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Examining The Effects Of Family Relationships OnMental And Physical Health: Testing TheBiobehavioral Family Model With An AdultPrimary Care SampleSarah Beth WoodsThe Florida State University

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Recommended CitationWoods, Sarah Beth, "Examining The Effects Of Family Relationships On Mental And Physical Health: Testing The BiobehavioralFamily Model With An Adult Primary Care Sample" (2012). Electronic Theses, Treatises and Dissertations. Paper 5277.

THE FLORIDA STATE UNIVERSITY

COLLEGE OF HUMAN SCIENCES

EXAMINING THE EFFECTS OF FAMILY RELATIONSHIPS ON MENTAL AND

PHYSICAL HEALTH: TESTING THE BIOBEHAVIORAL FAMILY MODEL WITH AN

ADULT PRIMARY CARE SAMPLE

By

SARAH B. WOODS

A dissertation submitted to the Department of Family and Child Sciences

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

Degree Awarded: Summer Semester, 2012

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Sarah Woods defended this dissertation on March 28, 2012.

The members of the supervisory committee were:

Wayne Denton Professor Directing Dissertation

Robert Glueckauf University Representative

Lenore McWey Committee Member

Ann Mullis Committee Member

The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements.

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ACKNOWLEDGEMENTS An enormous thank you to my dissertation committee, without whom I may not have had faith that this project was worthwhile or accomplishable. Thank you, especially, to Dr. Wayne Denton. I am incredibly grateful for your gentle reassurances that this dissertation was only one small test of my abilities. Your confidence in my aspirations has been encouraging and sustaining; I am very grateful for your enthusiasm in my ideas and your recognition of my passion for medical family therapy. Thank you also to Dr. Lenore McWey, whose endless support, guidance, and mentoring has had immeasurable influence on my personal and professional lives. Thank you to Dr. Ann Mullis for your consistent reminders of the (important) life that exists after graduate school. Lastly, I am very grateful for the perspectives and encouragement of Dr. Robert Glueckauf, who was always willing to talk through my ideas and build them into more meaningful and significant contributions to the field. I am also forever grateful for the love and support of my family throughout this doctoral process; through my frustrations and successes, my family has been unendingly giving. My parents deserve enormous credit for my ability to start, and finish, this degree. They consistently listened and offered thoughts that gave me perspective, strength, and resilience. Most importantly, I am thankful for my husband, Jesse, who constantly believed in my ability to succeed, and made my dreams his dreams. He gave me courage and humor when I was inconsolable, and praise when I was proud. Without my family, this process would not have been meaningful and I am ever appreciative of them and their love.

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TABLE OF CONTENTS List of Tables..vi List of Figuresvii

List of Abbreviationsviii Abstract...ix 1. INTRODUCTION...1

1.1 The Biobehavioral Family Model..1 1.2 Statement of the Problem...3 1.3 Study Purpose....4 1.4 Hypotheses.....5 2. LITERATURE REVIEW7

2.1 Families and Health.......7 2.2 The BBFM: An Organizing Framework....8 2.3 Summary..15 3. METHOD..16 3.1 Sample..16 3.2 Measures..19 3.3 Analyses...23

4. RESULTS..25 4.1 Exploratory Data Analysis...25 4.2 Construct Associations26 4.3 Stepwise Regressions...29 4.4 Model Testing..30 4.5 Bootstrapping Analyses...34 4.6 Conclusion...36 5. DISCUSSION37 5.1 Summary of Hypotheses..37 5.2 Model 1: Family Functioning..37 5.3 Model 2: Romantic Relationship Satisfaction.38 5.4 Limitations and Future Research.39 5.5 Implications for Clinical Practice44

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5.6 Conclusion...46 6. APPENDIX A: IRB Approval...54 7. APPENDIX B: Informed Consent Letter..55 8. APPENDIX C: Measures/Assessment...59 7. REFERENCES..70 8. BIOGRAPHICAL SKETCH.83

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LIST OF TABLES

1 FEC Variables, Biobehavioral Reactivity Variables, and Disease Activity Variables: Descriptive Statistics (N = 125).26

2 Bivariate Pearson Correlations of Study Variables...27

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LIST OF FIGURES

1 The Biobehavioral Family Model (Wood, 1993)6

2 Participation flow diagram.17

3 The initial model built in AMOS to test Model 1, with family functioning (GFS/FAD scores) as the endogenous variable, depression (QIDS) as the mediator, and disease activity as a latent outcome variable (n = 125) *p

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LIST OF ABBREVIATIONS 1. CDC - Centers for Disease Control and Prevention 2. BBFM - Biobehavioral Family Model 3. FEC Family Emotional Climate

4. NHLBI - National Heart, Lung, and Blood Institute 5. ACTH - Adrenocorticotropic Hormone 6. GFS/FAD General Functioning Subscale of the Family Assessment Device 7. QMI Quality of Marriage Index 8. DAS Dyadic Adjustment Scale 9. PC Perceived Criticism 10. NIAAA - National Institute on Alcohol Abuse and Alcoholism 11. AUDIT Alcohol Use Disorders Identification Test 12. QIDS - Quick Inventory of Depressive Symptomatology Self Report 13. OASIS - Overall Anxiety Severity and Impairment Scale 14. BMI Body Mass Index 15. RMSEA - Root Mean Square Error of Approximation 16. CFI Comparative Fit Index 17. CI Confidence Interval 18. MFT Marriage and Family Therapist

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ABSTRACT Family and romantic relationships have been linked to both mental and physical health outcomes. Previous research has lacked attention on precise pathways by which these associations occur and continue to use predominately White, middle-class, nuclear families as the basis of study. The Biobehavioral Family Model (BBFM) is a biopsychosocial approach to health that integrates family emotional climate, biobehavioral reactivity (emotion dysregulation), and physical health outcomes into a comprehensive model. The present study was conducted to examine the ability of the BBFM to explain connections between family processes and health for primarily uninsured, low-income adult primary care patients. Patient participants (ages 18-65 years) self-reported their family functioning, romantic relationship satisfaction, anxiety, depression, alcohol use, illness symptoms, and physical well-being (n = 125). Data were also collected from patient medical charts. Separate models using family functioning (Model 1) and romantic relationship satisfaction (Model 2) as measures of family emotional climate were tested using path analyses and bootstrapping. Results demonstrated support for the BBFM in explaining health quality for this sample. Applying the BBFM to diverse primary care patients demonstrates pathways by which family processes affect the mental and physical health of these individuals. Recommendations for future research and clinical implications are discussed.

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CHAPTER 1 INTRODUCTION

The World Health Organization (2006) defines health as a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity, asserting also that enjoyment of the highest attainable standard of health is one of the fundamental rights of every human being (p. 1). In the U.S., the Centers for Disease Control and Prevention (CDC) explains that well-being integrates mental health (mind) and physical health (body) resulting in more holistic approaches to disease prevention and health promotion (2011b, p. 1). Unfortunately, chronic illness and risk factors for poor physical health outcomes continue to be a concern in the United States. In 2008, almost 1 in 2 adults age 18 or older had 1 or more of the following chronic illnesses: diabetes, asthma, cancer, arthritis, cardiovascular disease, or chronic obstructive pulmonary disease (U.S. Department of Health and Human Services, 2011) and, more recently, 1 in 10 adults in the U.S. self-assessed their health to be fair or poor (National Center for Health Statistics, 2011). This is additionally concerning given the growth in health care costs (Agency for Healthcare Research and Quality, 2002); between 1998 and 2008, total personal health care expenditures almost doubled, growing to approximately $2 trillion, although almost 18% of the U.S. adult population under 65 years continues to be uninsured (National Center for Health Statistics, 2011). The mental and physical health of American adults are not unrelated to experiences of family and romantic relationships. Close relationships can both buffer and potentiate risk factors related to health, and there is now much evidence linking family and romantic relationships to health outcomes as researchers increasingly recognize the importance of understanding how social factors influence health (Carr & Springer, 2010). In addition, there is an increased focus in research tying relational variables to health outcomes on pathways by which these effects occur. Nevertheless, tests of the connections between social functioning and physical health continue to use White, middle-class, nuclear families as the basis of study, with a lack of attention on precise pathways and specific indicators of health (Carr & Springer, 2010; Wood, 2005). The Biobehavioral Family Model

The Biobehavioral Family Model (BBFM) is a biopsychosocial approach (Engel, 1977) to health that has successfully integrated family functioning, psychological factors, and physical

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health outcomes into one, comprehensive model (Wood, 1993). The BBFM connects principles of general systems theory (von Bertalanffy, 1969) with Minuchins (1974) structural family therapy model to explain the influence of psychosocial factors on biological processes and disease activity (Wood & Miller, 2002). The model theorizes the mutual influence of social, emotional, and physical factors on aspects of illness and purports that family/social relationships are a critical domain of functioning that can serve as protective or act to worsen health outcomes (Wood & Miller, 2002). Additionally, the BBFM addresses the limitation in the literature of a lack of organizing models that focus on factors relevant to explaining family-psychobiological pathways important in understanding disease (Wood et al., 2006, p. 1495). The BBFM theoretically applies to a broad developmental range of individuals and is able to address the processes affecting [the health of] any family member (adult or child) (Wood, Klebba, & Miller, 2000, p. 322). The authors justify this by individuals involvement with family regardless of age and stage of development, especially given the likely dependence on family members if ill (Wood et al., 2000; Wood & Miller, 2005). However, to date, studies of the BBFM have examined psychosocial processes affecting illness in children, specifically for children with asthma (Wood et al., 2000; Wood et al., 2006; Wood et al., 2007; Wood et al., 2008). Although this is logical given the stress-related nature of pediatric asthma (Miller & Wood, 2003), the BBFM is applicable to both psychologically manifested illnesses and diseases of primarily organic origin, as well as the illness experiences of adult family members (Wood & Miller, 2002). Lastly, because of its foundation in general systems theory, the model maintains neutrality with an unbiased framework of how families adapt; therefore, it is well-suited to the appreciation of cultural, racial, class, and gender factors as they relate to adaptive and maladaptive family process (Wood & Miller, 2002, p. 59-60). Constructs. The BBFM theorizes that family emotional climate (FEC) and biobehavioral reactivity (emotion regulation or dysregulation) are processes that influence one another and are either protective of or worsen physiological processes, including disease activity/severity and chronic illness (Wood, 1993; Wood et al., 2008). FEC describes the intensity and positivity or negativity of emotional processes within the family (Wood et al., 2008), while biobehavioral reactivity is the way in which an individual family member responds to emotional stimuli and is their degree of emotion regulation or dysregulation (Wood et al., 2008). Biobehavioral reactivity is typically measured as anxiety or depression (e.g., Wood et al., 2007; Wood et al., 2008).

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The BBFM theorizes that the effects of the FEC on physical health are through emotional influences on individual family members; therefore, it predicts (a) a direct relationship between FEC and biobehavioral reactivity, (b) a direct relationship between biobehavioral reactivity and disease activity, and (c) a nonsignificant pathway between FEC and disease activity (Figure 1). These hypotheses have been confirmed in tests of the model with child asthma outcomes (Wood et al., 2008). A further description of these constructs and pathways is provided in Chapter 2. Statement of the Problem Although evidence repetitively demonstrates associations between familial and romantic relationships and either mental or physical health outcomes, the body of research supporting these relationships fails to simultaneously integrate effects of relational functioning on both mental and physical health and lacks a focus on precise pathways (Carr & Springer, 2010). Although researchers point to potential mediators or moderators of the relationship between close relationships and health, there is a lack of consensus on which processes need investigation and consistent recommendations that future research explore these associations (Proulx & Snyder, 2009). Only in establishing pathways, mechanisms, and directions of effect by which biopsychosocial variables influence physical well-being in a comprehensive model can we fully understand how to intervene in these factors and prevent negative outcomes. It is critical to integrate social, psychological, and biological indicators of disease (National Research Council, 2001).

In addition, although research on relationships and heath has increased over the last decade, most studies of adult health focus on the protective effects of marriage and not on family relationships more broadly or non-traditional romantic relationships (Carr & Springer, 2010, p. 748). Neglecting to study the effects of relationships with family members on individual health outcomes ignores one of the most powerful influences on physical health and well-being; not all social relationships are created equal (Weihs, Fisher, & Baird, 2002). Although we increasingly understand how stress affects health, there is less literature examining the effects of family stress (Wood & Miller, 2005) and there has only recently been a growth in working to understand the impact of stressful close relationships and family relationship quality on physical health, especially for adults (Proulx & Snyder, 2009; Wickrama et al., 2001). Another concern with research to date is its reliance on models that privilege the White middle-class nuclear heterosexual family as the norm (Carr & Springer, 2010, p. 743). Given

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mixed findings regarding the effects of race (Kaplan & Kronick, 2006; Springer & Mouzon, 2011), gender (Gardner & Oswald, 2004; Johnson, Backlund, Sorlie, & Loveless, 2000), and family structure and stability (Sbarra & Nietert, 2009; Wienke & Hill, 2009) on health and chronic illness, this is an important limitation. Differences in physical health outcomes also

occur due in part to differing economic resources (Goldman, 2001; Rogers, Hummer, & Nam, 2000); research focusing only on middle-class individuals and families does not help us to understand processes related to health outcomes for families at lower socioeconomic levels.

Lastly, along with a lack of organizing, comprehensive models and a need to focus on precise pathways of effect, there is a need for research on adult health to focus on specific health outcomes and to link family processes to high-prevalence conditions and known risk factors of disease (Carr & Springer, 2010). Carr and Springer (2010), in their recent decade review of research on families and health, encouraged researchers to move beyond broad measures of physical health (e.g., all-cause mortality, self-rated health) and investigate family factors linked to actual health outcomes (p. 756). Research that provides evidence for these pathways and specific outcomes would indicate key information needed to translate our understanding of families and health into intervention and practice.

Study Purpose The purpose of this study is to address the shortcomings of the literature described above by (a) using a model integrating the effects of close relationships on both mental and physical health, (b) testing precise pathways in this model, including indirect mediation effects, (c) focusing on both family and romantic relationships as predictors of physical health outcomes, (d) using both self-rated health measures and specific indicators of health including current medical diagnoses as dependent variables, and (e) testing the biopsychosocial model with a diverse sample.

The present study is a test of the BBFM with a sample of adult primary care patients, in order to attend to the limitations of previous research and expand our understanding of the effects of close relationship functioning on physical health. Although the BBFM has a broad developmental application and can theoretically be used to explain illnesses primarily psychological or biological in origin (Wood et al., 2000; Wood & Miller, 2002; Wood & Miller, 2005), it has not yet been tested with adult family members, nor with a broad array of chronic conditions important to adult health. Additionally, the use of an adult primary care sample at a

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clinic serving a diverse, underserved patient population serves the studys goals of expanding the BBFM and addressing current limitations in the literature. A purpose of testing the models applicability to this population is to highlight factors that are protective of or detrimental to patient health; evidence of these linkages has several implications for practice, including the development of targeted, early interventions and tailored treatment plans.

Overall, the use of the BBFM in this study provides organization and structure to the hypotheses and a sound theoretical foundation on which to test pathways linking relational functioning, emotion dysregulation, and physical health.

Hypotheses

The purpose of this study is to further test the BBFM with a sample of adult primary care patients, as the model should be especially helpful in understanding links between close relationship functioning and physical health outcomes for adults. This study attempted to closely replicate previous tests of the BBFMs ability to predict health quality (e.g., Wood et al., 2008) by using similar variables that are developmentally appropriate for adult patients. Because this study is a new approach to using the BBFM, it is therefore exploratory. As a result, rather than preliminarily delineating the relationships expected between specific measures, relationships between constructs in the model are broadly hypothesized and exploratory data analysis is used to first examine the data and potentially significant relationships. Consequently, both family functioning and romantic relationship functioning are used as independent variables representative of FEC. This is not unlike previous tests of the BBFM, which use both traditional measures of family climate and parents relationship quality/conflict as endogenous predictors (e.g., Wood et al., 2008). These predictors will be tested in two separate models. Multiple indicators of biobehavioral reactivity, or emotion dysregulation, are also used, including measures of depression, anxiety, and alcohol abuse; again, this is not unlike previous research using the BBFM (e.g., Wood et al., 2006; Wood et al., 2007). Lastly, both self-report and objective indicators of physical health are used, similar to prior BBFM research (e.g., Wood et al., 2007), but unique to families and health research more broadly (Carr & Springer, 2010). In sum, the following are hypothesized for both models:

(1) A direct pathway between FEC and biobehavioral reactivity/emotion dysregulation; (2) A direct pathway between biobehavioral reactivity/emotion dysregulation and disease

activity; and

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(3) A nonsignificant pathway between FEC and disease activity (Figure 1).

Figure 1. The Biobehavioral Family Model (Wood, 1993)

FEC

Biobehavioral Reactivity

Disease Activity

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CHAPTER 2 LITERATURE REVIEW

Families and Health There is increasingly consistent evidence linking close relationships to health outcomes

and well-being (Carr & Springer, 2010). Familial relationships, including romantic relationships, can both buffer and potentiate risk factors related to health (Wood & Miller, 2002). This is in part because these relationships are more emotionally intense than most other social relationships and because they are longitudinal, continuing over time (Weihs, Fisher, & Baird, 2002). Stable, safe, and supportive close relationships help individual members coping with illness to regulate emotional distress that is due in whole or part to the chronic disease (Weihs et al., 2002). In contrast, conflictual and negative family relationships can interfere with emotion regulation (Fiscella, Franks, & Shields, 1997), while resulting physiological changes due to emotion dysregulation can influence the development of disease (Kiecolt-Glaser et al., 1997; McEwen, 1998). Overall, however, empirical evidence substantiating psychological mechanisms that link social support and health indicators (e.g., cardiovascular function) is lacking (Uchino, Cacioppo, & Kiecolt-Glaser, 1996). In addition, there is a lack of research connecting the findings that negative family relationships affect emotion regulation and that this reactivity influences disease activity (Weihs et al., 2002). This is in part because mental health and physical health outcomes are often studied separately.

Mental and physical health connections. Global reports of the contribution of either mental or physical health to mortality and disability underestimate the complexity of the interaction between the two, despite repetitive evidence that mental health concerns are related to and interact with physical health conditions (Prince et al., 2007). In a nationally representative sample of adults who completed the World Health Organization Disability Assessment Schedule, 53% reported 1 or more mental or physical conditions and these individuals reported an average 32 more role-disability days in the past year than matched controls; much of these effects could be accounted for by the effects of comorbid conditions (Merikangas et al., 2007). Of individuals with a serious mental illness, 74% have been given a diagnosis of 1 or more chronic health conditions, while 50% have a diagnosis of two or more. The number of health conditions an individual has is significantly related to annual costs of health care treatment (Jones et al., 2004).

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Recent research outlining the physiological effects of relationships on health, although

groundbreaking (Carr & Springer, 2010), continues to separately document effects on mental and physical health (e.g., Seeman, 2001). Mental and physical health issues in combination are complex (Prince et al., 2007), disabling (Merikangas et al., 2007), and costly (Egede, Zheng, & Simpson, 2002; Goetzel, Hawkins, Ozminkowski, & Wang, 2003; Jones et al., 2004). It is necessary to proceed with research on the effects of family relationships on health using a biopsychosocial model (Engel, 1977), such as the BBFM. The BBFM: An Organizing Framework The BBFM (Wood, 1993) is a biopsychosocial approach to understanding the mutual influences of family relationships and psychological processes on physical health outcomes. The model serves as the guiding framework for this study and maintains several theoretical assumptions based in general systems theory (von Bertalanffy, 1969) and the psychosomatic family model (Minuchin, Rosman, & Baker, 1978).

Theoretical assumptions. The BBFM builds upon several of the foundational premises of Minuchins (1974) structural family model, also known as the psychosomatic family model when used to explain the reciprocal effects of family relationships and illness (Minuchin et al., 1978). The first is that the family is a system; the BBFM maintains a general systems framework (von Bertalanffy, 1969). The second is that individual functioning and relational patterns are interactive and mutually impact each other. The third is that interpersonal patterns interact with individual biobehavioral processes and that some of these are related to health and illness (Wood & Miller, 2002, p. 60). The authors of the BBFM propose that three areas of functioning, which coexist and mutually influence one another, need to be considered when understanding physical health: the biological, psychological, and social (Wood & Miller, 2002). The model explains that family relationship patterns influence the psychological and biological processes of individuals within the family (Wood, 1993; Wood et al., 2000), and uses psychobiologic mediators to connect family emotional climate and disease activity (Miller & Wood, 2003; Wood et al., 2006; Wood et al., 2007; Wood et al., 2008).

Constructs. The BBFM was originally an attempt to address the limitations of Minuchins psychosomatic family model, which describes family relational processes and their association with child illness (Minuchin et al., 1975). Wood and others (1989) sought to define the limitations of the model, to reformulate it to include child biobehavioral reactivity (Wood,

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1993), and to add parent-child attachment security as a mediating or moderating factor (Wood, Klebba, & Miller, 2000). In sum, the BBFM models the interdependence of family relationships, emotional processes, and physiological changes, describing the contributions of family emotional climate (FEC) and biobehavioral reactivity to disease severity (Wood, 1993; Wood & Miller, 2002).

Family emotional climate. FEC is used in the BBFM to describe the intensity and positivity or negativity of emotional processes within the family (Wood et al., 2008). More specifically, the model posits that processes including proximity, relationship quality, and interpersonal responsivity are part of the familys emotional climate and interact with individual family member psychological and emotional processes. Proximity, within the FEC, is defined as sharing space, private information, and emotional experiences, while relationship quality is defined as interactions including mutual support, understanding, and respectful disagreement; responsivity is described as how family members react to one another (Wood & Miller, 2002). The BBFM specifically hypothesizes a direct relationship between FEC and biobehavioral reactivity, or more typically, a direct relationship between a negative FEC (characterized by hostility and criticism) and emotion dysregulation (typically measured as anxiety or depression) (e.g., Wood et al., 2007).

Research using the BBFM has tested the effects of FEC on illness in several ways. Wood et al. (2000) measured both childrens perceptions of their parents conflict and childrens beliefs they had caused interparental conflict (triangulation/self-blame), to represent a negative FEC. These authors found that childrens self-blame was significantly related to their feelings of hopelessness and vagal activation. Alternatively, Wood et al. (2006) used parent report of emotional expressiveness in the family as their measure of FEC, as have others (Wood et al., 2007). Findings indicate a significant pathway between a negative balance of FEC and child symptoms of depression and anxiety, although pathways at times appear to differ for mothers and fathers relationships with children (Wood et al., 2006). Additional tests of the BBFM have used child reports of parental psychological aggression, child reports of exposure to violence and hostility in the home (Woods & McWey, 2011), and observational coding of family interactions (Wood et al., 2008) to represent FEC. All find similar relationships between a negative FEC and child experiences of emotion dysregulation, including anxiety and depression.

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Family emotional climate is a highly relevant construct for adult family members (Carr & Springer, 2010; Weihs et al., 2002), and especially for understanding the well-being of adult primary care patients. Families are often the primary source of social support for adults (Fiscella et al., 1997), and family stress is often linked to a lower quality of life and higher healthcare utilization for primary care patients (Parkerson, Broadhead, & Tse, 1995). Additionally, higher perceived family criticism predicts an increase in primary care visits for both psychosocial and biomedical reasons (Fiscella et al., 1997). Family functioning is evident from the first primary care visit; whether family members are supportive of each other is critical to understanding patient coping and well-being (McDaniel, Campbell, Hepworth, & Lorenz, 2004). Lastly, in a sample of family practice patients surveyed about their families and relationship quality, the majority believed physicians should ask about family conflict (96%) and that their physicians could be helpful in intervening and providing information or referrals (93%); two-thirds of this same sample explained their physician had never asked about family conflict (Burge, Schneider, Ivy, & Catala, 2005).

Just as previous researchers using the BBFM include both family closeness and parental romantic relationship quality in their conceptualization of FEC (Wood et al., 2008), the present study includes measures of both family functioning and romantic relationship satisfaction as independent variables. In fact, family is typically defined as two ore more closely, intimately connected persons who have strong emotional bonds, a history, and a future as a group (Gilliss, Highly, Roberts, & Martinson, 1989; Weihs et al., 2002; Wood & Miller, 2005). Therefore, measuring family emotional climate does not preclude assessing for both the emotional and supportive nature of an individuals romantic relationships, as well as their family relationships more broadly defined (e.g., family-of-origin, family-of-procreation, spiritual family, etc.).

Biobehavioral reactivity. Biobehavioral reactivity is the portion of the BBFM that connects family process to disease-related physiological processes (Wood & Miller, 2002). Biobehavioral reactivity is the way in which a family member responds to emotional stimuli (Wood et al., 2008); it is the ability of an individual to regulate their emotions and is measured as emotion dysregulation, or, as symptoms of anxiety and depression (e.g., Wood et al., 2007; Wood et al., 2008). Emotions reflect an individual family members adaptations to changes in environmental stress and are self-regulatory responses that help to coordinate adjustments and behaviors (Thayer & Lane, 2000). Disorders of emotion regulation, including anxiety and

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depression (Kovacs et al., 2006; Wood et al., 2008), represent an individuals inability to adapt and assume emotions appropriate for the demands of the environment (Friedman & Thayer, 1998). Emotion dysregulation is accompanied by physiological dysregulation and transmits (or escalates) the effect of stress and emotional challenge to disease processes by way of psychophysiological pathways (Wood et al., 2008, p. 23).

This is a highly relevant construct in understanding the health and well-being of individual adult family members and primary care patients. Prevalence statistics of the rate of anxiety and depression in primary care vary, although all point to concerning, high rates (e.g., Ansseau et al., 2004). Uebelacker, Smith, Lewis, Sasaki, and Miller (2009) explain that depression is one of the most common presenting problems in primary care clinics, while others assert that anxiety and depression are the 2 most common mental health problems seen in primary care (Kroenke, Spitzer, Williams, Monahan, & Lowe, 2007). Disorders of emotion regulation are also often comorbid with psychosomatic medical conditions; for example, there is evidence that pain and depression are comorbid for 1 in 5 female outpatients (Poleshuck, Giles, & Tu, 2006). Interestingly, primary care patients with more severe medical illnesses are more likely to be recognized as having depression or anxiety by their providers (Robbins, Kirmayer, Cathebras, Yaffe, & Dworkind, 1994).

Recent estimates suggest 40% of primary care patients receive mental health care solely

from primary care providers (Uebelacker, Wang, Berglund, & Kessler, 2006). Additionally concerning is that they may not be receiving adequate treatment within these primary care visits (Wang et al., 2005) and would likely receive better follow-up care from a mental health specialist (Simon, VonKorff, Rutter, & Wagner, 2001). Depression screening significantly increases the duration of a primary care appointment (Schmitt, Miller, Harrison, & Touchet, 2010) and the recognition of depression and anxiety by primary care physicians is greatly affected by physician characteristics, including sensitivity to nonverbal emotional expressions and willingness to formulate a psychiatric diagnosis (Robbins et al., 1994).

Biobehavioral reactivity, connected with physiological dysregulation, involves several biological systems, including the hypothalamic-pituitary-adrenal axis and the autonomic nervous system; these arousal processes then influence the development and course of physical diseases (Wood et al., 2008; Wood & Miller, 2005). Our emotional interpretations of stimuli, including close relationships, affect the neuroendocrine activity of the body, which results in emotional and

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behavioral responses, including experiences of fear, anxiety, and other emotional states (Seeman, 2001). Recent research on close relationships and health is only beginning to link how relationships literally get under [the] skin (Seeman, 2001, p. 196) of adults, focusing on endocrine, metabolic, immune, and sympathetic nervous systems (Carr & Springer, 2010).

Disease activity as the dependent variable. The BBFM predicts physical health outcomes as exogenous variables, although theoretically the model hypothesizes that families are reciprocally affected by a family members illness (Wood, 1993). Because of the focus of previous research using the BBFM on pediatric asthma, measures of disease activity have focused mainly on health outcomes related to asthma severity. For example, Wood et al. (2006) used National Heart, Lung, and Blood Institute (NHLBI; 1997) criteria to diagnose asthma and type the severity level of the illness for children. The authors used measures of pulmonary function, frequency of daytime and nighttime symptoms, and both a research nurse and asthma specialist to diagnose the disease. Later tests used similar NHLBI criteria (e.g., Wood et al., 2007; Wood et al., 2008). In contrast, other research has measured heart rate fluctuations and respiratory sinus arrhythmia to measure vagal activation, used as the dependent variable in testing the BBFM (Wood et al., 2000).

Families and health research focusing on health outcomes for adult patients has focused on broad measures of physical health, including all-cause mortality and participant self-rated health (Carr & Springer, 2010). For example, there is repetitive evidence of the protective effect of marriage for general measures of health, including number of illnesses reported (Lorenz, Wickrama, Conger, & Elder, 2006) and self-rated health (Williams & Umberson, 2004), while marital strain leads to a decline in self-rated health (Umberson, Williams, Powers, Liu, & Needham, 2006). In general, there is less attention in families and health research on diseases of adulthood (Weihs et al., 2002). For example, of the three diseases with the highest costs to the U.S. healthcare system cardiovascular disease, COPD and asthma, and non-insulin dependent diabetes the latter two have been the subject of very little family-focused intervention research (Weihs et al., 2002, p. 16). This is despite the fact that family researchers have concluded that the influence of families on physical health is equally as powerful as traditional medical, biological risk factors (Campbell, 2003).

Pathways. Two significant pathways are predicted in the BBFM: a significant relationship between FEC and biobehavioral reactivity, and a significant relationship between

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biobehavioral reactivity and disease activity (Wood, 1993). Although previous research provides evidence of these pathways separately, tests of the BBFM are the first to tie these links together (e.g., Wood et al., 2007).

Pathway between FEC and biobehavioral reactivity. The connection between FEC and emotion regulation is a pertinent pathway for understanding adult functioning and health. Family relationships, including family expressed emotion, are stronger predictors of mental health than social relationships more broadly defined (Franks, Campbell, & Shields, 1992). There is also evidence of a relationship between perceived criticism and expressed emotion in ones family and depression and anxiety (Shields, Franks, Harp, Campbell, & McDaniel, 1994), a relationship between family cohesiveness and psychosocial risk factors for pregnant women (Balcazar, Krull, & Peterson, 2001), and evidence that supportive family processes affecting an individuals work experiences decrease the odds of problem drinking (Grzywacz & Marks, 2000).

Lastly, research suggests that adults exposed to family risk factors (e.g., conflict, aggression, lack of support) as a child have disruptions in their ability to regulate their emotions and adapt to stress; this interference in emotion regulation leads to later mental and physical health problems in adulthood (Taylor, Lerner, Sage, Lehman, & Seeman, 2004). In adulthood, marital distress and negative behaviors of ones partner are associated with poor mental health (Hawkins & Booth, 2005) and, although it has previously been hypothesized that emotion regulation (as part of psychological processes) serves as a mediator between marital quality (as part of marital functioning) and physiological outcomes (Burman & Margolin, 1992; Kiecolt-Glaser & Newton, 2001), this comprehensive view of close relationships and health has yet to be tested (Robles & Kiecolt-Glaser, 2003).

The pathway between FEC and biobehavioral reactivity is critical in the BBFM, as it links family processes to individual emotional responses to stress (Wood & Miller, 2005). There is increasing evidence demonstrating changes in neuroendocrine activity resulting from close relationships (Robles & Kiecolt-Glaser, 2003; Seeman, 2001). Kiecolt-Glaser et al. (1997) demonstrated that marital conflict in older adults is significantly related to endocrine changes; for wives, negative behavior during conflict and marital satisfaction accounted for 16% to 21% of the variance in changes in cortisol, adrenocorticotropic hormone (ACTH), and norepinephrine. This is also the case for newlyweds: hostile behaviors during conflict resulted in changes in epinephrine, norepinephrine, and ACTH for newly-married couples (Malarkey, Kiecolt-Glaser,

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Pearl, & Glaser, 1994). Moreover, more hostile couples demonstrated persistently higher rates of all three endocrine markers during conflict and for 15 minutes afterwards (Malarkey et al., 1994); elevated levels of epinephrine that lasted throughout the entire day (following the study-initiated conflict) predicted couples who eventually divorced compared to those who did not (Robles & Kiecolt-Glaser, 2003).

Recommendations for future research connecting relational processes and physical health through psychophysiological pathways include (a) use of objective health outcome assessments, (b) expanding samples to include more than healthy, happy participants, and (c) use of measures that assess both distress and support in close relationships (Robles & Kiecolt-Glaser, 2003).

Pathway between biobehavioral reactivity and disease activity. As described above, the comorbidity of mental and physical health conditions is considerable. Four out of 10 patients with a chronic illness have also had a recent or concurrent mental health disorder (Katon & Sullivan, 1990). Disorders of emotion regulation (mood and anxiety) are linked to physical illnesses through biological pathways, both in the BBFM and in prior research (Cohen & Rodriguez, 1995). Emotion dysregulation that includes the continual, persistent, excessive activation of endocrine systems, including the sympathetic-adrenal medullary system as an example, are likely to result in a medical condition (Cohen & Rodriguez, 1995). Constant activation of these physiological processes is implicated in diseases such as coronary heart disease (Manuck, Marsland, Kaplan, & Williams, 1995), hypertension (Krantz & Manuck, 1984), and susceptibility to infectious diseases (Cohen & Rodriguez, 1995; Robles & Kiecolt-Glaser, 2003).

In summary, the bodys regulatory systems connect emotional experience to the physiologic stress response (Weihs et al., 2002, p. 9). Changes that occur throughout the bodys systems, including neurochemical and endocrine systems, influence the development of disease (Kiecolt-Glaser et al., 1997). This is reflected in the notion of allostatic load, which is the cost of chronic exposure to fluctuating or heightened neural or neuroendocrine response resulting from repeated or chronic environmental challenge that an individual reacts to as being particularly stressful (McEwen & Stellar, 1993, p. 2093). Chronic stress from close relationships leads to sustained endocrine changes and emotion dysregulation, with long-term effects on physical health and increased vulnerability to developing illness (McEwen & Stellar, 1993). Evidence substantiates the connection between allostatic load and emotion regulation:

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increased allostatic load, measured using biomarker indices, is associated with increased, acute depressive symptoms (Juster et al., 2011). Disease outcomes for adult patients resulting from allostatic load include coronary heart disease, obesity, diabetes, hypertension, ulcers, and asthma, among others (McEwen, 1998). Reduction in allostatic load is related to later, lower odds of mortality for older adults (Karlamanga, Singer, & Seeman, 2006). Summary The goal of this study is to test the significance of pathways between (a) FEC and biobehavioral reactivity, and (b) biobehavioral reactivity and disease activity with an adult primary care sample. Despite evidence existing for the relevance of each of these three constructs for adult family members, and despite increasing families and health research on each of these two pathways, comprehensive models integrating these constructs and pathways have yet to be tested (Robles & Kiecolt-Glaser, 2003). Additionally, although the BBFM has broad developmental applications (Wood & Miller, 2005), it has yet to be tested with adults or for health outcomes other than disease activity related to pediatric asthma.

This study explores the relationships between (a) FEC, measured for both family relationships and romantic relationships, (b) biobehavioral reactivity, measured as symptoms of depression, anxiety, and alcohol abuse, and (c) disease activity, measured using both self-report and objective medical record data. The present test of the BBFM expands both literature on this biopsychosocial model and the families and health literature more broadly. Further, this study meets recent recommendations in this area to use (a) models that do not only privilege White, middle-class nuclear families, (b) specific health outcomes, including self-report and objective medical data, and (c) precise pathways linking family processes to health outcomes (Carr & Springer, 2010).

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CHAPTER 3 METHOD

Sample The sample included 125 adult patients receiving medical care at an urban primary care clinic in Leon County, Florida. The clinic provides comprehensive primary care services to primarily uninsured or underinsured adults and offers mental health care on-site to its patients. The clinic also offers specialty services including optometry, gynecology, and cardiology. This clinic operates on a sliding fee scale with the majority of patients paying a five-dollar copay for their care. Participants for the present study included primary care patients who presented for a previously scheduled appointment or for an appointment during open walk-in hours. Patients presented for a variety of reasons, including for follow-up care, chronic disease management (e.g., diabetes, hypertension, etc.), psychiatric concerns, and routine eye care. Recruitment. Approval for this study was obtained from the Human Subjects Committee at The Florida State University. Participants were recruited from the primary care site using approved flyers posted in the clinic that explained the process of providing their contact information to clinic staff. In addition, this author and two research team members positioned in clinic waiting rooms provided patients the ability to approach the researchers to participate and provided initial, basic information about the study to seemingly interested patients. Prospective participants were first asked if (a) they were between the ages of 18 and 65 years, (b) were current clinic patients, and (c) were comfortable reading and speaking English. Adults 18- to 65-years-old were included because persons under the age of 18 are considered children/minors and adults over 65 are considered elderly, both of which are protected, potentially vulnerable populations (Office of Human Subjects Research, 2006). Participants that met these inclusion criteria were then given further details of the study, provided a thorough consent form with additional information, and encouraged to ask questions about the study. They were explicitly told that the survey was confidential, that their medical providers at the clinic would not have access to the information included, and that their decision to participate would not affect their medical care. Patients that consented to participate completed our paper-and-pencil assessment. They were informed through the consent process that their answers would be reviewed following completion of the survey for any concerns. In addition, patients were able to complete the

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measures in the waiting room, the hallway of the clinic, their exam room, or in rooms used by the researcher for reviewing surveys following completion. All participants completed the survey confidentially, without the presence of a friend or family member; several participants required the researcher to read the measures aloud due to either vision or literacy concerns. Of those approached to participate (N = 232), 59.5% agreed to participate (n = 138). Several of these patients were interrupted during the consent process in order to meet with their medical provider. In addition, 6 patients began the survey but withdrew before completion for reasons of transportation, prior commitments, and/or discomfort with the content of the survey. Those who expressed feeling disconcerted by the survey (n = 2) were successfully referred to a mental health provider within the clinic. Finally, 2 participants completed the survey but later admitted they were not current patients, and 1 participant took the survey with them and did not return it following their appointment. A total of 125 patients completed the survey (Figure 2).

Figure 2. Participation flow diagram.

Approached (n=232)

Agreed to Participate (n=138)

Interrupted during consent process (n=4) Withdrew due to prior commitments (n=3) Withdrew due to discomfort with survey content (n=2) Withdrew due to transportation issues (n=1)

Excluded, not a patient (n=2) Excluded, did not return the survey (n=1)

Final Sample (n=125)

Completed Assessment (n=128)

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Demographic characteristics. Patients (N = 125) included 34% males (n = 43) and 65% females (n = 81); one participant did not report. The average age was 46 years (SD = 12.2), and the majority identified as Black or African American (59%), with other races identified as White (34%), American Indian or Alaska Native (2%), and Asian (1%); several participants identified as biracial (4%) and 1 participant did not report their race. In addition, 6% of participants identified as Hispanic/Latino ethnicity; 12 participants did not report their ethnicity. Regarding insurance status, 90% of the present sample was uninsured. Of the remainder, several had Medicaid coverage (n = 9) while others had Medicare (n = 2) and one participant had Universal Health Care as the insurance plan noted in their medical chart. The majority of patient participants reported being currently in a romantic relationship (62%, n = 76). In addition, 33% reported their current marital status as divorced, 31% reported never having married, 19% reported they were currently married and living together, 8% reported being currently married but separated, and 7% reported their current marital status as widowed. Participants also reported household income: the majority (66%) reported a household income of less than $10,000, while 14% reported $10,000-$19,999, 12% reported $20,000-$39,999, 2% reported $40,000-$59,999, and 3% reported $60,000-$79,999 (3 participants did not report). Participants most often reported having graduated from high school or having obtained a GED (35%), while 27% reported not having graduated from high school and 19% reported some college; the remainder (18.4%) reported a college degree (Associates, Bachelors, Masters, or Professional degrees). Lastly, participants reported their current employment status: the vast majority (71%) reported no current employment, while 19% reported part-time and 10% reported full-time employment.

Because the present sample included the full range of adults from ages 18- to 65-years-old, it is important to consider the broad developmental range of these persons. The participants span early to middle adulthood (Hewstone, Fincham, & Foster, 2005) and may be experiencing change that is maturational, normative, or in response to predictable events and contextual changes (Franz, 1997). According to the family life cycle perspective (Carter & McGoldrick, 1999), adults in the present sample may range in life cycle stages, including preparing to leave home, joining through marriage, having young children, adolescents, or launching young adults, or may be learning to support their own aging parents (Nichols & Schwartz, 2004). Thankfully, the vast majority of the present sample was able to identify persons with whom they have close

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relationships and consider to be family: 124 participants completed at least 75% of the family functioning measure, and were actively encouraged to respond to the items thinking of whoever they consider to be family.

Measures

The study questionnaire was completed using paper-and-pencil. All measures were offered in English. Family emotional climate. Three measures were used to assess family emotional climate; one measure represents participants perceptions of their family relationship quality and two are self-report measures of romantic relationship quality. The two romantic relationship

measures were only completed by participants who indicated they are currently married or in a romantic relationship.

General Functioning Family Assessment Device. Participants completed the General Functioning Subscale of the Family Assessment Device (GFS/FAD; Epstein, Baldwin, & Bishop, 1983). The GFS/FAD assesses relational functioning in families and has good reliability, demonstrated using Cronbachs alpha and spit-half correlations (Byles, Byrne, Boyle, & Oxford, 1988). The measure is valid, as demonstrated through correlations with other family variables, and is recommended as a global assessment of family functioning (Byles et al., 1988). The measure includes 12 items rated on a 4-point Likert scale (Strongly Agree to Strongly Disagree); example items include, We feel accepted for who we are, and, We can express feelings to each other. Scores on the GFS/FAD range from 1.00 (healthy family functioning) to 4.00 (unhealthy family functioning); the higher the score, the more problematic the family member perceives the familys overall functioning (Ryan, Epstein, Keitner, & Miller, 2005). A score of 2.00 or greater indicates problematic family functioning. Quality of Marriage Index. Participants completed the Quality of Marriage Index (QMI; Norton, 1983), a 6-item questionnaire assessing romantic relationship quality. The QMI has demonstrated high internal consistency coefficients ( = .96) for husbands and wives (Karney, Bradbury, Fincham, & Sullivan, 1994) and convergent validity is supported by correlations with the Dyadic Adjustment Scale (DAS; Spanier, 1976) and the Kansas Marital Satisfaction Scale (Schumm et al., 1986). Items 1 through 5 on the scale use a 7-point Likert scale ranging from 1 (very strong disagreement) to 7 (very strong agreement); item 6 uses a 10-point Likert scale ranging from 1 (very unhappy) to 10 (perfectly happy). Items are summed to produce a score

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ranging from 6 to 45; higher scores are indicative of greater relationship satisfaction. A cutoff score of 29 or less equates to a score of 97 or below on the DAS and indicates relationship distress (Heyman, Sayers, & Bellack, 1994). Perceived Criticism. Perceived criticism was measured with two items using 10-point Likert scales (Hooley & Teasdale, 1989). Those two items ask, How critical is your partner of you?, (PC-Partner) and, How critical are you of your partner? (PC-Self) and are anchored by not at all critical and very critical indeed. Perception of ones romantic partner as critical is strongly linked to relapses in symptoms of depression (Hooley & Teasdale, 1989), substance abuse (Fals-Stewart, OFarrell, & Hooley, 2001) and symptoms of anxiety disorders (Chambless & Steketee, 1999). The two-item measure has demonstrated discriminant validity and moderate convergent validity among depressed and dysthymic patients (Riso, Klein, Anderson, Ouimette, & Lizardi, 1996). There is presently no clinical cutoff for this measure. Biobehavioral reactivity. Four measures were used to assess participants responses to emotional stimuli (Wood et al., 2008) and the degree of their emotional regulation or dysregulation (Wood & Miller, 2002) by assessing alcohol use disorders, symptoms of depression, and anxiety severity. NIAAA Screen. The National Institute on Alcohol Abuse and Alcoholism (NIAAA) endorses the use of a one question to screen patients for alcohol consumption (NIAAA, 2005) This screen has been found to correctly predict whether patients meet either NIAAAs criteria for at-risk drinking or DSM-IV criteria for an alcohol use disorder (Taj, Devera-Sales, & Vinson, 1998). The question asks, On any single occasion during the past 3 months, have you had more than 5 drinks containing alcohol? and participants answered either yes or no (Taj et al., 1998). Alcohol Use Disorders Identification Test (AUDIT). Alcohol use was also assessed using the AUDIT (Saunders, Aasland, Babor, de la Fuente, & Grant, 1993), which includes 10 items that measure whether a patient has problems with alcohol but who may not be dependent. Example items include, How often do you have six or more drinks on one occasion? and, How often during the last year have you had a feeling of guilt or remorse after drinking? A cutoff score of 8 or more on this measure indicates harmful or hazardous drinking; the measure is equally appropriate for both males and females (Allen, Litten, Fertig, & Babor, 1997). The validity of the AUDIT is well-established by both relationships to other self-report measures and

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to biochemical measures of excessive drinking (Allen et al., 1997); internal consistency has been repetitively demonstrated to be high (e.g., = .94, Skipsey, Burleson, & Kranzler, 1997), including for primary care patients ( = .77, Schmidt, Barry, & Fleming, 1995). Quick Inventory of Depressive Symptomatology Self Report. The 16-item Quick Inventory of Depressive Symptomatology Self Report (QIDS; Rush et al., 2003) was used to assess the severity of depressive symptoms within the nine diagnostic symptom domains that characterize a major depressive episode in the DSM-IV (American Psychiatric Association, 2000). The measure demonstrates good internal consistency ( = .86, Rush et al., 2003; = .85, Trivedi et al., 2004) and content, criterion, and construct validity (Rush et al., 2003; Trivedi et al., 2004). Each item asks participants to describe how often each symptom of depression occurred in the past 7 days; an example asks respondents to rate feeling sad and includes response choices ranging from I do not feel sad to I feel sad nearly all the time. The exception is the QIDS item asking participants to describe whether they lost or gained weight; this item asks patients to reflect on the last 14 days. Scores on the QIDS range from 0 to 27; a cutoff score of 7 or higher indicates depression (Rush et al., 2003). More specifically, scores ranging from 7 to 10 indicate mild depression, scores from 11 to 15 indicate moderate depression, scores from 16 to 20 indicate severe depression, and scores from 21 to 27 indicate very severe depression (Health Technology Systems, 2012; Rush et al., 2003). Overall Anxiety Severity and Impairment Scale. Participants completed the 5-item Overall Anxiety Severity and Impairment Scale (OASIS), which addresses intensity and frequency of anxiety, interference with work or school, and interference with personal relationships (Norman, Cissell, Means-Christensen, & Stein, 2006). The measure was developed to capture anxiety severity and impairment in a brief manner and items are based on the DSM-IV guidelines of severity and impairment (American Psychiatric Association, 2000). Each question has a 5-point response scale, ranging from 0 to 4, with descriptors reflective of the aspect being assessed. For example, one item asks, In the past week, how often have you felt anxious?; participants can answer on a range of 0 (0 = NO ANXIETY in the past week) to 4 (4 = CONSTANT ANXIETY. Felt anxious all of the time and never really relaxed). The OASIS demonstrates adequate reliability ( = .80) and excellent convergent validity with other established anxiety measures (Norman et al., 2006). Scores on the OASIS range from 0 to 20;

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scores greater than or equal to 8 identifies clinically significant anxiety (Campbell-Sills et al., 2009). Disease activity. To assess the impact of family and romantic relationships and symptoms of emotion dysregulation on the physical health of these patient participants, we used both self-report data and indicators collected from patient electronic medical records. Health Review. To assess specific illness symptoms from the participants perspectives, we used the 21-item Health Review (Epidemiology Data Center, 2011; Jenkins, Kreger, Rose, & Hurst, 1980; Rose, Jenkins, & Hurst, 1978). The measure is a checklist of symptoms primarily related to infectious disease and continuing health problems and asks participants to answer yes or no to whether, in the past month, they have experienced any of the symptoms. The measure focuses on specific, well-operationalized symptom clusters (Kiecolt-Glaser & Newton, 2001, p. 480) and is consistently related to physicians diagnoses (Jenkins et al., 1980; Kiecolt-Glaser, Dura, Speicher, Trask, & Glaser, 1991; Orts et al., 1995), supporting its validity. The measure demonstrates good interrater reliability for individual symptoms when administered as an interview and excellent agreement between raters who applied International Classification of Diseases, Ninth Revision (ICD-9) criteria to patients self-reports; test-retest reliability is also high (Kiecolt-Glaser et al., 1991). The items used for this administration were retrieved from the baseline battery of the REACH II study at the University Center for Social and Urban Research (Epidemiology Data Center, 2011). Scores on the Health Review range from 0 to 21; higher scores indicate more illness symptoms. RAND-36. Patient perceptions of their general health were assessed using four subscales of the RAND 36-item Health Survey (Hays, Sherbourne, & Mazel, 1995), including the Physical Functioning (10 items), Role Limitations Due to Physical Health (4 items), Pain (2 items), and General Health (5) subscales. The measure has been demonstrated to be reliable and valid, is widely used for similar purposes (Hays & Morales, 2001), and has high internal consistency (VanderZee, Sanderman, Heyink, & Haes, 1996). The items of this scale are identical to the more widely used SF-36 (Ware & Sherbourne, 1992), with the exception of a simpler, more straightforward scoring method (Rand Health, 2010). All subscales for the RAND-36 are scored so that a high score demonstrates a more favorable health state (Hays et al., 1995). There are presently no clinical cutoffs for this measure.

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Medical chart data. Health data were also gathered directly from participants electronic medical records. Information collected included the participants most recent Body Mass Index (BMI; calculated from a persons height and weight), which provides an indicator of unhealthy weight that may potentially lead to health problems (CDC, 2011a). Each patients BMI was also categorized into the CDCs four weight statuses: underweight (BMI of below 18.5), normal (18.5 to 24.9), overweight (25.0 to 29.9) and obese (BMI of 30 or higher) (CDC, 2011a). Additional information collected included the number of primary care appointments the patient had within the past year, number of medical diagnoses (received in the past year and chronic), and the number of mental health diagnoses (given by NHS mental health providers and primary care providers). All medical diagnoses were verified using the ICD-9, published by the World Health Organization (CDC, 2009); diagnoses representing ICD-9 supplementary v-codes were excluded. The number of mental health diagnoses for the present study excluded diagnoses of depression and anxiety to avoid artificially inflating associations between measures of depression and anxiety included as mediators and the disease activity outcomes. Participant health insurance status and type were also gathered to characterize the sample.

Analyses Exploratory data analysis. Due to the exploratory nature of this study, exploratory data analysis was first used to better understand the data collected and to test for significant relationships among constructs prior to testing full models. An exploratory approach is recommended to more effectively use data to test hypotheses by first learning about the data, distributions of variables, and relationships between variables (Hartwig & Dearing, 1979; Smith & Prentice, 1993). Therefore, the first step in these analyses was to create boxplots of each variable to examine the data for outliers. Second, associations between constructs included in the BBFM models were tested using correlation analyses. Third, relationships between (a) FEC variables and biobehavioral reactivity variables, and (b) biobehavioral reactivity variables and physical health outcomes were tested using stepwise regressions. Variables that demonstrated significant relationships were then tested further using path analyses. Model testing. To test the BBFM models proposed in the hypotheses above (one model using family functioning as the independent variable, a second using romantic relationship satisfaction as indicative of FEC), path analyses were used. Variables with significant relationships highlighted in the exploratory data analyses were used to build the models we

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tested. Kline (2011) suggests a minimum sample size of 100 for structural equation modeling. He also suggests, along with Jackson (2003), that for statistical power to equal .80 in path analysis using maximum likelihood, the ratio of sample size to parameters in the model should be at least 10:1. Therefore, acceptable statistical power is achieved in this study, with a sample size of 125. Standardized path analysis coefficients were estimated in AMOS (Arbuckle, 1997) using direct maximum likelihood estimation. Model fit statistics are reported for each model. Consistent with

Byrnes (2010) recommendations, assessment of model fit was based on three criteria: 2

likelihood ratio statistics, comparative fit index (CFI) and root mean square error of approximation (RMSEA). The goal is to achieve a nonsignificant chi-square (2) value, indicating that the variances and covariances of [the] hypothesized modeldid not differ significantly from those in the data set (Bikos & Kocheleva, 2012, p. 11). Additionally, the goal is to have a CFI value greater than .95 and an RMSEA of less than .05. Regarding missing data, maximum likelihood estimation in AMOS ensures the full sample is used for model testing. Maximum likelihood creates estimates of data missing for individual participants. In this approach, missing values are not imputed, but all observed information is used to produce the maximum likelihood estimation of parameters (Acock, 2005, p. 1018). Therefore, cases are not deleted if data is incomplete; all cases are entered into the maximum likelihood estimation for path analysis (Kline, 2011). Maximum likelihood is standard in structural equation modeling and use of a different estimation method requires justification (Hoyle, 2000). In addition, bootstrapping methodology, a nonparametric resampling procedure (Preacher & Hayes, 2008), was used to confirm the results of the path analyses. As demonstrated in previous research (e.g., MacDonnell, Naar-King, Murphy, Parsons, & Harper, 2010), bootstrapping is appropriate for use with smaller sample sizes because it increases the power of statistical results (Shrout & Bolger, 2002) and involves repeatedly sampling from the actual data to generate an empirical approximation of the sampling distribution and confidence intervals for the indirect effect (Preacher & Hayes, 2008). Bootstrapping generates confidence intervals for the size of the indirect path; a statistically significant mediation effect is indicated if the values between the upper and lower confidence limits in the confidence interval (CI) do not include zero. Bias-corrected and accelerated intervals were examined, as recommended by Efron (1987) as an improvement on traditional CI and bootstrapping methods.

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CHAPTER 4 RESULTS

As outlined above, analyses will proceed in five steps: (1) examination of the data for outliers and scores outside expected scale ranges, (2) correlation analyses, (3) stepwise regressions, (4) model testing using path analyses, and (5) bootstrapping tests of mediation. Exploratory Data Analysis Family emotional climate independent variables. The range of scores for the QMI fell within expected (6 to 45), as did the range of scores for the perceived criticism items (both 1 to 10) and the scores for the GFS/FAD subscale (1.00 to 4.00). An examination of boxplots for each scale demonstrated one outlier for the GFS/FAD subscale: one participant scored a 4.00, which, although it falls within the acceptable scale score range, fell far from the mean of 2.09. Biobehavioral reactivity mediators. The range of scores for the AUDIT fell within

the expected range (0 to 23), as did the range of scores for the OASIS (0 to 20) and the QIDS (0 to 25). An examination of the boxplots for each scale demonstrated two outliers for the AUDIT: two participants scored a 23, which falls within the expected score range, although they fall far from the mean of 3.77.

Disease activity dependent variables. The range of scores for the Health Review fell within the expected range (0 to 20), as did all four RAND-36 subscales (0 to 100 for each). The boxplot for the Health Review scale revealed two outliers: one participant scored a 17 while a second scored a 20. While these two scores fall in the acceptable scale score range, they fell far from the mean of 4.89. None of the RAND-36 boxplots demonstrated outlier values. Because all values for each scale fell within the expected range, all cases will be used in the remainder of the statistical analyses. Study variables. A description of all study variables is provided in Table 1. As described above, the GFS/FAD, QMI, AUDIT, QIDS, and OASIS all have precise clinical cutoff scores; the BMI has clinical cutoff scores defined by the CDC for underweight, overweight and obese status (CDC, 2011a). In the present sample, the mean scores for the GFS/FAD, QIDS, and BMI measures were all at or above the recommended clinical cutoff scores. In other words, the average individual in this sample was obese, had problematic family functioning, and reported clinical levels of depression.

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Table 1. FEC Variables, Biobehavioral Reactivity Variables, and Disease Activity Variables: Descriptive Statistics (N = 125) Variables M SD n

GFS/FAD 2.09* .67 109 QMI 31.95 10.00 66 PC-Partner 5.43 2.92 77 PC-Self 5.68 2.92 76 NIAAAa .32 .469 124 AUDIT 3.77 5.07 119 QIDS 9.36* 5.76 124 OASIS 5.44 5.62 120 Health Review 4.89 3.97 115 RAND-36 Subscales Physical Functioning

60.33 32.35 123

Role Limitations 49.80 43.36 123 Pain 52.98 31.79 125 General Health 50.96 26.51 124 BMI 31.87* 8.40 116 Number of appointments

4.47 2.64 116

Number of medical diagnoses

1.91 1.90 116

Number of mental health diagnoses

.10 .333 116

aNIAAA Screen: 0 = no, 1 = yes. *Mean scores represent scores at or above clinical cutoff.

Construct Associations Associations between variables were initially tested using bivariate, two-tailed Pearson correlations (Table 2). These were conducted to examine the potential for scales to be used in later model testing (i.e., if demonstrated as significant in preliminary correlation tests). The correlations were also examined to check for multicollinearity; low multicollinearity is a statistical assumption of path analysis (Kline, 2011). Two correlations were greater than .70, the

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Table 2. Bivariate Pearson Correlations of Study Variables Measures 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1. GFS/ FAD

1.00

2. QMI -.27* 1.00 3. PC-S .13 -.08 1.00 4. PC-P .27* .01 .69** 1.00 5. NIAAA .04 .08 .01 .01 1.00 6. AUDIT .10 .02 -.03 .08 .63** 1.00 7. QIDS .28** -.31* -.05 .01 .07 .14 1.00 8. OASIS .12 -.24 -.06 .01 .11 .07 .70** 1.00 9. HR-21 .23* -.15 -.04 -.04 .03 .01 .40** .37** 1.00 10. R36- PF

-.27** -.03 -.01 -.08 .02 -.01 -.23* -.27** -.29** 1.00

11. R36- RL

-.18 -.04 .01 .023 -.04 -.01 -.39** -.46** -.45** .66** 1.00

12. R36- PA

-.10 -.02 .00 .08 -.01 .05 -.38** -.33* -.44** .43** .54** 1.00

13. R36- GH

-.10 .15 .10 .03 .01 -.07 -.44** -.32** -.43** .41** .48** .48** 1.00

14. BMI -.16 .10 .05 -.09 -.07 -.18 -.13 -.10 .16 -.07 -.08 .01 .03 1.00 15. BMI- CDC

-.10 -.02 .08 -.05 -.04 -.12 -.12 -.04 .03 -.03 -.03 .05 .03 .79** 1.00

16. APPTS -.12 -.13 -.16 -.11 -.07 -.07 -.03 -.11 -.07 -.07 .02 -.13 -.11 .05 .14 1.00 17. MED- DX

-.10 .02 -.12 -.18 -.07 .01 -.16 -.04 .015 -.14 -.12 -.11 -.10 .08 .21* .33** 1.00

18. MH- DX

-.06 .18 .24* -.00 -.02 .07 -.11 -.16 -.16 .02 .04 .02 .10 -.10 -.06 .02 .13 1.00

Note. Significant correlations are in boldface. GFS/FAD = General Functioning Subscale of the Family Assessment Device; QMI = Quality of Marriage Index; PC-S = Perceived criticism-self item; PC-P = Perceived criticism-partner item; NIAAA = NIAAA Screen; AUDIT = Alcohol Use Disorders Identification Test; QIDS = Quick Inventory of Depressive Symptomatology Self Report; OASIS = Overall Anxiety Severity and Impairment Scale; HR-21 = Health Review; R36-PF = RAND-36 Physical Functioning subscale; R36-RL = RAND-36 Role Limitations subscale; R36-PA = RAND-36 Pain subscale; R36-GH = RAND-36 General Health subscale; BMI = Body Mass Index; BMI-CDC = Body Mass Index weight status as determined by the CDC (CDC, 2011a); APPTS = Number of appointments in the past year; MED-DX = Number of medical diagnoses; MH-DX = Number of mental health diagnoses. *p

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recommended cutoff for testing multicollinearity (Tate, 1998). The first, a correlation of .70 between QIDS and OASIS scores, was examined by calculating the variables VIF and tolerance statistics. The collinearity statistics indicated the two variables are not replications of one another: the VIF was 2.09 (less than the recommended 10) and the tolerance was .48 (greater than the recommended 0.1) (UCLA Academic Technology Services, 2012). The second, a correlation between BMI and BMI-CDC of .79, was also examined by calculating the variables VIF and tolerance statistics. The VIF was 2.71 and the tolerance was .37, indicating the two do not require further investigation. However, it is logical that BMI and BMI-CDC are closely related, given that BMI-CDC is calculated using BMI. If the use of these variables together in model testing is indicated by stepwise regression analyses, their relationship will be closely analyzed. As predicted, several of the family emotional climate variables were significantly correlated with the biobehavioral reactivity variables. The GFS/FAD was significantly correlated with depression, as measured by the QIDS (r = .28, p = .003), as was the QMI (r = -.31, p = .011). In addition, two of the biobehavioral reactivity variables, depression and anxiety, were significantly associated with several disease activity outcome variables. More specifically, depression was significantly related to Health Review scores (r = .40, p = .000) and all four RAND-36 subscales. Anxiety, as measured by the OASIS, was also significantly related to Health Review scores (r = .37, p = .000) and all four RAND-36 subscales. These results indicate that as family functioning becomes more problematic, depression worsens; as marital satisfaction increases, depression decreases. Also, as depression increases, so do the number of illness symptoms reported by patients. As anxiety becomes more severe, the number of health symptoms reported by patients increases. Lastly, as depression and anxiety symptoms increase, patients self-reports of their physical health as reported on all four subscales of the RAND-36 worsened. Additional results include a significant correlation between family functioning and Health Review scores (r = .23, p = .018), as well as between family functioning and the RAND-36 Physical Functioning subscale scores. In addition, scores on the PC-Self item were significantly related to patients number of mental health diagnoses. Although correlations do not indicate a significant pathway, they are important to highlight as these associations may affect our

mediation results.

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Stepwise Regressions As outlined above, we tested relationships between (a) FEC variables and biobehavioral reactivity variables, and (b) biobehavioral reactivity variables and physical health outcomes using stepwise regressions. The goal, as stated above, is to conduct path analyses for two models: one with family functioning as the independent variable (Model 1) and one with romantic relationship satisfaction as the independent variable (Model 2). Therefore, preliminary stepwise regressions were conducted accordingly in order to explore which relationships make the most sense to test in AMOS.

Romantic relationship functioning. First, a stepwise regression was conducted using QMI and the perceived criticism items as predictors and depression scores as the outcome variable. Of those three, only QMI scores were a significant predictor of depression (t = -2.55, p = .013). We used a similar process to test QMI and perceived criticism as predictors of anxiety as measured by the OASIS; none of the variables were indicated as significant. This was also true when we used AUDIT scores as the dependent variable. A logistic regression was used to test QMI, PC-Partner, and PC-Self scores as predictors of NIAAA screen answers, given the dependent variables dichotomous nature; none of these relationships were significant. Family functioning. Because only one scale was used to assess family functioning (GFS/FAD), we conducted a linear regression to test the relationship between family functioning scores and depression. GFS/FAD scores were a significant predictor of QIDS scores (F = 9.04, p = .003). This indicates that as family functioning became more problematic, self-reported depression symptoms worsened. A similar process was used to test family functioning as a predictor of anxiety; this relationship was not significant (F = 1.53, p = .22). GFS/FAD scores were also not a significant predictor of alcohol use, as measured by the AUDIT (F = 1.02, p = .316). A logistic regression was used to test the NIAAA screen as a dependent variable; GFS/FAD was not a significant predictor (p = .672). Biobehavioral reactivity predicting disease activity. First, a stepwise regression was

used with all potential biobehavioral reactivity variables (QIDS, OASIS, AUDIT, and NIAAA screen scores) as independent variables and Health Review scores as the dependent variable. Only depression scores were highlighted as significant (t = 4.26, p = .000). This indicates that as the number and severity of self-reported depression symptoms increase, patients self-reported illness symptoms also increase. Similar analyses were used with all four RAND-36 subscales.

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Depression was the sole significant predictor of Physical Functioning subscale scores (t = -3.13, p = .002), Pain subscale scores (t = -4.13, p = .000), and General Health subscale scores (t = -5.07, p = .000). Both depression (t = -2.22, p = .029) and anxiety scores (t = -2.19, p = .031) were significant predictors of RAND-36 Role Limitations subscale scores. These results indicate that as self-reported symptoms and severity of depression (and anxiety) increase, physical health worsens (as indicated by decreasing RAND-36 subscale scores). Stepwise regressions were also used to test medical chart data as dependent variables. Again using QIDS, OASIS, AUDIT, and NIAAA screen scores as independent variables, we tested the number of primary care appointments in the past year, number of medical diagnoses, number of mental health diagnoses, BMI, and BMI weight status as determined by CDC guidelines as outcomes. Unfortunately, there were no significant relationships between our biobehavioral reactivity variables and these medical chart indicators of disease activity. Overall, significant relationships were found between marital satisfaction and depression; family functioning and depression; depression and illness symptoms; and both depression and anxiety and physical health. Because these findings were generally replicated in both the correlation analyses and stepwise regressions, this is how model testing will begin for both models. Therefore, initially for Model 1 (family functioning), anxiety, alcohol abuse, and medical chart disease activity outcome variables will not be tested. The same is true for the Model 2 (romantic relationship satisfaction). Anxiety will then be added as a potential additional mediating variable in both models.

Model Testing Both models of the BBFM were tested with path analyses, and standardized path analysis coefficients were estimated in AMOS using direct maximum likelihood estimation to handle missing data. A model generating approach (Byrne, 2010; Joreskog, 1993) was used when fitting the structural model. More specifically, testing began with an a priori specified model and, because it may be incorrectly specified given the exploratory nature of the study, the goal was to identify significant paths not included at first and to trim nonsignificant paths. First, the initial, hypothesized model was used to ensure the test was theoretically meaningful and parsimonious (Bikos & Kocheleva, 2012; Byrne, 2010; Kline, 2011). To modify each model and find the models with the best fit to the data and theory, a model trimming approach (Kenny, 2011; Kline, 2011) was used, which involves eliminating nonsignificant pathways one at a time on the basis

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of empirical considerations (Kline, 2011), or, examining the regression coefficients (Anderson, Parmenter, & Mok, 2002). Trimming models during the process of model testing by eliminating variables in order to improve model fit is common practice (e.g., Anderson et al., 2002; DeGarmo & Martinez, 2006; Driver & Gottman, 2004; Gallagher, Ting, & Palmer, 2008; Sidora-Arcoleo, Feldman, & Spray, 2012) and ensures the best-fitting, most parsimonious model. Model 1: Family functioning. The initial model (Figure 3) used the entire study sample (n = 125) and tested the relationships between family functioning (GFS/FAD), depression (QIDS), and disease activity. Disease activity was a latent, unobserved variable; Health Review scores and all four RAND-36 subscales were used as observed outcome variables loading onto disease activity based on preliminary findings of significant relationships derived from the exploratory analyses. Goodness-of-fit indices for the model indicate the first model does not fit the data (2 = 33.93, p = .001, CFI = .910, RMSEA = .114). Standardized regression weights are reported in Figure 3; these represent the path coefficients for the model. To maintain the test of the BBFM, pathways between family functioning and depression, depression and disease activity, or family functioning and disease activity were not eliminated. Instead, focus was on the RAND-36 measure, which included several subscales. In order to attempt a parsimonious, yet meaningful model, the RAND-36 subscale with the weakest path coefficient was trimmed: RAND-36 General Health scores. The second model (model 1a) is identical to the initial model, but with RAND-36 General Health scores (and the attached pathway) removed. A slight improvement in goodness-of-fit was observed, where the 2 decreased and the CFI improved a small amount (2 = 22.82, p = .004, CFI = .917, RMSEA = .122). The next weakest path coefficient was found between disease activity and RAND-36 Pain subscale scores; therefore, this variable and its pathway were trimmed next. The third model (model 1b) is identical to the first model, but with RAND-36 General Health and RAND-36 Pain subscale scores (and attached pathways) trimmed. Again, a slight improvement in goodness-of-fit was observed (2 = 14.25, p = .007, CFI = .919, RMSEA = .144). Next, RAND-36 Physical Functioning subscale scores were trimmed as a variable, due to its paths weaker standardized regression coefficient. The fourth model (model 1c; Figure 4) is again identical to the initial model, but with RAND-36 General Health, Pain, and Physical Functioning subscale scores (and attached

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pathways) removed from the model test. The improvement in goodness-of-fit was drastic and demonstrates acceptable agreement between the data and the model tested (2 = .324, p = .569, CFI = 1.000, RMSEA = .000). This model provides support of the theoretical model: pathways between family functioning and depression, and depression and disease activity were significant, while the pathway between family functioning and disease activity was nonsignificant. As discussed above, because anxiety demonstrated significant associations with disease activity outcomes in preliminary analyses, we added this into the model to test anxiety as an additional mediator in the BBFM. The last model (model 1c) was used as the base and a biobehavioral reactivity unobserved variable was created with anxiety and depression loading as observed variables (model 1d; Figure 5). This addition did not substantially affect goodness-of-fit statistics; this test demonstrated acceptable agreement between the data and the model tested (2 = 4.135, p = .247, CFI = .992, RMSEA = .055). In addition, the magnitude of the pathway coefficient between biobehavioral reactivity and anxiety was substantial. Of note is that this model altered the level of significance of the pathway between family functioning and biobehavioral reactivity (p < .05), as compared to in the previous model (model 1c; p

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activity, or romantic relationship satisfaction and disease activity were not eliminated in the model building and trimming process. Instead, the RAND-36 subscale with the weakest path coefficient was trimmed: RAND-36 Pain scores. The second romantic relationships model (model 2a) is identical to the initial model, but with RAND-36 Pain scores (and the attached pathway) removed. Unfortunately, the goodness-of-fit statistics worsened: the 2 became significant, the CFI decreased, and the RMSEA increased (2 = 19.657, p = .12, CFI = .890, RMSEA = .139). Therefore, Pain subscale scores were re-entered into the model. The next weakest path coefficient was between disease activity and RAND-36 Physical Functioning subscale scores; therefore, this variable and its pathway were trimmed. This third model (model 2b; Figure 7) demonstrated an enormous improvement in fit. The 2 became nonsignificant, the CFI was above our cutoff of .95, and the RMSEA was below .05 (2 = 6.126, p = .633, CFI = 1.000, RMSEA = .000). As with Model 1 (family functioning), anxiety was added into Model 2 to test these scores as an additional mediator in the BBFM. The last model (model 2b) was used as the basis of the test and a biobehavioral reactivity unobserved variable was created with anxiety and depression loading as observed variables (model 2c; Figure 8). This addition did not substantially affect goodness-of-fit statistics; this test demonstrated acceptable agreement between the data and the model tested (2 = 11.309, p = .503, CFI = 1.000, RMSEA = .000). In addition, the magnitude of the pathway coefficient between biobehavioral reactivity and anxiety was large. Therefore, this model is judged to be the best-fitting model for the data, and is the final model of this test of the BBFM using romantic relationship satisfaction as the endogenous variable. In summary, for both Model 1 and Model 2, pathways between FEC and biobehavioral reactivity were significant, as were pathways between biobehavioral reactivity and disease activity. Pathways between FEC and disease activity were not significant. For the final Model 1, this means that, as family functioning became more problematic (GFS/FAD scores increased), depression and anxiety scores worsened (QIDS and OASIS scores increased). It also indicates that, as patients experienced more biobehavioral reactivity, thei