vis-à-vis the depression-disability spiral
and the focus of the intervention.
The RAND-36 is a reliable and valid measure. Across the eight subscales, alpha
coefficients range from .78-.93 (Hays et al., 1993). In a sample of patients with COPD, alpha
coefficients range between .71 and .91 for the subscales (Moorer, Suurmeijer, Foets, Molenaar,
68
2001; VanderZee & Heyik, 1996). This suggests that there is specific evidence for the reliability
of using the RAND-36 with patients diagnosed with COPD. Moorer et al. (2001) reported
average alphas across subscales in other medical populations (e.g., multiple sclerosis, rheumatic
diseases, and COPD) to range between .76-.93. The measure has been described as being
unidimensional, all subscales factor on to the total health construct. The measure also has
moderate test-retest reliability after a two month interval (r= .58-.82; VanderZee & Heyik, 1996).
The physical component scale highly correlates with a measure of activity restriction (Groningen
Activity Restriction Scale; GARS), while the mental component scale highly correlates with
measures of anxiety (State and Trait Anxiety Questionnaire; STAI), depression (CES-D), and
general health [General Health Questionnaire (GHQ); VanderZee & Heyik, 1996], measures on
convergent and divergent validity. Overall, the RAND-36 is a reliable and valid measure to
assess health related concerns with patients particularly with COPD and HF.
69
CHAPTER IV: RESULTS
The results of the study are presented in this chapter. The first section includes sample
characteristics. Next, descriptive statistics are provided including comparisons with normative
data. Finally, results associated with each research question are presented.
Preliminary Analysis
A total of 201 participants participated in the COPE longitudinal study between May
2011 and January 2015. Of this total number, 36 dropped out during the intervention or followup phase of the study due to a variety of reasons. The remaining 185 participants provided data at
baseline, week 3, week 5, week 7, week 10 and follow-up via phone assessments and self-report.
The current study’s variables were collected between December 2012 and January 2015. During
this time, data were collected from a total of 75 participants. All scales and subscales were
examined for incomplete or missing data. A 90% cutoff for incomplete responses on any given
subscale was utilized to determine inclusion in data analysis. A total of 5 participants had more
than 10% of the items missing from at least one of the subscales on the variables. These
participants were excluded from all analyses involving the variable. A total of 11 participants
were missing less than 10% of items on a given subscale. Of these 11 participants, item
estimations were computed by averaging inter-participant subscale scores for subscales with
missing data and inputting the individuals subscale mean for each item missed.
Table 3 provides a summary of the demographic characteristics of the sample. Of the 75
study participants, the majority (69.3%) were male. There were significantly more male
participants in the current study compared to similar previous studies (Marty, 2012 and Segal et
al., 2012). The mean age of participants was 67.48 and age ranged between 56 and 89. The
majority of the sample were Caucasian, married or divorced, and highly educated (some college
70
or more). Age, race and education variables and proportion of sample size was equivalent to
other studies related using the RFLI. Compared to previous studies, the current sample included
a greater number of divorced participants (Segal et al., 2012).
Table 3
Demographic Characteristics of the Patient Sample
Variables
Sample size
Age
Gender
Male
Female
Ethnicity
African American/Black
Biracial/Multiracial
Caucasian/White
Marital Status
Married/Long term live partner
Widowed
Divorced/Separated
Single or Never married
Education
Less than high school
High school/GED completion
Some college
Bachelor’s Degree/4 year degree
Graduate/Professional degree
Living Environment
Alone
With spouse
With child
With other family
Other
71
N
75
67.48*
%
100
8.64*
52
23
69.3
30.7
3
3
69
4
4
92
32
9
27
7
42.7
12.0
36.0
9.3
10
26
30
4
4
13.3
34.7
40.0
6.7
5.3
32
35
2
5
1
42.7
46.7
2.7
6.6
1.3
Table 3--continued
Variables
Employment Status
Working
Retired
Discontinued work due to health
Yes
No
No response
Insurance
Medical
Yes
No
No response
Prescription
Yes
No
No response
Smoke History
Ever
Yes
No
Current
Yes
No
No response
Smoking
Cigarettes per day
Years smoked
Income
Less than 10,000
10,001-20,000
20,001-35,000
35,001-50,000
50,001-65,000
65,001-70,000
More than 70,000
N
%
14
61
18.67
81.33
21
46
8
28
61.3
10.7
65
9
1
86.7
12
.3
56
17
2
74.7
22.7
2.6
63
12
84
16
16
45
14
21.3
60
18.7
23.05*
33.16*
15.50*
15.47*
14
28
13
6
2
3
0
21.2
42.4
19.7
9.1
3.0
4.6
0
Note. *= Mean and Standard Deviation
Table 4 provides descriptive medical information for the sample. The majority of
participants were diagnosed with COPD (56%). Of the participants diagnosed with COPD or
72
COPD and HF, 61.6% were diagnosed more than 5 years prior to their participation in the study.
Many were diagnosed with emphysema COPD (40.4%) caused by smoking (67.6%). The
majority had stage II (32.7%) or III (30.7%) COPD. Similar to the COPD subset of the sample,
the majority of HF participants were diagnosed five years prior to participating in the current
study. Most participants were diagnosed with systolic HF (53.9%) and were type III NYHA
status (69.2%). The cause of HF varied greatly within the subset of HF participants.
Table 4
Medical Characteristics of the Patient Sample
Variables
Illness type
COPD
HF
COPD
Duration
Less than 1 year
1-5 years
More than 5 years
Type
Emphysema
Chronic Bronchitis
Both
Undocumented
Causes
Smoking
Environmental
Genetic
Other
Undocumented
Gold Stage
I
II
III
IV
Unable to determine
73
N
%
42
33
56
44
1
19
32
1.9
36.5
61.6
21
14
9
8
40.4
26.9
17.3
15.4
46
18
0
1
3
67.6
26.5
0
1.5
4.4
3
17
16
3
13
5.8
32.7
30.7
5.8
25
Table 4--continued
Variables
HF
Duration
Less than 1 year
1-5 years
More than 5 years
Type
Diastolic
Systolic
Undocumented
Cause
Coronary Artery Disease
Cardiomyopathy
High Blood Pressure
Viral
Alcohol
Heart Valve Problem
Diabetes
Heart Attack
Genetic/Familial
Unknown
Undocumented
Other
NYHA Status
I
II
III
IV
n
2
14
22
13
21
55
10
37
2
1
2
1
5
1
15
4
6
0
10
27
2
%
5.3
36.8
57.9
33.3
53.9
12.8
5.6
11.3
41.6
2.2
1.1
2.2
1.1
5.6
1.1
16.9
4.6
6.7
0
25.6
69.2
5.2
Table 5 provides descriptive statistics (measurement ranges, means, standard deviations,
and Cronbach’s alphas) for the study variables (e.g. depression, physical and mental HRQOL,
and reasons for living). Participants reported moderate levels of depression at baseline (M=21,
SD= 7.82), which appeared to decline by week five when the mean depression score was in the
mild range (M=17.36, SD=9.369). Baseline and week five HRQOL scores varied by subscale.
Cronbach’s alphas for this measure were all above .70 (standard cutoff for homogeneity), with
the exception of baseline mental HRQOL. The RFLI on the other hand showed variable internal
consistency. Specifically, some subscale Cronbach’s alphas were below .70 [e.g., Child-Related
Concerns, Fear of Suicide (FS), Fear of Social Disapproval (FSD), and Moral Objections (MO)],
indicating less than desirable reliability. Therefore, all analyses involving the RFLI utilized the
RFL total score instead of RFL subscale scores.
The present study targeted middle aged and older adults with major depression; therefore,
it is difficult to compare the severity of self-reported depression with other related studies, which
sampled from community dwelling older adults. With regard to HRQOL, fewer studies use a
general HRQOL measure (e.g. SF-36 or RAND-36,) and instead use disease specific quality of
life measures such as the Chronic Respiratory Disease Questionnaire (CRDQ), St. George’s
Respiratory Questionnaire (SGRQ), Quality of Life for Respiratory Illness Questionnaire
(QOLRIQ), Minnesota Living with Heart Failure Questionnaire (MLHFQ) and the Kansas City
Cardiomyopathy Questionnaire (KCCQ). One study, Park et al. (2008), reported MCS and PCS
means (i.e., 49 and 31.5, respectively) similar to this sample.
Reliability of the RFLI subscales in older populations is moderate ranging between .60.89 with a median alpha of .73 (Segal et al., 2008), which is congruent with the current study.
Previous studies exploring reasons for living in older adult populations reported lower mean
scores for fear of suicide (M= 2.42, SD= 1.15) and fear of social disapproval (M= 2.94,
SD=1.50; Marty, 2012; Segal, et al., 2007; & McLean et al., 2011) as protection against suicide,
compared to the current study. All other domains on the RFL inventory were equivalent to
previous studies.
75
Table 5
Psychological Variables
Variables
Depression
Baseline
Week 5
Health Related Quality of Life
Mental Composite Score
Baseline
Week 5
Physical Composite Score
Baseline
Week 5
Reasons for Living Total
Survival and Coping Beliefs (SCB)
Responsibility to Family (RF)
Child-Related Concerns (CRC)
Fear of Suicide (FS)
Fear of Social Disapproval (FSD)
Moral Objections (MO)
Note. M= Mean SD= Standard Deviation
Range
0-63
n
M
SD
Alpha
75
75
21.00
17.36
7.826
9.369
.841
.906
0-100
70
73
45.79
48.94
13.82
17.17
.695
.796
0-100
70
72
72
73
73
73
73
73
73
35.91
40.15
199.01
4.23
4.03
4.05
4.32
4.49
4.02
17.15
21.95
40.56
.85
1.13
1.58
1.07
1.13
1.34
.856
.892
.934
.881
.723
.567
.703
.651
.695
48-288
1-6
1-6
1-6
1-6
1-6
1-6
Research Questions
The first research question pertained to determining any group differences based on
gender (male vs. female), age, or illness type (COPD or HF) in reported total RFL. As
demonstrated in Table 6, there were no significant differences between male and female
participants’ self-reported total RFL. Similarly, the correlation between age and total RFL was
insignificant (r = .156, p=.191; see Table 8). Lastly, there was no significant mean differences in
illness type (COPD or HF) when compared based upon total RFL (Table 7).
76
Table 6
Gender and Reasons for Living
Measure
Reasons for
Living
Male
M
SD
196.97 43.90
Female
M
SD
203.65 32.11
t
-.641
df
70
p
.523
Table 7
Illness Type and Reasons for Living
Measure
Reasons for
Living
COPD
M
SD
200.08 43.34
M
197.75
HF
SD
37.63
t
.242
df
70
p
.809
To examine the associations between the key study variables (e.g. depression and
HRQOL) at the two study time points, baseline and week five, and RFL total scores, an intercorrelation matrix was calculated. As shown in Table 8, of note are the negative correlations
between depression with physical and mental HRQOL. Specifically, participants with lower
depressive symptoms reported higher physical and mental HRQOL at both time points. The
correlation between age and baseline depression marginally reached significance (r = -.211, p =
.069). There were no other significant correlations between study variables.
77
Table 8
Inter-correlation Matrix between Study Variables
Variables
1
2
1.Age
.156
2.Reasons for
Living
Total
3.Depression
baseline
4.Depression
week 5
5.Physical
HRQOL
baseline
6.Physical
HRQOL week 5
7.Mental
HRQOL
baseline
8.Mental
HRQOL week 5
Note. ** = p < .01 * = p < .05
3
4
-.211 .047
.021 -.161
.589**
5
-.034
-.42
6
.038
.044
7
.045
-.107
8
-.105
.140
-.347**
-.247*
-.422**
-.370**
-.457**
-.508**
-.429**
-.810**
.706**
.438**
.451**
.294*
.617**
.453**
The third research question addressed the influence of physical HRQOL at baseline and
week 5 on total RFL. I ran a hierarchical linear regression model for total RFL. Table 9 shows
the first step of the model, physical HRQOL at baseline did not significantly account for RFL
total scores (F (1, 63) = .003, R2 = .000, p = .954). The second step, week five physical HRQOL
also did not significantly account for additional variability in total RFL, after controlling for the
variance accounted for by baseline physical HRQOL (F (1, 62) = .217, R2 = .004, p = .643).
There was a significant difference between baseline and week five physical HRQOL scores (t= 2.26, df= 66, p=. 027), as demonstrated by a paired sample t-test. Participants’ physical HRQOL
improved from baseline to week five (see Figure 3).
78
Table 9
Hierarchical Regression of Physical HRQOL Predicting Reasons for Living
𝛽
Variable
B
SE
Step 1
R2
F
∆R2
.003 .000 .000
Physical HRQOL(Baseline)
.017
.007
.301
Step 2
p
.954
.954
.217 .004 .003
.643
Physical HRQOL (Baseline)
-.121
-.051
..424
.776
Physical HRQOL (Week 5)
.154
.083
.329
.643
Physical HRQOL
45
40
35
30
25
20
Baseline
Week 5
Physical HRQOL
Figure 3. Change in physical HRQOL from baseline to week five.
The fourth research question pertained to the influence of depression at baseline and
week five on RFL total scores. I ran a hierarchical linear regression model for total RFL. As
shown in Table 10, the first step of the regression model, baseline depression did not
79
significantly account for variance in RFL total scores (F (1, 70) = .015, R2 = .000, p = .902). In
the second step, week five depression approached statistical significance. Specifically, week five
depression accounted for additional variability in total RFL after controlling for the variance
accounted for by baseline depression (𝛽 =.834, ∆R2 = .043, p= .084) but was not statistically
significant (F (1, 69) = .217, R2=.043, p = .084). Week five depression scores incrementally
accounted for 4% of the variance in RFL total scores.
Table 10
Hierarchical Regression of Depression Predicting Reasons for Living
Variable
𝛽
B
SE
Step 1
BDI-II (Baseline)
.077
.015
F
R2
∆R2
p
.015
.000
.000
.902
.623
Step 2
.902
3.079
.043
.043
.084
BDI-II (Baseline)
.834
.160
.750
.270
BDI-II (Week 5)
-1.096
-.253
.625
.084
Additional Analyses
Several analyses were added to the original research questions. Because of the range in
age of the study participants and the marginal significance in the correlation between age and
baseline depression, an additional regression was completed to assess the predictive ability of
depression after controlling for age. Table 11 displays the three step regression model. In step
one, age non-significantly predicted total RFL (F (1, 70) = 1.746, R2= .024, p = .191). Baseline
depression was added during step two and did not account for additional variance in total RFL
after the variability accounted for by age was controlled (F (1, 69) =.155, R2=.027 p=.695).
Lastly, week five depression was added to the model and significantly accounted for additional
80
variability in RFL after age and baseline depression were controlled for (F (1,68) = 4.704, R2 =
.089, p= .034). Thus, week five depression accounts for 6% of the variance in total RFL (𝛽 = 1.369, ∆R2 = .063, p= .034). Additionally, there was a significant difference between baseline
and week five depression, participants were less depressed at week five than baseline as measure
by a paired t-test (t= 3.98, df= 74, p< .00; see Figure 4).
Table 11
Hierarchical Regression of Age and Depression Predicting Reasons for Living
Variable
𝛽
B
SE
Step 1
Age
.725
.156
F
R2
∆R2
p
1.746
.024
.024
.191
.549
Step 2
.191
.155
.027
.002
.695
Age
.770
.165
.564
.177
BDI II (Baseline)
.249
.048
.632
.695
Step 3*
4.704
.089
.063
.034
Age
1.053
.226
.564
.066
BDI-II (Baseline)
1.258
.241
.771
.108
-1.368
-.315
.631
.034*
BDI-II (Week 5)*
Note: *p<.05
81
Depression
Severity
22
20
18
16
14
12
10
Baseline
Week 5
Depression
Severity
Figure 4. Change in depression severity from baseline to week five.
Because mood related concerns (i.e., depression) significantly affected RFL, an
additional regression was conducted with mental HRQOL excluding and including age as a
control variable for age (Table 12 and 13, respectively). Similar to the previous regression
models, baseline mental HRQOL was entered into step one. The first model did not significantly
predict RFL (F (1, 63) = .116, p = .735). In step two, week five mental HRQOL was added;
however, it did not account for additional variance in RFL with the variability accounted for by
baseline mental HRQOL controlled (F (1, 62) = .789, p=.459). Table 13 includes the regression
analysis of the incremental effect of age, baseline mental HRQOL, and week 5 mental HRQOL.
Step 1 including age only approached significance (F (1, 63), ∆R2 = .046, .085); however, all
other steps were insignificant. There was no significant differences in mental HRQOL scores.
82
Table 12
Hierarchical Regression of Mental HRQOL Predicting Reasons for Living
Variable
𝛽
B
SE
Step 1
MCS (Baseline)
-.130
-.043
F
R2
∆R2
P
.116
.002
.002
.735
.381
Step 2
.735
1.460 .025
.023
.231
MCS (Baseline)
-.343
-.113
.419
.416
MCS (Week 5)
.416
.167
.344
.231
Table 13
Hierarchical Regression of Age and Mental HRQOL Predicting Reasons for Living
Variable
𝛽
B
SE
Step 1
Age
1.101
.630
F
R2
∆R2
p
3.060
.046
.046
.085
.215
Step 2
.085
.158
.049
.002
.212
Age
1.109
.217
.634
.085
MCS (Baseline)
-.149
-.049
.375
.692
Step 3*
2.289
.635
.083
.034
.149
Age
1.250
.244
MCS (Baseline)
-.415
-.137
.411
.316
MCS (Week 5)
.515
.207
.340
.135
83
.053
CHAPTER V: DISSCUSSION
In this chapter, I will relate the results presented in Chapter IV to the a priori hypotheses.
First, the core research questions and additional analyses are discussed, with regard to how they
align or deviate from previous literature and theory in the areas of geropsychology, depression,
quality of life, and reasons for living. The limitations of the study is described. Finally, I will
provide implications for future practice and research.
Demographics
The hypotheses generated for the first research question stated that older participants
would likely report fewer RFL than the younger participants; women would likely report greater
RFL than men; and COPD and HF patients’ would likely not differ in reported RFL. The first
two hypotheses were not supported by the results of this study. Specifically, there was no
correlation between age and RFL, and the mean RFL score did not differ by gender. These
findings do not completely conflict with previous literature since several studies reported mixed
results related to age and gender group differences.
The current literature on age and RFL is inconclusive. Studies exploring the relationship
between age and RFL demonstrated significant differences between older and younger adults on
RFL subscales such as MO, RF, FS, and CRC (Miller et al., 2001 & McLaren, 2011). Previous
research findings highlight lifespan specific characteristics such as the role of grand parenting
through RFL subscales’ responsibility to family and child related concerns. The current study
primarily focused on total RFL rather than subscales, due to the low reliability of the RFL
subscales within the current sample. Therefore, it is difficult to make a direct comparison
between the current study and previous studies. Previous studies as well as the current study
used the RFLI, a general population measure of cognitive deterrents to consider suicide as an
84
option. Edelstein, Heisel, McKee, Martin, Koven, Duberstein, and Britton (2009) are in the
process of validating a population specific measure of RFL in older adults, which may better
reflect the unique lifespan characteristics in late life and provide strong reliability and validity.
Previous research exploring gender differences is also mixed. The current study aligns
with Segal and Needham (2007) and June et al., (2010) who found no significant differences
related to gender on total RFL; and differs from Kissane et al., (2006) and Range et al., (1996),
who found older women endorsed higher total RFL and RF. Research by Kissane et al., (2006)
and Range et al. (1996) shed light on the gender differential observed in completed suicides
across the lifespan, but particularly in later life. Suicide statistics indicate that older men are
more likely to complete suicide than older women; therefore, gender is viewed as a risk factor
(CDC, 2009). The risk of suicide is particular high among older, those 85 and older, Caucasian
men. Kissane et al. (2006) and Range et al. (1996) indicate that higher reports of protective
factors or RFL among women may contribute to the observed lower rate of completed suicides
by gender.
Additionally, there is a significant age and gender effect related to mental health helpseeking (Mackenzie, Gekoski, and Knox, 2006). Research suggests that older women tend to
hold more favorable attitudes toward help-seeking and more openness to psychological concerns
than men, mediated by education level and marital status. Similar to patterns observed in the
general older adult population, Mackenzie and colleagues (2006) found that older adults reported
being more likely to seek mental health treatment in the context of their primary care visits.
Moreover, exploring the literature holistically suggests that the role of gender in explaining
suicide protective factors and rates of completed suicide is inconclusive, due partially to
disproportionate sampling of older adult men and women. The current study’s sample was
85
primarily female similar to Kissane et al., (2006). Although other samples have been more
mixed, they remain disproportionately female (Segal et al., 2007 and June et al., 2010).
Chronic illness in late life is a pervasive medical and mental health concern. 80% of
Americans aged 70 and over are diagnosed with a chronic medical condition and experience
physical and functional impairments. This study sought to explore reported RFL in a sample of
older adults diagnosed with chronic illness. As hypothesized, COPD and HF patients’ mean total
RFL scores did not differ. There are both natural aging and disease related changes in organ
system functioning. Although COPD and HF affect different organ systems-- respiratory and
cardiovascular, respectively-- COPD and HF have several similarities, particularly with regard to
prominent symptoms, etiologies, and psychosocial consequences. The overlapping medical and
psychosocial characteristics are also reflected in patients’ reported suicide protective factors.
Chronic illness and suicide are viewed as a public health epidemics. The CDC and the
National Center for Chronic Disease Prevention and Health Promotion are working towards
preventing chronic disease through four domains: epidemiology and surveillance, environmental
approaches, health care interventions, and community clinical links (for a full review of the CDC
and NCCDPHP efforts visit their website at http://www.cdc.gov/chronicdisease/about/publichealth-approach.htm). Similarly, the CDC advocates for evaluation and actionable research
related to suicide prevention to advance science and practice through a multi-level approach:
population approach, primary prevention, commitment to science, and multi-disciplinary
perspective (to see a full review of to CDD’s suicide prevention efforts visit http://www.cdc.gov/
violenceprevention/pdf/asap_suicide_issue2-a.pdf). The present study fits well within the public
health model exploring two major public health concerns with an emphasis on prevention.
86
Due to the exploratory nature of the second research question, there was no stated
hypothesis. The results of the inter-correlation matrix, exploring relationships between the core
independent and dependent variables, revealed negative correlations between depression and
HRQOL (physical and mental) at baseline and week five. Patients with lower perceived physical
and mental quality of life were more depressed. This is consistent with Anderson’s (1995) results
which indicated that COPD patients with lower reported QOL on the Quality of Life Scale were
more depressed. Similarly, researchers utilizing general and illness specific QOL measures
consistently report significant and negative relationships between depression and HRQOL
(Yohannes et al., 2000, Faller et al., 2009, and Cully et al., 2006). Compared to non-depressed
COPD (Yohannes et al., 2000) and HF (Faller et al., 2009) patients, depressed patients reported
lower QOL. The current study focused exclusively on depressed patients and supports previous
research on COPD and HF patients, which concludes that depression plays a strong role in
perceived QOL and life satisfaction. In sum, the analyses conducted to answer the first two
research questions suggest that the strength of overall RFL or suicide protective factors of
patients diagnosed with COPD and/or HF from varying demographics (e.g., gender and age)
should be conceptualized similarly. Additionally, there are strong relationships between
reported depression and physical and mental HRQOL, which is discussed further in this study’s
subsequent research questions and hypotheses.
Depression
The main findings for this study pertained to predicting RFL, specifically the effects of
depression and physical HRQOL. The original hypothesis was that week five depression, after
the influence of baseline depression was removed, would significantly predict variability in RFL
scores. The hypothesis was not supported by the results of this study but neared accepted levels
87
of statistical significance. Therefore, a follow-up analysis was conducted. Exploratory analyses
revealed a moderate correlation between age and baseline depression, which suggests that age
may better account for the observed effects of baseline depression predicting RFL. At baseline,
younger participants, those in the middle aged range, were more depressed, and older
participants reported lower depression. Therefore, the follow-up regression predicted variability
in RFL, by age and depression. These results supported the original hypothesis. Specifically,
after participation in an intervention designed to aid middle aged and older adult patients in the
process of grieving and coping with transitions, managing their illness and symptoms, and
actively engaging in enjoyable activities, depression severity significantly predicted RFL, when
the effects of age and baseline depression were held constant.
The sample’s mean depression scores significantly reduced over time, between week one
and week five. The patient sample was middle aged; also older adults experiencing physical
limitations due to medical symptoms tended to be homebound. Illness management and
psychotherapy interventions were provided for the duration of the data collection process.
Equality in group effects by intervention and illness type was assumed, due to the stage of data
collection and corresponding intervention modalities. Participants were suspected to experience
a reduction in depression from baseline to week five due to psychotherapy or intervention
common factors (i.e., active engagement in treatment process, therapeutic alliance, reduction in
isolation, and conversations related to biopsychosocial changes), which was supported by this
study’s findings.
Patients were active involved in managing their illness. Both intervention groups were
provided basic information packets about the cause of COPD and HF, common symptoms, and
behavioral interventions to manage symptoms and improve self-care at the onset of treatment.
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During subsequent sessions, both groups were asked to set weekly goals focused on illness
management. Treatment coaches assessed progress toward goals, barriers, and readiness to set
new goals or revisit previous goals. Patients socially engaged with an empathic treatment coach.
The larger clinical trial treatment manual encourages treatment coaches to actively listen to
patient concerns in both treatment interventions. Treatment coaches are recommended to avoid
discussing mood related concerns with IMO patients; however, empathy and active listening are
used. This form of contact with a neutral provider outside of the patient’s medical provider,
family, and friendship group was suspected to be beneficial. As previously mentioned, sample
participants tended to be residents of rural communities, homebound, and isolated due to
relationship strain and changes in roles; therefore, weekly contact from a provider likely reduced
isolation and subsequent distress.
Finally, participants enrolled in the psychotherapy and illness management group
(COMBO group) discussed issues related to physical and social changes since diagnosis of
chronic illness. Patients in the COMBO treatment group were asked about changes in
relationship such as roles, quality and quantity of contact, help-seeking, etc. Likewise, patients
were asked to describe their functional changes and how they impacted patients’ sense of
identity. Ultimately, these conversations were aimed toward setting realistic expectations related
to functioning and create a “new normal,” through behavioral activation during subsequent
sessions.
Overall, the regression model demonstrates that depression severity over time accounted
for significant amount variability in RFL. Researchers and theorists consistently report
depression as a common risk factor of suicide across the lifespan, but particularly in late life
(Conwell et al., 2011; Gallagher-Thompson & Osgood, 1997; Klinger, 1999; Szanto et al., 2001).
89
Older adults often do not seek mental health treatment due to stigma, low access to care, and lack
of specialized professionals; instead older adults tend to report depression to medical providers
(Conwell et al., 2011; Gallagher-Thompson & Osgood, 1997; Klinger, 1999; Szanto et al., 2001).
Participants of the current study met criteria for moderate or major depression; therefore, there
was limited variability in depression scores. Nevertheless, the follow-up analysis of the current
study supports the theory of depression as a suicide risk factor and advances the literature by
emphasizing the importance of considering the influence of depression on unique suicide
protective factors.
This study was the first of its kind to predict RFL in a specific older adult medical
population. Several studies have found increased risk of suicide related to medical factors such
as disease severity, comorbidity, frequency of hospitalizations. Additionally, COPD patients
have been reported to have higher risk of suicide (suicidal ideation and attempt; Goodwin, 2011)
compared to health controls even after the effects of depression are held constant (Fiske et al.,
2008). However, previous research related to suicide protective factors has focused primarily on
healthy community dwelling older adult populations or younger depressed patients. The present
study is consistent with Oquendo et al.’s (2004) study, which found that depressed patients
reported lower RFL and were more likely to attempt suicide. Segal el al. (2008) highlighted the
importance of older adults’ perceived health and its effect on predicting RFL. In that study,
perceived health predicted RFL above and beyond depression, age, life stress, and optimism
(Segal et al., 2008). Although the current study did not explore the role of perceived health,
health was captured by patient demographics (i.e., diagnosis of COPD and/or HF).
90
Health Related Quality of Life
The present study also sought to consider the hypothesis that physical HRQOL
significantly predicts RFL. Baseline and week five physical HRQOL did not significantly
predict total RFL. This finding remained true for week five physical HRQOL, even after the
accounted variability in RFL by baseline physical HRQOL were removed. There was significant
change in the sample’s mean physical HRQOL between week one and week five. The current
study’s participants reported HRQOL was comparable to previous research (Park, et al., 2008).
However, Park’s et al., (2008) study investigated the relationship between various coping
strategies and HRQOL, specifically related to creating a meaningful life. The present study
uniquely investigated the effects of HRQOL on predicting reasons for living.
Cully et al. (2006) posited that depression may better explain mental HRQOL, which was
also determined to insignificantly predict RFL in an additional analysis of the current study.
Since there is some construct overlap between physical and mental HRQOL component scales,
depression may also better explain physical HRQOL. The current study’s correlational data
confirms the overlap in variables; baseline and week five depression and physical and mental
HRQOL were significantly related, which suggests some construct overlap. The relationships
between depression and mental and physical HRQOL is noteworthy because the present study’s
participants met criteria for depression; therefore, the range of depression severity was limited
(i.e., BDI-II scores 10-43 at baseline and 2-47 at week 5), which was not true for the range in
HRQOL scores. Overall, the results from the current study support Cully’s et al., (2006)
research.
Beyond the role of depression, the chronic and progressive nature of COPD and HF may
also explain why physical HRQOL did not predict RFL. The present study is a part of a larger
91
RCT investigating the effectiveness of an illness management and psychotherapy intervention,
particularly targeting depression. Illness management addressed self-care and following
physician recommendations. Although depression and self-perceptions of QOL are modifiable,
as demonstrated in the present study, depression is the more ideal targeted variable for
intervention and an important predictor variable of suicide protective factors over time.
Study Limitations
The current study’s limitations included sample characteristics and inclusion criteria.
The total sample size of the present study lacked ethnic diversity and was relatively small which
reduces generalizability. Sampling took place in a rural Midwestern area resulting in limited
ethnic diversity. The current sample was predominately of Caucasian descent, despite efforts to
diversify the sample (e.g. posting advertisement in areas accessible to diverse demographics).
Previous research has found unique demographic differences based on RFL; the current study
was unable to detect these differences.
Despite the lack of ethnic diversity the present study explored rural culture. Many rural
citizens have low access to medical and mental health treatment. The current study and the
larger RCT enhance the literature related to psychosocial issues and interventions in rural
populations. Research involving rural populations is important for several reasons: degree of
isolation, risk of suicide completion, and lack of access to care. Chronically ill older adults and
older adults in general have a tendency to become isolated particularly when living in rural
neighborhoods due to proximity to neighbors and family members. Subsequently, these older
adults not less likely to be found following an attempt and consequently not receive emergency
care. Some rural residents must travel long distances to receive medical and mental health
treatment, research exploring the effectiveness of accessible treatment such as home-based
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services is important to support modifications to treatment services. There are several initiatives
in the VA setting and geriatrics care addressing the issue of older adults’ access to care.
Specifically, the VHA Office of Rural Health is working toward increasing telehealth and Home
Based Primary Care services to treat rural residents.
Additionally, due to the clinical trial nature of the larger study, the present study
participants met criteria for Minor and Major Depressive Disorder. Exclusion of participants
with varying severity of depression limits generalizability to patients diagnosed with comorbid
depression and COPD and HF. Additionally, it excludes patients who are coping or adjusting
within normal limits or unimpaired by mental health concerns related to depression. Similarly,
older adults with cognitive impairments were excluded from the sample; cognitive impairment is
a common issue in geropsychology, which is often excluded in intervention research but an
important variable to explore further with regard to depression, HRQOL and RFL.
Implications for Future Practice
Results from this study provides a rationale for the use of psychotherapy targeting
depression with older adults to potentially strengthen RFL and reduce risk of suicide.
Specifically, mental health providers working independently, on multidisciplinary, or
interdisciplinary teams should assess depression and other life stressors common among COPD
or HF patients (i.e., loss, role transitions, and interpersonal issues). Clinicians may also be called
to complete suicide risk assessments. In addition to exploring intent, plan, and means of suicide,
clinicians should also assess unique aspects of protective factors. When determining and
communicating (e.g., written clinical notes or verbally during team meetings) a patient’s risk
level, clinicians should incorporate both risk and protective factors. Specifically, a separate
section of risk assessment clinical notes should be devoted to protective factors. The RFLI may
93
offer some suggestions related to categories of RFL explored during an assessment and
communicated verbally or written in clinical notes. Geropsychologist’s will likely find the older
adult RFL measure, currently under construction and validation, helpful due to its ability to offer
lifespan specific RFL and address salient lifespan issues (Edelstein, et al., 2009). Overall,
geropsychologist’s should be clinically trained to assess risk and protective factors and
depression in late life.
Interventions such as behavioral activation and problem solving strategies should be
employed to target and manage depressive symptoms. The present study found that patients
enrolled in the RCT and involved in early discussions related to behavioral activation with
primary focus on problem solving strategies such as identifying illness management problems,
determining options to address these problems, goal setting, and implementing these options
experienced changes in depression which influenced reported RFL.
There do not appear to be significant demographic differences with regard to reported
RFL; therefore, there are limited unique cultural considerations based on this study.
Additionally, physical HRQOL did not significantly predict RFL; however, the role of medical
professionals, occupational and rehabilitative therapists remains important related to behavioral
activation interventions. These professionals play an important role in assessing COPD and HF
patient’s functional abilities and may assist in determining behavioral activation activities.
Clinicians in primary care settings or others settings involving interdisciplinary services have a
unique position with regard to the ease in collaboration between providers to address both
psychological and medical concerns related to suicide risk. Independent practitioners are
encouraged to obtain a Release of Information to contact patient’s medical providers.
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Overall, this study also applies to Counseling Psychologists with regard to the
dependent variable, research lens and approach, and population and demographic characteristics.
Particularly, counseling psychology’s core tenants of orientation toward strength-based,
developmental, and prevention are applicable. The field of counseling psychology has a long
history of focusing on the strengths and resources of those with whom they work. Clifford Beers
suggested that clients have the capability to overcome and recover from mental health issues
(Gelso & Fretz, 2001). Lightner and Witmer extended this concept by conceptualizing mental
health issues as developmental in nature, in contrast to viewing problems as deficits (Gelso &
Fretz, 2001). The RFLI explores strengths which preclude individuals from considering suicide
as an option, rather than focusing on risk factors or the lack of risk. Framing suicide as a two
part phenomena involving risk and strength or protective factors leads to a holistic understanding
of the individual and minimizes clinicians’ bias or assumptions during assessments. The RFLI is
one of the first measures to assess strengths or life-sustaining beliefs in the context of suicide,
which aligns with the clinical approach taken by counseling psychologists. Counseling
psychologists may be more inclined to guide clients by identifying resources and helping them
engage with positive aspects of life. Resources for older adults may include relationships (e.g.
friends, family, spouse, or neighbors), leisure activities, religion and spirituality, and/or
volunteerism. These aspects of life may also be identified as protective factors against suicide.
Prevention is an additional identifying quality central to counseling psychology and a
focus of the current study. According to Morgan and Vera (2012), counseling psychology has
been one of the only mental health professions to embrace the concept of prevention.
Traditionally, mental health professionals work with severely ill patients and attempt to reduce
symptoms (Gelso & Fretz, 1992). In addition to the curative nature of clinical work, counseling
95
psychologists also work to prevent future illness. This study underscores the value in assessing
protective factors as a means of reducing risk of suicide. The RFLI specifically taps into the
protective factors and prevention by framing items stems that challenge responders to consider
“why killing yourself is not or would never be an alternative for you,” which is congruent with a
counseling psychology perspective. Other suicidology based measures tend to focus on risk
factors such as intent, ideation, suicide history, etc.
Counseling psychology has long been concerned with the way individuals, families, and
larger systems exist in and adapt to the various stages of human development (APA &
Lichtenberg, 1999). Murdock, Alcorn, Heesacker, and Stoltenberg (1998) wrote that “attention
to life-span development and transitions has traditionally been and remains a critical element in
the practice of counseling psychology” (p. 662).
In its “Archival Description of Counseling
Psychology,” the APA and Lichtenberg (1999) list “personal and interpersonal functioning
across the lifespan” first in its characterization of the field (p. 589). Rather than believing that all
people should respond similarly to certain situations, counseling psychologists appreciate that
specific factors—such as age and life experience—contribute to individuals’ reactions and
struggles.
This commitment to individuals throughout the lifespan is also visible through the
wide-ranging environments in which counseling psychologists work, such as hospitals,
community mental health centers, universities, and veterans’ administrations. The current study
also takes a developmental approach by exploring the phenomena in later life with both middle
aged and older adults. Age played an important role in the predictor model for RFL, specifically
accounting for the effects of baseline depression on predicting RFL.
96
Implications for Future Research
This study demonstrated the role of depression predicting or accounting for RFL.
Although participants were all involved in intervention, the aim of the present study was not to
determine treatment effects. However, the next step of this study is to determine treatment
effectiveness pertaining to reducing depression and predicting RFL. Additionally, the future
treatment effectiveness study will explore dose effects to determine the length of treatment
necessary to create clinically significant change in depression (i.e., five weeks versus ten weeks)
and if additional treatment or change in depression influences accounted variability in RFL.
The present study determined that age effects the relationship between depression and
RFL. Future research should also explore alternative social and demographic variables which
may modify the relationship between depression and RFL (i.e., social support and spirituality).
Researchers should also assess RFL in other older adult populations. Reasons for living need to
be studied in populations of non-depressed chronically ill older adults. This type of study will
increase the variability in the severity of depression. Research on non-depressed chronically ill
patients may provide additional information on the psychosocial profile of patients and may
better address the progression of distress related to adjusting to chronic illness. An exploration
of suicide protective factors in other medical populations is the next step in this line of research.
The current study investigated two overlapping chronic illnesses, but older adults cope with a
variety of other or comorbid illnesses.
The present study’s finding that physical and mental HRQOL does not significantly
predict RFL is somewhat inconsistent with previous research. Disease specific measures may
better capture QOL issues effecting the unique experiences of chronically ill patients. These
measures likely also have better reliability and validity within specific patient populations.
97
Similarly, future research should explore suicide protective factors using population specific
depression and RFL measures. For instance, the full scale 30-item or short form 15-item
Geriatric Depression Scale (GDS) can be administered to patients 65 and older (Yesavage, Blink,
Rose, Lum, et al., 1983). The GDS accounts for population specific concerns related to distress
and can be used in research and clinical contexts. As mentioned previously, the older adult
version of the RFLI under validation is a 69 item measure, scored and administered similar to the
48 item general RFLI.
Conclusions
This study demonstrated the role of depression on reported suicide protective factors
among COPD and HF patients. It has shown that improvement in depression severity over time
predicts reported reasons for living. Specifically, after the effects of age and baseline depression,
which are correlated, were held constant, week five depression scores predict suicide protective
factors. However, the same is not true for health related quality of life which does not predict
suicide protective factors. Based on the results of this study, it is suggested that clinicians
working with chronically ill patients diagnosed with depression intervene by conducting
thorough suicide risk and protective assessments and providing psychotherapy interventions to
reduce depression severity.
98
REFERENCES
American Association of Suicidology. (2009). Elderly suicide fact sheet. Retrieved January 16,
2013, from http://www.suicidology.org/c/documentlibrary/ getfile?folderId=232&name=
DLFE-242.pdf
American Heart Association. (2012). About heart failure. Retrieved January 03, 2013, from
http://www.heart.org/HEARTORG/Conditions/HeartFailure/ AboutHeartFailure/AboutHeart-Failure_UCM_002044_Article.jsp
American Heart Association. (2012). Heart disease and stroke statistics--2012 update: A report
from the American Heart Association. Circulation, 3-218.
doi:10.1161/CIR.0b013e31823ac046
American Psychological Association. (2000). Diagnostic and statistical manual of mental
disorders (fourth text rev. ed.). Washington: Author.
American Psychological Association & Lichtenberg, J. W. (1999). Archival description of
counseling psychology [Division 17]. The Counseling Psychologist, 27, 589-592.
doi:10.1177/0011000099274006
Anderson, K. L. (1995). The effect of chronic obstructive pulmonary disease on quality of life.
Research in Nursing & Health, 18(6), 547-556. doi: 10.1002/nur.4770180610
Angermann, C. E., Gelrich, G., Stork, S., Schowalter, M., Deckert, J., Ertl, G., & Faller, H.
(2011). Somatic correlates of comorbid major depression in patients with systolic heart
failure. International Journal of Cardiology, 147, 66-73.
Bahrampour, R. (2013, June 3). Baby boomers are killing themselves at an alarming rate, raising
question: Why?. The Washington Post. Retrieved from
http://www.washingtonpost.com/local/baby-boomers-are-killing-themselves-at-an-alarmingrate-begging-question-why/2013/06/03/d98acc7a-c41f-11e2-8c3b-0b5e9247e8ca_story.html
Balsis, S., & Cully, J. A. (2008). Comparing depression diagnostic symptoms across younger and
older adults. Aging & Mental Health, 12(6), 800-806. doi: 10.1080/13607860802428000
Beck, A. T. (1993). Beck anxiety inventory (BAI). San Antonio, TX: Psychological Corporation.
Beck, A., Steer, R., & Brown, G. (1996). Beck depression inventory—II (BDI-II). San Antonio,
TX: Psychological Corporation.
Blazer, D. G. (2002). Depression in late life (3rd ed.). New York: Springer.
Blazer, D. G. (2003). Depression in late life: Review and commentary. The Journals of
Gerontology: Series A: Biological Sciences and Medical Sciences, 58A (3), 249-265. doi:
10.1093/gerona/58.3.M249
99
Blazer, D. G. (2003). Depression in late life: Review and commentary. Journal of Gerontology:
Medical Sciences, 58A, M249-M265.
Böhm, M., & Erdmann, E. (1991). The pathophysiological basis, clinical presentation, and
therapy of chronic heart failure. In H. Viefhues (Ed.), Chronic heart failure (pp. 5-26). New
York: Springer-Verlag.
Braden, A. L., Overholser, J. C., & Silverman, E. (2011). Depression and reasons for living
among AIDS patients: Protecting quality of life when the end is in sight. International
Journal of Psychiatry in Medicine, 41(2), 173-185. doi: 10.2190/PM.41.2.f
Braido, F., Baiardini, I., Menoni, S., Bagnasco, A. M., Balbi, F., Bocchibianchi, S., . . .
Canonica, G. W. (2011). Disability in COPD and its relationship to clinical and patientreported outcomes. Current Medical Research and Opinion, 27(5), 981-986. doi:
10.1185/03007995.2011.563285
Bratås, O., Espnes, G. A., Rannestad, T., & Walstad, R. (2012). Relapse of health related quality
of life and psychological health in patients with chronic obstructive pulmonary disease 6
months after rehabilitation. Scandinavian Journal of Caring Sciences, 26(2), 219-227. doi:
10.1111/j.1471-6712.2011.00921.x
Britton, P. C., Duberstein, P. R., Conner, K. R., Heisel, M. J., Hirsch, J. K., & Conwell, Y.
(2008). Reasons for living, hopelessness, and suicide ideation among depressed adults 50
years or older. The American Journal of Geriatric Psychiatry, 16(9), 736-741. doi:
10.1097/JGP.0b013e31817b609a
Bruce, M. L. (2001). Depression and disability in late life: Directions for future research. The
American Journal of Geriatric Psychiatry, 9(2), 102-112. doi: 10.1176/appi.ajgp.9.2.102
Carver, C., Scheier, M., & Weintraub, J. (1989). Assessing coping strategies: A theoretically
based approach. Journal of Personality and Social Psychology, 56, 267–283.
Center for Disease Control. (2012). Heart failure fact sheet. Retrieved January 03, 2013, from
http://www.cdc.gov/dhdsp/data_statistics/fact_sheets/fs_heart_failure.htm
Chiles, J. A., Strosahl, K. D., Ping, Z. Y., & Michael, M. C. (1989). Depression, hopelessness,
and suicidal behavior in Chinese and American psychiatric patients. The American Journal
of Psychiatry, 146(3), 339-344.
Conwell, Y., Van Orden, K., & Caine, E. D. (2011). Suicide in older adults. Psychiatric Clinics
of North America, 34(2), 451-468. doi: 10.1016/j.psc.2011.02.002
Cully, J. A., Graham, D. P., Stanley, M. A., Ferguson, C. J., Sharafkhaneh, A., Souchek, J., &
Kunik, M. E. (2006). Quality of life in patients with chronic obstructive pulmonary disease
and comorbid anxiety or depression. Psychosomatics: Journal of Consultation Liaison
Psychiatry, 47(4), 312-319. doi: 10.1176/appi.psy.47.4.312
100
Cully, J. A., Johnson, M., Moffett, M. L., Khan, M., & Deswal, A. (2009). Depression and
anxiety in ambulatory patients with heart failure. Psychosomatics: Journal of Consultation
Liaison Psychiatry, 50(6), 592-598.
Cully, J. A., Phillips, L. L., Kunik, M. E., Stanley, M. A., & Deswal, A. (2010). Predicting
quality of life in veterans with heart failure: The role of disease severity, depression, and
comorbid anxiety. Behavioral Medicine, 36(2), 70-76. doi: 10.1080/08964280903521297
de Voogd, J. N., Sanderman, R., Postema, K., van Sonderen, E., & Wempe, J. B. (2011).
Relationship between anxiety and dyspnea on exertion in patients with chronic obstructive
pulmonary disease. Anxiety, Stress & Coping: An International Journal, 24(4), 439-449.
doi: 10.1080/10615806.2010.520081
Delville, C. L., & McDougall, G. (2008). A systematic review of depression in adults with heart
failure: Instruments and incidence. Issues in Mental Health Nursing, 29(9), 1002-1017. doi:
10.1080/01612840802274867
Dervic, K., Grunebaum, M. F., Burke, A. K., Mann, J. J., & Oquendo, M. A. (2006). Protective
factors against suicidal behavior in depressed adults reporting childhood abuse. Journal of
Nervous and Mental Disease, 194(12), 971-974. doi:
10.1097/01.nmd.0000243764.56192.9c
Desikan, R., Mason, H. L., Rupp, M. T., & Skehan, M. (2002). Health-related quality of life and
healthcare resource utilization by COPD patients: A comparison of three instruments.
Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment,
Care & Rehabilitation, 11(8), 739-751. doi: 10.1023/A:1020836719321
Dozois, D. J. A., Dobson, K. S., and Ahnberg, J. L. (1998). A psychometric evaluation of the
Beck Depression Inventory-II. Psychological Assessment, 10 (2), 83-89.
Durkheim, E. (1979). Suicide: A study in sociology. New York: Free Press.
Edelstein, B. A., Heisel, M. J., McKee, D. R., Martin, R. R., Koven, L. P., Duberstein, P. R., and
Britton, P. C. (2009). Development of psychometric evaluation of the Reasons for LivingOlder Adults Scale: A suicide risk assessment inventory. The Gerontologist, 49 (6), 736745.
Faller, H., Steinbüchel, T., Störk, S., Schowalter, M., Ertl, G., & Angermann, C. E. (2010).
Impact of depression on quality of life assessment in heart failure. International Journal of
Cardiology, 142, 133-137. doi: 10.1016/j.ijcard.2008.12.093
Faller, H., Störk, S., Schowalter, M., Steinbüchel, T., Wollner, V., Ertl, G., & Angermann, C. E.
(2007). Is health-related quality of life an independent predictor of survival in patients with
chronic heart failure? Journal of Psychosomatic Research, 63(5), 533-538. doi:
10.1016/j.jpsychores.2007.06.026
101
Faller, H., Störk, S., Schuler, M., Schowalter, M., Steinbüchel, T., Ertl, G., & Angermann, C. E.
(2009). Depression and disease severity as predictors of health-related quality of life in
patients with chronic heart failure- A structural equation modeling approach. Journal of
Cardiac Failure, 15(4), 286-292. doi: 10.1016/j.cardfail.2008.10.022
First, M., Spitzer, R., Gibbon, M., & Williams, J. (2002). Structured clinical interview for DSMIV-TR--axis I disorders--research version--patient edition (SCID-IV-P). New York: New
York: State Psychiatric Institute, Biometrics Research Department.
Fiske, A., O'Riley, A., & Widoe, R. (2008). Physical health and suicide in later life: An
evaluative review. Clinical Gerontologist: The Journal of Aging and Mental Health, 31(4),
31-50. doi: 10.1080/07317110801947151
Folstein, M. F., Folstein, S. E., and McHugh, P. R. (1975). Mini-mental state: A practical method
for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research,
12 (3), 189-198.
Furlanetto, L. M., & Stefanello, B. (2011). Suicidal ideation in medical inpatients: Psychosocial
and clinical correlates. General Hospital Psychiatry, 33(6), 572-578. doi:
10.1016/j.genhosppsych.2011.08.002
Galfalvy, H., Oquendo, M. A., Carballo, J. J., Sher, L., Grunebaum, M. F., Burke, A., & Mann, J.
J. (2006). Clinical predictors of suicidal acts after major depression in bipolar disorder: A
prospective study. Bipolar Disorders, 8(5), 586-595. doi: 10.1111/j.13995618.2006.00340.x
Gallagher-Thompson, D., & Osgood, J. (1997). Suicide in later life.28, 23-41.
Gellis, Z. (Ed.). (2006). Older adults with mental and emotional problems (6th ed.). New York:
Oxford University Press.
Gelso, C., & Fretz, B. (2001). Counseling psychology: An introduction to the field. Counseling
psychology (2nd ed., pp. 2-25). Belmont, CA: Wadsworth Group/Thomson Learning.
Goodwin, R. D. (2011). Is COPD associated with suicide behavior? Journal of Psychiatric
Research, 45(9), 1269-1271. doi: 10.1016/j.jpsychires.2011.01.014
Green, C., Porter, C., Bresnahan, D., & Spertus, J. (2000). Development and evaluation of
the Kansas City Cardiomyopathy Questionnaire: A new health status measure for heart
failure. Journal of the American College of Cardiology, 35(5), 1245-1255.
Grunebaum, M. F., Galfalvy, H. C., Mortenson, L. Y., Burke, A. K., Oquendo, M. A., & Mann,
J. J. (2010). Attachment and social adjustment: Relationships to suicide attempt and major
depressive episode in a prospective study. Journal of Affective Disorders, 123(1-3), 123130. doi: 10.1016/j.jad.2009.09.010
102
Guyatt, G., Berman, L., Townsend, M., Pugsley, S., & Chambers, L. (1987). A measure of
quality of life for clinical trials in chronic lung disease. . Thorax, 157, 185-190.
Heart Failure Society of America. (2011). Questions about heart failure. Retrieved January 03,
2013, from http://www.abouthf.org/questions_what_is_hf.htm
Heisel, M. J. (2006). Suicide and its prevention among older adults. The Canadian Journal of
Psychiatry / La Revue Canadienne De Psychiatrie, 51(3), 143-154.
Hirsch, J. K., Duberstein, P. R., Conner, K. R., Heisel, M. J., Beckman, A., Franus, N., &
Conwell, Y. (2006). Future orientation and suicide ideation and attempts in depressed adults
ages 50 and over. The American Journal of Geriatric Psychiatry, 14(9), 752-757. doi:
10.1097/01.JGP.0000209219.06017.62
Hirsch, J. K., Duberstein, P. R., & Unützer, J. (2009). Chronic medical problems and distressful
thoughts of suicide in primary care patients: Mitigating role of happiness. International
Journal of Geriatric Psychiatry, 24(7), 671-679. doi: 10.1002/gps.2174
Hooyman, N., & Kiyak, A. (2010). Social gerontology: A multidisciplinary perspective (ninth
ed.). Boston: Ally and Bacon.
Hybels, C., & Blazer, D. G. (2003). Epidemiology of late life mental disorders. Public Policy
and Aging Report, 16, 663-696.
Hynninen, K. M. J., Breitve, M. H., Wiborg, A. B., Pallesen, S., & Nordhus, I. H. (2005).
Psychological characteristics of patients with chronic obstructive pulmonary disease: A
review. Journal of Psychosomatic Research, 59(6), 429-443. doi:
10.1016/j.jpsychores.2005.04.007
Johnson, J., Wood, A. M., Gooding, P., Taylor, P. J., & Tarrier, N. (2011). Resilience to
suicidality: The buffering hypothesis. Clinical Psychology Review, 31(4), 563-591. doi:
10.1016/j.cpr.2010.12.007
Jones, P., Quirk, F., & Baveystock, C. (1991). The St. Georges Respiratory Questionnaire.
Respiratory Medicine, 85, 25-37.
Jones, R., Marcantonio, E., & Rabinowitz, T. (2003). Prevalence and correlates of recognized
depression in U.S. nursing homes. Journal of the American Geriatrics Society, 51, 14041409.
June, A., Segal, D. L., Coolidge, F. L., & Klebe, K. (2009). Religiousness, social support and
reasons for living in African American and European American older adults: An exploratory
study. Aging & Mental Health, 13(5), 753-760. doi: 10.1080/13607860902918215
Juurlink, D. N. (2004). Does the treatment of dyslipidemia affect the risk of depression or
suicidal behaviour? Canadian Medical Association Journal, 170(2), 192.
103
Kissane, M., & McLaren, S. (2006). Sense of belonging as a predictor of reasons for living in
older adults. Death Studies, 30(3), 243-258. doi: 10.1080/07481180500493401
Klein, D. M., Turvey, C. L., & Pies, C. J. (2007). Relationship of coping styles with quality of
life and depressive symptoms in older heart failure patients. Journal of Aging and Health,
19(1), 22-38. doi: 10.1177/0898264306296398
Klinger, J. (1999). Suicide among seniors. Australasian Journal on Ageing, 18(3), 114-118. doi:
10.1111/j.1741-6612.1999.tb00110.x
Lawrence, D., Almeida, O. P., Hulse, G. K., Jablensky, A. V., D'Arcy, C., & Holman, J. (2000).
Suicide and attempted suicide among older adults in Western Australia. Psychological
Medicine, 30(4), 813-821. doi: 10.1017/S0033291799002391
Levin, A. (2014, October 10). Will Baby Boomers Rewrite Data on Elderly Suicides?.
Psychiatric News. Retrieved from http://psychnews.psychiatryonline.org/doi/full/
10.1176/pn.46.16.psychnews_46_16_17_1
Linehan, M. M., Goodstein, J. L., Nielsen, S. L., & Chiles, J. A. (1983). Reasons for staying
alive when you are thinking of killing yourself: The reasons for living inventory. Journal of
Consulting and Clinical Psychology, 51(2), 276-286. doi: 10.1037/0022-006X.51.2.276
Lizardi, D., Sher, L., Sullivan, G. M., Stanley, B., Burke, A., & Oquendo, M. A. (2009).
Association between familial suicidal behavior and frequency of attempts among depressed
suicide attempters. Acta Psychiatrica Scandinavica, 119(5), 406-410. doi: 10.1111/j.16000447.2009.01365.x
Lizardi, D., Currier, D., Galfalvy, H., Sher, L., Burke, A., Mann, J., & Oquendo, M. (2007).
Perceived reasons for living at index hospitalization and future suicide attempt. Journal of
Nervous and Mental Disease, 195(5), 451-455.
Lizardi, D., Dervic, K., Grunebaum, M. F., Burke, A. K., Mann, J. J., & Oquendo, M. A. (2008).
The role of moral objections to suicide in the assessment of suicidal patients. Journal of
Psychiatric Research, 42(10), 815-821. doi: 10.1016/j.jpsychires.2007.09.007
Lorig, K., Sobel, D., & Laurent, D. (2000). Living a healthy life with chronic conditions: Selfmanagement of heart disease, arthritis, diabetes, asthma, bronchitis, emphysema & others.
Boulder: Bull Publishing Company.
Lossnitzer, N., Müller-Tasch, T., Löwe, B., Zugck, C., Nelles, M., Remppis, A., . . . Wild, B.
(2009). Exploring potential associations of suicidal ideation and ideas of self-harm in
patients with congestive heart failure. Depression and Anxiety, 26(8), 764-768. doi:
10.1002/da.20587
104
Low G. D. and Hubley, A. M. (2007). Screening for depression after cardiac events using the
Beck Depression Inventory II and geriatric depression scale. Social Indicators Research, 82,
527-543.
Maille, A., Koning, C., Zwinderman, A., Willems, L. N., Kijkman, J., & Kaptein, A. (1997). The
development of the Quality-of-Life for Respiratory Illness Questionnaire (QOL-RIQ): A
disease-specific quality-of-life questionnaire for patients with mild to moderate chronic nonspecific lung disease.
. Respiratory Medicine, 91, 297-309.
Malone, K. M., Oquendo, M. A., Haas, G. L., Ellis, S. P., Li, S., & Mann, J. J. (2000). Protective
factors against suicidal acts in major depression: Reasons for living. The American Journal
of Psychiatry, 157(7), 1084-1088. doi: 10.1176/appi.ajp.157.7.1084
Marty, M. A. (2012). Older adults and the desire for suicide: The role of perceived
burdensomeness and thwarted belongingness. ProQuest Information & Learning). 72(8),
4989.
Marty, M. A., Segal, D. L., & Coolidge, F. L. (2010). Relationships among dispositional coping
strategies, suicidal ideation, and protective factors against suicide in older adults. Aging &
Mental Health, 14(8), 1015-1023. doi: 10.1080/13607863.2010.501068
McLaren, S. (2011). Age, gender, and reasons for living among Australian adults. Suicide and
Life-Threatening Behavior, 41(6), 650-660. doi: 10.1111/j.1943-278X.2011.00061.x
Mikkelsen, R. L., Middelboe, T., Pisinger, C., & Stage, K. B. (2004). Anxiety and depression in
patients with chronic obstructive pulmonary disease (COPD). A review. Nordic Journal of
Psychiatry, 58(1), 65-70. doi: 10.1080/08039480310000824
Miller, J. S., Segal, D. L., & Coolidge, F. L. (2001). A comparison of suicidal thinking and
reasons for living among younger and older adults. Death Studies, 25(4), 357-365. doi:
10.1080/07481180151143105
Moorer, P., Suurmeijer, T., Foets, M., Molenaar, I., (2001). Psychometric properties of the
RAND-36 among three chronic diseases (multiple sclerosis, rheumatic diseases and COPD)
in the Netherlands. Quality of Life Research, 10, 637-645.
Morgan, M., & Vera, E. (2012). Prevention and psychoeducationin counseling psychology. In E.
Altmaier, & J. Hansen (Eds.), The Oxford handbook of counseling psychology (1st ed., pp.
529-544). New York, NY: Oxford University Press.
Moussas, G., Tselebis, A., Karkanias, A., Stamouli, D., Ilias, I., Bratis, D., & Vassila-Demi, K.
(2008). A comparative study of anxiety and depression in patients with bronchial asthma,
chronic obstructive pulmonary disease and tuberculosis in a general hospital of chest
diseases. Annals of General Psychiatry, 7 doi: 10.1186/1744-859X-7-7
105
Murdock, N. L., Alcorn, J., Heesacker, M., & Stoltenberg, C. (1998). Model training program in
counseling psychology. The Counseling Psychologist, 23, 658–672.
doi:10.1177/0011000098264008
National Institute of Mental Health. Any anxiety disorder among adults. Retrieved January 03,
2013, from http://www.nimh.nih.gov/statistics/1anyanx_adult.shtml
National Institute of Mental Health. Major depressive disorder among adults. Retrieved January
03, 2013, from http://www.nimh.nih.gov/statistics/1mdd_adult.shtml
Osman, A., Gilfford, J., Jones, T., Lickiss, L., Osman, J., and Wenzel, R. (1993). Psychometric
Evaluation of the Reasons for Living Inventory. Psychological Assessment, 5 (2), 154-158
Osman, A., Gregg, C. L., Osman, J. R., and Jones, K. (1992). Factor structure and reliability of
The Reasons for Living Inventory. Psychological Reports, 70, 107-112.
Osman, A., Jones, K., & Osman, J. R. (1991). The Reasons for Living Inventory: Psychometric
Properties. Psychological Report, 69, 271-278.
Oquendo, M. A., Dragatsi, D., Harkavy-Friedman, J., Dervic, K., Currier, D., Burke, A. K., . . .
Mann, J. J. (2005). Protective factors against suicidal behavior in Latinos. Journal of
Nervous and Mental Disease, 193(7), 438-443. doi: 10.1097/01.nmd.0000168262.06163.31
Oquendo, M. A., Friedman, J. H., Grunebaum, M. F., Burke, A., Silver, J. M., & Mann, J. J.
(2004). Suicidal behavior and mild traumatic brain injury in major depression. Journal of
Nervous and Mental Disease, 192(6), 430-434. doi: 10.1097/01.nmd.0000126706.53615.7b
Oquendo, M. A., Galfalvy, H., Russo, S., Ellis, S. P., Grunebaum, M. F., Burke, A., & Mann, J.
J. (2004). Prospective study of clinical predictors of suicidal acts after a major depressive
episode in patients with major depressive disorder or bipolar disorder. The American
Journal of Psychiatry, 161(8), 1433-1441. doi: 10.1176/appi.ajp.161.8.1433
Parker-Pope, T. (2013, May 2). Suicide Rates Rise Sharply in U.S. The New York Times.
Retrieved from http://www.nytimes.com/2013/05/03/health/suicide-rate-rises-sharply-inus.html?_r=0.
Paukert, A. L., LeMaire, A., & Cully, J. A. (2009). Predictors of depressive symptoms in older
veterans with heart failure. Aging & Mental Health, 13(4), 601-610. doi:
10.1080/13607860802459823
Range, L. M., & Stringer, T. A. (1996). Reasons for living and coping abilities among older
adults. The International Journal of Aging & Human Development, 43(1), 1-6. doi:
10.2190/5JEE-K1KF-AFN6-DGJW
106
Rector, T., Kubo, S., & Cohn, J. (1987). Patients' self-assessment of their
congestive heart failure: Vol. 2. Content, reliability, and validity of a new measure-The Minnesota Living with Heart Failure Questionnaire. Heart Failure, 3, 198-209.
Richardson-Vejlgaard, R., Sher, L., Oquendo, M. A., Lizardi, D., & Stanley, B. (2009). Moral
objections to suicide and suicidal ideation among mood disordered whites, blacks, and
Hispanics. Journal of Psychiatric Research, 43(4), 360-365. doi:
10.1016/j.jpsychires.2008.03.008
Richardson-Vejlgaard, R., Sher, L., Oquendo, M. A., Lizardi, D., & Stanley, B. (2009).
Objections to suicide among depressed patients with alcohol use disorders. Journal of
Affective Disorders, 117(3), 197-201. doi: 10.1016/j.jad.2009.01.005
Roehrig, H. R., & Range, L. M. (1995). Recklessness, depression, and reasons for living in
predicting suicidality in college students. Journal of Youth and Adolescence, 24(6), 723729. doi: 10.1007/BF01536953
Rustᴓen, T., Stubhaug, A., Eidsmo, I., Westheim, A., Paul, S., & Miaskowski, C. (2008). Pain
and quality of life in hospitalized patients with heart failure. Journal of Pain and Symptom
Management, 36(5), 497-504. doi: 10.1016/j.jpainsymman.2007.11.014
Schane, R. E., Woodruff, P. G., Dinno, A., Covinsky, K. E., & Walter, L. C. (2008). Prevalence
and risk factors for depressive symptoms in persons with chronic obstructive pulmonary
disease. Journal of General Internal Medicine, 23(11), 1757-1762. doi: 10.1007/s11606008-0749-z
Scharloo, M., Kaptein, A. A., Schlösser, M., Pouwels, H., Bel, E. H., Rabe, K. F., & Wouters, E.
F. M. (2007). Illness perceptions and quality of life in patients with chronic obstructive
pulmonary disease. Journal of Asthma, 44(7), 575-581. doi: 10.1080/02770900701537438
Segal, D. L., Coolidge, D. L., Cahill, B. S., and O’Riley, A. A. (2008). Psychometric Properties
of the Beck Depression Inventory-II (BDI-II) among community dwelling older adults.
Behavior Modification, 32 (3), 3-20.
Segal, D. L., Lebenson, S., & Coolidge, F. L. (2008). Global self-rated health status predicts
reasons for living among older adults. Clinical Gerontologist: The Journal of Aging and
Mental Health, 31(4), 122-132. doi: 10.1080/07317110802144006
Segal, D. L., Marty, M. A., Meyer, W. J., & Coolidge, F. L. (2012). Personality, suicidal
ideation, and reasons for living among older adults. The Journals of Gerontology: Series B:
Psychological Sciences and Social Sciences, 67B(2), 159-166. doi: 10.1093/geronb/gbr080
Shen, B., Eisenberg, S. A., Maeda, U., Farrell, K. A., Schwarz, E. R., Penedo, F. J., . . . Mallon,
S. (2011). Depression and anxiety predict decline in physical health functioning in patients
with heart failure. Annals of Behavioral Medicine, 41(3), 373-382. doi: 10.1007/s12160010-9251-z
107
Sher, L., Oquendo, M. A., Galfalvy, H. C., Grunebaum, M. F., Burke, A. K., Zalsman, G., &
Mann, J. J. (2005). The relationship of aggression to suicidal behavior in depressed patients
with a history of alcoholism. Addictive Behaviors, 30(6), 1144-1153. doi:
10.1016/j.addbeh.2004.12.001
Steer, R. A., Ball, R., Ranieri, W. F., and Beck A. T. (1999). Dimensions of the Beck Depression
Inventory-II in clinically depressed outpatients. Journal of Clinical Psychology, 55, 117128.
Steer, R. A., Rissmiller, D. J., & Beck, A. T., (2000). Use of the Beck Depression Inventory-II
with depressed geriatric inpatients. Behaviour Research and Therapy, 38, 311-318.
Strosahl, K., Chiles, J. A., & Linehan, M. (1992). Prediction of suicide intent in hospitalized
parasuicides: Reasons for living, hopelessness, and depression. Comprehensive Psychiatry,
33(6), 366-373. doi: 10.1016/0010-440X(92)90057-W
Stucky, K., & Greenblatt, J. (2008). Chronic obstructive pulmonary disease. In B. Boyer, & I.
Paharia (Eds.), Comprehensive handbook of clinical health psychology (pp. 277-297) John
Wiley & Sons.
Szanto, K., Prigerson, H., & and Reynolds, C. (2001). Suicide in the elderly. Clinical
Neuroscience Research, 366-376.
Thombs, B. D., Ziegelstein, R. C., Beck, C. A., and Pilote, L. (2008). A general factor model for
the Beck Depression Inventory-II: Validation in a sample of patients hospitalized with acute
myocardial infarction. Journal of Psychosomatic Research, 65(2), 115-121.
Thombs, B. D., Ziegelstein, R. C., Pilote, L., Dozois, D. J. A., Beck, A. T., Dobson, K. S., …
Abbey, S. E. (2010). Somatic symptom overlap in Beck Depression Inventory- II scores
following myocardial infarction. The British Journal of Psychiatry, 197, 61-66
Titov, N., Dear, B. F., McMillian, D., Anderson, T., Zou, J., and Sunderland, M. (2011).
Psychometric comparison of the PHQ-9 and BDI-II for measuring response during
treatment of depression. Cognitive Behaviour Therapy, 40 (2), 126-136.
Turvey, C. L., Klein, D. M., & Pies, C. J. (2006). Depression, physical impairment, and
treatment of depression in chronic heart failure. Journal of Cardiovascular Nursing, 21(3),
178-185.
U.S. Census Bureau. (2012). The older population in the United States: 2011. Retrieved January
03, 2013, from http://www.census.gov/population/age/data/2011.html
Ulmer, A. S., Range, L. M., & Gale, T. R. (1992). Loneliness and depression related to reasons
for living in military personnel. Death Studies, 16(2), 183-189. doi:
10.1080/07481189208252567
108
VanderZee K. I., Sanderman, R., and Heyink, J. (1996). A comparison of two multidimensional
measures of health status: The Nottingham Health Profile and the RAND 36- Item Health
Survey 1.0. Quality of life Research, 5, 165-174.
Wakabayashi, R., Motegi, T., Yamada, K., Ishii, T., Gemma, A., & Kida, K. (2011). Presence of
in‐home caregiver and health outcomes of older adults with chronic obstructive pulmonary
disease. Journal of the American Geriatrics Society, 59(1), 44-49. doi: 10.1111/j.15325415.2010.03222.x
Wang, M., Lightsey, O. R., Pietruszka, T., Uruk, A. C., & Wells, A. G. (2007). Purpose in life
and reasons for living as mediators of the relationship between stress, coping, and suicidal
behavior. The Journal of Positive Psychology, 2(3), 195-204. doi:
10.1080/17439760701228920
Ware, J., & Sherbourne, C. (1992). The MOS 36-item short-form health survey (SF-36): I.
conceptual framework and item selection. . Medical Care, 30(6), 473-483.
Westefeld, J., Range, L., Rogers, J., Maples, M., Bromley, J., & Alcorn, J. (2000). Suicide: An
overview. The Counseling Psychologist, 28(4), 445-510.
Whitbourne, S., & Whitbourne, S. (2011). Adult development and aging. New York: Wiley.
Williams, S. A., Kasl, S. V., Heiat, A., Abramson, J. L., Krumholz, H. M., & Vaccarino, V.
(2002). Depression and risk of heart failure among the elderly: A prospective communitybased study. Psychosomatic Medicine, 64(1), 6-12.
Yesavage, J., Brink, T., Rose, T., & et al. (1983). Development and validation of a geriatric
depression screening scale: A preliminary report. Journal of Psychiatric Research, 17, 3749.
Yohannes, A. M., Baldwin, R. C., & Connolly, M. J. (2000). Mood disorders in elderly patients
with chronic obstructive pulmonary disease. Reviews in Clinical Gerontology, 10(2), 193202. doi: 10.1017/S0959259800002100
Zalaquett, C., & Stens, A. (2006). Psychosocial treatments for major depression and dysthymia
in older adults. Journal of Counseling & Development, 84, 192-201.
Zarit, S. (Ed.). (2009). A good old age: Theories of mental health and aging. New York:
Springer.
109