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Social support, health, and treatment activity in adults with cystic fibrosis

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Title:
Social support, health, and treatment activity in adults with cystic fibrosis
Creator:
Flewelling, Kassie D. ( author )
Place of Publication:
Denver, Colo.
Publisher:
University of Colorado Denver
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English
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1 electronic file (75 pages) : ;

Thesis/Dissertation Information

Degree:
Master's ( Master of science)
Degree Grantor:
University of Colorado Denver
Degree Divisions:
Department of Psychology, CU Denver
Degree Disciplines:
Psychology

Subjects

Subjects / Keywords:
Cystic fibrosis ( lcsh )
Genre:
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Review:
Background: Adults with cystic fibrosis (CF) may face a unique set of clinical and psychosocial barriers affecting the attainment and maintenance of social support; however, research in this area is limited. The current study examines the role of age, gender, income, education, marital status, employment, and disease severity on social support in adults with CF. Mental and physical health symptoms, treatment activity, and disease-specific quality of life are evaluated as outcomes of support. ( ,, )
Review:
Methods: Participants in the study included 250 adults with CF who took part in a larger longitudinal study known as the Project on Adult Care in Cystic Fibrosis (PAC-CF). Participants were administered a battery of measures including a social support evaluation (Interpersonal Support Evaluation List, ISEL), a health assessment (Memorial Symptom Assessment Scale, MSAS), treatment activity questionnaires (Tool for Adherence Behaviour Screening, TABS and other surveys), and a health-related quality of life measure (Cystic Fibrosis Questionnaire - Revised, CFQ-R). Disease severity measures (forced expiratory volume in one second [FEV1] and exacerbations) were also assessed over a 45-month period.
Review:
Results: Latent growth curve modeling revealed that social support remained stable over time. Linear regression analyses indicated that females and those who were employed had greater social support. In turn, greater social support predicted fewer mental and physical health symptoms, digestive symptoms, and eating disturbances. Social support predicted emotional, social, and role functioning as well as vitality and improved body image. Moreover, those with more support experienced less treatment burden and better overall perceptions of their health. Social support mediated the relationship between employment and mental health symptoms. Conclusions: The current study fills gaps in the literature by examining predictors and outcomes of social support in adults with CF as well as studying social support over time. Interventions that target support, and are in accordance with disease prevention and control efforts, are of utmost importance.
Bibliography:
Includes bibliographical references.
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System requirements: Adobe Reader.
Statement of Responsibility:
Kassie D. Flewelling.

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University of Colorado Denver
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Auraria Library
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All applicable rights reserved by the source institution and holding location.
Resource Identifier:
on10225 ( NOTIS )
1022564186 ( OCLC )
on1022564186
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LD1193.L645 2017m F54 ( lcc )

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1
SOCIAL SUPPORT, HEALTH, AND TREATMENT ACTIVITY IN ADULTS WITH
CYSTIC FIBROSIS by
KASSIE D. FLEWELLING B.A., University of Nebraska Lincoln, 2015
A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Master of Arts Psychology Program
2017


This thesis for the Master of Arts degree by Kassie D. Flewelling has been approved for the Psychology Program by
Edward Dill, Chair Barbara Walker
Walter Robinson


iii
Date: December 16, 2017
Flewelling, Kassie D (MA, Psychology Program)
Social Support, Health, and Treatment Activity in Adults with Cystic Fibrosis Thesis directed by Assistant Professor Edward Dill
ABSTRACT
Background: Adults with cystic fibrosis (CF) may face a unique set of clinical and psychosocial barriers affecting the attainment and maintenance of social support; however, research in this area is limited. The current study examines the role of age, gender, income, education, marital status, employment, and disease severity on social support in adults with CF. Mental and physical health symptoms, treatment activity, and disease-specific quality of life are evaluated as outcomes of support. Methods: Participants in the study included 250 adults with CF who took part in a larger longitudinal study known as the Project on Adult Care in Cystic Fibrosis (PAC-CF). Participants were administered a battery of measures including a social support evaluation (Interpersonal Support Evaluation List, ISEL), a health assessment (Memorial Symptom Assessment Scale, MSAS), treatment activity questionnaires (Tool for Adherence Behaviour Screening, TABS and other surveys), and a health-related quality of life measure (Cystic Fibrosis Questionnaire Revised, CFQ-R). Disease severity measures (forced expiratory volume in one second [FEVi] and exacerbations) were also assessed over a 45-month period. Results: Latent growth curve modeling revealed that social support remained stable over time. Linear regression analyses indicated that females and those who were employed had greater social support. In turn, greater social support predicted fewer mental and physical health symptoms, digestive symptoms, and eating disturbances. Social support predicted emotional, social, and role


IV
functioning as well as vitality and improved body image. Moreover, those with more support experienced less treatment burden and better overall perceptions of their health. Social support mediated the relationship between employment and mental health symptoms. Conclusions: The current study fills gaps in the literature by examining predictors and outcomes of social support in adults with CF as well as studying social support over time. Interventions that target support, and are in accordance with disease prevention and control efforts, are of utmost importance.
The form and content of this abstract are approved. I recommend its publication.
Approved: Edward Dill


V
TABLE OF CONTENTS
CHAPTER
I. INTRODUCTION.............................................................1
Models of the Relationship between Social Support and Health..........1
Social Support and Health in the General Population...................3
Social Support and Health in Clinical Populations.....................6
Social Support and Health among Adults with Cystic Fibrosis...........9
Longitudinal Trends and Predictors of Social Support.................13
Research Aims and Hypotheses.........................................15
II. METHODS.................................................................16
Participants.........................................................16
Procedures...........................................................17
Measures.............................................................17
Demographic Information.......................................17
Disease Severity..............................................18
Social Support................................................18
Mental and Physical Health Symptoms...........................19
Treatment Activity............................................20
Disease Specific Health Related Quality of Life...............22
Covariates....................................................22
Statistical Analyses.................................................23
III. RESULTS.................................................................25
Longitudinal Trends in Social Support
25


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Predictors of Social Support............................................26
Outcomes Related to Social Support......................................26
Mediators of the Relationship between Social Support and Health.........28
IV. DISCUSSION..............................................................29
Implications............................................................35
Limitations and Future Directions.......................................36
Conclusion..............................................................38
REFERENCES....................................................................57
APPENDIX
A. Interpersonal Support Evaluation List (ISEL)........................46
B. Memorial Symptom Assessment Scale (MSAS)............................48
C. Tool for Adherence Behaviour Screen (TABS)..........................50
D. Treatment Activity..................................................51
E. Cystic Fibrosis Questionnaire Revised (CFQ-R).....................52


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LIST OF TABLES
TABLE
1. Descriptive statistics for variables included in analyses................39
2. Bivariate correlations of variables predicting social support............40
3. Bivariate correlations of outcome variables predicted by social support..41
4. Regression Results for Predictors of Social Support......................42
5. Regression Results for Outcomes of Social Support........................43


LIST OF FIGURES
viii
FIGURE
1. Latent growth curve examining support across four time points....44
2. Latent growth curve examining support across three time points...45


1
CHATPERI
Social Support, Health, and Treatment Activity in Adults with Cystic Fibrosis
Cystic fibrosis (CF) is a chronic, genetic condition affecting the respiratory, digestive, endocrine and reproductive systems of nearly 70,000 individuals worldwide (Cystic Fibrosis Foundation, 2013). In those with the most common clinical pattern of CF, mucus accumulates in the lungs, pancreas and other organs, causing significant damage and eventually death (Cystic Fibrosis Foundation, 2013). With nearly 1,000 new diagnoses each year, CF is the most common life-shortening inherited disease among Caucasians (Cystic Fibrosis Foundation, 2013; Krauth, Jalilvand, Welte, & Busse, 2003). In 2006, direct costs associated with CF care in the U.S. exceeded $48,000 per patient (Ouyang, Grosse,
Amendah, & Schechter, 2009).
The prognosis of CF has improved dramatically over the past three decades with predicted longevity estimates of nearly 40 years of age. In fact, nearly half of those with CF are over the age of 18 (Cystic Fibrosis Foundation, 2013). The improvements in life expectancy have resulted in a new population of CF adults who face disease-specific psychosocial challenges. Although social support has been linked to a variety of mental and physical health outcomes in those with and without chronic illness, this construct has rarely been studied in the CF population, especially in adult samples.
Models of the Relationship between Social Support and Health
Social support can be defined as the material and emotional resources that are available to a person through interpersonal contacts (Aslund, Larm, Starrin, & Nilsson, 2014, p. 1). Historically, social support has been separated into two distinct constructs: perceived and received support (Tardy, 1985; Uchino, 2009). The current paper examines perceived


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social support, a more subjective belief that emotional, informational and instrumental support is available from family, friends, colleagues and loved ones (Nurullah, 2012; Schulz & Schwarzer, 2004). Although researchers have known for decades that social support is associated with various health outcomes, the exact mechanisms underlying the relationships remain unknown (Thoits, 2011). Previous research suggests that social support is related both directly and indirectly to mental and physical health (Glasgow, Barrera, McKay, & Boles, 1999; Romero, 2013; Thoits, 2011). The most widely accepted models of social support are the stress buffering model and the main effects model, also known as the direct effects model. The stress buffering model suggests that social support buffers negative effects of stress on health, but only when stress levels are high (Cohen & Wills, 1985; Romero, 2013). According to this model, social support improves ones ability to cope with stressful events, which leads to improvements in mental health. When stress is low or nonexistent, however, this model predicts that social support is not associated with health outcomes (Lakey & Orehek, 2011).
Despite its prevalence in the literature, many researchers have found that the link between social support and health is not fully explained by the role of stress, thus supporting the main effects model (e.g., Lakey & Cronin, 2008; Lakey & Orehek, 2011; Wade & Kendler, 2000). For example, although Cohen and Wills (1985) found evidence for the stress buffering model, the authors also concluded that support may be related to well-being in ways other than improved coping in stressful situations. They proposed that social support may lead to better health irrespective of the level of stress (Cohen & Wills, 1985). Romero (2013) reported that when stress varies widely or is chronic, such as in those with CF, the


main effects model should be adopted as the underlying theory of support (Romero, 2013; Quittner, Glueckauf, & Jackson, 1990).
3
Several pathways linking social support to improved well-being exist. For example, Cohen and Wills (1985) argued that social networks might generate feelings of stability, predictability and self-worth (Cohen & Wills, 1985; Lakey & Orehek, 2011). Lakey and Orehek (2011) developed the Relational Regulation Theory, proposing that support improves health through the regulation of emotions rather than through stress coping. Others have argued that social support benefits health through improved psychophysiological processes such as immune, autonomic and neuroendocrine systems, although results of these studies have been inconsistent (e.g. Adler & Matthews, 1994; Loucks, Sullivan, DAgostino, Larson, Berkman, & Benjamin, 2006; Miyazaki et al., 2005; Steptoe, Wardle, Pollard, Canaan, & Davies, 1996). Others believe that social support enhances motivation to engage in lifestyle behaviors beneficial to health, such as adhering to medical treatments, maintaining a healthy diet, participating in regular exercise, and transporting oneself to medical visits (e.g.
Croezen, Picavet, Haveman-Nies, Verschuren, de Groot, & vant Veer, 2012; Emmons, Barbeau, Gutheil, Stryker, & Stoddard, 2007; Kouvonen et al., 2011; Tamers, Beresford, Cheadle, Zheng, Bishop, & Thompson, 2011; Umberson, 1987).
Social Support and Health in the General Population
In the general population, there is a strong association between social support and key health behaviors as well as mental and physical health outcomes. Studies show, for instance, that individuals with higher levels of social support are more likely to engage in physical activity, have healthy diets, and avoid smoking and excessive drinking (e.g. Croezen et al.,


4
2012; Emmons, Barbeau, Gatheil, 2007; Kouvonen et al., 2011; Tamers et al., 2011; Umberson, 1987).
Additionally, social support has been related to a variety of mental health outcomes including better psychological well-being (e.g. Aslund et al., 2014; Turner, 1981), lower rates of depression (Glass, 2006; Lakey & Cronin, 2008), and less psychological distress (Barrera, 1986; Cohen & Wills, 1985; Boen, Dalgard, & Bjertness, 2012; Holahan & Moos, 1981; Procidano, 1992). Patten and colleagues (2010) found that low social support was a predictor of future depressive episodes in an eight-year longitudinal study (Patten, Williams, Lavorato, & Bulloch, 2010). In a meta-analysis conducted by Brewin and colleagues (2000), low social support during or after a traumatic event was viewed as a risk factor for subsequent a diagnosis of posttraumatic stress disorder (Brewin, Andrews, & Valentine, 2000). Studies have also found links between social support and increased happiness (Lakey, 2013) as well as adaptation to life stressors such as job loss and academic adjustment (Gore, 1978; Rueger, Malecki, & Demaray, 2010).
Social support has also been associated with a variety of physical health outcomes within the general population. Consistent with the stress buffering theory of social support, researchers have found considerable evidence supporting the belief that social support buffers the negative effects of stress. In a study conducted by Steptoe (2000), 104 schoolteachers recorded levels of stress experienced throughout the work day. Participants ambulatory blood pressure and heart rate were collected every 20 minutes and participants were divided into low and high self-reported social support categories. Results of the study showed that high levels of social support reduced the impact of stress on blood pressure (Steptoe, 2000). Specifically, social support buffered the effects of stress as evidenced by the


5
lack of significant increases in blood pressure or heart rate among those with high levels of support. Spitzer and colleagues (1992) found similar results indicating that ambulatory blood pressure is lower when surrounded by family members as opposed to strangers (Spitzer, Llabre, Ironson, Gellman, & Schneiderman, 1992).
Moreover, some studies have shown that social support is related to inflammation and immune system function (Loucks et al., 2006; Copertaro, Bracci, Manzella, Barbaresi, Copertaro, & Santarelli, 2014; Miyazaki et al., 2005). Copertaro and colleagues (2014) studied the role of social support on T lymphocytes CD8+CD57 (markers of immune senescence) and TNF- a (cytokine involved in infection and immunity) in 232 individuals. Results indicated that low levels of social support were related to an expansion of CD8+CD57 lymphocytes as well as increased TNF- a levels, possibly resulting in sustained chronic inflammation.
A 2003 meta-analysis conducted by Wang and colleagues examined 208 primary studies investigating the role of social support on health. Participants ranged in age from 15 to 83, with 36% of the studies examining participants with chronic illness. Results of the meta-analysis revealed that social support had a large effect on quality of life and self-actualization. Social support had small to medium effects on mental health outcomes such as well-being, psychosocial adjustment, depression, stress, and health beliefs, as well as physical health outcomes such as physical symptoms and health behaviors (Wang, Wu, Liu, 2003).
Social support remains of utmost importance on an epidemiological level as well. A seminal study by Berkman and Syme (1979) showed that individuals with fewer social ties had higher mortality rates (Berkman & Syme, 1979). A review by House and colleagues


6
(1988) supplemented this literature by showing that social support added a protective effect to mortality, similar to other factors such as blood pressure, smoking and physical activity (House, Landis, & Umberson, 1998). In a more recent study, Smith and colleagues (2010) analyzed 148 independent studies in a meta-analysis. Results of the study indicated that perceived social support was significantly related to lower all-cause mortality rates amongst 308,849 study participants. Additionally, social support was as predictive as many other mortality risk factors including smoking, alcohol use, physical activity and air pollution (Smith, Holt-Lunstad, & Layton, 2010).
Social Support and Health in Clinical Populations
The relationship between social support and health in chronically ill individuals is particularly important to study given that they are likely to experience higher levels of stress and poorer overall functioning than those in the general population. Social support in clinical populations has been associated with improved disease prognosis, improved mental and physical health, and better treatment adherence. Most research examining the relationship between social support and disease prognosis has been conducted in individuals with cardiovascular disease (e.g. Barth, Schneider, & von Kanel, 2010; Coyne, Rohrbaugh, Shoham, Sonnega, Nicklas, & Cranford, 2001; Orth-Gomer, Rosengren, & Wilhelmsen, 1993). In a prospective study by Berkman and colleagues (1992), for instance, lack of social support was related to mortality six months later in elderly patients hospitalized for acute myocardial infarction (Berkman, Leo-Summers, Horwitz, 1992). Similarly, a study by Brummett and colleagues found that mortality rates were higher for patients with coronary artery disease who experienced social isolation (Brummett et al., 2001). A more recent population-based study conducted in Sweden found that social participation predicted


7
incidence of first acute myocardial infarction. Results indicate that individuals with low levels of support were 1.5 times more likely to experience a heart attack than those with greater levels of support (Ali, Merlo, Rosvall, Lithman, Lindstrom, 2006; Reblin & Uchino, 2008)
Although further research is needed, some evidence suggests that social support may improve the prognosis for cancer patients as well. Pinquart and Duberstein (2010) analyzed 87 studies examining the role of social support in over one million patients with varying types of cancer. Results of the meta-analysis revealed that patient longevity was associated with patients perceptions of social support (Pinquart & Duberstein, 2010). Despite the impressive results of this study, the authors noted that many studies in the meta-analysis did not control for important factors such as personality and perceptions of health (Pinquart & Duberstein, 2010) and therefore, more research is needed. Social support has also been shown to affect mortality in those with diabetes; in one study, those with high levels of support had a 55% lower risk of death than those with low levels of support (Zhang, Norris, Gregg, & Beckles, 2007).
Social support in patient populations has also been related to improved psychosocial adaptation to illness (Blanchard, Albrecht, Ruckdeschel, Grant, & Hemmick, 1995; Helgeson & Cohen, 1996), and is believed to buffer the stress associated with disease. Several studies, for instance, have shown relationships between social support and well-being in those with cancer (e.g. Bloom, 1982; Bolger, Foster, Vinokur, & Ng, 1996, Ganz, Guadagnoli,
Landrum, Lash, Rakowski, & Silliman, 2003). Bloom and colleagues (2001) found that greater support amongst those with breast cancer was related to improved mental well-being (Bloom, Stewart, Johnston, Banks, & Fobair, 2001). Additionally, a review by Strom and


8
Egede (2012) found that people with Type 2 diabetes who had high levels of social support reported fewer depressive and stress-related symptoms than those with less support (Strom & Egede, 2012).
Social support is also related to various physical health outcomes in patient populations (Uchino, 2004; Uchino, 2009). A study examining social support in patients with chronic hepatitis C who were receiving antiviral therapy showed that lower levels of social support at the beginning of treatment were related to higher symptom reporting during treatment. Specifically, these patients reported more fatigue, pain, irritability and overall symptomology (Evon, Esserman, Ramcharran, Bonner, & Fried, 2011).
In addition to these outcomes, social support among those with chronic and acute diseases has been associated with clinical outcomes such as treatment adherence. Researchers have suggested that social support promotes patient adherence by encouraging self-efficacy, providing aid and buffering the stress associated with illness. Improved adherence, in turn, may yield better mental and physical health outcomes (DiMatteo, 2004; Gallant, 2003). A meta-analysis of 122 studies by DiMatteo (2004) provided strong evidence that social support has significant effects on patient adherence across a variety of disease conditions.
Other studies have shown that individuals with cancer or HIV are more adherent to treatment when they have high levels of social support (Alfonso, Geller, Bermbach, Drummond, &Montaner, 2006; Thompson, Littles, Jacob, & Coker, 2006; Reblin & Uchino, 2008). In a study examining follow-up care in African-American breast cancer survivors,
70% of individuals indicated that social support from the family facilitated follow-up care (Thompson et al., 2006). In a review of the literature on the impact of social support on adults with Type 2 diabetes, Strom and Egede (2012) found that individuals with higher


social support often had improved clinical outcomes including diabetes self-management, medication adherence and healthier lifestyles (Strom & Egede, 2012).
9
Social Support and Health among Adults with Cystic Fibrosis
Despite the many studies linking social support to health outcomes, surprisingly little is known about the effects of social support in adults with CF. Of the few reports to date, most are anecdotal or qualitative and focus on pediatric and adolescent samples. These studies indicate that youth with CF often view support from caregivers, friends and family as a valuable resource that encourages coping and disease management (Christian & DAuria, 1997; Harrop, 2007). Young patients with CF often rely on family and peers to encourage coping, monitor treatment adherence and provide assistance with medical visits; however, many report feeling isolated due to their illness (Jamieson, Fitzgerald, Singh-Grewal, Hanson, Craig, & Tong, 2014). Other studies have demonstrated the efficacy of social support interventions on emotional functioning in youth with CF (Christian & DAuria,
2006; MacDonald & Greggans, 2010).
To the authors knowledge, only one study has been published that explicitly addresses the relationship between social support and health in adults with CF. In a study examining the overall psychological functioning of adults with CF, Anderson and colleagues (2001) demonstrated that psychosocial support and better lung functioning predicted better psychological health. It is also important to note that a 2002 study by Goldbeck and colleagues, examining longitudinal trends and predictors of quality of life, demonstrated that partnership and parental closeness predicted better scores on a life satisfaction instrument in a sample of adolescents and adults with CF.


10
Studying social support in adults with CF is particularly important because the disease presents a variety of unique physical, social and emotional challenges and stressors that are distinct from other populations (Harrop, 2007; Mador & Smith, 1989). Although some research indicates that those with CF do not have impaired functioning (e.g., Anderson, Flume, Hardy, 2001; Shepherd et al., 1990), other studies have found that those with CF may have delayed puberty and insecurities regarding their physical appearance, resulting in poor-body image and self-esteem (Harrop, 2007; Pfeffer, Pfeffer, Hodson, 2003). Health complications, infertility in males, restrictions on physical activity, and hospitalizations may result in frustration, loneliness, anxiety and depression (Harrop, 2007; Mador & Smith, 1989; Pearson, 1991; Pfeffer, Pfeffer, Hodson, 2003; Riekert, Bartlett, Boyle, Krishnan, & Rand, 2007). Additionally, those with CF may feel embarrassed by symptoms (e.g., coughing, wheezing, low weight, delayed puberty) that prevent them from engaging in social activities (Harrop, 2007).
Another unique concern associated with social support among individuals with CF regards the infection prevention and control procedures introduced in 2003 (Romero, 2013; Saiman & Siegel, 2003). In an effort to reduce the chance of individuals with CF from infecting each other, these guidelines prohibit physical contact among CF patients in clinics, hospitals and at home, which severely limits their opportunities for social interaction and support (Romero, 2013).
The increasing population of adults with CF presents new opportunities for the study of the psychosocial aspects related to the disease. The transition from pediatric to adult care can be a challenging and frustrating process that may cause anxiety (Tuchman, Schwartz, Sawicki, & Britto, 2010). A loss of social support may result from individuals introduction


11
into a new setting and interactions with unfamiliar medical staff (Jamieson, Fitzgerald, Singh-Grewal, Hanson, Craig, & Tong, 2014). Social support may be even more difficult to establish for adults with CF as they gain autonomy, live on their own, and leave school settings. The parents of those with CF may have greater difficulty transporting their adult children to medical visits and providing support during treatment. Additionally, primary support persons may switch from parents to significant others, leading to unstable and changing social relationships (George, Rand-Giovannetti, Eakin, Borrelli, Zettler, & Riekert, 2010).
Research in other disease populations indicates that social support may improve treatment adherence; however, this relationship is largely understudied in adults with CF. Although findings are varied, some evidence indicates that those with CF are largely nonadherent to treatment regimens (Harrop, 2007; George et al., 2010; Passero, Remor, & Salomon, 1981). Treatment regimens for adults with CF are intensive and demanding, sometimes preventing social participation in the workplace and other settings (MacDonald & Greggans, 2010). George and colleagues (2010) studied barriers to and facilitators of treatment adherence in older adolescents and adults with CF. Results indicate that those with CF may be nonadherent to their prescribed treatment regimens due to treatment burden, social demands, work demands, forgetfulness, absence of perceived benefit, fatigue, and embarrassment. Importantly, support and reminders from significant others was reported as a facilitator of treatment adherence in this study (George et al., 2010). Prasad and Cerney (2002) propose that social support from parents, spouses, or friends may improve adherence to exercise by improving activity-related self-esteem and the desire to complete routines, as well as through shared commitment to activity (Prasad & Cerney, 2002).


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Some researchers have found that older age among individuals with CF was associated with less treatment adherence (Rosen, Blum, Britto, Sawyer, & Siegel, 2003; Harrop, 2007; Pearson, 1991). Pfeffer and colleagues have argued that adherence in adult patients with CF is poorer as they try to balance their treatment activities with the demands of daily living (Conway, Pond, Hamnett, & Watson, 1996; Pfeffer, Pfeffer, Hodson, 2003). In addition, adults with CF may also experience more emotional disturbance and social isolation (Blair, Cull, & Freeman, 1994; Pearson, 1991). George and colleagues (2010) add that the shift in social relationships from parents to significant others may enhance or hinder treatment adherence. In their study, some participants reported that the switch in support persons allowed for freedom from treatment regimens and that they found it difficult to integrate their activities into new romantic relationships. Conversely, other participants reported having romantic relationships that encouraged adherence, particularly through the use of support and reminders (George, 2010).
A study by Eakin and colleagues (2011) indicated that poor medication adherence in those with CF (ages 6 years and older) was related to worsened health outcomes, including more pulmonary exacerbations (Eakin, Bilderback, Boyle, Mogayzel, & Riekert, 2011). Exacerbations of the illness may lead to hospitalization, resulting in more social isolation, loneliness and maladjustment (Pfeffer, Pfeffer, Hodson, 2003; Sinenema, 1983). Given previous literature and theory supporting the mediating effects of adherence in the relationship between social support and health, examining this construct in those with CF is of utmost importance.


13
Longitudinal Trends and Predictors of Social Support
In addition to studying social support in an adult population of individuals with CF, it is also important to do so longitudinally. Most studies examining social support and health have done so using cross-sectional designs which ignore the changes in support that may take place over time. Schwarzer and Leppin (1991) propose that support may be high at the onset of an illness and decrease significantly over time due to burnout and other factors (Schwarzer & Leppin, 1991). More recently, Uchino and colleagues (2012) have recommended investigators use more sensitive longitudinal designs to study the relationships between social support and health.
Additionally, some research has shown that social support may not be stable over time. Research on women with breast cancer, for example, has found that perceptions of social support may decline over the course of treatment (Bloom & Kessler, 1994; Courtens, Stevens, Crebolder, & Philipsen, 1996; Den Oudsten, Van Heck, Van der Steeg, Roukema, & De Vries, 2010; Levy et al., 1992; Thompson, Littles, Jacob, & Coker, 2006). A longitudinal study by Bolger and colleagues (1996) found that the distress experienced by breast cancer patients was largely due to a decline in social support as opposed to the illness itself (Bolger et al., 1996). Further research by Evon and colleagues (2011) demonstrated that social support declined in those with hepatitis C from baseline to treatment week 24. In a qualitative study of treatment barriers in individuals who are HIV positive, participants remarked that lack of social support hindered continuing treatment. One participant noted that social support was largely present at the onset of the diagnosis; however, support declined after the patient started to look and feel better (Alfonso, 2006). When facing terminal illness, it is possible that support declines because the supporter no longer knows how to be of service.


14
Previous studies have demonstrated that predictors of social support may include age, gender, income, education, marital status, and employment status. A cross-sectional study examining social support in 292 women with breast cancer found that younger patients (below age 50) reported higher levels of support than older patients (above age 50) (Sammarco, 2009; Thompson, Rodebaugh, Perez, Schootman, & Jeffe, 2013). Although findings are varied, some evidence suggests that gender is related to social support as well, with many studies finding that women are more likely to provide and receive support than men (Matthews, Stansfeld, & Power, 1999; Vaux, 1985). Matthews and colleagues postulate that greater social networks may yield better health outcomes in women; however, women may also be more likely to experience negative support interactions that may result in poorer health (Matthews et al., 1999; Turner, 1994). Further studies show that social support may be related to income and education. A study by Mickelson and Kubzansky (2003) using data from the National Comorbidity Survey (NCS) showed that those with low levels of income and education reported less support than those with higher income and educational attainment (Mickelson & Kubzansky, 2003).
Being married or employed provides greater access to social networks that may promote a sense of belonging and social integration (e.g., Freedman & Fesko, 1996; Johnson, Yorkston, Klasner, Kuehn, Johnson, & Amtmann, 2004). Being employed specifically, may improve health due to increased social networks or improved financial stability and healthcare coverage (Johnson et al., 2004). In a meta-analysis examining over 250,000 elderly participants across 53 studies, those who were married had a lower risk of mortality than those who were not married. In fact, those who were married had a 9-15% reduction in mortality risk (Manzoli, Villari, Pirone, & Boccia, 2007). Other cross-sectional studies


15
suggest that married adults have better mental and physical health than those who are unmarried (e.g., Ross & Mirowsky, 1989; Sherbourne & Hays, 1990).
Research Aims and Hypotheses
Based on previous research and theory, the current study aims to answer the following questions: 1) Does social support change over time for adults with CF? 2) What predicts differences in social support and changes over time in social support? and 3) Does social support, in turn, predict health outcomes in this sample at a single time point and/or longitudinally? Given previous literature, social support was hypothesized to decline over time. Specifically, it was hypothesized that support would decline more for those who are older, male, who have less income and education, who are not married or employed, and who have lower FEVi scores and more exacerbations. Furthermore, it was predicted that individuals who report lower social support would have poorer mental and physical health as well as treatment activity and disease-specific health-related quality of life. To the authors knowledge, this is the first study that has examined predictors and outcomes of social support in adults with CF in a longitudinal fashion.


16
CHAPTER II Methods
Participants
Participants in the current study were part of a larger, longitudinal study known as the Project on Adult Care in Cystic Fibrosis (PAC-CF). This larger study examined health-related quality of life (HRQoL) in adults with CF in 10 participating CF centers across the United States. The study examined HRQoL in participants with high disease severity. Therefore, a stratified sample was formed using Liou et als. (2001) validated prognostic model, which calculated an individuals predicted probability of surviving five years. All individuals with a five-year probability of survival less than 0.975 were invited to participate in the study. In addition, a randomly selected 25% of individuals who had a five-year survival predicted probability equal to or greater than 0.975 (60 individuals) were also invited to participate.
Of the 575 individuals selected to participate, 333 enrolled in the study (301 with predicted survival less than or equal to 0.975 and 32 with predicted survival greater than 0.975). The remaining 242 adults either declined to participate in the study or could not be contacted. Individuals who participated in the study were more likely to be white, women, and older. Participants were also more likely to have better weight-for-age z scores and more exacerbations than nonparticipants. Of the 333 participants who began the study in the fall of 2004, 185 participants remained in the study until its conclusion in February of 2009. Reasons that participants did not complete the study included death, transplant, personal decision, or administrative difficulty. To be included in the current study, participants had to complete at least one wave of the four waves examined in the current study (see below).


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Therefore, the current study included 250 participants. Participants in the current study ranged in age from 20 to 65 years (M = 34.67, SD = 10.17) and 61% of the sample were female. The PAC-CF was approved by the institutional review boards at Education Development Center, Inc. and at the 10 participating CF centers.
Procedures
Surveys were administered and returned via mail in eleven waves over a 46-month period. Waves were conducted every 3-7 months with the first wave administered in April 2005 and the final, eleventh wave in February of 2009. In each wave, 70% to 93% of participants completed and returned their surveys. Surveys were mailed as opposed to being administered at the clinic to promote honesty and reduce response bias. Upon consent, medical records and other clinical data were extracted from the CF Foundation patient registry.
In an attempt to decrease patient burden, some measures were not administered during all of the eleven waves. Therefore, the data presented in this study were collected over a period of four waves: 5, 7, 8, and 9. Corresponding dates of these waves are May 2006, January 2007, June 2007, and January 2008 respectively. Wave 5 was used as the baseline time point in the current study.
Measures
Demographic Information. Self-report surveys captured the following variables, which were included as predictors of social support: age, gender (male or female), income (less than $50,000 or greater than or equal to $50,000), education (nopost high school degree or a vocational degree or higher), marital status (currently married or not married), and employment (employed or unemployed).


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Disease Severity. Forced expiratory volume in one second (FEVi) and pulmonary exacerbations are commonly used measures of disease severity amongst those with CF and are included as predictors in the current study. FEVi, which objectively assesses airway obstruction, is a measure of the lung disease associated with CF. Higher scores are indicative of better functioning and lower disease severity. FEVi was collected at the CF center providing care during routine clinical visits, generally occurring on a quarterly basis or within three months of the respective survey. Pulmonary exacerbations are defined as episodes requiring IV antibiotics either during an inpatient hospitalization or at home. A higher number of pulmonary exacerbations represents more disease severity. For this study, the number of pulmonary exacerbations was calculated as the number of exacerbations per month since the last survey wave.
Social Support. Perceived social support (support that is believed to be available) was measured by using a 24-item version of the Interpersonal Support Evaluation List (ISEL), designed to assess the availability of four separate functions of social support (Cohen, Mermelstein, Kamarck, & Hoberman, 1985). The four functions of support include: tangible (instrumental aid), appraisal (having someone to talk to), belonging (having someone to do activities with), and self-esteem (positive comparisons to others) (Cohen, Mermelstein, Kamarck, & Hoberman, 1985). Participants rate each item on a 4-point scale ranging from 1 (Completely False) to 4 (Completely True) with higher scores representing more perceived social support. Some items are reverse scored so that the measure includes both positive and negative statements about support. An overall measure of perceived support was computed by summing all items across the four separate functions. Scores were
then converted to a 0-100 scale.


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Example items include: If I were very sick and needed someone to drive me to the doctor, I would have trouble finding someone (tangible); There is at least one person I know whose advice I really trust (appraisal); lam usually invited to do things with others (belonging); and I have someone who takes pride in my accomplishments (self-esteem). Research indicates that the ISEL is a good predictor of mental and physical health (Uchino, 2004). The ISEL was administered at waves 5, 7, 8 and 10 of the PAC-CF study. Internal consistency was rated as good at wave 5 (a = .897), and excellent at waves 7 (a = .902), 8 (a = .911), and 10 (a =.915).
Mental and Physical Health Symptoms. The Memorial Symptom Assessment Scale (MSAS) is a self-report measure originally developed to assess the frequency, severity and distress associated with a variety of psychological and physical symptoms of those with chronic illness (Portenoy et al., 1994). Specifically, the MSAS has been validated in patients with cancer, heart disease and HIV (Portenoy et al., 1994; Nelson, Meier, Litke, Natale, Siegel, & Morrison, 2004; Zambroski, Moser, Bhat & Ziegler, 2005). Investigators of the study altered the measure to inquire about 18 symptoms experienced during the past two weeks instead of the past week as originally written.
The MSAS comprises the Psychological Symptom Subscale (MSAS-PSYCH) and the Physical Symptom Subscale (MSAS-PHYS). The MSAS-PSYCH subscale includes the following six psychological symptoms: worrying, feeling sad, feeling nervous, difficulty sleeping, feeling irritable, and difficulty concentrating. The MSAS-PHYS subscale includes twelve physical symptoms: lack of appetite, lack of energy, pain, feeling drowsy, constipation, dry mouth, nausea, vomiting, change in taste, weight loss, feeling bloated, and dizziness. Patients rate the severity, frequency, and distress of their symptoms on separate


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five-point Likert scales. Each overall symptom score was calculated by averaging the severity, distress, and frequency scale scores for that symptom. Overall symptom scores range from 0-4, with higher scores representing more symptom burden. If a respondent did not report having the symptom, the symptom score was zero. The MSAS-PSYCH and the MSAS-PHYS subscale scores were computed by averaging the respective individual symptom scores. The MSAS was administered at all waves of the PAC-CF study except 3 and 5 and was used as an outcome variable at wave 9.
Treatment Activity. Information regarding treatment activity was assessed by the use of two surveys. Both surveys were administered at wave 9 of the study and are included as outcome variables.
The Tool for Adherence Behaviour Screening (TABS) is an 8-item subscale of the Beliefs and Behavior Questionnaire (BBQ). This instrument was designed to measure patients beliefs and behaviors surrounding disease management (George, Mackinnon, Kong, & Stewart, 2006). The TABS assesses adherence to pharmacological and non-pharmacological treatment regimens and is commonly used in both clinical and research settings. For the purpose of this study, items were tailored to screen for beliefs about airway clearance treatments; therefore, one question pertaining to medication availability was removed, leaving seven items in the measure, three of which pertained to adherence and the other four to nonadherence. Preliminary analyses revealed that internal consistency for the nonadherence items was poor (a = .556) and therefore, this scale was not included in the study.
The adherence scale was computed by summing the responses within that category. Internal consistency was rated as good for the adherence items (a = .813) at wave 9 of the


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study. Examples of adherence items include, I have strict routines for completing my airway clearance treatment, and I keep supplies for airway clearance treatment close to where I use them. Participants rated each item on a 5-point Likert Scale ranging from 1 {Never) to 5 {Always) with higher scores representing more treatment activity.
Treatment activity was also measured by asking participants how many times in the previous day they completed six various airway clearance treatments including the vest, flutter/acapella device, positive expiratory pressure mask, intrapulmonary percussive ventilation mask, autogenic draining/huffing/special breathing techniques, and chest physical therapy/physio/clapping on chest. Participants responded on a 1-5 scale for each type of activity with 1 = once, 2 = twice, 3 = three or more times, 4 = none, I do this, but not yesterday, and 5 = none, my doctor recommends it, but I don 7 do it.
For analyses, items were recoded on a 0-3 scale, such that 0 = none, my doctor recommends it, but I don 7 do it or none, I do this, but not yesterday, 1 = once, 2 = twice, and 3 = three or more times. Therefore, higher scores represent more treatment activity. The reported frequency of each airway clearance treatment was then summed to result in an overall treatment activity score. As clinically recommended, if huffing was listed in combination with any other treatment activity, the reported frequency of huffing was not included in the count. If huffing was listed as the only treatment activity completed the previous day, it was included as a 1 on the 0-3 scale. Based on recommendations from clinical providers, the total amount of treatment activities completed the previous day was capped at four.


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Disease Specific Health Related Quality of Life (HRQoL). The Cystic Fibrosis Questionnaire Revised (CFQ-R) is a disease-specific instrument designed to measure the health and symptoms of adults with cystic fibrosis. The CFQ-R is a 50-item self-report survey that contains the following 9 HRQoL domains: physical functioning (e.g., how difficult is it to walk without getting tired), role functioning (e.g., how often does CF interfere with daily activities), vitality (e.g., how often did you feel exhausted), emotional functioning (e.g., how often didyou feel lonely), social functioning (e.g., how often do you get together with friends), body image (e.g., to what extent do you feel you look different than others your age), eating disturbances (e.g., have you had problems eating), treatment burden (e.g., how much time do you spend on your treatments each day), and health perceptions (e.g., how do you think your health is now). The measure also contains 3 symptom domains: weight (e.g., have you had trouble gaining weight), respiratory symptoms (e.g., have you had trouble breathing), and digestive symptoms (e.g., have you had abdominal pain). Participants were asked to think of the past two weeks and respond on a 4-point Likert scale {always, often, sometimes, never), such that higher scores indicated better HRQoL. Each domain was converted to 0-100 scale. The CFQ-R domains at wave 9 were included as outcome measures in the current study. Internal consistency for all CFQ-R items ranged acceptable to excellent with the exception of the social functioning and treatment burden domains, which were rated as poor (Quittner et al., 2012).
Covariates. CF Centers provided clinical data on PAC-CF participants through the CF Foundation patient registry. Data included weight percentile, diagnosis of diabetes, pancreatic sufficiency status, and colonization with Burkholderia cepacia, Staphylococcus aureus, and Pseudomonas aeruginosa. These variables were included and controlled for


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within the current study analyses. The study design (oversampling of individuals with a probability of five-year survival less than 0.975) was also controlled for in analyses. Statistical Analyses
Latent growth curve (LGC) modeling was employed using Mplus statistical software (Version 7.31; Muthen & Muthen, 2014). Using LGC modeling within a structural equation modeling (SEM) framework has several advantages including the ability to conduct longitudinal analyses and examine trajectories over time. Additionally, using LGC modeling allows for the examination of multiple outcome variables within a single model as well as sophisticated handling of missing data points using Full Information Maximum Likelihood (FIML). The root Mean Square Error of Approximation (RMSEA), Tucker-Lewis Index/ Non-Normed Fit Index (TLI/NNFI) and Comparative Fit Index (CFI) were used as indices of model fit. Although recommendations vary, many researchers suggest that RMSEA cut-off values should be 0.06 or lower (Hooper et al., 2008). The TLI/NNFI was created to address problems with sample size in regards to the Normed-fit Index (NFI) with scores typically ranging from 0.0 to 1.0; scores closer to 1.0 indicate better fit. The CFI is a revised version of the NFI and is one of the most commonly reported fit statistics, also ranging from 0.0 to 1.0. Scores below 0.90 on both the TLI/NNFI and CFI indicate poor fit, scores between 0.90 and 0.95 indicate adequate fit, and scores above 0.95 indicate good fit (Hooper et al., 2008; Little, 2013).
Separate linear regression analyses were also used in order to examine the predictors and outcomes associated with social support in a cross-sectional manner. All regression analyses were conducted using IBM SPSS software (version 20). For all tests, a P value of .05 was used as an indicator of statistical significance. Post-hoc mediational analyses were


24
conducted to examine if social support mediates the relationships between significant predictors of support and mental and physical health symptoms. Mediational analyses were conducted using IBM SPSS software following the recommendations of Baron and Kenny (1986), stating that 1) the total effect of the independent variable (IV) on the dependent variable (DV) should be significant, 2) the relationship between the IV and the mediator (M) should be significant, and 3) the direct relationship between M and the DV should be significant when controlling for the IV, which should no longer be significantly related to the DV.


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CHAPTER III Results
Descriptive statistics for all study variables are provided in Table 1. Bivariate correlations of primary study variables were also obtained (see Tables 2 and 3). Longitudinal Trends in Social Support
Preliminary analyses indicated that ratings of social support were high overall (M = 79.14, SD = 14.36, at baseline). Latent growth curve modeling revealed good model fit when examining social support across waves 5, 7, 8 and 10, CFI = 1.0, TLI = 1.0, RMSEA = 0.0. As shown in Figure 1, results supported a statistically significant decline in social support across these four waves (p = 0.02), with the mean and variance of the intercept equaling 79.78 and 164.00 and the mean and variance of the slope equaling -.06 and .01 respectively. Specifically, social support declined 1.62 points from baseline (wave 5) to the last wave examined (wave 10). Although these results support a statistically significant negative trend, the results are not clinically meaningful on a 0-100-point scale of social support.
In addition to examining longitudinal trends of social support, this study aimed to examine the outcomes associated with change in social support. All outcome measures in the current study were collected at wave 9. Therefore, the intercept and slope of all waves containing the social support measure prior to wave 9 (waves 5, 7, and 8) were calculated using Mplus. Latent growth curve modeling revealed good model fit when examining social support across these three waves, CFI = 1.0, TLI = 0.99, RMSEA = 0.03. As Figure 2 depicts, results of this model did not suggest a significant change in support over time (p = 0.49), indicating that social support did not significantly change from baseline (wave 5) to


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the last wave examined (wave 8). The mean and variance of the intercept equaled 79.63 and 165.02 and the mean and variance of the slope equaled -.03 and .04 respectively.
Given that there was no meaningful change in social support across waves 5, 7 and 8, no predicted outcomes of social support change could be examined longitudinally. Therefore, multiple linear regressions in SPSS were used to examine the second and third research aims. Predictors of Social Support
One multiple linear regression analysis was conducted to examine predictive variables of social support in a cross-sectional manner. As hypothesized, both gender and employment predicted social support; females reported more social support than males (8=6.18) and those who were employed reported more social support than those who were unemployed (8=5.59). Contrary to initial hypotheses, age (8=-. 12), income (8=2.0), education (8=-.53), marital status (8=2.15), FEVi (8=-,01) or exacerbations (8=,3 1) failed to predict social support. As shown in Table 4, the overall effect size equaled 0.11. Unstandardized coefficients are reported for interpretability of results. Covariates included in this study (design, weight percentile, diabetes, pancreatic sufficiency, and colonization with Burkholderia cepacia, Staphylococcus aureus, and Pseudomonas aeruginosa) did not significantly predict baseline social support.
Outcomes Related to Social Support
Separate linear regression analyses were employed in order to examine outcomes associated with social support. As hypothesized, social support predicted better mental health (8=-.02, R2=. 18), and physical health (8=-.01, R2=. 14), such that those with more support reported fewer mental and physical health symptoms.


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Social support was significantly associated with 9 HRQoL domains. Specifically, social support predicted less treatment burden (8=26, R2 .08) and better emotional (5= 69, R2=. 28), social (5=61, 82 .24), and role (5=42, 82 .17) functioning. Having more support also predicted improved vitality (5= 35, R2=.20), better body image (5= 63,52=. 31) and health perceptions (5=.65, R2 .35), Moreover, support predicted fewer eating disturbances (5=44, 52=.17) and digestive symptoms (5= 25, 52=.10).
Social support did not predict physical functioning (5= 27, 52=. 15), problems gaining weight (5=. 28, 82 .32), or respiratory symptoms (5=01, 52=. 06). Social support also did not predict higher treatment activity on either measure: TABS (5= 02, 52=.04) nor treatment activity (5=-.00,52=.07). See Table 5.
In addition to social support predicting health outcomes, some covariates included in analyses also significantly predicted variables of interest. For example, those with a greater weight percentile reported fewer mental (5=-.006,/?=.008) and physical health symptoms (5=-.004,/)=.003), less treatment burden (5= 12,/)=.049), and better physical (5= 15, p=.049), emotional (5= 13,/)=. 02), and role functioning (5= 19, p=.006). Those with greater weight percentile also reported better vitality (5=22,/)=. 000), body image (5=36, /;< 001), health perceptions (5=.24,/>< 001), ability to gain weight (5=,28, /;< 001) and fewer eating disturbances (5=.14,/>=.01) and respiratory symptoms (5= 13, /;=,04), Having a diabetes diagnosis significantly predicted poorer vitality (5=-6.66,/>=.04) and body image (5=-8.98, p=.03). Those with pancreatic insufficiency reported significantly worse physical functioning (5=-24.91, p=.02). Moreover, the design of the study predicted outcomes, such that those who were more ill reported less treatment activity (5=-.79,/)=.03) and poorer physical functioning (5= 21.24,/)=.008). Finally, colonization with Burkholderia cepacia predicted


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better health perceptions (Z?=-15.40,/>=.003); however, colonization with the bacteria Pseudomonas aeruginosa predicted better digestion (5=1 1.71,/?=.03).
Mediators of the Relationship between Social Support and Health
In order to determine if social support mediates the relationships between gender/employment status and mental and physical health, post-hoc mediation analyses were conducted. Analyses revealed that gender was not predictive of either mental or physical health symptoms in this sample. Although employment status did not predict physical health symptoms (p=.08), being employed did predict fewer mental health symptoms (B=-.31, fi=-.18, SE=.13, l -2,5,/;=02), Moreover, when employment status and social support were included in the same model, social support remained significant (B=-.02, /?=-.36, SE=.004, t=-5.18, /K.001); however, employment status was no longer significant (B=-.21, /?=-.12, SE=.12, ^=-1.73, p=.08). This indicates a full mediation by social support of the relationship between employment and mental health symptoms.


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CHAPTER IV Discussion
The current study examined the role of social support in adults with CF. Specifically, this study assessed longitudinal trends of social support as well as predictors and health outcomes associated with social support in this population. Overall, ratings of perceived social support were high. As hypothesized, perceived social support decreased significantly across the 27 months it was examined, but because it decreased only 1.62 points, the results were not clinically meaningful. There were also no significant changes in social support examined across the three time-points (a period of 13 months) prior to the measurement of the health outcome variables. Therefore, these findings do not uphold the hypothesis that support would significantly decline over time in this sample.
Similarly, others have found that Quality of Life (QoL) in those with CF remains relatively stable over time (e.g., Goldbeck et al., 2007; Sawicki et al., 2011). Rapkin and Schwartz (2004) proposed that patients with CF may effectively cope with their disease and have the ability to adjust their subjective experiences as their illness progresses in a phenomenon known as response shift. It is possible that perceptions of social support are adjusted over time as well. Others have proposed that QoL is a trait rather than a state, thus leading to more stability over time (Goldbeck et al., 2007). Perceptions of social support may also be heavily influenced by traits. Still, it is possible that more time is required to detect substantial changes in social support over time.
As hypothesized, gender significantly predicted social support, such that women reported more support than men. Specifically, being female was associated with a six-point increase on the ISEL. This finding is consistent with the literature on gender and social


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support in the general population, suggesting that women are more likely to receive support than men (Matthews et al., 1999; Turner, 1994). Despite females reporting more support than males, gender did not predict fewer mental or physical health symptoms in this sample.
In addition to gender, individuals who were employed also reported having more support than those who were unemployed. Specifically, employment was associated with a five-point increase on the ISEL. Further analyses revealed that employment status predicted increased perceived social support, which in turn, predicted fewer mental health symptoms.
In other words, social support mediated the relationship between employment status and mental health symptoms. This finding is consistent with the literature suggesting that employment offers increased opportunities to connect with others (e.g., Freedman & Fesko, 1996; Johnson, Yorkston, Klasner, Kuehn, Johnson, & Amtmann, 2004), which may lead to better mental health.
Given the association between employment and social support, it is reasonable to postulate that marriage would also lead to an increased opportunity for support; however, marital status did not predict social support in this sample. Post-hoc analyses further revealed that marital status at baseline was not associated with either mental (5= 15,/?=.23) or physical health (B=~. 13,/)=. 12) symptoms at wave 9. Perhaps adults with CF acquire support from friends and family at a similar rate as those who obtain support through a spouse. It is also possible that marriage does not provide an added benefit to adults with CF due to marital distress caused by having a chronic illness. Partners of those with a chronic illness may experience a variety of negative outcomes including higher rates of stress and depression which may contribute to decreased marital satisfaction among both individuals (e.g.,
Pruchno, Wilson-Genderson, & Cartwright, 2009). It may be possible that, in other


31
populations, marital status leads to better health through mechanisms other than increased social support.
Contrary to the hypothesis, age did not predict ratings of social support in this sample. Younger patients with CF may not report more support for a variety of reasons. First, early diagnosis of the disease may allow individuals to develop confidence surrounding symptom management, resulting in less needed assistance as they transition into early adulthood. Second, those with CF may have a strengthened ability to foster relationships throughout their illness and course of treatment. Third, caretakers, family, and friends may be understanding of the chronic disease and better able to provide consistent and continued support throughout the lifespan.
Previous studies indicate that those of lower SES have less perceived emotional support and more negative social interactions, perhaps due to decreased access to social resources, more frequent negative life events, and an inability to mobilize support in time of need (Mickelson and Kubzansky, 2003). Contrary to these findings, neither income nor education predicted social support in the current study. This finding may be due to the classification of categories within these dichotomous variables: income (less than $50,000 or greater than or equal to $50,000) and education (no post high school degree or a vocational degree or higher). The amount of variability within this categorization system may have been too large to detect significant differences. In other words, the social support received by those who earn $49,000 a year may be remarkably different from those who earn $19,000 a year. Similarly, there may be differences in the level of support between those with vocational degrees versus those with post-baccalaureate degrees.


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Finally, measures of disease severity, FEV1 and exacerbations in the past month, failed to predict social support in this sample. Previous studies have shown that social support often declines over time in those with chronic illness (e.g., Bloom & Kessler, 1994; Bolger et al., 1996). This decline may be attributable to a decreased opportunity for support, such as missing school/work and other opportunities for engagement. Those who are more ill may also experience frequent hospitalizations that limit their access to their social networks. In this sample, however, measures of disease severity did not predict less social support. It is possible that as adults with CF become more ill, they begin to require more assistance from family and friends. This increased support may mitigate the loss of support opportunity in other areas.
The current study also examined the health outcomes associated with social support in adults with CF. As hypothesized, those with more support reported fewer mental and physical health symptoms. Specifically, for every 10-unit increase on the ISEL, there was a .2 and 1 reduction on mental and physical health symptoms respectively, on a 0-4 point scale. Social support also predicted better emotional, social, and role functioning. Therefore, social support may improve mental and physical health symptoms, which may lead to an increased ability to function in various areas of life. Social support also predicted improved vitality, enhanced body image, and better overall health perceptions. Moreover, support predicted fewer eating disturbances and digestive symptoms in this sample. It is possible that fewer eating problems influenced better digestive functioning. It is also possible, given previous literature and theory, that support directly affects the physiological process of digestion through improved diet (Croezen, Picavet, Haveman-Nies, Verschuren, de Groot, & vant Veer, 2012).


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Although social support predicted fewer digestive symptoms, it did not predict respiratory symptoms, such as wheezing, coughing, and difficulty breathing. Mucus accumulation in the lungs is the most common clinical pattern in those with CF; therefore, it is likely that many individuals in the current study experienced respiratory symptoms. Due to study design, participants in this study were are also more likely to be severely ill. It is possible that social support did not predict respiratory symptoms as these symptoms were already advanced and unremitting.
Although higher levels of support predicted fewer physical symptoms on the MSAS, it did not predict physical functioning on the CFQ-R. It is possible that although support was associated with fewer symptoms, CF patients still feel the negative effects of their disease on their ability to perform physical tasks of daily living. For example, although individuals with more support may report less nausea, shortness of breath, or dizziness, they may continue to struggle to perform vigorous activities such as running or playing sports.
Research in other disease populations indicates that social support may improve treatment adherence (Prasad & Cerney, 2002), which may, in turn, yield better mental and physical health outcomes due to increased disease management. In the current study, social support did not predict either measure of treatment activity. Moreover, neither measure of treatment activity was related to either mental or physical health symptoms. Some have proposed the mediating effects of treatment adherence on the relationship between social support and health (Prasad & Cerney, 2002). Although the results of this study do not support either relationship, it is important to note that the measures used assessed beliefs about treatment and activity completed, not necessarily adherence to prescribed treatments. Although higher levels of support did not predict better beliefs about treatment or more


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treatment activity, it did predict less treatment burden. Perhaps, support is not related to beliefs about treatment or actual treatment activity, but does lessen the burden experienced by providing increased assistance.
In addition to examining the relationship between social support and key health outcomes, covariates of interest were also included in analyses. Greater weight percentile, indicating better health, significantly predicted several outcomes, including the ability to gain weight, fewer mental and physical health symptoms and better physical, emotional, and role functioning. Greater weight percentile also predicted better vitality, body image, health perceptions, and less treatment burden, eating disturbances, and respiratory symptoms. Having a diabetes diagnosis was significantly related to poorer vitality and body image while pancreatic insufficiency was significantly related to poorer physical functioning.
Moreover, individuals with poorer prognoses reported poorer physical functioning and less treatment activity, despite the fact that treatment activity is crucial for this group. These individuals may feel incapable of completing their treatments due to poor physical functioning or decide to discontinue treatment due to their poor prognosis. Due to the cross-sectional nature of these findings, directionality remains unknown. Therefore, it is possible that those with less treatment activity have poorer health. Further, colonization with Burkholderia cepacia was associated with worse perceptions of health. Specifically, colonization with cepacia predicted a fifteen-point decrease in health perceptions. Although startling, this discovery is consistent with the finding that cepacia can cause increased risk of mortality (e.g., Tablan et al., 1987). Interestingly, colonization with the bacteria Pseudomonas aeruginosa was associated with better digestive symptoms.


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Implications
Studying the relationship between social support and health is of utmost importance, as implementations of research findings may improve health-care costs and quality of life (Aslund et al., 2014). Most studies involving social support examine the relationship between support and either physical or mental health in a cross-sectional manner. Previous researchers have proposed that future studies should examine the relationship between social support and health in a longitudinal design (Bloom, 1990) and should explore mental and physical health outcomes simultaneously (Thoits, 2011). Therefore, the current study fills gaps in the literature by providing a longitudinal investigation of social support while examining multiple health outcomes in an understudied population. The current study also includes both subjective (self-report) and objective (disease severity) measures of health.
Further, the current study broadens our understanding of the psychosocial components of those living with CF. In accordance with the biopsychosocial framework, researchers have studied the origins of CF {biological) as well as the emotional vulnerability associated with the illness (psychological), but have overlooked the many social components (Gotz, 2000; Harrop, 2007). In fact, few studies examine the role of support on health outcomes in the CF population, and those that do are primarily qualitative in nature and focused on pediatric samples. Given the improved longevity of those with CF, studying support in adults is of concern and deserving of further research.
Prevention and control procedures promote health and decrease infection in those with CF. However, the procedures also prohibit interaction between patients and make support difficult to acquire and maintain. Given the influence of support on health in those with CF, finding alternatives to support is of greatest importance. For example, some have


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suggested the use of online support groups (Romero, 2013) to allow patients the opportunity for interaction and the sense of belonging. In addition, mentoring programs between patients of different ailments may prove useful in providing support opportunities. Clinicians may wish to encourage their patients to maintain close contact with family members, friends, or significant others. Including support persons in medical visits may also prove particularly useful in fostering support. Moreover, the transition from adolescence to adulthood may be particularly isolating as adults move away from home, transition from pediatric care, and leave the school system. Opportunities for adult support and an increased sensitivity to these issues during the transition by medical staff may lessen the isolation experienced.
The current study also demonstrates the relationship between employment status and social support. This finding is particularly important as CF may drastically affect ones ability to remain employed, despite its benefits. Establishing creative solutions to maintain employment status in those with CF is imperative.
Limitations and Future Directions
Despite the strengths of this study, there are some limitations that should be addressed. First, participant survey data and clinical outcomes collected at the CF centers are not synchronous due to the method of data collection. Time between surveys and clinical data, however, was generally no more than 12 weeks apart. Second, participants in the current study were part of a larger study (PAC-CF) that aimed to recruit adults with high disease-severity, thus skewing the sample and making results less generalizable to healthier adults with CF. Despite this study design, the average lung function of adults in PAC-CF was only slightly lower than the U.S. average. Third, income was dichotomously coded for analyses; however, this may have led to less sensitivity in determining how various levels of income are related to social support. For example, perhaps the categories of above or below


37
$50,000 annually are too broad to capture true economic differences.
Although this study provides further insight into longitudinal trends, predictors, and outcomes of social support in adults with CF, more research in this area is needed. It will be important for future investigators to carefully consider the distinction between perceived support (the subjective belief that emotional, informational, and instrumental support is available from family, friends, colleagues, and loved ones) and received support (the actual amount of support acquired) (Nurullah, 2012; Schulz & Schwarzer, 2004; Tardy, 1985; Thoits, 1995; Uchino, 2009) as they relate to health outcomes. Although perceived social support has been consistently linked to beneficial health-related outcomes, including mental and physical health, health behaviors, and mortality rates, findings on the influence of received support on health outcomes widely vary (Haber, Cohen, Lucas, & Baltes, 2007; Nurullah, 2012; Thoits, 2011; Uchino, 2009). Some research indicates that received support may have negative effects on health including higher mortality rates (Forster & Stoller, 1992; Krause, 1997; Pennix, Van Tilburg, Kriegsman, Deeg, Boeke, & van Eijk, 1997; Uchino, 2009) depression (Frese, 1999), and poorer mental health (Iwata & Suzuki, 1997; Nurullah, 2012). In addition, researchers generally agree that social support is a multidimensional construct with different types of support yielding different outcomes (Schwarzer & Leppin, 1991; Uchino, 2004). Researchers may wish to differentiate between types of perceived support (e.g., positive appraisals, emotional belonging, tangible support, etc.).
Future studies focusing on the mechanisms by which social support predicts better health outcomes in this population will yield important findings for the field. Previous research indicates that support may yield better treatment adherence that, in turn, leads to better health. In this study, however, neither treatment activity nor beliefs about treatment


38
were associated with social support. Perhaps other constructs, such as level of optimism, emotional well-being, resiliency, or other behaviors mediate this relationship.
Finally, little research has examined the use of a social support intervention on improving health outcomes in any population. Results of the current study suggest that an intervention of this nature may be particularly beneficial to those with CF who show improved health outcomes with more support, but are unable to attend support groups with individuals of the same illness.
Conclusion
In summary, this study demonstrated that social support remained relatively stable over time for adults with CF. Being female and employed predicted more perceived social support. Social support, in turn, predicted fewer mental and physical health symptoms, better emotional, role, and social functioning, improved body image and vitality, fewer eating disturbances and digestive symptoms, and less treatment burden. This study fills gaps in the literature by providing a longitudinal examination of support and an analysis of relationships between social support and multiple health outcomes in adults with CF. Findings illustrate that those who are male and unemployed are at the most risk of experiencing low levels of social support, which may result in poorer health outcomes. It is crucial to develop employment opportunities and support interventions for those with CF, especially for those at most risk. Future research should differentiate between types of support and examine mechanisms by which support influences health outcomes.


39
Table 1. Descriptive statistics for variables included in analyses
Variable N Mean (SD) Frequency (%) Min Max
Social Support Wave 5 233 79.14(14.36) 30.56 100.00
Wave 7 203 79.32 (14.62) - 20.29 100.00
Wave 8 209 78.55 (14.60) - 18.06 100.00
Wave 10 184 77.52 (15.34) - 23.61 100.00
Predictors of Support Age 250 33.17 (10.28) 19 64
Gender (female) 250 - 60.4% - -
Income (more than 197 - 54.3% - -
50,000 annually) Education (vocational 229 63.3%
degree or higher) Marital Status (married) 230 54.3%
Employment (employed) 233 - 57.9% - -
FEV! 218 53.85 (20.04) - 8.63 119.08
Exacerbations 224 0.17 (.31) - 0 2
Outcomes of Support Mental Elealth Symptoms 195 1.10 (.85) 0 3.56
Physical Elealth Symptoms 194 0.72 (.54) - 0 2.74
TABS 185 11.07 (3.16) - 3 15
T reatment Activity 184 1.43 (1.16) - 0 4
Treatment Burden 193 52.33 (20.63) - 0 100
Physical Functioning 198 52.45 (27.36) - 0 100
Emotional Functioning 198 69.90 (21.00) - 0 100
Social Functioning 193 58.76 (19.47) - 0 100
Role Functioning 195 71.82 (23.95) - 0 100
Vitality 198 49.20 (19.58) - 0 100
Body Image 194 64.78 (27.39) - 0 100
Eating Disturbances 196 83.59 (20.85) - 11.11 100
Digestive Symptoms 196 75.17 (18.89) - 11.11 100
Respiratory Symptoms 192 55.22 (19.86) - 0 100
Elealth Perceptions 192 54.72 (22.62) - 0 100
Weight 193 68.91 (37.43) 0 100


40
Table 2. Bivariate correlations of variables predicting social support
Variable Social Support Wave 5 Age Sex Income Education Marital Status Employment FEV1
Age -.07 - - - - - - -
Gender .20** -.11 - - - - - -
Income .10 .32*** .02 - - - - -
Education .04 .24*** -.12 .21** - - - -
Marital Status .06 .40*** -0.02 .42*** .16* - - -
Employment .13* -.00 -0.20** .13 .22*** -.07 - -
FEV1 .07 -.20** .11 .09 .02 -.12 .12 -
Exacerbations .02 -.17* .12 -.07 -.12 .02 -.15* -.13
*p < .05; **p < ,Q1;***P < .001


Table 3. Bivariate correlations of outcome variables predicted by social support
Variable Social Mental Physical TABS Treat Treat Physical Emotion Social Role Vital Body Eat Dig Respir Health
Support (8) Health Health Activity Burden
Mental Health .34*** -
Physical Health _ 22*** .62*** -
TABS .09 -,ii -.11 -
Treat -.08 -.02 -.05 42*** -
Activity Treat .17* _ 29*** -.28*** _ 26*** -.20**
Burden Physical 19** _ 34*** _ 49*** -.15* -.22** 33***
Emotion .50*** _ 73*** -.58*** .08 -.03 39*** 3g*** -
Social 47*** -.53*** -.55*** .00 -.04 .35*** 40*** .65*** -
Role 27*** -.50*** _ 59*** -.10 -.19* 44*** .60*** .66*** .55***
Vital 27*** -.53*** -65*** .05 -.00 .36*** 60*** .58*** 53*** 59*** _
Body 31*** -.36*** -.35*** .14 .04 21 ** 23*** 42*** .31*** 38*** 40*** -
Eat 29*** _ 40*** -.58*** .04 -.01 23*** 38*** 52*** 46*** 49*** 47*** 47*** -
Dig .13 _ 39*** -55*** -.01 .10 .14 19** 38*** 37*** 39*** 40*** .16* 30*** -
Respir .07 _ 26*** _ 40*** -.08 _ 24*** 37*** 52*** 26*** 34*** 43*** 51*** 2i** .23** .18* -
Health 47*** _ 49*** -58*** -.08 _ 20** 41 *** .65*** 67*** 64*** 73*** 71*** 44*** 49*** 33*** 51***
Weight ,ii -.07 -.212** -.01 -.01 .14 .10 .15* .13 2i** 19** .52*** 37*** .00 .13 .21**
*p < .05; **p <.01 -***p < .001


42
Table 4. Regression Results for Predictors of Social Support
Variable B SEB P t P
Age -0.12 .14 -.09 -.87 .386
Gender (female=l) 6.18 2.45 .21 2.52 .013
Income 2.00 2.70 .07 .74 .463
Education -0.53 2.62 -.02 -.20 .841
Marital Status 2.15 2.67 .07 .81 .420
Employment 5.59 2.54 .19 2.20 .029
FEVj -.01 .06 -.01 -.14 .891
Exacerbations .31 4.40 .01 .07 .944
Note. The table reflects results from a single linear regression with all covariates and time-varying predictors (wave 5) entered in the same step. The dependent variable was social support at wave 5. The overall R2 = 0.11 *


43
Table 5. Regression Results for Outcomes of Social Support
Variable B SE P t P R2 AR2
Mental Health -.02 .004 -.32 -4.13 <.001 .18 .10
Symptoms Physical Health -.01 .003 -.23 -2.95 .004 .14 .05
Symptoms TABS .02 .02 .09 1.02 .309 .04 .007
Treat -.004 .01 -.05 -.55 .59 .07 .002
Activity Treat .26 .12 .18 2.23 .028 .08 .03
Burden Physical .27 .15 .14 1.81 .072 .15 .02
Emotional .69 .10 .48 6.60 <.001 .28 .22
Social .61 .10 .45 6.02 <.001 .24 .19
Role .42 .13 .25 3.17 .002 .17 .06
Vitality .35 .10 .25 3.32 .001 .20 .06
Body .63 .13 .34 4.78 <.001 .31 .11
Eat .44 .11 .31 4.03 <.001 .17 .09
Digest .25 .10 .20 2.43 .016 .10 .04
Respiration .01 .11 .01 .10 .917 .06 .000
Health .65 .11 .41 6.00 <.001 .35 .16
Weight .28 .18 .11 1.58 .117 .32 .01
Note. Separate regression analyses were conducted with covariates (wave 9) entered on the first step and social support (wave 8) entered on the second step. The variable of interest was entered as the dependent variable (wave 9).


44
Figure 1. Longitudinal Examination of Support Across Four Waves
el
e2
e3
e4
0.032
Note. I=Intercept, S=Slope, M=Mean, V=Variance, e=error.


45
Figure 2. Longitudinal Examination of Support Across Three Waves
el
e2
e3
-0.102
Note. I=Intercept, S=Slope, M=Mean, V=Variance, e=error.


46
APPENDIX A
Interpersonal Support Evaluation List (ISEL)
The following is a list of statements that may or may not be true about you. Please read each statement, then circle the one number that best describes how true or false that statement is about you.
Completely Somewhat Somewhat Completely
False False True True
If I had to go out of town for a few weeks, someone I know would look after my home, such as watering the plants or taking care of the
pets. If I were very sick and needed someone to drive me to the doctor, I would have 1 2 3 4
trouble finding someone. If I were very sick, I would have trouble finding someone to help me with my 1 2 3 4
daily chores. f I needed help moving, I would be able to find 1 2 3 4
someone to help me. If I needed a place to stay for a week because of an emergency, such as the water or electricity being out in my home, I could easily find someone who would put 1 2 3 4
me up. There is at least one person I know whose advice I really 1 2 3 4
trust. There is no one I know who will tell me honestly how I 1 2 3 4
am handling my problems When I need suggestions 1 2 3 4
about how to deal with a 1 2 3 4


47
personal problem, I know there is someone I can turn to.
i. There isn't anyone I feel comfortable talking to about intimate personal problems. 1 2 3 4
j. There is no one I trust to give me good advice about money matters. 1 2 3 4
k. I am usually invited to do things with others. 1 2 3 4
1. Most people I know think highly of me. 1 2 3 4
m. Most of my friends are more interesting than I am. 1 2 3 4
n. I am more satisfied with my life than most people are with theirs. 1 2 3 4
o. I think my friends feel that Im not very good at helping them solve problems. 1 2 3 4
p. I am closer to my friends than most other people. 1 2 3 4
q. I am able to do things as well as most other people. 1 2 3 4


48
APPENDIX B
Memorial Symptom Assessment Scales (MSAS)
Please review the following list of symptoms. If you have had the symptom during the past two weeks, let us know how often you had it, how severe it usually was and how much it distressed or bothered you by circling the appropriate number. If you did not have the symptom, circle the O in the column marked Did Not Have.
If Yes, If yes, If yes,
During the past two weeks: How OFTEN did you have How SEVERE was it How much did it DISTRESS
it? usually? or BOTHER you?
w
Did you have any of the following symptoms? > < X H o z, Q 1 >> Id G (/) cd o o >> G 2 D > (D C/3 (h (D < <1 o s JD 3 cd ID B s cd U 3 43 O G 2 !i (D
Q & o U-i < u 00 2 00 > '4,
a. Cough 0 1 2 3 4 1 2 3 4 0 1 2 3 4
b. Diarrhea 0 1 2 3 4 1 2 3 4 0 1 2 3 4
c. Shortness of breath 0 1 2 3 4 1 2 3 4 0 1 2 3 4
d. Nausea 0 1 2 3 4 1 2 3 4 0 1 2 3 4
e. Feeling irritable 0 1 2 3 4 1 2 3 4 0 1 2 3 4
f. Weight loss 0 1 2 3 4 1 2 3 4 0 1 2 3 4
g. Lack of energy 0 1 2 3 4 1 2 3 4 0 1 2 3 4
h. Pain 0 1 2 3 4 1 2 3 4 0 1 2 3 4
i. Feeling drowsy 0 1 2 3 4 1 2 3 4 0 1 2 3 4
j. Dry mouth 0 1 2 3 4 1 2 3 4 0 1 2 3 4
k. Feeling nervous 0 1 2 3 4 1 2 3 4 0 1 2 3 4


49
During the past two
WEEKS:
If Yes,
How OFTEN did you have it?
If YES,
How SEVERE was it usually?
IF YES,
How much did it DISTRESS or BOTHER you?
Did you have any of the following symptoms?
a. Lack of appetite
b. Difficulty
c. Change in taste
d. Constipation
e. Dizziness
f. Feeling bloated
g. Vomiting
h. Worrying
i. Difficulty
j. Sinus discharge
w > si H o c o S3 G 13 U G C/3 od 2 G Vh o o V. 3 > O C/3 < s o G -a £ 3 G JS o 3 £
z Q 1 g o o 3 O" B 2 S a it o .3 M o a o t-H 1) > >. Vh o <11 o 4 Q oi o ft. < O 55 s C/3 > Z < 03 a >
0 1 2 3 4 1 2 3 4 0 1 2 3 4
0 1 2 3 4 1 2 3 4 0 1 2 3 4
0 1 2 3 4 1 2 3 4 0 1 2 3 4
0 1 2 3 4 1 2 3 4 0 1 2 3 4
0 1 2 3 4 1 2 3 4 0 1 2 3 4
0 1 2 3 4 1 2 3 4 0 1 2 3 4
0 1 2 3 4 1 2 3 4 0 1 2 3 4
0 1 2 3 4 1 2 3 4 0 1 2 3 4
0 1 2 3 4 1 2 3 4 0 1 2 3 4
0 1 2 3 4 1 2 3 4 0 1 2 3 4
0 1 2 3 4 1 2 3 4 0 1 2 3 4
k. Feeling sad


50
APPENDIX C
Tool for Adherence Behaviour Screen (TABS)
Now, thinking in general, please indicate how often the following statements about airway clearance treatment apply to you:
Never T Rarely Sometimes T Often Always T
a. I have strict routines for completing my airway clearance treatment 1 2 3 4 5
b. I keep supplies for airway clearance treatment close to where I use them 1 2 3 4 5
c. I strive to follow the instructions of my doctors about completing airway clearance treatment 1 2 3 4 5


51
APPENDIX D
Treatment Activity
Yesterday, how many times did you do each of the following airway clearance treatments?
None, my None, I doctor
Three or do this, recommends
more but not it, but I dont
Once Twice times yesterday do it

a. The Vest 1 2 3 4 5
b. Flutter/Acapella device 1 2 3 4 5
c. PEP mask 1 2 3 4 5
d. IPV mask 1 2 3 4 5
e. Autogenic draining/huffing/special breathing techniques 1 2 3 4 5
f. Chest PT /Physio/clapping on chest 1 2 3 4 5


52
APPENDIX E
Cystic Fibrosis Questionnaire Revised (CFQ-R)
Please think about your health and well-being over the past two weeks when answering the following questions.
1. During the past two weeks, to what extent have you had difficulty:
A Lot of Some A Little No
Difficulty Difficulty Difficulty Difficulty
a. Performing vigorous activities such QP T T T
do running or playing sports 1 2 3 4
b. Walking as fast as others c. Carrying or lifting heavy things 1 2 3 4
such as books, groceries, or school bags 1 2 3 4
d. Climbing one flight of stairs 1 2 3 4
e. Climbing stairs as fast as others 1 2 3 4
2. During the past two weeks, indicate how often:
Always Often Sometimes Never
T
a. You felt well 1 2 3 4
b. You felt worried 1 2 3 4
c. You felt useless 1 2 3 4
d. You felt tired 1 2 3 4
e. You felt energetic 1 2 3 4
f. You felt exhausted 1 2 3 4
g- You felt sad 1 2 3 4
To what extent do you have difficulty walking?
1 You can walk a long time without getting tired
2 You can walk a long time but you get tired
3 You cannot walk a long time, because you get tired quickly
4 You avoid walking whenever possible, because it's too tiring for you


53
4. How do you feel about eating?
1 Just thinking about food makes you feel sick
2 You never enjoy eating
3 You are sometimes able to enjoy eating
4 You are always able to enjoy eating
5. To what extent do your treatments make your daily life more difficult?
1 Not at all
2 A little
3 Moderately
4 A lot
6. How much time do you currently spend each day on your treatments?
1 A lot
2 Some
3 A little
4 Not very much
7. How difficult is it for you to do your treatments (including medications) each day?
1 Not at all
2 A little
3 Moderately
4 Very
8. How do you think your health is now?
1 Excellent
2 Good
3 Fair
4 Poor


54
9. Thinking about your health during the past two weeks, indicate the extent to which each sentence is true or false for you.
Very Somewhat Very
True True Somewhat False False
T T T
a. I have trouble recovering after physical effort 1 2 3 4
b. I have to limit vigorous activities such as running or playing sports 1 2 3 4
c. I have to force myself to eat 1 2 3 4
d. I have to stay at home more than I want to 1 2 3 4
e. I feel comfortable discussing my illness with others 1 2 3 4
f. I think I am too thin 1 2 3 4
g- I think I look different from others my age 1 2 3 4
h. I feel bad about my physical appearance 1 2 3 4
i. People are afraid that I may be contagious 1 2 3 4
j- I get together with my friends a lot 1 2 3 4
k. I think my coughing bothers others 1 2 3 4
1. I feel comfortable going out at night 1 2 3 4
m. I often feel lonely 1 2 3 4
n. I feel healthy 1 2 3 4
o. It is difficult to make plans for the future (for example, going to college, getting married, advancing in a job, etc.) 1 2 3 4
P- I lead a normal life 1 2 3 4
10. To what extent did you have trouble keeping up with your schoolwork, professional work, or other daily activities during the past two weeks?
1 I have had no trouble keeping up
2 I have managed to keep up but it has been difficult
3 I have been behind
4 I have not been able to do these activities at all


55
11. How often:
a. Were vou absent from school, work, or unable to complete daily activities during the last two Always Often T Sometimes T Never
weeks because of your illness or treatments? b. Does CF get in the way of meeting your 1 2 3 4
school, work or personal goals? c. Does CF interfere with getting out of the house to run errands such as shopping or 1 2 3 4
going to the bank? 1 2 3 4
12. During the past two weeks:
a. Have you had trouble gaining weight?
b. Have you been congested?
c. Have you been coughing during the day?
d. Have you had to cough up mucus?
A Great Deal Somewhat A Little Not at All
T T
1 2 3 4
1 2 3 4
1 2 3 4
1 I 2 1 3 _l 4
Has your mucus been mostly:
1 Clear
2 Clear to yellow
3 Yellowish-green
4 Green with traces of blood
5 Dont know


56
13. How often during the past two weeks:
a. Have you been wheezing?
b. Have you had trouble breathing?
c. Have you woken up during the night because you were coughing?
d. Have you had problems with gas?
e. Have you had diarrhea?
f. Have you had abdominal pain?
g. Have you had eating problems?
Always Often Sometimes Never
T
1 2 3 4
1 2 3 4
1 2 3 4
1 2 3 4
1 2 3 4
1 2 3 4
1 2 3 4


57
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i SOCIAL SUPPORT, HEALTH, AND TREATMENT ACTIVITY IN ADULTS WITH CYSTIC FIBROSIS by KASSIE D. FLEWELLING B.A., University of Nebraska Lincoln, 2015 A thesis submitted to the F aculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Master of Arts Psychology Program 2017

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ii This thesis f or the Master of Arts degree by Kassie D. Fl ewelling has been approved for the Psychology Program by Edward Dill Chair Barbara Walker Walter Robinson

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iii Date: December 16, 2017 Flewelling, Kassie D (MA, Psychology Program) Social Support, Health, and Treatment Activity in Adults with Cystic Fibrosis Thesis directed by Assistant Professor Edward Dill ABSTRACT Background: Adults with cystic fibrosis (CF) may face a unique set of clinical and psychosocial barriers affecting the attainment and maintenance of social support; however, research in this area is limited. The current study examines the role of age, gender, income, education, marital status, employment, and disease severity on social support in adults with CF. Mental and physical health symptoms, treatment activity, and disease specific quality of life are evaluated as outcomes of support. Methods: Participants in th e study included 250 adults with CF who took part in a larger longitudinal study known as the Project on Adult Care in Cystic Fibrosis (PAC CF). Participants were administered a battery of measures including a social support evaluation (Interpersonal Suppo rt Evaluation List, ISEL ), a health assessment (Memorial Symptom Assessment Scale, MSAS), treatment activity questionnaires (Tool for Adherence Behaviour Screening, TABS and other surveys), and a health related quality of life measure (Cystic Fibrosis Ques tionnaire Revised, CFQ R). D isease severity measures (forced expiratory volume in one second [FEV 1 ] and exacerbations) were also assessed over a 45 month period. Results: Latent growth curve modeling revealed that social support remained stable over time Linear regression analyses indicated that females and those who were employed had greater social support. In turn, greater social support predicted fewer mental and physical health symptoms, digestive symptoms, and eating disturbances. Social support pre dicted emotional, social, and role

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iv functioning as well as vitality and improved body image. Moreover, those with more support experienced less treatment burden and better overall perceptions of their health. Social support mediated the relationship between employment and mental health symptoms. Conclusions: The current study fills gaps in the literature by examining predictors and outcomes of social support in adults with CF as well as studying social support over time. Interventions that target support, an d are in accordance with disease prevention and control efforts, are of utmost importance. The form and content of this abstract are approved. I recommend its publication. Approved: Edward Dill

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v TABLE OF CONTENTS CHAPTER I. INTRODUCTION .1 Models of the Relationship between Social Support and Health .. 1 Social Support and Health in the General Population .. 3 Social Support and Health in Clinical Populations .. 6 Social Support and Health among Adults with Cystic Fibrosis ... 9 Longitudinal Trends and Predictors of Social Support 13 Research Aims and Hypotheses .. 15 II. METHOD S ..16 Participants 16 Procedures .17 Measures .... 17 Demographic Information .. 17 Disease Severity 18 Social Support 18 Mental and Physical Health Symptoms 19 Treatment Activity 20 Disease Specific Health Related Quality of Life 22 Covariates .... 22 Statistical Analyses 23 III. RESULTS ....25 Longitudinal Trends in Social Support .. 25

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vi Predictors of Social Support .. 26 Outcomes Related to Social Support .. ..26 Mediators of the Relationship between Social Support and Health.....28 IV. DISCUSSION ......29 Implications .. .35 Limitations and Future Directions ... ..36 Conclusion ......... 38 REFERENCES ....57 APPENDIX A. Interpersonal Support Evaluation List (ISEL)......................... ..............................46 B. Memorial Symptom Assessment Scale (MSAS).................................................. 48 C. Tool for Adherence Behaviour Screen (TABS) .............. ..............................50 D. Treatment Activity ............................................... ..............................51 E. Cystic Fibrosis Questionnaire Revised (CFQ R) .. ......................... .52

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vii LIST OF TABLES TABLE 1. Descriptive s tatistics for variables included in analyses 39 2. Bivariate correlations of variables predicting social support .40 3. Bivariate correlations of outcome variables predicted by social support ...41 4. Regression Results for Predictors of Social Support ........ ..42 5. Regression Results for Outcomes of Social Support ... .. 43

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viii LIST OF FIGURES FIGURE 1. Latent growth curve examining support across four time points .44 2. Latent growth curve examining support across three time points 45

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1 CHATPER I Social Support, Health, and Treatment Activity in Adults with Cystic Fibrosis Cystic f ibrosis (CF) is a chronic, genetic condi tion affecting the respiratory, digestive endocrine and reproductive systems of nearly 70,000 individuals worldwide (Cystic Fibrosis Foundation, 2013) In those with the most common clinical pattern of CF, mucus accumulates in the lungs, panc reas and other organs, causing significant damage and eventually death (Cystic Fibrosis Foundation, 2013). With nearly 1,000 new diagnoses each year, CF is the most common life shortening inherited disease among Caucasians (Cystic Fibrosis Foundation, 2013 ; Krauth, Jalilvand, Welte, & Busse, 2003). In 2006, direct costs associated with CF care in the U.S. exceeded $48,000 per patient (Ouyang, Grosse, Amendah, & Schechter, 2009). T he prognosis of CF has improved dramatically over the past three decades with predicted longevity estimates of nearly 40 years of age In fact, nearly half of those with CF are over the age of 18 (Cystic Fibrosis Foundation, 2013). The improvements in life expectancy have resulted in a new population of CF adults who face disease s pecific psychosocial challenges. Although social support has been linked to a variety of mental and physical health outcomes in those with and without chronic illness, this construct has rarely been studied in the CF population, especially in adult samples Models of the Relationship between Social Support and Health Social support can be defined as the material and emotional resources that are available to a person through interpersonal conta cts (Aslund, Larm, Starrin, & Nilsson 2014 p. 1 )." Historica lly, social support has been separated into two distinct constructs: perceived and received support (Tardy, 1985 ; Uchino, 2009 ). The current paper examines perceived

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2 social support, a more subjective belief that emotional, informational and instrumental su pport is available from family, friends, colleagues and loved ones (Nurullah, 2012; Schulz & Schwarzer, 2004) Although researchers have known for decades that social support is associated with various health outcomes, the exact mechanisms underlying the r elationships remain unknown (Thoits, 2011). Previous research suggests that social support is related both directly and indirectly to mental and physical health (Glasgow, Barrera, McKay, & Boles, 1999; Romero, 2013; Thoits, 2011). The most widely accepted models of social support are the stress buffering model and the main effects model, also known as the direct effects model. The stress buffering model suggests that social support buffers negative effects of stress on health, but only when stress levels a re high (Cohen & Wills, 1985; Romero, 2013). According to this model, social support improves one's ability to cope with stressful events, which leads to improvements in mental health. When stress is low or nonexistent, however, this model predicts that so cial support is not associated with health outcomes (Lakey & Orehek, 2011). Despite its prevalence in the literature, many researchers have found that the link between social support and health is not fully explained by the role of stress, thus supporting the main effects model (e.g., Lakey & Cronin, 2008; Lakey & Orehek, 2011; Wade & Kendler, 2000). For example, although Cohen and Wills (1985) found evidence for the stress buffering model, the authors also concluded that support may be related to well bei ng in ways other than improved coping in stressful situations. They proposed that social support may lead to better health irrespective of the level of stress (Cohen & Wills, 1985). Romero (2013) reported that when stress varies widely or is chronic, such as in those with CF, the

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3 main effects model should be adopted as the underlying theory of support (Romero, 2013; Quittner, Glueckauf, & Jackson, 1990). Several pathways linking social support to improved well being exist. For example, Cohen and Wills (1985) argued that social networks might generate feelings of stability, predictability and self worth (Cohen & Wills, 1985; Lakey & Orehek, 2011). Lakey and Orehek (2011) developed the Relational Regulation Theory proposing that support improves health through the regulation of emotions rather than through stress coping. Others have argued that social support benefits health through improved psychophysiologi cal processes such as immune, autonomic and neuroendocrine systems, although results of these studies have been inconsistent (e.g. Adler & Matthews, 1994; Loucks, Sullivan, D'Agostino, Larson, Berkman, & Benjamin, 2006; Miyazaki et al., 2005; Steptoe, Ward le, Pollard, Canaan, & Davies, 1996). Others believe that social support enhances motivation to engage in lifestyle behaviors beneficial to health, such as adhering to medical treatments, maintaining a healthy diet, participating in regular exercise, and t ransporting oneself to medical visits (e.g. Croezen, Picavet, Haveman Nies, Verschuren, de Groot, & van't Veer, 2012; Emmons, Barbeau, Gutheil, Stryker, & Stoddard, 2007; Kouvonen et al., 2011; Tamers, Beresford, Cheadle, Zheng, Bishop, & Thompson, 2011; U mberson, 1987). Socia l Support and Health in the General Population In the general population, there is a strong association between social support and key health behaviors as well as mental and physical health outcomes S tudies show for instance, that individuals with higher levels of social support are more likely to engage in physical activity, have healthy diets, and avoid smoking and excessive drinking (e.g. Croezen et al.,

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4 2012; Emmons, Barbeau, Gatheil, 2007; Kouvonen et al., 2011; Tamers et al., 2011; Umberson, 1987). Additionally, social support has been related to a variety of mental health outcomes including better psychological well being ( e.g. Aslund et al., 2014 ; Turner, 1981 ), lower rates of depression ( Glass, 2006; Lakey & Cronin, 2008) a nd less psychological distress (Barrera, 1986; Cohen & Wills, 1985; B e n, Dalgard, & Bjertness, 2012; Holahan & Moos, 1981; Procidano, 1992). Patten and colleagues (2010) found that low social support was a predictor of future depressive episodes in an eig ht year longitudinal study (Patten, Williams, Lavorato, & Bulloch, 2010). In a meta analysis conducted by Brewin and colleagues (2000), low social support during or after a traumatic event was viewed as a risk factor for subsequent a diagnosis of posttraum atic stress disorder (Brewin, Andrews, & Valentine, 2000). Studies have also found links between social support and increased happiness (Lakey, 2013) as well as adaptation to life stressors such as job loss and academic adjustment (Gore, 1978; Rueger, Male cki, & Demaray, 2010). Social support has also been associated with a variety of physical health outcomes within the general population. Consistent with the stress buffering theory of social support, researchers have found considerable evidence supporting the belief that social support buffers the negative effects of stress. In a study conducted by Steptoe (2000), 104 school teachers recorded levels of stress experienced throughout the work day. Participants' ambulatory blood pressure and heart rate were c ollected every 20 minutes and participants were divided into low and high self reported social support categories. Results of the study showed that high levels of social support reduced the impact of stress on blood pressure (Steptoe, 2000). Specifically, social support buffered the effects of stress as evidenced by the

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5 lack of significant increases in blood pressure or heart rate among those with high levels of support. Spitzer and colleagues (1992) found similar results indicating that ambulatory blood pr essure is lower when surrounded by family members as opposed to strangers (Spitzer, Llabre, Ironson, Gellman, & Schneiderman, 1992). Moreover, some studies have shown that social support is related to inflammation and immune system function (Loucks et al ., 2006; Copertaro, Bracci, Manzella, Barbaresi, Copertaro, & Santarelli, 2014; Miyazaki et al., 2005). Copertaro and colleagues (2014) studied the role of social support on T lymphocytes CD8+CD57 (markers of immune senescence) and TNF (cytokine involve d in infection and immunity) in 232 individuals. Results indicated that low levels of social support were related to an expansion of CD8+CD57 lymphocytes as well as increased TNF levels, possibly resulting in sustained chronic inflammation. A 2003 met a analysis conducted by Wang and colleagues examined 208 primary studies investigating the role of social support on health. Participants ranged in age from 15 to 83, with 36% of the studies examining participants with chronic illness. Results of the meta analysis revealed that social support had a large effect on quality of life and self actualization. Social support had small to medium effects on mental health outcomes such as well being, psychosocial adjustment, depression, stress, and health beliefs, as well as physical health outcomes such as physical symptoms and health behaviors (Wang, Wu, Liu, 2003). Social support remains of utmost importance on an epidemiological level as well. A seminal study by Berkman and Syme (1979) showed that individuals wit h fewer social ties had higher mortality rates (Berkman & Syme, 1979). A review by House and colleagues

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6 (1988) supplemented this literature by showing that social support added a protective effect to mortality, similar to other factors such as blood pressu re, smoking and physical activity (House, Landis, & Umberson, 1998). In a more recent study, Smith and colleagues (2010) analyzed 148 independent studies in a meta analysis. Results of the study indicated that perceived social support was significantly rel ated to lower all cause mortality rates amongst 308,849 study participants. Additionally, social support was as predictive as many other mortality risk factors including smoking, alcohol use, physical activity and air pollution (Smith, Holt Lunstad, & Layt on, 2010). Social Support and Health in Clinical Populations The relationship between social support and health in chronically ill individuals is particularly important to study given that they are likely to experience higher levels of stress and poorer overall functioning than those in the general population. Social support in clinical populations has been associated with improved disease prognosis, improved mental and physical health, and better treatment adherence. Most research examining the relation ship between social support and disease prognosis has been conducted in individuals with cardiovascular disease (e.g. Barth, Schneider, & von KŠnel 2010; Coyne, Rohrbaugh, Shoham, Sonnega, Nicklas, & Cranford, 2001; Orth GomŽr, Rosengren, & Wilhelmsen, 19 93). In a prospective study by Berkman and colleagues (1992), for instance, lack of social support was related to mortality six months later in elderly patients hospitalized for acute myocardial infarction (Berkman, Leo Summers, Horwitz, 1992). Similarly, a study by Brummett and colleagues found that mortality rates were higher for patients with coronary artery disease who experienced social isolation (Brummett et al., 2001). A more recent population based study conducted in Sweden found that social partici pation predicted

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7 incidence of first acute myocardial infarction. Results indicate that individuals with low levels of support were 1.5 times more likely to experience a heart attack than those with greater levels of support (Ali, Merlo, Rosvall, Lithman, L indstršm 2006; Reblin & Uchino, 2008) Although further research is needed, some evidence suggests that social support may improve the prognosis for cancer patients as well. Pinquart and Duberstein (2010) analyzed 87 studies examining the role of social support in over one million patients with varying types of cancer. Results of the meta analysis revealed that patient longevity was associated with patients' perceptions of social support (Pinquart & Duberstein, 2010). Despite the impressive results of thi s study, the authors noted that many studies in the meta analysis did not control for important factors such as personality and perceptions of health (Pinquart & Duberstein, 2010) and therefore, more research is needed. Social support has also been shown t o affect mortality in those with diabetes; in one study, those with high levels of support had a 55% lower risk of death than those with low levels of support (Zhang, Norris, Gregg, & Beckles, 2007). Social support in patient populations has also been rela ted to improved psychosocial adaptation to illness (Blanchard, Albrecht, Ruckdeschel, Grant, & Hemmick, 1995; Helgeson & Cohen, 1996), and is believed to buffer the stress associated with disease. Several studies, for instance, have shown relationships bet ween social support and well being in those with cancer (e.g. Bloom, 1982; Bolger, Foster, Vinokur, & Ng, 1996, Ganz, Guadagnoli, Landrum, Lash, Rakowski, & Silliman, 2003). Bloom and colleagues (2001) found that greater support amongst those with breast c ancer was related to improved mental well being (Bloom, Stewart, Johnston, Banks, & Fobair, 2001). Additionally, a review by Strom and

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8 Egede (2012) found that people with Type 2 diabetes who had high levels of social support reported fewer depressive and s tress related symptoms than those with less support (Strom & Egede, 2012). Social support is also related to various physical health outcomes in patient populations (Uchino, 2004; Uchino, 2009). A study examining social support in patients with chronic he patitis C who were receiving antiviral therapy showed that lower levels of social support at the beginning of treatment were related to higher symptom reporting during treatment. Specifically, these patients reported more fatigue, pain, irritability and ov erall symptomology (Evon, Esserman, Ramcharran, Bonner, & Fried, 2011). In addition to these outcomes, social support among those with chronic and acute diseases has been associated with clinical outcomes such as treatment adherence. Researchers have sugg ested that social support promotes patient adherence by encouraging self efficacy, providing aid and buffering the stress associated with illness. Improved adherence, in turn, may yield better mental and physical health outcomes (DiMatteo, 2004; Gallant, 2 003). A meta analysis of 122 studies by DiMatteo (2004) provided strong evidence that social support has significant effects on patient adherence across a variety of disease conditions. Other studies have shown that individuals with cancer or HIV are more adherent to treatment when they have high levels of social support (Alfonso, Geller, Bermbach, Drummond, &Montaner, 2006; Thompson, Littles, Jacob, & Coker, 2006; Reblin & Uchino, 2008). In a study examining follow up care in African American breast cance r survivors, 70% of individuals indicated that social support from the family facilitated follow up care (Thompson et al., 2006). In a review of the literature on the impact of social support on adults with Type 2 diabetes, Strom and Egede (2012) found tha t individuals with higher

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9 social support often had improved clinical outcomes including diabetes self management, medication adherence and healthier lifestyles (Strom & Egede, 2012). Social Support and Health among Adults with Cystic Fibrosis Despite th e many studies linking social support to health outcomes, surprisingly little is known about the effects of so cial support in adults with CF. Of the few reports to date, most are anecdotal or qualitative and focus on pediatric and adolescent samples. These studies indicate that youth with CF often view support from caregivers, friends and family as a valuable resource that encourages coping and disease management (Christian & D'Auria, 1997; Harrop, 2007). Young patients with CF often rely on family and peer s to encourage coping, monitor treatment adherence and provide assistance with medical visits; however, many report feeling isolated due to their illness (Jamieson, Fitzgerald, Singh Grewal, Hanson, Craig, & Tong, 2014). Other studies have demonstrated the efficacy of social support interventions on emotional functioning in youth with CF (Christian & D'Auria, 2006; MacDonald & Greggans, 2010). To the author's knowledge, only one study has been published that explicitly addresses the relationship between so cial support and health in adults with CF. In a study examining the overall psychological functioning of adults with CF, Anderson and colleagues (2001) demonstrated that psychosocial support and better lung functioning predicted better psychological health It is also important to note that a 2002 study by Goldbeck and colleagues, examining longitudinal trends and predictors of quality of life, demonstrated that partnership and parental closeness predicted better scores on a life satisfaction instrument in a sample of adolescents and adults with CF.

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10 Studying social support in adults with CF is particularly important because the disease presents a variety of unique physical, social and emotional challenges and stressors that are distinct from other populatio ns (Harrop, 2007; Mador & Smith, 1989). Although some research indicates that those with CF do not have impaired functioning (e.g., Anderson, Flume, Hardy, 2001; Shepherd et al., 1990), other studies have found that those with CF may have delayed puberty a nd insecurities regarding their physical appearance, resulting in poor body image and self esteem (Harrop, 2007; Pfeffer, Pfeffer, Hodson, 2003). Health complications, infertility in males, restrictions on physical activity, and hospitalizations may result in frustration, loneliness, anxiety and depression (Harrop, 2007; Mador & Smith, 1989; Pearson, 1991; Pfeffer, Pfeffer, Hodson, 2003; Riekert, Bartlett, Boyle, Krishnan, & Rand, 2007). Additionally, those with CF may feel embarrassed by symptoms (e.g., co ughing, wheezing, low weight, delayed puberty) that prevent them from engaging in social activities (Harrop, 2007). Another unique concern associated with social support among individuals with CF regards the infection prevention and control procedures int roduced in 2003 (Romero, 2013; Saiman & Siegel, 2003). In an effort to reduce the chance of individuals with CF from infecting each other, these guidelines prohibit physical contact among CF patients in clinics, hospitals and at home, which severely limits their opportunities for social interaction and support (Romero, 2013). The increasing population of adults with CF presents new opportunities for the study of the psychosocial aspects related to the disease. The transition from pediatric to adult care can be a challenging and frustrating process that may cause anxiety (Tuchman, Schwartz, Sawicki, & Britto, 2010). A loss of social support may result from individuals' introduction

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11 into a new setting and interactions with unfamiliar medical staff (Jamieson, F itzgerald, Singh Grewal, Hanson, Craig, & Tong, 2014). Social support may be even more difficult to establish for adults with CF as they gain autonomy, live on their own, and leave school settings. The parents of those with CF may have greater difficulty t ransporting their adult children to medical visits and providing support during treatment. Additionally, primary support persons may switch from parents to significant o thers, leading to unstable and changing social relationships (George, Rand Giovannetti, Eakin, Borrelli, Zettler, & Riekert, 2010). Research in other disease populations indicates that social support may improve treatment adherence; however, this relationship is largely understudied in adults with CF. Although findings are varied, some evide nce indicates that those with CF are largely non adherent to treatment regimens (Harrop, 2007; George et al., 2010; Passero, Remor, & Salomon, 1981). Treatment regimens for adults with CF are intensive and demanding, sometimes preventing social participati on in the workplace and other settings (MacDonald & Greggans, 2010). George and colleagues (2010) studied barriers to and facilitators of treatment adherence in older adolescents and adults with CF. Results indicate that those with CF may be nonadherent to their prescribed treatment regimens due to treatment burden, social demands, work demands, forgetfulness, absence of perceived benefit, fatigue, and embarrassment. Importantly, support and reminders from significant others was reported as a facilitator of treatment adherence in this study (George et al., 2010). Prasad and Cerney (2002) propose that social support from parents, spouses, or friends may improve adherence to exercise by improving activity related self esteem and the desire to complete routines as well as through shared commitment to activity (Prasad & Cerney, 2002).

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12 Some researchers have found that older age among individuals with CF was associated with less treatment adherence (Rosen, Blum, Britto, Sawyer, & Siegel, 2003; Harrop, 2007; Pearso n, 1991). Pfeffer and colleagues have argued that adherence in adult patients with CF is poorer as they try to balance their treatment activities with the demands of daily living (Conway, Pond, Hamnett, & Watson, 1996; Pfeffer, Pfeffer, Hodson, 2003). In a ddition, adults with CF may also experience more emotional disturbance and social isolation (Blair, Cull, & Freeman, 1994; Pearson, 1991). George and colleagues (2010) add that the shift in social relationships from parents to significant others may enhanc e or hinder treatment adherence. In their study, some participants reported that the switch in support persons allowed for freedom from treatment regimens and that they found it difficult to integrate their activities into new romantic relationships. Conve rsely, other participants reported having romantic relationships that encouraged adherence, particularly through the use of support and reminders (George, 2010). A study by Eakin and colleagues (2011) indicated that poor medication adherence in those with CF (ages 6 years and older) was related to worsened health outcomes, including more pulmonary exacerbations (Eakin, Bilderback, Boyle, Mogayzel, & Riekert, 2011). Exacerbations of the illness may lead to hospitalization, resulting in more social isolation loneliness and maladjustment (Pfeffer, Pfeffer, Hodson, 2003; Sinenema, 1983). Given previous literature and theory supporting the mediating effects of adherence in the relationship between social support and health, examining this construct in those wit h CF is of utmost importance.

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13 Longitudinal Trends and Predictors of Social Support In addition to studying social support in an adult population of individuals with CF, it is also important to do so longitudinally. Most studies examining social support and health have done so using cross sectional designs which ignore the changes in supp ort that may take place over time. Schwarzer and Leppin (1991) propose that support may be high at the onset of an illness and decrease significantly over time due to burnout and other factors (Schwarzer & Leppin, 1991). More recently, Uchino and colleague s (2012) have recommended investigators use more sensitive longitudinal designs to study the relationships between social support and health. Additionally, some research has shown that social support may not be stable over time. Research on women with bre ast cancer, for example, has found that perceptions of social support may decline over the course of treatment (Bloom & Kessler, 1994 ; Courtens, Steven s, Crebolder, & Philipsen, 1996; Den Oudsten, Van Heck, Van der Steeg, Roukema, & De Vries, 2010 ; Levy et al., 1992; Thompson, Littles, Jacob, & Coker, 2006 ). A longitudinal study by Bolger and colleagues (1996) found that the distress experienced by breast cancer patients was largely due to a decline in social support as opposed to the illness itself (Bolger et al., 1996). Further research by Evon and colleagu es (2011) demonstrated that social support declined in those with hepatitis C from baseline to treatment week 24. In a qualitative study of treatment barriers in individuals who are HIV positive, participants remarked that lack of social support hindered c ontinuing treatment. One participant noted that social support was largely present at the onset of the diagnosis; however, support declined after the patient started to look and feel better (Alfonso, 2006). When facing terminal illness, it is possible that support declines because the supporter no longer knows how to be of service.

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14 Previous studies have demonstrated that predictors of social support may include age, gender, income, education, marital status, and employment status. A cross sectional study ex amining social support in 292 women with breast cancer found that younger patients (below age 50) reported higher levels of support than older patients (above age 50) (Sammarco, 2009; Thompson, Rodebaugh, PÂŽrez Schootman, & Jeffe, 2013). Although findings are varied, some evidence suggests that gender is related to social support as well, with many studies finding that women are more likely to provide and receive support than men (Matthews, Stansfeld, & Power, 1999; Vaux, 1985). Matthews and colleagues pos tulate that greater social networks may yield better health outcomes in women; however, women may also be more likely to experience negative support interactions that may result in poorer health (Matthews et al., 1999; Turner, 1994). Further studies show t hat social support may be related to income and education. A study by Mickelson and Kubzansky (2003) using data from the National Comorbidity Survey (NCS) showed that those with low levels of income and education reported less support than those with highe r income and educational attainment (Mickelson & Kubzansky, 2003). Being married or employed provides greater access to social networks that may promote a sense of belonging and social integration (e.g., Freedman & Fesko, 1996; Johnson, Yorkston, Klasner, Kuehn, Johnson, & Amtmann, 2004). Being employed specifically, may improve health due to increased social networks or improved financial stability and health care coverage (Johnson et al., 2004). In a meta analysis examining over 250,000 elderly participa nts across 53 studies, those who were married had a lower risk of mortality than those who were not married. In fact, those who were married had a 9 15% reduction in mortality risk (Manzoli, Villari, Pirone, & Boccia, 2007). Other cross sectional studies

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15 s uggest that married adults have better mental and physical health than those who are unmarried (e.g., Ross & Mirowsky, 1989; Sherbourne & Hays, 1990). Research Aims and Hypotheses Based on previous research and theory, the current study aims to answer t he following questions: 1) Does social support change over time for adults with CF? 2) W hat pre dicts differences in social support and changes over time in social support? and 3) Does social support, in turn, predict health outcomes in this sample at a sin gle time point and/or longitudinally ? Given previous literature, social support was hypothesized to decline over time. Specifically, it was hypothesized that support would decline more for those who are older, male, who have less income and education, who are not married or employed, and who have lower FEV 1 scores and more exacerbations. Furthermore, it was predicted that individuals who report lower social support would have poorer mental and physical health as well as treatment activity and disease specif ic health related quality of life. To the author's knowledge, this is the first study that has examined predictors and outcomes of social support in adults with CF in a longitudinal fashion.

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16 CHAPTER II Methods Participants Participants in the curr ent study were part of a larger, longitudinal study known as the Project on Adult Care in Cystic Fibrosis (PAC CF). This larger study examined health related quality of life (HRQoL) in adults with CF in 10 participating CF centers across the United States. The study examined HRQoL in participants with high disease severity. Therefore, a stratified sample was formed using Liou et al's. (2001) validated prognostic model, which calculated an individual's predicted probability of surviving five years. All indiv iduals with a five year probability of survival less than 0.975 were invited to participate in the study. In addition, a randomly selected 25% of individuals who had a five year survival predicted probability equal to or greater than 0.975 (60 individuals) were also invited to participate. Of the 575 individuals selected to participate, 333 enrolled in the study (301 with predicted survival less than or equal to 0.975 and 32 with predicted survival greater than 0.975). The remaining 242 adults either decli ned to participate in the study or could not be contacted. Individuals who participated in the study were more likely to be white, women, and older. Participants were also more likely to have better weight for age z scores and more exacerbations than nonpa rticipants. Of the 333 participants who began the study in the fall of 2004, 185 participants remained in the study until its conclusion in February of 2009. Reasons that participants did not complete the study included death, transplant, personal decision or administrative difficulty. To be included in the current study, participants had to complete at least one wave of the four waves examined in the current study (see below).

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17 Therefore, the current study included 250 participants. Participants in the cur rent study ranged in age from 20 to 65 years ( M = 34.67, SD = 10.17) and 61% of the sample were female. The PAC CF was approved by the institutional review boards at Education Development Center, Inc. and at the 10 participating CF centers. Procedures Surveys were administered and returned via mail in eleven waves over a 46 month period. Waves were conducted every 3 7 months with the first wave administered in April 2005 and the final, eleventh wave in February of 2009. In each wave, 70% to 93% of parti cipants completed and returned their surveys. Surveys were mailed as opposed to being administered at the clinic to promote honesty and reduce response bias. Upon consent, medical records and other clinical data were extracted from the CF Foundation patien t registry. In an attempt to decrease patient burden, some measures w ere not administered during all of the eleven waves. Therefore the data presented in this study were collected over a period of four waves: 5, 7, 8, and 9. Corresponding dates of these waves are May 2006, January 2007, June 2007, and January 2008 respectively. Wave 5 was used as the baseline time point in the current study. Measures Demographic Information. Self report surveys captured the following variables, which were included as pred ictors of social support: age gender ( male or female ), income ( less than $50,000 or greater than or equal to $50,000 ), education ( no post high school degree or a vocational degree or h igher ) marital status ( currently married or not married ), and employme nt ( employed or unemployed ).

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18 Disease Severity. Forced expiratory volume in one second (FEV 1 ) and pulmonary exacerbations are commonly used measures of disease severity amongst those with CF and are included as predictors in the current study FEV 1 which objectively assesses airway obstruction is a measure of the lung disease associated with CF. Higher scores ar e indicative of better functioning and lower disease severity. FEV 1 was collected at the CF center provid ing care during routine clinical visits, generally occurring on a quarterly basis or within three months of the respective survey. Pulmonary exacerbations are defined as episodes requiring IV antibiotics either during an inpatient hospitalization or at hom e. A higher number of pul monary exacerbations represents more disease severity For this study, the number of pulmonary exacerbations was calculated as the number of exacerbations per month since the last survey wave. Social Support. Perceived social sup port (support that is believed to be available) was measured by using a 24 item version of the Interpersonal Support Evalu ation List (ISEL), designed to assess the availability of four separate functions of social support (Cohen, Mermelstein, Kamarck, & Ho berman, 1985). The four functions of support include: tangible (instrumental aid), appraisal (having someone to talk to), belonging (having someone to do activities with), and self esteem (positive comparisons to others) (Cohen, Mermelstein, Kamarck, & Hob erman, 1985). Participants rate each item on a 4 point scale ranging from 1 ( Completely False ) to 4 ( Completely True) with higher scores representing more perceived social support. Some items are reverse scored so that the measure includes both positive an d negative statements about support. An overall measure of perceived support was computed by summing all items across the four separate functions. Scores were then converted to a 0 100 scale.

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19 Example items include: If I were very sick and needed someone to drive me to the doctor, I would have trouble finding someone (tangible); There is at least one person I know whose advice I really trust (appraisal);" I am usually invited to do things with others (belonging); and I have someone who takes pride in my accomplishments (self esteem). Research indicates that the ISEL is a good predictor of mental and physical health (Uchino, 2004). The ISEL was administered at waves 5, 7, 8 and 10 of the PAC CF study. Internal consistency was rated as good at wave 5 ( = .897), and excellent at waves 7 ( = .902), 8 ( = .911), and 10 ( = .915). Mental and Physical Health Symptoms The Memorial Symptom Assessment Scale (MSAS) is a self report measure originally developed to assess the frequency, severity and distress associated with a variety of psychological and physical symptoms of those with chronic illness (Portenoy et al., 1994). Sp ecifically, the MSAS has been validated in patients with cancer, heart disease and HIV (Portenoy et al., 1994; Nelson, Meier, Litke, Natale, Siegel, & Morrison, 2004; Zambroski, Moser, Bhat & Ziegler, 2005). Investigators of the study altered the measure to inquire about 18 symptoms experienced during the past two weeks instead of the past week as originally written. The MSAS comprises the Psychological Symptom Subscale (MSAS PSYCH) and the Physical Symptom Subscale (MSAS PHYS). The MSAS PSYCH subscale in cludes the following six psychological symptoms: worrying, feeling sad, feeling nervous, difficulty sleeping, feeling irritable, and difficulty concentrating. The MSAS PHYS subscale includes twelve physical symptoms: lack of appetite, lack of energy, pain, feeling drowsy, constipation, dry mouth, nausea, vomiting, change in taste, weight loss, feeling bloated, and dizziness. Patients rate the severity, frequency, and distress of their symptoms on separ ate

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20 five point Likert scales. Each overall symptom score was calculated by averaging the severity, distress, and frequency scale scores for that symptom Overall symptom scores range from 0 4, with higher scores representing more symptom burden. If a respondent did not report having the symptom, the symptom sco re was zero. The MSAS PSYCH and the MSAS PHYS subscale scores were computed by averaging the respective individual symptom scores. The MSAS was administered at all waves of the PAC CF study except 3 and 5 and was used as an outcome variable at wave 9. Tr eatment Activity Information regarding treatment activity was assessed by the use of two surveys. Both surveys were administered at wave 9 of the study and are included as outcome variables. The Tool for Adherence B ehaviour Screening (TABS) is an 8 item subscale of the Beliefs and Behavior Questionnaire (BBQ) This instrument was designed to measure patients' beliefs and behaviors surrounding disease management (George, Mackinnon, Kong, & Stewart, 2006). The TABS assesses adherence to pharmacological and non pharmacological treatment regimens and is commonly used in both clinical and research settings. For the purpose of this study, items were tailored to screen for beliefs about airway clearance treatments ; therefore, one question pertaining to medication availability was removed, leaving seven items in the measure, three of which pertained to adherence and the other four to nonadherence Preliminary analyses revealed that internal consistency for the nonadherence items was poor ( = .556) and therefore, t his scale was not included in the study. The adherence scale was computed by summing the responses within that category. Internal consistency was rated as good for the adherence items ( = .813) at wave 9 of the

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21 study. Examples of adherence items include, "I have strict routines for completing my airway clearance treatment, and "I keep supplies for airway clearance treatment close to where I use them." Participants rated each item on a 5 point Likert Scale ranging from 1 ( Never ) to 5 ( Always ) with higher scores representing more treatment activity Treatment activity was also measured by asking participants how many times in the previous day they completed six various airway clearance treatments including the vest, flutter/acapella device, positive expir atory pressure mask, intrapulmonary percussive ventilation mask, autogenic draining/huffing/special breathing techniques, and chest physical therapy/physio/clapping on chest. Participants responded on a 1 5 scale for each type of activity with 1 = once 2 = twice 3 = three or more times 4 = none, I do this, but not yesterday and 5 = none, my doctor recommends it, but I don't do it For analyses, items were recoded on a 0 3 scale such that 0 = none, my doctor recommends it, but I don't do it or none, I do this, but not yesterday 1 = once 2 = twice and 3 = three or more times Therefore, higher scores represent more treatment activity. The reported frequency of each airway clearance treatment was then summed to result in an overall treatment activity score. As clinically recommended, if huffing was listed in combination with any other treatment activity, the reported frequency of huffing was not included in the count. If huffing was listed as the only treatment activity completed the previous day, it w as included as a 1 on the 0 3 scale Based on recommendations from clinical providers, the total amount of treatment activities completed the previous day was capped at four.

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22 Disease Specific Health Related Quality of Life (HRQoL ). The Cystic Fibrosis Questionnaire Revised (CFQ R) is a disease specific instrument designed to measure the health and symptoms of adults with cystic fibrosis The CFQ R is a 50 item self report survey that contains the following 9 HRQoL domains: physical functioning ( e.g., how difficult is it to walk without getting tired ) role functioning ( e.g., how often does CF interfere with daily activities ) vitality ( e.g., how often did you feel exhausted ) emotional functioning ( e.g., how often did you feel lonely ) social functioning ( e.g., how often do you get together with friends ) body image ( e.g., to what extent do you feel you look different than others your age ) eating disturbances ( e.g., have you had problems eating ) treatment burden ( e.g., how much time do you sp end on your treatments each day ) and health perceptions ( e.g., how do you think your health is now) The measure also contains 3 symptom domains: weight ( e.g., have you had trouble gaining weight ), respiratory symptoms (e.g., have you had trouble breathin g ), and digestive symptoms (e.g., have you had abdominal pain). Participants were asked to think of the past two weeks and respond on a 4 point Likert scale ( always, often, sometimes, never ) such that higher scores indicated better HRQoL Each domain was converted to 0 100 scale. The CFQ R domains at wave 9 were included as outcome measures in the current study. Internal consistency for all CFQ R items ranged acceptable to excellent with the exception of the social functioning and treatment burden domains, which were rated as poor (Quittner et al., 2012). Covariates CF Center s provided c linic al data on PAC CF participants through the CF Foundation patient registry. Data included weight percentile, diagnosis of diabetes, pancreatic sufficiency status and colonization with Burkholderia cepacia Staphylococcus aureus and Pseudomonas aeruginosa These variables were included and controlled for

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23 within the current study analyses. The study design (oversampling of individuals with a probability of five year su rvival less than 0.975) was also controlled for in analyses. Statistical Analyses Latent growth curve (LGC) modeling was employed using Mplus statistical software (Version 7.31; MuthÂŽn & MuthÂŽn, 2014). Using LGC modeling within a structural equation modeling (SEM) framework has several advantages including the ability to conduct longitudinal analyses and examine trajectories over time. Additionally, using LGC modeling allows for the exami nation of multiple outcome variables within a single model as well as sophisticated handling of missing data points using Full Information Maximum Likelihood (FIML). The root Mean Square Error of Approximation (RMSEA), Tucker Lewis Index/ Non Normed Fit In dex (TLI/NNFI) and Comparative Fit Index (CFI) were used as indices of model fit. Although recommendations vary, many researchers suggest that RMSEA cut off values should be 0.06 or lower (Hooper et al., 2008). The TLI/NNFI was created to address problems with sample size in regards to the Normed fit Index (NFI) with scores typically ranging from 0.0 to 1.0; scores closer to 1.0 indicate better fit. The CFI is a revised version of the NFI and is one of the most commonly reported fit statistics, also ranging from 0.0 to 1.0. Scores below 0.90 on both the TLI/NNFI and CFI indicate poor fit, scores between 0.90 and 0.95 indicate adequate fit, and scores above 0.95 indicate good fit (Hooper et al., 2008; Little, 2013). Separate linear regression analyses were also used in order to examine the predictors and outcomes associated with social support in a cross sectional manner. All regression analyses were conducted using IBM SPSS software (version 20). For all tests, a P value of .05 was used as an indicator of s tatistical significance. Post hoc mediational analyses were

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24 conducted to examine if social support mediates the relationships between significant predictors of support and mental and physical health symptoms. Mediational analyses were conducted using IBM S PSS software following the recommendations of Baron and Kenny (1986), stating that 1) the total effect of the independent variable (IV) on the dependent variable (DV) should be significant, 2) the relationship between the IV and the mediator (M) should be significant, and 3) the direct relationship between M and the DV should be significant when controlling for the IV, which should no longer be significantly related to the DV.

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25 CHAPTER III Results Descriptive statistics for all study variables are provided in Table 1. Bivariate correlations of primary study variables were also obtained (see Table s 2 and 3 ). Longitudinal Trends in Social Support Preliminary analyses indicated that ratings of social support were high overall ( M = 79.14, SD = 14.36, at baseline). Latent growth curve modeling revealed good model fit when examining social support across waves 5, 7, 8 and 10, CFI = 1.0, TLI = 1.0, RMSEA = 0.0. As shown in Figure 1, results supporte d a statistically significant decline in social support across these four waves ( p = 0.02) with the mean and variance of the intercept equaling 79.78 and 164.00 and the mean and variance of the slope equaling .06 and .01 respectively. Specifically, socia l support declined 1.62 points from baseline (wave 5) to the last wave examined (wave 10). Although these results support a statistically significant negative trend, the results are not clinically meaningful on a 0 100 point scale of social support. In ad dition to examining longitudinal trends of social support, this study aimed to examine the outcomes associated with change in social support. All outcome measures in the current study were collected at wave 9. Therefore, the intercept and slope of all wave s containing the social support measure prior to wave 9 (waves 5, 7, and 8) were calculated using Mplus Latent growth curve modeling revealed good model fit when examining social support across these three waves, CFI = 1.0, TLI = 0.99, RMSEA = 0.03 As Fi gure 2 depicts, results of this model did not suggest a significant change in support over time ( p = 0.49), indicating that social support did not significantly change from baseline (wave 5) to

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26 the last wave examined (wave 8). T he mean and variance of the intercept equaled 79.63 and 165.02 and the mean and v ariance of the slope equaled .03 and .04 respectively. Given that there was no meaningful change in social support across waves 5, 7 and 8, no predicted outcomes of social s upport change could be examined longitudinally. Therefore, multiple linear regressions in SPSS were used to examine the second and third research aims. Predictors of Social Support One multiple linear regression analysis was conducted to examine predictiv e variables of social support in a cross sectional manner. As hypothesized, both gender and employment pred icted social support; females reported more social support than males ( B = 6.1 8 ) and those who were employed reported more social support than those wh o were unemployed ( B =5.59 ) Contrary to initial hypotheses, age ( B = .12 ) income ( B =2.0 ) education ( B = .53 ) marital status ( B =2.15 ) FEV 1 ( B = .01) or exacerbations ( B =.31) failed to predict social support. As shown in Table 4, t he overall effect size equ aled 0.11. Unstandardized coefficients are reported for interpretability o f results. Covariates included in this study ( design, weight percentile, diabetes, pancreatic sufficiency, and colonization with Burkholderia cepacia Staphylococcus aureus and Pseudomonas aeruginosa ) did not significantly predict baseline social support. Outcomes Related to Social Support S eparate linear regression analyses were employed in order to examine outcomes associated with social support. As hypothesized, social support predicted better mental health ( B = .02 R 2 = .18 ), and physical health ( B = .01 R 2 = .14 ) such that those with more support reported fewer mental and physical health symptoms.

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27 Social support was significantly associated with 9 HRQoL domains. Specifically, s ocial support predicted less treatment burden ( B =.26 R 2 = .08 ) and better emotional ( B =.69 R 2 =. 28 ) social ( B =.61 R 2 = .24 ) and role ( B =.42 R 2 =. 17 ) functioning. Having more support also predicted improved vitality ( B =.35 R 2 =. 20 ) better body image ( B =.63 R 2 =. 31 ) and health perceptions ( B =.65 R 2 = .35 ) Moreover, support predicted fewer eating disturbances ( B =.44 R 2 = .17 ) and digestive symptoms ( B =.25 R 2 = .10 ) Social support did not predict physical functioning ( B =.27 R 2 =. 15 ), problems gaining weight ( B =.28 R 2 =. 32 ), or respiratory symptoms ( B =.01 R 2 =. 06 ) Social support also did not predict higher treatment activity on either measure: TABS ( B =.02 R 2 = .04 ) nor treatment activity ( B = .00 R 2 =. 07 ) See Table 5. In addition to social support predicting health outcomes, some covariates included in analyses also significantly predicted variables of interest. For example, those with a greater weight percentile reported fewer mental ( B = .006, p =.008 ) and physical health symptoms ( B = .004, p =.003 ), less treat ment burden ( B =.12, p =.049 ), and better physical ( B =.15, p =.049 ), emotional ( B =.13, p =.02 ), and role functioning ( B =.19, p =.006 ). Those with greater weight percentile also reported better vitality ( B =.22, p =.000 ), body image ( B =.36, p <.001 ), health percept ions ( B =.24, p <.001 ), ability to gain weight ( B =.28, p <.001 ) and fewer eating disturbances ( B =.14, p =.01 ) and respiratory symptoms ( B =.13, p =.04 ). Having a diabetes diagnosis significantly predicted poorer vitality ( B = 6.66, p =.04 ) and body image ( B = 8.98, p =.03 ). Those with pancreatic insufficiency reported significantly worse physical functioning ( B = 24.91, p =.02 ). Moreover, the design of the study predicted outcomes, such that those who were more ill reported less treatment activity ( B = .79, p =.03 ) and poorer physical functioning ( B =.21.24, p =.008 ). Finally, colonization with Burkholderia cepacia predicted

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28 better health perceptions ( B = 15.40, p =.003 ); however, colonization with the bacteria Pseudomonas aeruginosa predicted better digestion ( B =11.71 p =.03 ). Mediators of the Relationship between Social Support and Health In order to determine if social support mediates the relationships between gender/employment status and mental and physical health, p ost hoc mediation analyses were conducted. Analy ses revealed that gender was not predictive of either mental or physical health symptoms in this sample. Although employment status did not predict physical health symptoms ( p =.08), being employed did predict fewer mental health symptoms ( B = .31, = .18, SE=.13, t= 2.5 p =.02 ). Moreover, when employment status and social support were included in the same model, social support remained significant ( B = .02, = .36, SE=.004, t= 5.18 p <.001 ); however, employment status was no longer significant ( B = .21 = .12, SE=.12, t= 1.73 p =.08 ). Th is indicates a full mediation by social support of the relationship between employment and mental health symptoms.

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29 CHAPTER IV Discussion The current study examined the role of social support in adults with C F. S pecifically, this study assessed longitudinal trends of social support as well as predictors and health outcomes associated with social support in this population Overall, ratings of perceived social support were high. As hypothesized, perceived social support decreased significantly across the 27 months it was examined, but because it decreased only 1.62 points, the results were not clinically meaningful. There were also no significant changes in social support examined across the three time poin ts (a period of 13 months) prior to the measurement of the health outcome variables. Therefore, these findings do not uphold the hypothesis that support would significantly decline over time in this sample. Similarly, others have found that Quality of Lif e (QoL) in those with CF remains relatively stable over time (e.g., Goldbeck et al., 2007; Sawicki et al., 2011). Rapkin and Schwartz ( 2004 ) proposed that patients with CF may effectively cope with their disease and have the ability to adjust their subject ive experiences as their illness progresses in a phenomenon known as "response shift." It is possible that perceptions of social support are adjusted over time as well. Others have proposed that QoL is a trait rather than a state, thus leading to more stab ility over time (Goldbeck et al., 2007). Perceptions of social support may also be heavily influenced by traits. Still, it is possible that more time is required to detect substantial changes in social support over time. As hypothesized, gender significan tly predicted social support, such that women reported more support than men. Specifically, being female was associated with a six point increase on the ISEL. This finding is consistent with the literature on gender and social

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30 support in the general popula tion, suggesting that women are more likely to receive support than men (Matthews et al., 1999; Turner, 1994). Despite females reporting more support than males, gender did not predict fewer mental or physical health symptoms in this sample. In addition t o gender, individuals who were employed also reported having more support than those who were unemployed. Specifically, employment was associated with a five point increase on the ISEL. Further analyses revealed that employment status predicted increased p erceived social support, which in turn, predicted fewer mental health symptoms. In other words, social support mediated the relationship between employment status and mental health symptoms. This finding is consistent with the literature suggesting that em ployment offers increased opportunities to connect with others (e.g., Freedman & Fesko, 1996; Johnson, Yorkston, Klasner, Kuehn, Johnson, & Amtmann, 2004), which may lead to better mental health. Given the association between employment and social suppor t, it is reasonable to postulate that marriage would also lead to an increased opportunity for support; however, marital status did not predict social support in this sample. Post hoc analyses further revealed that marital status at baseline was not associ ated with either mental ( B =.15, p =.23) or physical health ( B = .13, p =.12) symptoms at wave 9. Perhaps adults with CF acquire support from friends and family at a similar rate as those who obtain support through a spouse. It is also possible that marriage d oes not provide an added benefit to adults with CF due to marital distress caused by having a chronic illness. Partners of those with a chronic illness may experience a variety of negative outcomes including higher rates of stress and depression which may contribute to decreased marital satisfaction among both individuals (e.g., Pruchno, Wilson Genderson, & Cartwright, 2009). It may be possible that, in other

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31 populations, marital status leads to better health through mechanisms other than increased social s upport. Contrary to the hypothesis, age did not predict ratings of social support in this sample. Younger patients with CF may not report more support for a variety of reasons. First, early diagnosis of the disease may allow individuals to develop confide nce surrounding symptom management, resulting in less needed assistance as they transition into early adulthood. Second, those with CF may have a strengthened ability to foster relationships throughout their illness and course of treatment. Third, caretake rs, family, and friends may be understanding of the chronic disease and better able to provide consistent and continued support throughout the lifespan. Previous studies indicate that those of lower SES have less perceived emotional support and more negat ive social interactions, perhaps due to decreased access to social resources, more frequent negative life events, and an inability to mobilize support in time of need ( Mickelson and Kubzansky, 2003 ). Contrary to these findings, neither income nor education predicted social support in the current study. This finding may be due to the classification of categories within these dichotomous variables: income ( less than $50,000 or greater than or equal to $50,000 ) and education ( no post high school degree or a vo cational degree or h igher ). The amount of variability within this categorization system may have been too large to detect significant differences. In other words, the social support received by those who earn $49,000 a year may be remarkably different from those who earn $19,000 a year. Similarly, there may be differences in the level of support between those with vocational degrees versus those with post baccalaureate degrees.

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32 Finally, measures of disease severity, FEV 1 and exacerbations in the past mont h, failed to predict social support in this sample. Previous studies have shown that social support often declines over time in those with chronic illness (e.g., Bloom & Kessler, 1994 ; Bolger et al., 1996). This decline may be attributable to a decreased o pportunity for support, such as missing school/work and other opportunities for engagement. Those who are more ill may also experience frequent hospitalizations that limit their access to their social networks. In this sample, however, measures of disease severity did not predict less social support. It is possible that as adults with CF become more ill, they begin to require more assistance from family and friends. This increased support may mitigate the loss of support opportunity in other areas. The current study also examined the health outcomes associated with social support in adults with CF. As hypothesized, those with more support reported fewer mental and physical health symptoms. Specifically, for every 10 unit increase on the ISEL, there was a .2 and .1 reduction on mental and physical health symptoms respectively, on a 0 4 point scale. Social support also predicted better emotional, social, and role functioning. Therefore, social support may improve mental and physical health symptoms, wh ich may lead to an increased ability to function in various areas of life. Social support also predicted improved vitality, enhanced body image, and better overall health perceptions. Moreover, support predicted fewer eating disturbances and digestive symp toms in this sample. It is possible that fewer eating problems influenced better digestive functioning. It is also possible, given previous literature and theory, that support directly affects the physiological process of digestion through improved diet (C roezen, Picavet, Haveman Nies, Verschuren, de Groot, & van't Veer, 2012).

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33 Although social support predicted fewer digestive symptoms, it did not predict respiratory symptoms, such as wheezing, coughing, and difficulty breathing. Mucus accumulation in the lungs is the most common clinical pattern in those with CF; therefore, it is likely that many individuals in the current study experienced respiratory symptoms. Due to study design, participants in this study were are also more likely to be severely ill. I t is possible that social support did not predict respiratory symptoms as these symptoms were already advanced and unremitting. Although higher levels of support predicted fewer physical symptoms on the MSAS, it did not predict physical functioning on the CFQ R. It is possible that although support was associated with fewer symptoms, CF patients still feel the negative effects of their disease on their ability to perform physical tasks of daily living. For example, although individuals with more support ma y report less nausea, shortness of breath, or dizziness, they may continue to struggle to perform vigorous activities such as running or playing sports. Research in other disease populations indicates that social support may improve treatment adherence (P rasad & Cerney, 2002), which may, in turn, yield better mental and physical health outcomes due to increased disease management. In the current study, social support did not predict either measure of treatment activity. Moreover, neither measure of treatme nt activity was related to either mental or physical health symptoms. Some have proposed the mediating effects of treatment adherence on the relationship between social support and health (Prasad & Cerney, 2002). Although the results of this study do not support either relationship, it is important to note that the measures used assessed beliefs about treatment and activity completed, not necessarily adherence to prescribed treatments. Although higher levels of su pport did not predict better beliefs about treatment or more

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34 treatment activity, it did predict less treatment burden. Perhaps, support is not related to beliefs about treatment or actual treatment activity, but does lessen the burden experienced by provid ing increased assistance. In addition to examining the relationship between social support and key health outcomes, covariates of interest were also included in analyses. Greater weight percentile, indicating better health, significantly predicted severa l outcomes, including the ability to gain weight, fewer mental and physical health symptoms and better physical, emotional, and role functioning. Greater weight percentile also predicted better vitality, body image, health perceptions, and less treatment b urden, eating disturbances, and respiratory symptoms. Having a diabetes diagnosis was significantly related to poorer vitality and body image while pancreatic insufficiency was significantly related to poorer physical functioning. Moreover, individuals wi th poorer prognoses reported poorer physical functioning and less treatment activity, despite the fact that treatment activity is crucial for this group. These individuals may feel incapable of completing their treatments due to poor physical functioning o r decide to discontinue treatment due to their poor prognosis. Due to the cross sectional nature of these findings, directionality remains unknown. Therefore, it is possible that those with less treatment activity have poorer health. Further, colonization with Burkholderia cepacia was associated with worse perceptions of health. Specifically, colonization with cepacia predicted a fifteen point decrease in health perceptions. Although startling, this discovery is consistent with the finding that cepacia can cause increased risk of mortality (e.g., Tablan et al., 1987). Interestingly, colonization with the bacteria Pseudomonas aeruginosa was associated with better digestive symptoms.

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35 Implications Studying the relationship between social support and health is of utmost importance, as implementations of research findings may improve health care costs and quality of life (Aslund et al., 2014). Most studies involving social support examine the relationship between support and either physical or mental health in a cross sectional manner. Previous researchers have proposed that future st udies should examine the relationship between social support and health in a longitudinal design (Bloom, 1990) and should explore mental and physical health outcomes simultaneously (Thoits, 2011). Therefore, the current study fills gaps in the literature b y providing a longitudinal investigation of social support while examining multiple health outcomes in an understudied population. The current study also includes both subjective ( self report) and objective (disease severity) measures of health. Further, the current study broadens our understanding of the psychosocial components of those living with CF. In accordance with the biopsychosocial framework, researchers have studied the origins of CF ( bio logical ) as well as the emotional vulnerability associated with the illness ( psycho logical ) but have overlooked the many social components (Gotz, 2000 ; Harrop, 2007 ). In fact, f ew studies examine the role of support on health outcomes in the CF population, and those that do are primarily qualitative in nature and focused on pediatric samples G iven the improved longevity of those with CF, studying support in adults is of concern and deserving of further research Prevention and contro l procedures promote health and decrease infection in those with CF. However, the procedures also prohibit interaction between patients and make support difficult to acquire and maintain. Given the influence of support on health in those with CF, finding a lternatives to support is of greatest importance. For example, some have

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36 suggested the use of online support groups (Romero, 2013) to allow patients the opportunity for interaction and the sense of belonging. In addition, mentoring programs between patient s of different ailments may prove useful in providing support opportunities. Clinicians may wish to encourage their patients to maintain close contact with family members, friends, or significant others. Including support persons in medical visits may als o prove particularly useful in fostering support. Moreover, the transition from adolescence to adulthood may be particularly isolating as adults move away from home, transition from pediatric care, and leave the school system. Opportunities for adult suppo rt and an increased sensitivity to these issues during the transition by medical staff may lessen the isolation experienced. The current study also demonstrates the relationship between employment status and social support. This finding is particularly important as CF may drastically affect one's ability to remain employed, despite its benefits. Establishing creative solutions to maintain employment status in those with CF is imperative. Limitations and Future Directions Des pite the strengths of this st udy there are some limitations that sho uld be addressed First participant survey data and clinical outcomes collected at the CF centers are not synchronous due to the method of data collection. Time between surveys and clinical data, however, was genera lly no more than 12 weeks apart. Second, participants in the current study were part of a larger study (PAC CF) that aimed to recruit adults with high disease severity, thus skewing the sample and making results less generalizable to healthier adults with CF. Despite this study design, the average lung function of adults in PAC CF was only slightly lower than the U.S. average. Third, income was dichotomously coded for analyses; however, this may have led to less sensitivity in determining how various levels of income are related to social support. For example, perhaps the categories of above or below

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37 $50,000 annually are too broad to capture true economic differences. Although this study provides further insight into longitudinal trends, predictors, and out comes of social support in adults with CF, more research in this area is needed. It will be important for future investigators to carefully consider the distinction between perceived support (the subjective belief that emotional, informational and instrum ental support is available from family, friends, colleagues and loved ones ) and received support ( the actual amount of support acquired ) ( Nurullah, 2012; Schulz & Schwarzer, 2004 ; Tardy, 1985 ; Thoits, 1995 ; Uchino, 2009) as they relate to health outcomes Although p erceived social support has been consistently linked to beneficial health related outcomes including mental and physical health, health behaviors, and mortality rates, f indings on the influence of received support on health outcomes widely var y (Haber, Cohen, Lucas, & Baltes, 2007 ; Nurullah, 2012; Thoits, 2011; Uchino, 2009) Some research indicates that received support may have negative effects on health including higher mortality rates (Forster & Stoller, 1992; Krause, 1997; Pennix, Van Tilbu rg, Kriegsman, Deeg, Boeke, & van Eijk, 1997; Uchino, 2009) depression (Frese, 199 9) and poorer mental health (Iwata & Suzuki, 1997; Nurullah, 2012). In addition, researchers generally agree that social support is a multidimensional construct with differe nt types of support yielding different outcomes (Schwarzer & Leppin, 1991; Uchino, 2004) Researchers may wish to differentiate between types of perceived support (e.g., positive appraisals, emotional belonging, tangible support, etc.). Future studies foc using on the mechanisms by which social support predicts better health outcomes in this population will yield important findings for the field. Previous research indicates that support may yield better treatment adherence that, in turn, leads to better hea lth. In this study, however, neither treatment activity nor beliefs about treatment

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38 were associated with social support. Perhaps other constructs, such as level of optimism, emotional well being, resiliency, or other behaviors mediate this relationship. Finally, little research has examined the use of a social support intervention on improving health outcomes in any population. Results of the current study suggest that an intervention of this na ture may be particularly beneficial to those with CF who show improved health outcomes with more support, but are unable to attend support groups with individuals of the same illness. Conclusion In summary, this study demonstrated that social support remained relatively stable over time for adults with CF. Being f emale and employed predicted more perceived social support. Social support, in turn, predicted fewer mental and physical health symptoms, better emotional, role, and social functioning, improved body image and vitality, fewer eating disturbances and digest ive symptoms, and less treatment burden. This study fills gaps in the literature by providing a longitudinal examination of support and an analysis of relationships between social support and multiple health outcomes in adults with CF. Findings illustrate that those who are male and unemployed are at the most risk of experiencing low levels of social support, which may result in poorer health outcomes. It is crucial to develop employment opportunities and support interventions for those with CF, especially for those at most risk. Future research should differentiate between types of support and examine mechanisms by which support influences health outcomes.

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39 Table 1. Descriptive statistics for variables included in analyses Variable N Mean ( SD ) Frequency (%) Min Max Social Support Wave 5 233 79.14 (14.36) 30.56 100.00 Wave 7 203 79.32 (14.62) 20.29 100.00 Wave 8 209 78.55 (14.60) 18.06 100.00 Wave 10 184 77.52 (15.34) 23.61 100.00 Predictors of Support Age 250 33.17 (10.28) 19 64 Gender (fem ale ) 250 60.4% Income (more than 50,000 annually) 197 54.3% Education (vocational degree or higher) 229 63.3% Marital Status (married) 230 54.3% Employment (employed) 233 57.9% FEV 1 218 53.85 (20.04) 8.63 119.08 Exacerbations 224 0.17 (.31) 0 2 Outcomes of Support Mental Health Symptoms 195 1.10 (.85) 0 3.56 Physical Health Symptoms 194 0.72 (.54) 0 2.74 TABS 185 11.07 (3.16) 3 15 Treatment Activity 184 1.43 (1.16) 0 4 Treatment Burden 193 52.33 (20.63) 0 100 Physical Functioning 198 52.45 (27.36) 0 100 Emotional Functioning 198 69.90 (21.00) 0 100 Social Functioning 193 58.76 (19.47) 0 100 Role Functioning 195 71.82 (23.95) 0 100 Vitality 198 49.20 (19.58) 0 100 Body Image 194 64.78 (27.39) 0 100 Eating Disturbances 196 83.59 (20.85) 11.11 100 Digestive Symptoms 196 75.17 (18.89) 11.11 100 Respiratory Symptoms 192 55.22 (19.86) 0 100 Health Perceptions 192 54.72 (22.62) 0 100 Weight 193 68.91 (37.43) 0 100

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40

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41

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42 Table 4. Regression Results for Predictors of Social Support Variable B SE B t p Age 0.12 .1 4 .0 9 .87 .386 Gender (female=1) 6.18 2. 45 .2 1 2.52 .013 Income 2.00 2. 70 .07 .74 .463 Education 0.53 2.62 .02 .20 .841 Marital Status 2.15 2. 67 .0 7 .81 .420 Employment 5.59 2. 54 .19 2.20 .029 FEV 1 .01 .06 .01 .14 .891 Exacerbations .31 4.4 0 .01 .07 .944 Note. The table reflects results from a single linear regression with all covariates and time varying predictors (wave 5) entered in the same step. The dependent variable was social support at wave 5. The overall R 2 = 0.11*

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43 Table 5. Regression Results for Outcomes of Social Support Note. Separate regression analyses were conducted with covariates (wave 9) entered on the first step and social support (wave 8) entered on the second step. The variable of interest was entered as the dependent variable (wave 9). Variable B SE t p R 2 R 2 Mental Health Symptoms .02 .004 .32 4.13 <.001 .18 .10 Physical Health Symptoms .01 .003 .23 2.95 .004 .14 .05 TABS .02 .02 .09 1.02 .309 .04 .007 Treat Activity .004 .01 .05 .55 .59 .07 .002 Treat Burden .26 .12 .18 2.23 .028 .08 .03 Physical .27 .15 .14 1.81 .072 .15 .02 Emotional .69 .10 .48 6.60 <.001 .28 .22 Social .61 .10 .45 6.02 <.001 .24 .19 Role .42 .13 .25 3.17 .002 .17 .06 Vitality .35 .10 .25 3.32 .001 .20 .06 Body .63 .13 .34 4.78 <.001 .31 .11 Eat .44 .11 .31 4.03 <.001 .17 .09 Digest .25 .10 .20 2.43 .016 .10 .04 Respiration .01 .11 .01 .10 .917 .06 .000 Health .65 .11 .41 6.00 <.001 .35 .16 Weight .28 .18 .11 1.58 .117 .32 .01

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44 Figure 1. Longitudinal Examination of Support Across Four Waves Note. I=Intercept, S=Slope, M=Mean, V=Variance e=error.

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45 Figure 2. Longitudinal Examination of Support Across Three Waves Note. I=Intercept, S=Slope, M=Mean, V=Variance e=error.

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46 APPENDIX A Interpersonal Support Evaluation List (ISEL) The following is a list of statements that may or may not be true about you. Please read each statement, then circle the one number that best describes how true or false that statement is about you. Completely False Somewhat False Somewhat True Completely True a. If I had to go out of town for a few weeks, someone I know would look after my home, such as watering the plants or taking care of the pets. 1 2 3 4 b. If I were very sick and needed someone to drive me to the doctor, I would have trouble finding someone. 1 2 3 4 c. If I were very sick, I would have trouble finding someone to help me with my daily chores. 1 2 3 4 d. f I needed help moving, I would be able t o find someone to help me. 1 2 3 4 e. If I needed a place to stay for a week because of an emergency, such as the water or electricity being out in my home, I could easily find someone who would put me up. 1 2 3 4 f. There is at least one person I know whose advice I really trust. 1 2 3 4 g. There is no one I know who will tell me honestly how I am handling my problems 1 2 3 4 h. When I need suggestions about how to deal with a 1 2 3 4

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47 personal problem, I know there is someone I can turn to. i. There isn't anyone I feel comfortable talking to about intimate personal problems. 1 2 3 4 j. There is no one I trust to give me good advice about money matters. 1 2 3 4 k. I am usually invited to do things with others. 1 2 3 4 l. Most people I know think highly of me. 1 2 3 4 m. Most of my friends are more interesting than I am. 1 2 3 4 n. I am more satisfied with my life than most people are with theirs. 1 2 3 4 o. I think my friends feel that I'm not very good at helping them solve problems. 1 2 3 4 p. I am closer to my friends than most other people. 1 2 3 4 q. I am able to do things as well as most other people. 1 2 3 4

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48 APPENDIX B Memorial Symptom Assessment Scales (MSAS) Please review the following list of symptoms. If you have had the symptom during the past two weeks, let us know how often you had it, how seve re it usually was and how much it distressed or bothered you by circling the appropriate number. If you did not have the symptom, circle the 0' in the column marked Did Not Have.' !

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49

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50 APPENDIX C Tool for Adherence Behaviour Screen (TABS) Now, thinking in general, please indicate how often the following statements about airway clearance treatment apply to you:

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51 APPENDIX D Treatment Activity Yesterday, how many times did you do each of the following airway clearance treatments?

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52 APPENDIX E Cystic Fibrosis Questionnaire Revised (CFQ R) Please think about your health and well being over the past two weeks when answering the following questions.

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53

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54 ! !

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55 ! ! !

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56 ! ! !

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57 REFERENCES Adler, N., & Matthews, K. (1994). Health psychology: Why do some people get sick and some stay well?. Annual Review of P sychology 45 229. Alfonso, V., Geller, J., Bermbach, N., Drummond, A., & Montaner, J. S. (2006). Becoming a"treatment success": What helps and what hinders patients from achieving and sustaining undetectable viral loads. AIDS Patient Care & STDs 20 (5), 326 334. Ali, S. M., Merlo, J., Rosvall, M., Lithman, T., & Lindstršm, M (2006). Social capital, the miniaturisation of community, traditionalism and first time acute myocardial infarction: A prospective cohort study in southern Sweden. Social Science & M edicine 63 (8), 2204 2217. Anderson, D. L., Flume, P. A., & Hardy, K. K. (2001). Psychological functioning of adults with cystic fibrosis. CHEST Journal 119 (4), 1079 1084. slund, C., Larm, P., Starrin, B., & Nilsson, K. W. (2014). The buffering effect of tangible social support on financial stress: influence on psychological well being and psychosomatic symptoms in a large sample of the adult general population. International Journal for Equity in H ealth 13 (1), 1 Baron, R. M., & Kenny, D. A. (1986). The moderator mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology 51 (6), 1173. Barrera Jr, M. (1986). Distinctions between social support concepts, measures, and models. American Journal of Comm unity P sychology 14 (4), 413 445. Barth, J., Schneider, S., & von KŠnel, R. (2010). Lack of social support in the etiology and the prognosis of coronary heart diseas e: a systematic review and meta analysis. Psychosomatic M edicine 72 (3), 229 238. Berkman, L. F. (1995). The role of social relations in health promotion. Psychosomatic M edicine 57 (3), 245 254. Berkman, L. F., Leo Summers, L., & Horwitz, R. I. (1992). Emotional support and survival after myocardial infarction: a prospective, population based study of the elderly. Annals of Internal Medicine 117 (12), 1003 1009. Berkman, L. F., & Syme, S. L. (1979). Social networks, host resistance, and mortality: a nine year follow up study of Alameda County residents. American J ournal of Ep idemiology 109 (2), 186 204. Blair, C., Cull, A., & Freeman, C. P. (1994). Psychosocial functioning of young adults with cystic fibrosis and their families. Thorax 49 (8), 798 802.

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