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An examination of the association between spousal support and type 2 diabetes self-management

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An examination of the association between spousal support and type 2 diabetes self-management

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AN EXAMINATION OF THE ASSOCIATION BETWEEN SPOUSAL SUPPORT
AND TYPE 2 DIABETES SELF-MANAGEMENT

by

Keosha R. Partlow

________________________________________________________________________

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(PREVENTIVE MEDICINE (HEALTH BEHAVIOR))

May 2011

Copyright 2011 Keosha R. Partlow

ii
DEDICATION

This dissertation is dedicated to my wonderful children: Amiri, Kalei, Makai and Anaya.
Being your mommy is the biggest honor that I will ever receive.

iii

ACKNOWLEDGEMENTS

I sincerely would like to thank the members of my Dissertation Committee, Drs.
Kim Reynolds, Steve Sussman, Lourdes Baezconde-Garbanati, Anny Xiang, and Karen
Lincoln, for their continued support and assistance in helping me to complete this
document. Their guidance, advice, and patience have truly been invaluable. I would
especially like to thank Dr. Reynolds for being a wonderful mentor. I will be eternally
grateful for the time and effort he has spent over the years- from reviewing drafts, to
always challenging me to think critically. Thank you Drs. Sussman and Baezconde-
Garbanati for their constant words of encouragement, and for believing in me. Thank you
to Dr. Xiang for always taking the time to discuss my data and research findings. Thank
you to Dr. Lincoln for being such a wonderful role model. Her critical feedback,
guidance, and never-ending support have helped me tremendously. I would also like to
thank the students and staff at IPR who have supported me over the years. Thank you so
much to Dr. Luanne Rohrbach, for her support and understanding. Thank you to my
colleagues and friends, Dr. Lilia Espinoza, Claradina Soto, T. Em Arpawong, Dr. Selena
Nguyen-Rodriquez, and Dr. Kari-Lyn Sakuma, who have all been amazing. My sincere
thanks to Marny Barovich, who has been an incredible support to me and who has always
believed in me even when I failed to believe in myself. I would especially like to thank
my family, for being there for me unconditionally. I truly thank God that I have the most
amazing parents, Cedric and Ruth Partlow, and I am eternally grateful for their
unwavering, unconditional love. Words simply cannot express my sincere gratitude for
their support and encouragement through all of my endeavors. Thank you so much to my

iv
beautiful younger sisters, Kimberli and Kaia. Whenever I doubted myself, their love and
encouragement has always been there for me. Thank you to my Grandparents who taught
me that I could conquer the impossible. Thank you to my Godparents for their prayers,
and for instilling in me that no height is unreachable, and no goal is unattainable. Thank
you so much to my aunts, who have been wonderful examples of strong women. Thank
you to my in-laws, who have provided so much support, especially with the children.
Thank you so much to my beautiful children, Amiri, Kalei, Makai, and Anaya, who have
truly been my inspiration to persevere despite adversity. I wanted to especially thank my
wonderful husband, Dustin Johnson, for being my rock through all of these years. His
love and laughter has helped me to endure many difficult days through this process. I am
truly looking forward to our next chapters together. Finally, thank you Jesus for carrying
me when I could not walk by myself.

v

TABLE OF CONTENTS

Dedication

ii
Acknowledgements

iii
List of Tables

vi
List of Figures

x
Abstract

xi
Chapter One: Introduction

1
Chapter Two: Methods

30
Chapter Three: The Association Between Spousal Support, Saturated Fat
Consumption and Physical Activity Levels

43
Chapter Four: The Association Between Spousal Support and Diabetes
Distress

73
Chapter Five: The Association Between Spousal Support, BMI and
HbA1c
87

vi
Chapter Six: Conclusion

107
References

121
Appendix

137

vii
LIST OF TABLES

Table 1 Characteristics of the Sample by Gender

45
Table 2 Characteristics of the Sample by Race

46
Table 3 Correlations with Support, Gender, Race, and Self-Management
Variables for Type 2 Diabetes Patients

48
Table 4 Correlation Among Support Variables, Race, and Self-
Management Outcomes for Men with Type 2 Diabetes

50
Table 5 Correlation Among Support Variables, Race, and Self-
Management Outcomes for Women with Type 2 Diabetes

51
Table 6 Correlation Among Support Variables, Gender, and Self-
Management Outcome Variables for African Americans, Asian
Americans, Native Americans, and Asian Pacific Islanders with
Type 2 Diabetes

53
Table 7 Correlation Among Support Variables, Gender, and Self-
Management Outcomes for Whites with Type 2 Diabetes

54
Table 8 Correlation Among Support Variables, Gender, and Self-
Management Outcomes for Hispanics with Type 2 Diabetes

55
Table 9 Associations Between Spousal Support and Saturated Fat
Consumption

57

viii
Table 10 Associations Between Spousal Support and Light Physical
Activity

59
Table 11 Associations Between Spousal Support and Moderate Physical
Activity

63
Table 12 Associations Between Spousal Support and Vigorous Physical
Activity

65
Table 13 Associations Between Spousal Support and Diabetes Distress

75
Table 14 Associations between Spousal Support for Physical Activity and
Diabetes Distress for Men

80
Table 15 Associations Between Spousal Support for Physical Activity and
Diabetes Distress for Women

81
Table 16 Associations Between Spousal Support for Diet and Body Mass
Index among African Americans, Asian Americans and Native
Americans

91
Table 17 Associations Between Spousal Support for diet and Body Mass
Index among Hispanics

93
Table 18 Associations Between Spousal Support for Diet and Body Mass
Index among Whites

94
Table 19 Associations Between Spousal Support for Physical Activity and
Body Mass Index

95
Table 20 Associations Between Spousal Support for Diet and HbA1c
Among Women
98

ix

Table 21 Associations Between Spousal Support for Diet and HbA1c
Among Men

98
Table 22 Associations Between Spousal Support for Physical Activity and
HbA1c

100

x
LIST OF FIGURES

Figure 1 Main Effect Model of Social Support

22
Figure 2 The Stress-Buffering Model of Social Support

23
Figure 3 Main Effect Model of Proposed Study

26
Figure 4 The Interaction between Appraisal Support for Physical Activity
and Acculturation

61
Figure 5 Interaction between Unsupportive Behavior for Physical Activity
and Body Mass Index (BMI)

64
Figure 6 The Interaction Between Unsupportive Behavior for Diet and
Health of Spouse

77
Figure 7 The Interaction Between Instrumental Support for Physical
Activity and Gender

79
Figure 8 The Interaction Between Race and Emotional Support for Diet

90
Figure 9 The Interaction Between Gender and Unsupportive Behavior for
Diet
97

xi
ABSTRACT

The present study sought to explore the effects of spousal support on Type 2
diabetes self-management. Cross-sectional data from 305 spousal pairs, ages 30-70, were
collected from a tailored self-management intervention study for type 2 diabetes entitled
Prevention and Control of Diabetes in Families (PCDF). Three separate studies were
conducted in order to examine spousal support effects on three different self-management
domains: behavioral (diet, physical activity), psychological (diabetes distress), and
biological (BMI, HbA1c). The studies were also designed to assess gender and race as
moderators of the relationship between spousal support and self-management measures.
Findings from Study 1 revealed that increased instrumental support and unsupportive
behavior for physical activity was associated with decreased moderate physical activity
levels. Additionally, increased appraisal support for physical activity was associated with
higher levels of light activity. Individuals who had lower levels of acculturation and
perceived higher levels of appraisal support reported higher levels of light physical
activity. Findings from Study 2 revealed that for women, increased levels of instrumental
support were associated with higher levels of diabetes distress. Further, higher levels of
perceived unsupportive behavior for diet were associated with increased levels of
diabetes distress, particularly when the non-diabetic spouse was healthier. Finally,
findings from Study 3 revealed that increased unsupportive behavior for diet and
emotional support for diet was associated with negative health outcomes, including
increased BMI among Whites, and increased HbA1c levels. Findings support and expand

xii
prior research on spousal support literature, particularly regarding the negative impacts of
support. Results indicate that support can have negative health outcomes, even if the
provider has well-meaning intentions. Results emphasize the importance of contextual
factors, such as gender and race, in addition to the social context when tailoring family-
based interventions. Further, the role of unsupportive behavior and appraisal support in
spousal relationships deserves further attention.
1
CHAPTER ONE: INTRODUCTION

Specific Aims
According to the Center for Disease Control, Type 2 diabetes is a chronic illness
that affects approximately 25 million individuals in the United States. This number is
expected to triple by the year 2050, largely due to rising rates of obesity, the number of
children who are increasingly being diagnosed with Type 2 diabetes, and our aging
population (Department of Health and Human Services, CDC, 2005; Deshpande, Harris-
Hayes , & Schootman, 2008). Health care costs of Type 2 diabetes are also increasing.
According to the American Diabetes Association, the estimated health care costs
associated with diabetes is $132 billion per year (American Diabetes Association, 2003).
Common complications from Type 2 diabetes include stroke, chronic kidney damage,
heart attacks, foot damage, and eye damage (American Diabetes Association, 2009;
Stamler et al, 1993; Zimmet, 2003). In addition to the physical and economic burdens of
Type 2 diabetes, the psychological burden resulting from diabetes, such as depression, is
also of great concern (Jacobson, 1993; Rubin & Peyrot, 1992; Gayvard et al., 1993;
Anderson et al., 2001). Thus, Type 2 diabetes is a chronic illness with immense impact.
The goals of effective Type 2 diabetes self-management are to improve a patient’s
quality of life by minimizing the complications and psychological burden that is
associated with the illness (de Weerdt et al., 1989; Norris et al., 2001; Norris et al., 2002).
Successful Type 2 diabetes self-management is largely influenced by self-care, much of
which occurs in one’s home environment (Von Korff, 1997). According to Von Korff ,

2
self-care and medical care are often treated as two competing entities (Von Korff, 1997).
That is, although much of management takes place in the home, the patient is typically
treated without much consideration of what occurs outside of the physician’s office.
Thus, the home environment deserves increased attention (Glasgow, 1994; Glasgow &
Toobert, 1988; Gallant, 2003).
Spousal support is an important environmental influence that may improve or
impact diabetes self-management. A person spends a large amount of time with their
spouse. In addition, most spouses are like-minded individuals, and may share the same
values and beliefs (Booth &Welch, 1978; Larson & Hoffman, 1994). Although there has
been research on the general benefits of spousal support (e.g. decreased depression, stress
and overall better health outcomes) (Syrotuik & D’Arcy, 1984; Ell, 1984; Kiecolt-Glaser
& Newton, 2001), as well as social support and diabetes self-management (VanDam et
al., 2005; Gallant, 2003; VonKorff et al., 1997; Tillotson & Smith; 1996; Gleeson-Kreig,
2002), very few studies have directly examined the impact of spousal support on Type 2
diabetes self-management (Wing et al., 1991; Akimoto et al., 2004; Beverly, Miller &
Wray, 2008; Beverly, Penrod & Wray, 2007). Of these few studies, all have examined
overall spousal support, but have not examined how specific types of spousal support
may affect different domains of type 2 diabetes self-management. Additionally, no
studies have examined the possible moderating influence of race, and few studies have
examined gender of the diabetic spouse as a moderator of the relationship between
spousal support and diabetes self-management (Hagedoorn et al., 2006; Wing et al.,
1999).

3
The present research will investigate the relationship between spousal support and
Type 2 diabetes self-management. Specifically, in addition to examining whether or not
there is an association between types of spousal support and diabetes self-management,
the degree to which gender and race moderate this relationship will be examined. The
association with unsupportive behavior (i.e., criticizing and complaining) and Type 2
diabetes self-management will also be explored. Cross-sectional baseline data from an
intervention program designed to prevent and control diabetes in 305 married couples
where one spouse was diabetic, and one spouse was non-diabetic, will be utilized. The
following aims are proposed for the present research:
(1) To assess the association between spousal support and three indicators of type 2
diabetes self-management including behavioral, psychological, and biological
(2) To investigate whether gender and race moderate the association between spousal
support and Type 2 diabetes
(3) To determine whether unsupportive spousal interactions are negatively associated
with Type 2 diabetes self-management

Background and Significance
Type 2 diabetes affects approximately 25 million adults in the United States, and
this number is expected to increase (Department of Health and Human Services, CDC,
2005; Cowie et al., 2006). Previous research has indicated that the daily self-management
regimen for Type 2 diabetes is challenging and can be overwhelming for the average
The Problem of Type 2 Diabetes

4
person (Gallant, 2003; Glasgow & Eakin, 1998; Rubin, 2005; Park et al., 2004; Lin et al.,
2004; Cramer, 2004).
There are three major domains that comprise diabetes self-management:
behavioral (diet, exercise, regularly taking prescribed medication), psychological
(morale, quality of life, depression), and biological (measures of HbA1c, BMI) (Glasgow
& Toobert, 1988). Self-management can include medication, which involves
administering insulin shots to regulate glucose, frequent moderate to vigorous physical
activity, following a healthy diet, recognizing and responding to symptoms, and making
informed decisions about care (Gallant, 2003; Glasgow & Toobert, 1988; Glasgow &
Eakin, 1998). Much of this management has to take place in one’s home, where social
support is an important contributor. Additionally, successful diabetes self-management
includes managing relations with family members, which frequently requires the roles
and tasks of family members to change in order to accommodate the diabetic patient
(Gallant, 2003).
Definition of Spousal Support
Spousal Support
Spousal support is typically identified as the assistance and protection given to an
individual, where one spouse is the giver and/or the receiver of support (Langford et al.,
1997). Spousal support may provide a wider range of support types compared to other
sources of support (Gardner & Cutrona, 2004). Spousal support has four major types.
These include emotional support, informational support, instrumental support, and
appraisal support. Emotional support is the reassurance that one is valued, trusted, and

5
cared for. This can also include encouraging a spouse to eat healthier and to be physically
active. Informational support refers to giving advice and information to assist with
problem solving. Instrumental support involves providing behavioral support, in addition
to tangible goods and services such as preparing healthy meals, physically exercising
with one’s spouse, and assisting with transportation to and from the doctor. Appraisal
support (also referred to as esteem support) is to recognize, acknowledge and reinforce
the appropriateness of one’s actions or statements (Miller & Davis, 2005; Langford et al.,
1997; House & Kahn 1985).
Effects of Specific Types of Support
There has been a limited amount of research seeking to answer the question
whether individuals with chronic illness have better or worse health outcomes depending
on the type of spousal support received. Findings, in general, conclude that the impact of
spousal support on health depends on the type and source of support. The work of
Cutrona and colleagues have examined the differences between types of spousal support
and their effects on health-related outcomes (i.e., stress). They concluded that support
was helpful to a spouse based on the type of support, the stage and severity of an illness,
as well as the provider of support (Cutrona & Russell, 1990; Cutrona & Suhr, 1992).
Among individuals with a chronic illness that was in later stages, emotional support was
found to be most effective to a spouse when their spouse was the provider. However,
receiving instrumental support from a spouse was associated with lower levels of power
and control. That is, emotional support from spouses, or the feeling of caring and
encouragement, was well received among patients. Instrumental support, or having the

6
spouse assist with administering medication or with cooking and preparing healthy meals,
was not well received due to the patient feeling as if he or she was asking too much, and
seeking too much assistance and attention. Similar results were also found among a
sample of patients with osteoarthritis. Women felt that the receipt of instrumental support
from their spouses stymied their ability to feel independent (Martire et al., 2002; Nadler
and Fisher, 1986). Findings about informational support has revealed that informational
support may be more readily accepted from their health care professionals, compared to
spouses (Cutrona, 1996). Further, one’s age and internal levels of self-control may
determine whether informational support received from spouses will result in negative
consequences (Baltes &Wahl, 1992; Seeman et al., 1996; Martire et al., 2002). Thus,
emotional support appears to be well received among spouses, however instrumental and
informational support appears to be only helpful depending on the source of support and
under specific circumstances, and at times may be harmful to one’s well-being (Cutrona
& Suhr, 1992).
Although more attention needs to be placed on the effects of types of spousal
support on chronic illness self-management, the work that has taken place to date has
been encouraging. It also brings attention to the importance of measuring specific types
of spousal support, rather than assessing general measures of support. More research in
this area should occur to further determine what diabetes self-management behaviors are
associated with different types of support, in addition to how and why discrepancies in
the types of support desired and received affect health outcomes. This knowledge will
help to inform researchers and clinicians on which types of support to emphasize when

7
working with families.
Unsupportive Behavior
In addition to research on spousal support, the effects of spousal unsupportive
behaviors have also been discussed in the literature. The effects of spousal unsupportive
behaviors on Type 2 diabetes self-management deserve further attention, as much of
previous research has focused primarily on the positive aspects of spousal interactions.
There is evidence that spousal nagging, criticizing and complaining may have a greater
effect on health outcomes compared to spousal support. For example, one study found
that negative support from a spouse was statistically associated with more chronic health
problems, greater physical disability and greater physical symptoms such as headaches
and stiffness in joints (Bookwala, 2005). Mann and colleagues, who examined support
among cancer patients, reported similar findings. They found that across gender, negative
behaviors from a partner were more highly associated with psychological distress and
well-being compared to positive behaviors from a partner (Manne et al., 1997).
Additionally, among a sample of patients with Type 2 diabetes, negative social support
from family and friends was found to be a stronger predictor of depression than positive
social support from family and friends (Skarbeck et al., 2006). Thus, researchers should
work to further understand the effects of unsupportive behaviors on health and well-being
outcomes in order to inform health providers and intervention studies. However, it is
important to note that many of the studies that have examined unsupportive behaviors
have solely looked at the patient’s perception of support. It is possible that support
received from a patient may be different than the support reported by a spouse.

8
Invisible support
Invisible support is a term used to explain when the patient’s perception of
support received and the spouse’s perception of support provided are not concordant
(Bolger, Zuckerman Kessler, 2000). That is, the spouse may indicate that they have
provided positive support, however, the patient reports that positive support has not been
received. According to Bolger, Zuckerman and Kessler, there are at least two types of
invisible support. One type occurs when the spouse provides support without the patient
knowing – it is “outside of the recipient’s awareness”. The second type occurs when the
spouse provides support without drawing attention to the support needs of the patient. In
other words, the spouse may not code their actions as supportive in order to minimize the
patient’s need for help as an effort to bolster the patient’s level of confidence. For
example, the spouse may indirectly provide information about diabetes in such a way that
the patient may think they supplied this information themselves. Thus, invisible support
in this context may buffer a stressor and may lead to increased health outcomes, such as a
decrease in depression and distress (Bolger, Zuckerman & Kesler, 2000; Bolger and
Amarel, 2007; Shrout , Herman & Bolger 2006). Additional diary and observational
studies are needed to further clarify additional types of invisible support and when
invisible support occurs. However, a further explanation of why the level of perceived
support does not agree with support provided may be linked to the fact that perceptions of
support may vary based on characteristics of the individual who perceives the support.
These individual characteristics will be addressed in the following section.
Factors that may influence perceptions and the provision of spousal support

9
Spousal Support and Personality Type
Personality type may play a key role in how support is perceived, and may be
important to understanding social support. According to Cohen and colleagues, support is
a cognitive process, and is influenced by the personality of the giver and receiver of
support (Cohen et al., 2005). Cutrona, Hesling and Suhr found that personality type
influences supportive relationships, in addition to how social support is perceived and
received (Cutrona, Hesling & Suhr, 1997). The work of Dehle and Landers suggest that
wives consider the personality traits of their husbands when providing support. Husbands
who were less emotionally stable and/or less conscientious (less reliable, less organized)
received more compliments, validation of their abilities, advice, suggestions, and
information from their wives. Wives who were more conscientious and/or emotionally
stable were reportedly more satisfied with the support they received from their husbands
(Dehle and Landers, 2005). Thus, research in this arena is promising and may shed light
on how support is perceived and provided. However, more research should occur in order
to elucidate whether certain types of support are associated with certain types of
personality types, and whether there are any moderating and/or mediating factors.
Spousal Support and Coping Style
Although social support may influence coping, one may choose to seek support
from their spouse as a way of coping with stress because of their illness (Scheurs & de
Ridder, 1997). Coping is a process that is often initiated in an emotional environment,
because it often arises as a response to a situation that is perceived as negative, and
therefore determined to be stressful (Lazarus & Folkman, 1984; Folkman & Moskowitz,

10
2004; White, Richter & Fry, 1992). Thus, when one is diagnosed with an illness such as
Type 2 diabetes, one can have intense emotions that are typically stressful. Coping
emerges as a way to regulate these emotions. There are eight individual coping styles that
have been found to affect the management of a chronic illness: confrontive (constructive
problem solving, seeking knowledge about the illness), evasive (avoidance), optimistic
(positive thinking), fatalistic (feeling hopeless), emotive (doing something impulsive,
letting off steam), palliative (doing something in efforts to feel better, such as exercise, or
trying to keep busy), supportant (seeking support systems), and self-reliant (depending on
yourself to deal with the situation) (Folkman & Moskowitz, 2004). An examination of
these coping styles suggests the presence of two main types including problem-focused
(focus is on changing the situation rather than adjusting to it), or emotion-focused coping
(focus is on reducing the stress and anxiety associated with a given situation) (White,
Richter & Fry, 1992). Additional coping studies that have involved the spouse typically
include terms such as protective buffering (hiding one’s concerns), overprotection
(underestimating the patient’s abilities) and active engagement (collaborating and using
constructive problem solving, similar to common coping) (Berg, Meegan & Deviney,
1998). Research indicates that problem-focused strategies are more associated with
positive outcomes (Lazarus & Folkman, 1984; Folkman &, Moskowitz, 2004). Patients
who exhibit more problem-focused coping tools may report receiving more social
support. Sollner and colleagues suggest this interdependence between coping strategies
and social support by showing that individuals who exhibit an active, problem-focused
coping style are more likely to report receiving support (Sollner et al., 1999).

11
There is some evidence that dyadic coping, or the utilization of emotion and
problem-focused coping by the patient and the spouse in order to benefit the health of the
diabetic patient and the marital relationship, occurs in addition to individual coping
efforts (Bodenmann, 2005). Moreover, a couple’s ability to engage in effective coping
mechanisms may influence their perception of support (Fekete et al., 2007). According to
Bodenmann, when a stressor arises, the spouse becomes stressed, ignores the stressor, or
assists with dyadic coping. Dyadic coping can be positive or negative. Types of positive
dyadic coping include supportant (helping with tasks, providing advice and
understanding) delegated (one partner takes over the chores of another) and common
coping (joint problem solving). Negative dyadic coping includes hostile (disparagement,
distancing and mocking), ambivalent (spouse is unwillingly supportive) and superficial
coping (insincere, unemphatic support) (Bodenmann, 2005). Positive dyadic coping
styles may be associated with perceiving greater spousal support. Spouses who reported
receiving more emotional support were more emotionally responsive to their spouse
(Fekete et al., 2007).
Spousal support and gender
There are many contextual factors that can influence spousal support.
Additionally, these factors also may serve as moderators between spousal support and
Type 2 diabetes self-management. One such contextual factor is gender. Previous
literature has shown that women may perceive social support differently than men
(Primomo et al., 1990). A review examining findings on gender differences in coping and
social support among patients with myocardial infarction revealed that women used a

12
larger variety of coping strategies and reported receiving less social support compared to
men. Women were less likely to seek support from others, outside of the home, to avoid
being burdensome. On the other hand, men were more likely to involve their spouses
with their care (Kristofferzon, Lofmark & Carlsson, 2003). One study also found that
women were more likely to seek support outside the marriage, and that men who sought
support outside the marriage were more likely to be depressed (Edwards, Nazroo, &
Brown, 1998). Similar findings were reported in a study that examined social support
among African American church members. Married men were more likely to rely on their
spouse for support compared to married women for eating a healthy diet, being physically
active and complying to colorectal cancer screening (Thrasher et al., 2004). These studies
indicate that while women are likely to seek support from multiple sources, men may rely
more on their spouse for support.
In addition to the differences in the perception of support, women and men have
also been postulated to be more likely to provide different amounts and types of support.
The “marital support gap” posits that women provide more support than men, and that
this support is more helpful than the support that men provide (Belle, 1992; Schwarzer
and Gutierrez-Dona, 2006). Current explanations for the occurrence of the marital
support gap include the differences in social role expectations between men and women,
in addition to innate, inherent propensities for women to be more supportive (Fausto-
Sterling, 1985; Barbee et al., 1993). Additionally, women are thought to provide more
emotional support, while men are thought to provide more informational support
(Cutrona, 1996; Cutrona, Hesling & Suhr, 1997; Steil, 2000; Luszczynska et al., 2007).

13
While the “marital support gap” has been tested in several studies, it has not been fully
substantiated. Many of the studies that have commented on this hypothesis have found
that women do provide more emotional support that men, however, many have been
solely based on self-report measures, although one self-report measure did not find
evidence of the marital support gap and concluded that men and women do not differ in
the amount of support received (Xu & Burleson, 2001). A study was conducted that used
observational and self-report methods among married couples, and determined that
although self-report methods concluded that women provide more emotional and
informational support than men, effect sizes were low. Both men and women reported the
same levels of overall perceived positive and negative spousal support. Further,
observational methods revealed that men and women did not differ with respect to the
type of support they provided and perceived (Verhofstadt, Buysse & Ickes, 2007). This
finding was consistent with other observational studies of marital support (Neff &Karney
2005; Pasch et al., 1997; Verhofstadt et al., 2005). Clearly, more research should examine
gender differences in support processes. This research should include an investigation of
the underlying gender differences in perceptions of support, as well as gender differences
in the providance of support.
Spousal support and race and ethnicity
While there has been research on the effects of gender on the relationship between
spousal support and health-related outcomes, there exists little data on the moderation
effects of race and ethnicity on spousal support. Among a sample of newly married
Chinese and American married couples, Xu and Burleson found that American spouses

14
reported experiencing more emotional support than Chinese spouses (Xu & Burleson,
2001). Further, the work of Burleson suggests that perceptions of support may be
moderated by cultural differences. For example, Burleson suggests that the role of
emotional support may be more relevant to Caucasian Americans compared to other
ethnicities such as Asian and African Americans, largely due to cultural differences.
Additionally, prior research has shown that Asian and African Americans belong to
cultures that demonstrate higher levels of collectivism compared to Caucasian
Americans, who demonstrate higher levels of independence (Hofstede, 1980; Burleson,
2003; Mortenson, 1999). Research has shown that collectivist cultures are less likely to
verbalize their distress in front of family members and close friends (Argyle et al., 1986;
Samter et al., 1997). On the other hand, western cultures are more reliant on explicit,
verbal cues to express feelings of emotions (Samter et al., 1997; Brunueau & Ishii,
1988;). According to the work of Burleson and colleagues, emotional support may be
perceived as person-centered, as it is aimed at comforting and lessening a person’s
distress and woes on an emotional level. As a result, expressiveness, or constructs largely
associated with emotional support, may be highly valued and more often perceived by
Caucasian Americans, largely due to cultural differences that place more value on the
self.
Additional data on the moderation effects of race and ethnicity on social support
remain mixed. Some research has suggested that African Americans perceive greater
amounts of social support compared to Caucasian Americans (Nicholas & D’Meza, 1999;
Heckman et al., 2000). On the other hand, there has been literature suggesting that

15
African American patients report receiving less or about the same amount of support
compared to Caucasian Americans (Leserman et al., 1992; Bae et al., 2001; Silverstein &
Waite, 1993). One study reported that among Hispanics, social support was not strongly
associated with diabetes self-efficacy, although the authors reported potential problems
with measuring spousal support (Gleeson-Kreig et al., 2002). Examining the moderation
effects of race and ethnicity on spousal support and diabetes self-management is an area
that deserves further attention. Further examinations of the moderating effects of race and
ethnicity are greatly needed not only to allow for generalizable comparisons between
racial and ethnic groups, but also to provide more insight into the mechanisms that
underlie spousal support. In my exploration of the moderating effects of race and
ethnicity on spousal support, I expect clear distinctions to emerge between racial groups.
These distinctions will emerge not only due to cultural differences as researchers may
suggest, but also due to differences in barriers and opportunities based on race and
ethnicity that may effect one’s place in society (House, Umberson & Landis, 1988).
Thus, diverse experiences based on race and ethnicity may effect one’s perception and
providance of spousal support.
Spousal support and marital quality
Positive marital quality has been found to be associated with better diabetes-
related outcomes (Trief et al., 2001; Trief et al., 2002), including among elderly patients
(Trief et al., 2006; Gilden et al., 1989). Increased marital stress and low marital quality
were related to poor adherence to the diabetes care regimen, including following exercise
and dietary guidelines. High marital quality may buffer stress that arises due to being

16
diagnosed with diabetes, attempting to self-manage one’s illness, or from complications
that may arise due to diabetes. If a couple does not have a positive marital relationship,
the spouse will be less likely to provide essential spousal support, and the patient may be
less likely to seek support from the spouse. This does not mean that outside sources of
support will compensate for a lack of support from the spouse. Prior evidence shows that
outside sources of support cannot compensate for a negative marital relationship
(Lieberman, 1982; Coyne & DeLongis, 1986). Spousal support is important to diabetes
care and is greatly affected by the marital relationship. It is important for researchers and
clinicians to provide the tools that will aid in a more satisfying marital relationship for
both partners during the intervention, and long after the intervention has ended.
Qualitative studies on spousal support and type 2 diabetes self-management have
primarily involved married couples, where at least one spouse has diabetes. Guided
interviews and focus groups have been the most utilized methods. Overall, qualitative
studies have provided evidence for the positive and negative effects of spousal support on
Type 2 diabetes self-management. They have concluded that the spouse has a large
positive influence on the patient’s dietary behaviors, physical activity levels, glycemic
control, knowledge about diabetes, and adherence to prescribed medication (Savoca and
Miller, 2005; Beverly et al., 2007; Beverly, Miller, & Wray 2008; Beverly & Wray,
2008; Jerant et al., 2005; Miller and Brown, 2005; Nagelkerk, Reick, & Meengs, 2006;
Wong et al., 2005; Sandberg et al., 2006). They have also found that negative spousal
Quantitative and Qualitative Studies on Spousal support and Type 2 diabetes self-
management

17
behaviors are a barrier to diabetes self-management and contribute to less healthful eating
behaviors (Jerant et al., 2005; Nagelkerk et al., 2006). Finally, qualitative studies have
provided evidence for the “marital support gap” hypothesis by concluding that women
provide more support than men, particularly for diet. This may be largely due to
customary gender roles, where women are traditionally responsible for food preparation
in the home (Savoca & Miller, 2001; Beverly et al., 2008; Beverly & Wray, 2008).
While conclusions from qualitative studies on the effects of spousal support on
Type 2 diabetes have been encouraging, it must be noted that findings are less
generalizable due to very small sample sizes. They also utilize designs that are not strong
for drawing conclusions about cause-effect relationships. Nonetheless, qualitative studies
have provided the impetus for conducting quantitative studies. To date, there have only
been two quantitative studies that have directly explored spousal support and Type 2
diabetes self-management. Wing, Marcus, Epstein and Jawad (1991) conducted a family-
based intervention with obese Type 2 diabetes patients to determine if spouses could
support each other in weight loss, where both the spouse and the patient were obese.
There was a 20-week weight behavior control session for each condition, family-based
and individual. The spouses in the family-based condition were taught to offer support,
positive reinforcement, and to identify shared problems. Changes in fat intake and
exercise activity, listening and support strategies (evaluated by how well they adhered to
listening and social support strategies taught by the intervention) were not statistically
different between patients in the family-based condition and patients in the individual
condition. However, female patients did better when they were treated with their male

18
spouses, and male patients did better treated alone. Spouses in the family-based condition
lost significantly more weight than spouses in the individual condition, and exhibited
greater changes in eating behavior strategies (Wing et al., 1991).
The second study aimed to determine which psychosocial factors influenced
diabetes self-management. Diabetic patients whose meals were provided by their spouses,
but had no prior diabetes education and had less perceived social support were more
likely to relapse (show an increase in HbA1c values) than patients whose spouse did not
prepare their meals and who did have prior diabetes education, but had more perceived
social support. The majority of the group whose meals were prepared by the spouse
consisted mainly of men, and the meal preparers were mainly women. Interpretation of
these results should be with caution however due to serious methodological issues: 42.2%
of subjects were eliminated from analyses due to a prior administrative error (Akimoto et
al., 2004).
In short, although results from qualitative studies have been encouraging, they
have also accomplished two important tasks. First, they have helped to identify salient
constructs to test quantitatively. These constructs may help to shed light on the
underlying mechanisms and processes of spousal support. Moreover, testing these
constructs quantitatively will aid researchers in the construction of a theoretical
framework to explain how spousal support can positively and negatively affect type 2
diabetes self management. Second, qualitative studies have revealed potential constructs
to incorporate in interventions, as well as key topics to be addressed by clinicians and
health educators. Unfortunately, there is a lack of quantitative studies that have been able

19
to test these constructs. More quantitative studies are essential in order to substantiate
findings concluded from qualitative studies that are generalizable and will help test
theoretical constructs generated from qualitative studies.
In many studies, support is defined in general terms, and survey items about
support do not adequately separate the types of social support (Akimoto et al., 2004;
Wing et al., 1999). Thus, it is often difficult to assess the mechanism by which social
support has an effect on the outcome. In my review of the literature, it became clear that
the methodological limitations in the spousal support literature mirrored many of the
same limitations mentioned in the social support literature. First, sample sizes remained
low across studies, especially among studies that included both the spouse and the
patient, largely due to the difficulty of recruiting the family unit as opposed to simply one
person. Because it is more challenging to recruit two members from the family compared
to one, the studies that included both spouses could have represented couples that were
healthier than typical diabetic couples, or who were more likely to seek help from the
medical community. Thus, low sample sizes affected the generalizability of findings and
increased the chance of Type II error due to decreased statistical power. Second, there are
only few randomized controlled studies of spousal support, which results in few studies
that systematically vary types of support. This is problematic because randomized
controlled studies are recognized as a sound method designed to establish causal
relationships (Richter & Berger, 2000). In addition to the low number of randomized
controlled studies, there were also few prospective studies, which further limited our
The methods of spousal support

20
ability to determine cause-effect relationships. Third, many of the studies did not include
complete information about their sample. For example, information about HbA1c, or the
percent of participants taking insulin could have been helpful in determining the general
health of samples. A large majority of the studies sampled predominately whites, or did
not provide enough data on ethnic and racial demographics of the sample, which also
limited the generalizability of findings. Finally, there are a variety of measures used for
spousal support, which make results difficult to interpret. Additional standardized
measures will allow for compelling, replicable conclusions that will serve as an improved
guide for interventions.
Based on the methods of reviewed studies, future work can be improved by
additional prospective studies. First, additional intervention and prospective studies
should be utilized in order to establish causal relationships. Second, larger sample sizes
that are made up of various ethnic groups are needed in order to increase statistical power
and generalizability of study results. Third, multi-modal outcome assessments are needed
in order to provide additional insight on factors relating to perceptions, provision and
receipt of support, and the influence of contextual factors such as race and marital quality
on these constructs. Suggested methods can include self-report surveys, daily diary
studies, in addition to behavioral observation methods. Finally, a standardized measure of
spousal support should be established and used across studies to enhance comparability.
This tool should measure different domains of support. A standardized measure will
make it easier to not only draw conclusions, but also to further understand the processes
of support.

21
The mechanisms underlying spousal support are not clearly understood. Although
there is overwhelming evidence about the positive effects of social support, researchers
have worked to explain how social support influences health. Specifically, does social
support influence health directly, or does it influence health by minimizing stress or the
perceived threat of illness?
Theoretical Foundations of Social Support
Main Effect Model of Social Support
The main effect model posits that social support is an ongoing fulfillment of a
basic need. Further, according to Finney and colleagues, “A main effect refers to the
influence of a variable in the absence of conditions that, when present to some degree,
modify that effect” (Finney et al., 1984). Emotional, instrumental, appraisal, and
informational support from a loved one all translate to an environment where an
individual feels loved and cared for. Thus, according to this model, higher levels of social
support will directly lead to better health. Moreover, the absence of social support is
associated with poorer health outcomes (Thoits, 1983; House, 1981; Cohen &Wells,
1985; Cutrona, 1996; Cohen et al., 2000). Because social support from this perspective is
essential to health and a fulfillment of a basic need, it is not linked to crisis or adverse life
events.

22

Figure 1. Main Effect Model of Social Support

For example, a spouse can provide instrumental support by exercising with the
patient daily. According to the main effects model, this support is helpful and present
regardless of life stressors, such as a diabetes diagnosis. However, support from the
spouse will have a direct positive effect on diabetes self-management by increasing
physical activity levels of the patient. Higher levels of social support will positively
influence health behaviors and health outcomes. The main effects of spousal support on
diabetes self-management may vary based on various contextual factors, such as gender,
age and race.
There has been a large amount of research supporting the main effects model
(Thoits, 1982; Berkman and Syme, 1979; Eriksson and Rosenqvist, 1993; Goz et al.,
2007). In a literature review that examined the effects of social support on chronic illness
management, Gallant concluded that the majority of the findings showed that greater
levels of social support was associated with better self-management behaviors, especially
for diet and exercise (Gallant, 2003).
Social
Support
Health
Outcomes
Contextual
Variables

23
Because the effects of spousal support on health outcomes are typically viewed as
positive, researchers rarely examine the potential negative influences social support can
have (Gallant, 2003; Gallant, 2007). Further, the types of social support are rarely
distinguished when examining this model. In a disease context, the main effect model
does not take into account how support may vary based on the stage and severity of a
chronic illness. For example, individuals may perceive and require different types of
support based on the nature of their illness.
The Stress-Buffering Model of Social Support
The Stress-Buffering model maintains that social support only exerts a positive
influence when stress is present. Thus, from this perspective, rather than fulfilling a basic,
ongoing need, social support is the fulfillment of a time-limited need only when there is
crisis and hardship (Cutrona, 1996). When a stressor arises from the diagnosis or
management of a chronic illness, social support will act to buffer the stress, thereby
lessening the damage that would have occurred from the illness.

Figure 2. The Stress-Buffering Model of Social Support

In the stress-buffering model, social support moderates the relationship between stress
and health outcomes. Thus, in the presence of a stressor, higher levels of social support
Stressor

Health Outcome
Social
Support

24
are associated with more positive health outcomes. In the presence of lower levels of
stress, social support does not have an effect on health outcomes. There has been a great
deal of empirical evidence supporting the stress-buffering model. Prior studies have
found that when social support is high, increasing levels of stress and diabetes distress
were not associated with higher levels of HbA1c (Polonsky et al., 1995; Peyrot &
McMurry, 1985; Frenzel et al., 1988; Griffith, Field, & Lustman, 1990). This is because
social support buffers stress so that it will not affect health outcomes as negatively as
when support is low or not present at all.
According to Thoits, the presence of a life event, such as the diagnosis of a
chronic illness, may directly affect the level of support one receives. Support may
diminish, increase, or remain the same. That is, the diagnosis of a chronic illness may
cause a decrease in one’s social support, which may cause stress, rather than the
diagnosis of a chronic illness itself. Thus, the stress-buffering model may be problematic
because the effects of the stressor on social support may be confounded with the
interaction of the stressor with social support. One way to control for this would be to
measure support levels before and after life events. However, Thoits notes this may still
be problematic (Thoits, 1982).
In summary, the main effects model and the stress-buffering model were proposed
by researchers in an attempt to explain why social support has an effect on health
outcomes. While there is evidence for both models, further research needs to occur to
determine which model explains the most variance. An ideal test would be longitudinal in

25
order to examine support levels before and after life events, particularly after the
diagnosis of a chronic illness.
Proposed Research Model
Although both the main effects and the stress-buffering model deserve further
testing, the proposed research will only explore the association between types of spousal
support and Type 2 diabetes self-management using the framework from the main effects
model. Because the proposed research utilizes secondary data, an acceptable, stand-alone
measure of stress was not available.
The proposed main effects model maintains that one’s self-management is
influenced by social factors, including the level and type of support received from one’s
spouse. Additionally, different types of support are perceived based on various
contextual factors, each having an impact on diabetes self-management outcomes.

26
Independent Variables Moderators Dependent Variables

Figure 3. Main Effect Model of Proposed Study

Demographic factors, such as gender and race, can have an effect on one’s
perceptions about the types of support received, which can affect health outcomes. For
example, women may respond well to higher levels of instrumental support for diet from
their spouse if they are primarily responsible for preparing food for their family. If they
perceive higher levels of instrumental support for physical activity from their spouse, this
may have a positive effect on their moderate physical activity levels. Additionally,
Whites may perceive higher levels of emotional support compared to other races, which
may have a negative effect on levels of diabetes distress.
Just as the positive effects of spousal support will be examined in this model, the
negative effects of spousal support will also be examined. Types of spousal support can
Emotional
Support
Gender
Race

Unsupportive
Behaviors
Appraisal
Support
Instrumental
Support
Fat Consumption
Moderate PA
Diabetes distress
HbA1c
BMI

27
negatively or positively effect self-management outcomes. Higher levels of
encouragement to increase moderate levels of physical activity may be all it takes for a
diabetic spouse to increase their physical activity levels. On the other hand, higher levels
of criticism may lead to an increase in fat consumption, or an increase in HbA1c levels.
Rationale
The overall goal of this research, consisting of three separate studies, is to expand
on the previous literature by examining the role of spousal support on Type 2 diabetes
self-management. Specifically, this research will investigate whether certain types of
spousal support are associated with Type 2 diabetes self-management, and whether these
types are moderated by gender and race. This research will also examine whether
unsupportive behaviors are associated with Type 2 diabetes self-management, and
whether this relationship is also moderated by gender and race. This research will utilize
cross-sectional data from 305 spousal pairs, ages 30-70, who were recruited from the
Kaiser Permanente Southern California membership in the greater Los Angeles area.
The specific aims and hypotheses of these studies are:
Specific Aim 1: To assess the association between spousal support and type 2 diabetes
self-management
Hypothesis 1: Higher levels of emotional support for diet will be positively
associated with lower levels of fat consumption.
Hypothesis 2: Higher levels of emotional, instrumental, and appraisal support for
physical activity will be associated with higher levels of moderate physical
activity.

28
Hypothesis 3: Higher levels of emotional support for diet, and emotional,
instrumental and appraisal support for physical activity will be associated with
lower levels of diabetes distress.
Hypothesis 4: Higher levels of emotional support for diet and emotional,
instrumental and appraisal support for physical activity will be associated with
lower levels of HbA1c and BMI.

Specific Aim 2: To determine whether unsupportive spousal interactions are
negatively associated with Type 2 diabetes self-management
Hypothesis 5: Higher levels of unsupportive behavior for diet and physical
activity will be associated with higher levels of fat consumption, lower levels of
moderate physical activity, higher levels of diabetes distress, and higher BMI and
HbA1c values.

Specific Aim 3: To investigate whether gender and race moderate the association
between spousal support and Type 2 diabetes
Hypothesis 6: The observed effect of emotional support for diet and physical
activity on fat consumption, moderate physical activity, diabetes distress, BMI
and HbA1c will be more pronounced among men than women and among Whites
than Hispanics.
Hypothesis 7: The observed effect of instrumental and appraisal support for
physical activity on moderate physical activity, diabetes distress, BMI and HbA1c

29
will be more pronounced among women than men and among Hispanics than
Whites.
Hypothesis 8: The observed effect of unsupportive behaviors for diet and physical
activity on saturated fat consumption, moderate physical activity, diabetes
distress, BMI and HbA1c will be more pronounced among women than men and
among Hispanics than Whites.
The following work will examine three sub-studies that were conducted to
examine whether associations exist between spousal support and diabetes self-
management. To recapitulate, diabetes self-management consists of several components,
including behavioral, psychological, and biological (Glasgow & Toobert, 1988). Thus,
the goals of the three sub-studies were to explore the relationship between spousal
support variables and each self-management domain separately. The next chapter will
discuss the methods and data analyses for the three sub-studies. Chapter 3 will discuss
Sub-study 1, where the association between spousal support and the behavioral domain of
diabetes self-management, including saturated fat consumption and physical activity was
examined. Chapter 4 will discuss Sub-study 2, where the relationship between spousal
support and the psychological domain of diabetes self-management, including quality of
life, as measured by diabetes distress was explored. Chapter 5 will discuss Sub-study 3,
where the relationship between spousal support and the biological domain of diabetes
self-management, including HbA1c and BMI was investigated. Finally, Chapter 6 will
integrate the findings across studies, discuss implications for future research and
interventions, and will discuss the limitations of the study findings.

30
CHAPTER TWO: METHODS

Cross-sectional data were collected from a tailored self-management intervention
study for type 2 diabetes entitled Prevention and Control of Diabetes in Families (PCDF),
a practical randomized trial (Tunis et al., 2003; Glasgow, Magid et al., 2005). Spousal
pairs, where one spouse had Type 2 diabetes and one spouse did not, were sampled and
recruited from the diabetes registry of Kaiser Permanente Southern California (KPSC).
At the time of recruitment, all participants received care through KPSC medical centers
in the greater Los Angeles area. Additional inclusion criteria for participants included: (a)
30-70 years of age, (b) living with spouse independently (e.g., not in an institution or
nursing home), (c) the diabetic spouse having been diagnosed with diabetes for at least
one year, (d) the ability to give informed consent, (e) adequate cognition and literacy to
complete self-administered questionnaires in English, and (f) having a telephone.
Potential participants were excluded if they were diagnosed before age 30; began taking
insulin when first diagnosed with diabetes; had a heart condition or other conditions that
required intensive treatment regimes; were unable to travel to a Kaiser Permanente
facility and/or if they or their spouse were pregnant. PCDF was an innovative
intervention that aimed to improve self-management diet and physical activity behaviors
among the diabetic spouse, while reducing the risk for diabetes in the non-diabetic
spouse.
Measures were obtained from the PCDF baseline assessment. Since the goals of
the study are to assess the effects of spousal support on diabetes self-management
outcomes, this study only utilizes the responses of the diabetic spouse, with the exception

31
of the perceived health status of the non-diabetic spouse. The perceived health status of
the non-diabetic spouse is examined as a potential covariate due to existing research that
suggests that the health of the spouse may impact their ability to be supportive (Booth
and Johnson, 1994; Fisher et al., 2004).
Spousal Support
Measures
Spousal support was assessed using two scales, The Social Support and Eating
Habits Survey (13-items) and The Social Support and Exercise Survey (10-items) (Sallis
et al., 1987) (see appendix for a complete list of items used). These scales were originally
created to assess support from “friends” and “family”, therefore the wording was
replaced with “spouse/partner” for the purposes of the PCDF study. The Social Support
and Exercise scale was originally comprised of two factors, Participation and Support
(e.g., support and encouragement from friends and/or family to exercise), and Rewards
and Punishment (e.g., friends and/or family offering rewards to exercise, and
complaining or criticizing about time spent exercising) (Sallis et al., 1987). Sallis and
colleagues reported that test-retest reliability for “”Participation and Support” was 0.77,
with an internal consistency score of 0.91, while test-retest reliability for “Rewards and
Punishment” was 0.55, with an internal consistency score of 0.61. The Social Support
and Eating Habits scale was originally composed of two factors, “Encouragement” and
“Sabotage” ” (e.g., words and/or actions from friends and family that are perceived as a
discouraging gesture). Sallis and colleagues reported that test-retest reliability for

32
“Encouragement” was 0.86, with an internal consistency score of 0.87. Test-retest
reliability for “Sabotage was 0.57, with an internal consistency score of 0.83.
Exploratory factor analyses were performed on each support scale using SAS 9.1
for two reasons. First, Sallis and colleagues created their support scales based on
responses from a sample that included primarily young Caucasian women in college.
Since the PCDF sample is an older, more diverse sample, it is possible that a different
factor structure will emerge. Secondly, the PCDF research team changed the wording of
original scales in order to measure perceived spousal support, rather than support from
friends and family. Thus, it was important to determine whether the new sample and
wording changes could also result in different factor structures.
Items with a minimum factor loading of 0.40 on at least one factor of its
respective scale were retained in the subscale score. Each subscale score consisted of the
mean score of items retained based on factor loadings. Cronbach’s alphas were computed
on each subscale to determine internal consistency (Cronbach’s alphas are reported later).
The factor solution for the Social Support and Eating Habits scale consisted of
two factors: emotional support for diet, and unsupportive behavior for diet. It is important
to note that this factor structure was identical to the structure reported by Sallis and
colleagues. However, for the present research the wording of the factors was changed
from encouragement and sabotage to emotional support and unsupportive behavior. This
was done in order to align with extant literature (encouragement is typically identified as
a form of emotional support), in addition to the terminology of the current research. The
factor solution for the Social Support and Exercise scale consisted of four factors,

33
emotional support for physical activity, instrumental support for physical activity,
appraisal support for physical activity, and unsupportive behavior for physical activity.
This is inconsistent with the original scale, as participation and involvement, and rewards
and punishment were the only two scales that emerged from the original publication
(Sallis et al., 1987).
Five items were retained for Emotional Support for diet. On a 5-point scale,
participants were asked how often their spouse provided encouragement for eating
healthy. Sample questions include, how often has your spouse “discussed [your] eating
habit changes with [you]”, “or encouraged [you] not to eat unhealthy foods when
[you’ve] attempted to do so?” Based on the PCDF data, the Cronbach’s alpha internal
consistency score was 0.88. Five items were retained for Unsupportive Behavior for diet.
Sample items include, “how often has your spouse eaten high fat or high salt foods in
front of [you]”, or “ brought home foods [you’re] trying not to eat”. The internal
consistency score was 0.77. Four items were retained for Emotional Support for Physical
Activity. Sample questions included encouragement items, such as, “how often has your
spouse [given] helpful reminders to exercise?” The internal consistency score was 0.86.
Three items were retained for Instrumental Support for Physical Activity. On a 5-point
scale, participants were asked to what extent their spouses were directly involved in
healthy behaviors. Sample items included, how often has your spouse “exercised with
(you)” or “offered to exercise with you”. The internal consistency score was 0.84. Four
items were retained for appraisal support. On a 5-point scale, participants were asked to
what extent their spouses showed supportive behavior that reinforced their positive

34
behavior. Example questions included, how often has your spouse provided “rewards for
exercising”, or “helped plan activities around my exercise?” The internal consistency
score was 0.80. Finally, two items were retained for Unsupportive Spousal Behaviors for
Physical Activity. On a 5-point scale, participants were asked to what extent their spouse
exhibited unsupportive behaviors in response to them making an effort to improve their
physical activity levels. Sample questions included, how often has your spouse
“complained about the time I spend exercising”, or “criticized me or made fun of me for
exercising?” The internal consistency score was 0.54 (For a detailed list of all items, see
appendix).
Diet
The NCI Quick Food Scan - Fat Screener was utilized to assess saturated fat
intake among participants. The screener employed 17 items to estimate “percent calories
from fat” (Thompson et al., 2007) and was validated by Thompson and colleagues. Using
data from 412 participants, the authors of the screener validated the tool by correlating
the response of the screener completed by participants to two non-consecutive, 24-hour
dietary recalls completed by the same sample (Thompson et al., 2007). The estimated
correlation between actual dietary intake and the screener that was reported by Thompson
and colleagues was 0.64 and 0.58 for men and women, respectively. Sample questions
included, “Over the last month, how often did you eat eggs, fried or scrambled in
margarine, butter or oil?”, or “Over the last month, how often did you eat sausage or
bacon, regular-fat?”. Participants were able to respond to questions using an 8-point scale
ranging from “never” to “2 or more times per day”.

35
Physical Activity
Physical activity was assessed utilizing the International Physical Activity
Questionnaire (IPAQ). The IPAQ is a 7-item scale designed to assess frequency of light,
moderate and vigorous levels of physical activity. Participants were asked about time
spent being physically active during the last 7 days prior to completing the survey. When
the long and short versions of the IPAQ were tested for reliability, Craig and colleagues
reported that Spearman’s rho clustered around 0.80, which indicated reliable responses
between repeat administrations for all versions of the IPAQ. Further, they also found a
median rho of 0.30 for criterion validity when compared against the CSA accelerometer
for minutes of moderate, vigorous, walking, and sedentary behaviors per week (Craig et
al, 2003). Physical activity data derived from IPAQ can be scored categorically (light,
moderate, or vigorous activity), or continuously as minutes of activity. The proposed
study will utilize physical activity as a continuous variable. MET values will be
computed by multiplying the MET score (metabolic equivalent intensity level) of a given
activity by the minutes performed. Continuous physical activity scores will be computed
by multiplying the MET value of an activity by the number of minutes of the activity per
day. This product will be multiplied by the number of days per week participating in the
activity. Specifically, physical activity will be broken down into three categories:
walking, moderate and vigorous. Walking activity is defined as lower levels of activity
not meeting the criteria for moderate or vigorous activity. Moderate activity is defined as
any one of the following three criteria: 3 or more days of vigorous activity of at least 20
minutes per day, 5 or more days of moderate-intensity activity or walking of at least 30

36
minutes per day, or 5 or more days of any combination of walking, moderate-intensity or
vigorous-intensity activities achieving a minimum of at least 600 MET-min/week.
Vigorous activity is defined as any one of the following 2 criteria: Vigorous-intensity
activity on at least 3 days and accumulating at least 1500 MET-minutes/week, or 7 or
more days of any combination of walking, moderate-intensity or vigorous-intensity
activities achieving a minimum of at least 3000 MET-minutes/week. (Pate et al., 1995,
Scoring Protocol for IPAQ, accessed from IPAQ website).
Diabetes Distress
Diabetes distress was measured using the Diabetes Distress Scale (DDS)
(Polonsky, 2005), a 17-item scale that was created to measure diabetes-related emotional
distress. Four subscales are present in the diabetes distress scale, including 5 items
measuring emotional burden (“Feeling angry, scared, and/or depressed when I think
about living with diabetes”), 5 items measuring Physician-related distress (“Feeling that
my doctor doesn’t give me clear enough directions on how to manage my diabetes”),
Four items measuring regimen-related distress (“Feeling that I am not testing my blood
sugars frequently enough”), and 3 items measuring diabetes-related interpersonal distress
(“Feeling that friends or family don’t appreciate how difficult living with diabetes can
be”). Diabetic participants were asked to rate each item on a 5-point Likert scale, from 1
(no problem) to 5 (serious problem). According to Polonsky and colleagues, internal
consistency of the Diabetes Distress scale and the four subscales were adequate (all
reported alphas were > 0.87), and validity coefficients yielded significant associations

37
with the Center for Epidemiological Studies Depression Scale, meal planning, exercise,
and total cholesterol (Polonsky et al., 2005).
Health Status
Perceived health Status was obtained by self-report for non-diabetic participants
alone. The health status of the non-diabetic spouse was treated as a covariate. It was
measured using the CDC Healthy Days Core Module (CDC-HRQOL-4), a 4-item set of
Healthy Days core questions included in the State-based Behavioral Risk Factor
Surveillance System (BRFSS), the National Health and Nutrition Examination Survey
(NHANES), and the Medicare Health Outcome Survey (HOS). Test-retest reliability was
excellent (0.75 or higher) for Self-Reported Health and Healthy Days measures
(Andresen et al., 2001). For the first question, which is a 5-point scale that asks about
one’s general health status, higher scores indicate poorer health. The additional items ask
about one’s physical and mental health, and have dichotomous response options (0=
‘None’ and 1= ‘Number of days.’).
Barriers
Barriers to Physical Activity were measured using the Barriers to Physical
Activity Scale, a 19-item scale adapted from the Project GRAD Health Assessment
Survey (Sallis et al., 1989, Calfas et al., 1994). Barriers to healthy eating were assessed
using the Barriers to Healthy Eating Scale, an 11-item scale adapted from the pan-EU
consumer attitudinal survey (Kearney & McElhone, 1999; Kearney, Kearney & Gibney,

38
1997). Both measures utilized a 5-point scale and asked participants questions about what
kinds of obstacles get in the way when trying to eat a healthy diet or be physically active.
Participants were instructed to put a check next to the box that explained how often
certain statements were obstacles. Responses ranged from “never” to “always”. Sample
statements included, “I am usually too tired to exercise”, and “Busy lifestyle”.
Psychometrics for Barriers to Physical Activity and Barriers to Health Eating scales were
not available.
HbA1c
HbA1c, or glycolsylated hemoglobin, is the average plasma concentration of blood
glucose over a prolonged period of time. Thus, it has been widely used as a marker of
long- term glycemic control among diabetic patients (Norberg et al., 2006). Total
hemoglobin was measured photometrically. HbA1c was determined
immunoturbidimetrically. The ratio of both concentrations yielded the final percent
HbA1c result (HbA1c (%)). Diabetes patients with HbA1c levels below 7% meet the goal
of the American Diabetes Association for having good glycemic control (American
Diabetes Association, 1995). HbA1c was drawn and analyzed at the KPSC laboratory.

Body Mass Index (BMI)
A Tanita BWB-800-S digital scale was used to obtain weight for each participant in
kilograms. Height was measured in centimeters using a stadiometer. Weight and height

39
was measured three separate times and a mean weight and height was calculated. BMI
(weight (kg)/(height (m))
2
was calculated from these measurements.

Demographics
Race, gender, date of birth, and education was assessed by self-report. Race was
coded into 3 groups: White, Latino, and other (African Americans, Asian Americans,
Native Americans, and Asian/Pacific Islanders). Gender and race were measured
separately and served as moderators of the relationships between the independent and
dependent variables in the analysis (see appendix for a detailed list of all demographic
variables).

Language Usage/Acculturation
Language use/acculturation was measured in participants using one 5-point item
adapted from the Short Acculturation Scale for Hispanics. Participants were asked to
check the box next to the language participants primarily spoke at their house. Responses
included “English only, “Mostly English”, “English and another language”, “Mostly
another language”, and “Another language only” (Marin and Gamba, 1996).
SAS 9.0 was used to conduct all analyses. Because subjects participated in an
intervention that may have influenced their perceived level of spousal support, all
analyses were performed using cross-sectional data from baseline only. Additionally,
only the diabetic spouse’s responses were utilized for this study, with the exception of the
Data Analyses

40
health status of the non-diabetic spouse. Characteristics of the sample were examined by
computing descriptive statistics (mean, standard deviation, frequencies and percentages)
for all variables in the sample. Dependent variables include saturated fat intake, physical
activity level (MET-minutes/week), BMI, HbA1c, and diabetes distress. Independent
variables include spousal support variables, such as emotional support for diet, emotional
support for physical activity, instrumental support for physical activity, appraisal support
for physical activity, unsupportive behavior for diet, and unsupportive behavior for
physical activity. Additional covariates are gender, race, perceived health status of the
non-diabetic spouse, age, education, acculturation, and barriers for diet and physical
activity. Gender and race were also tested as moderators of the relationship between
social support and self-management outcomes. All continuous variables were
standardized to a mean of 0 and a standard deviation of 1 in order to produce
standardized parameter estimates.
Variable means, standard deviations, and frequency distributions were computed.
The relationship between all variables was examined using bivariate correlations to
determine the associations among independent variables, dependent variables, and
covariates. Correlations among independent variables were also examined for
collinearity. Bivariate associations between spousal support, demographic and diabetes
self-management variables (saturated fat consumption, physical activity, diabetes
distress, BMI and HbA1c) were examined in order to control for potential confounding
variables in the multiple regression analyses. With the exception of spousal support, race,
gender, age and acculturation, variables that were associated with self-management

41
outcomes at p<0.10 were retained as covariates in the regression models. Multivariate
regression model construction is further discussed below.

Main Analyses
Using Proc GLM, main effects were tested using a series of multiple regression
models that included all support variables , while controlling for gender, race, age and
acculturation, as well as those covariates determined to be significant confounders.
Constructing interaction terms that represented the product of gender and each support
variable, as well as race and each support variable, was employed to test the potential
moderation by gender and race of the relationship between support and self-management
outcomes. The moderating effects of race and gender were explored in all three studies. A
test of the moderating effect of race and gender is illustrated in the equation below:

Y=b
0
+b
1
X+b
2
Z+b
3
XZ+e (eq.1)

Where Y=self-management outcomes (fat consumption, moderate physical activity,
HbA1c, BMI and diabetes distress), X=spousal support variables, Z=gender and race, and
e=the error term. In conducting these analyses, it was hypothesized that the influence of
spousal support will vary based on race and gender, therefore it was predicted that b
3

would be significantly different from zero (Cohen and Cohen, 1975; Finney et al., 1984).
Data was stratified according to gender and/or race only when the interaction term
was significant. Gender and race main effects were retained in the model to control for

42
confounding factors when the interaction term was not significant. The level of
significance for all tests was set to alpha=0.05. Following model construction, residual
analyses were conducted to determine any influential data.
Five separate regression models were constructed:
Model 1 included fat consumption as the dependent variable. Independent
variables for model one include emotional support for diet, unsupportive behavior for
diet, race and gender. Race, gender, age and acculturation were always retained in the
model to control for confounding factors.
Model 2 included moderate physical activity as the dependent variable.
Independent variables include emotional support for physical activity, appraisal support
for physical activity, instrumental support for physical activity, unsupportive behavior for
physical activity, race, gender, acculturation and age.
Models 3-5 included diabetes distress, HbA1c, and BMI as dependent variables,
respectively. With the exception of potential covariates and interaction terms,
independent variables are consistent across models 3-5 and test the same support
variables in each model -, emotional support for diet, emotional support for physical
activity, appraisal support for physical activity, instrumental support for physical activity,
unsupportive behavior for diet, unsupportive behavior for physical activity, race, gender,
age and acculturation.

43
CHAPTER THREE: THE ASSOCIATION BETWEEN SPOUSAL SUPPORT,
SATURATED FAT CONSUMPTION AND PHYSICAL ACTIVITY LEVELS

The purpose of this study was to examine whether types of perceived spousal
support were associated with saturated fat consumption and physical activity levels, and
whether race and gender moderated these relationships.
The specific hypotheses that were tested in this study were:
Hypothesis 1: Higher levels of emotional support for diet will be positively
associated with lower levels of fat consumption.
Hypothesis 2: Higher levels of emotional support for physical activity,
instrumental support for physical activity, and appraisal support for physical
activity will be positively associated with higher levels of moderate physical
activity.
Hypothesis 3: Higher levels of unsupportive behavior for diet will be associated
with higher levels of fat consumption.
Hypothesis 4: Higher levels of unsupportive behavior for physical activity will be
associated with lower levels of moderate physical activity.
Hypothesis 5: The observed effect of emotional support for diet on fat
consumption and emotional support for physical activity on moderate physical
activity will be more pronounced among men than women and among Whites
than Hispanics.
Hypothesis 6: The observed effect of instrumental and appraisal support for diet
on fat consumption and instrumental and appraisal support for physical activity on

44
moderate physical activity will be more pronounced among women than men and
among Hispanics than Whites.
Hypothesis 7: The observed effect of unsupportive behaviors for diet on fat
consumption and unsupportive behaviors for physical activity on moderate
physical activity will be more pronounced among women than men and among
Hispanics than Whites.
Description of Sample
Results
Demographic characteristics of the study sample are displayed in Tables 1 and 2.
The sample was mostly Hispanic (42.9%) and male (64%). The mean age of the sample
was 56 years, with men being slightly older than women. The mean level of education
was 14 years. Men perceived higher levels of emotional support for diet compared to
women (p<0.0001). Further, the spouses of diabetic men reported a significantly higher
number of unhealthy days compared to the spouses of diabetic women (p=0.001).
Emotional support for diet was significantly higher for the Other Race category compared
to Whites (p=0.03). Appraisal support for physical activity was significantly higher for
Hispanics compared to Whites (p=0.05) (See Table 2). BMI was significantly higher for
Hispanics compared to those in the Other Race category (p=0.003). Finally, HbA1c was
significantly lower for Whites compared to Hispanics (p=0.018).

45

Table 1. Characteristics of the sample (by gender)
Variable
N = 335
Overall
N = 215 (64%)
Male
N= 120 (46%)
Female
Range
M (SD) M (SD) M (SD)
Age in years 55.74 (7.99) 56.22 (8.16) 54.89 (7.63) 34-69
Education (years) 14.27 (2.98) 14.23 (3.04) 14.35 (2.88) 10-30
Race*†
Other 83 (24.78) 50 (23.26) 33 (27.50)
White 100 (29.85) 61 (28.37) 39 (32.50)
Hispanic 144 (42.99) 98 (45.58) 38 (38.33)
Emotional Support for Diet 1.98 (1.0) 2.16 (0.96) 1.64 (1.01) 0 - 4
Unsupportive Diet 2.79 (0.82) 2.85 (0.73) 2.69 (0.94) 0 – 4
Emotional Support for PA 1.54 (1.14) 1.53 (1.11) 1.55 (1.19) 0 – 4
Appraisal Support PA 0.69 (0.83) 0.67 (0.82) 0.71 (0.85) 0 – 4
Instrumental Support PA 1.04 (1.14) 1.06 (1.12) 1.04 (1.18) 0 – 4
Unsupportive PA 3.74 (0.58) 3.73 (0.60) 3.75 (0.55) 0 – 4
Spouse’s No. of unhealthy
days in the past 30 days
7.15 (9.59) 8.47 (10.09) 4.74 (8.10) 0 – 30
HbA1c 7.68 (1.48) 7.69 (1.46) 7.65 (1.51) 5.2-12.3
BMI 34.34 (6.85) 33.9 (7.31) 34.84 (5.92) 19.28-
67.09
*N (%)
† Missing =2.38%

46

Table 2. Characteristics of the sample (by race)
Variable
N = 83 (25%)
Other
N = 100 (30%)
White
N= 144 (43%)
Hispanic
Range
M (SD) M (SD) M (SD)
Age in years 56.77 (7.58) 58.47 (6.51) 53.15 (8.52) 33-71
Education (years) 14.67 (3.11) 14.68 (3.11) 13.77 (2.77) 6-31
Gender*†
Male 50 (60.24) 61 (61.00) 98 (68.06)
Female 33 (39.76) 39 (39.00) 46 (31.94)
Emotional Support for Diet 2.16 (1.05) 1.74 (0.95) 2.02 (0.81) 0 - 4
Unsupportive Diet 2.76 (0.87) 2.89 (0.78) 2.75 (0.81) 0 – 4
Emotional Support for PA 1.54 (1.19) 1.41 (1.12) 1.61 (1.11) 0 – 4
Appraisal Support PA 0.70 (0.88) 0.51 (0.72) 0.80 (0.86) 0 – 4
Instrumental Support PA 0.90 (1.12) 1.06 (1.13) 1.12 (1.14) 0 – 4
Unsupportive PA 3.75 (0.58) 3.83 (0.44) 3.66 (0.67) 0 – 4
Spouse’s No. of unhealthy
days in the past 30 days
5.74 (7.89) 6.96 (9.62) 7.76 (10.15) 0 – 30
HbA1c 7.55 (1.36) 7.37 (1.41) 7.95 (1.54) 5.2-12.3
BMI 32.10 (6.12) 34.44 (6.85) 35.10 (7.05) 19.28-
67.09
*N (%)
† Missing =2.38%

47

Correlation Analyses
Emotional support for diet and unsupportive behaviors for diet were not
significantly correlated with fat consumption. Emotional, instrumental support, and
unsupportive behavior for physical activity were not significantly correlated with
moderate physical activity (r=0.07, p=0.22; r=0.04, p=0.48, and r=0.02, p=0.77,
respectively). However, appraisal support for physical activity was marginally
significantly correlated with moderate physical activity (r=0.10, p=0.07). Additionally,
emotional support for physical activity was significantly correlated with fat consumption
(r=-0.11, p=-0.05) (see Table 3).

48
Table 3. Correlations with Support, Gender, Race, and Self-Management Variables for Type 2 Diabetes Patients

Emotional Support
Diet
Unsupportive Behavior
Diet Emotional Support PA
Instrumental Support
PA Appraisal Support PA
Unsupportive Behavior
PA
Emotional Support Diet

1.00

0.21**
0.49***

0.40***

0.39*** -0.19***
Unsupportive Behavior Diet

0.20**

1.00

0.16*

0.14**

0.12*

0.11*

Emotional Support PA
0.49***

0.16*

1.00

0.70***

0.68***

-0.28***

Instrumental Support PA
0.40***

0.14*

0.70***

1.00

0.67***

-0.27***

Appraisal Support PA 0.39***
0.12*

0.68***

0.67***

1.00

-0.23***

Unsupportive Behavior PA

-0.19***

0.11*

-0.28***

-0.27***

-0.23***

1.00

Gender

-0.24***

-0.09

0.01

-0.002

0.02

0.02

Physical Activity

0.07

0.07

0.14**

0.11*

0.16**

-0.01

Fat Consumption

0.09

-0.04

-0.11*

-0.06

-0.09

-0.09

Race

-0.04

-0.01

0.02

0.07

0.06

-0.08

Diabetes Distress

-0.14**

-0.31***

-0.05

0.01

-0.03

-0.04

BMI

-0.09

-0.13*

-0.01

-0.04

-0.12*

0.03

HbA1c

-0.06

-0.05

-0.05

-0.04

0.01

-0.10

* p<.05; ** p<.01; ***p<.001

49

When correlations were stratified by gender, the data showed that among men and
women, there were no significant correlations between emotional support for diet and
unsupportive behavior for diet and fat consumption. Additionally, for both men and
women, there were no significant correlations between emotional, appraisal, instrumental
support, and unsupportive behavior for physical activity and moderate physical activity
levels. However, unsupportive behavior for physical activity was significantly correlated
with fat consumption for women (r=-0.27, p=0.005) but not among men (r=-0.01,
p=0.86) (see Tables 4 and 5).

50

Table 4. Correlation Among Support Variables, Race, and Self-Management Outcomes for Men with Type 2 Diabetes
Race
Emotional Support
Diet
Unsupportive
Behavior Diet Emotional Support PA
Instrumental Support
PA Appraisal Support PA
Unsupportive Support
PA
Fat Consumption

-0.003

-0.08

-0.07

-0.21**

-0.15*

-0.16*

-0.01

Physical Activity

-0.01

0.07

0.06

0.17*

0.15*

0.18**

-0.02

Diabetes Distress

0.17*

-0.17*

-0.25***

-0.11

-0.04

-0.08

-0.11

BMI

0.16*

-0.07

-0.17**

-0.01

-0.04

-0.15*

0.01
HbA1c

0.06

-0.08

-0.20**

-0.008

-0.02

0.02

-0.12

* p<.05; ** p<.01; ***p<.001

51

Table 5. Correlations with Support Variables, Race, and Self-Management Outcomes for Women with Type 2 Diabetes
Race
Emotional Support
Diet
Unsupportive Behavior
Diet Emotional Support PA
Instrumental Support
PA Appraisal Support PA
Unsupportive Support
PA
Fat Consumption
NCI Estimated total
fat(g)
-0.04

0.12

-0.12

0.06

0.10

0.04

-0.27*

Physical Activity
IPAQ Physical Activity
0.15

-0.05

0.04

0.08

0.03

0.14

0.02

Diabetes Distress
Mean of 17 items, range
0-5
0.08

0.01

-0.36***

0.01

0.09

0.04

0.07

BMI

0.11

-0.09

-0.05

-0.01

-0.04

-0.05

0.05

HbA1c

0.20*

-0.01

0.17^

-0.12

-0.06

-0.01

-0.06

* p<.05; ** p<.01; ***p<.001
^ Marginally significant p=.07

52
When correlations were stratified by race, the data showed that emotional support
and unsupportive behavior for diet were not significantly correlated with fat consumption
among Whites, Hispanics, or Other Races. Among Whites, Hispanics and Other Races,
there were no significant correlations between emotional, appraisal and unsupportive
behavior for physical activity and moderate physical activity levels. However, appraisal
support for physical activity was marginally significantly correlated with physical activity
levels among the Other Race group (p=0.20, p=0.07). Emotional support for physical
activity was also significantly correlated with fat consumption for those that comprised
the Other Race group (p=-0.22, p<0.05) (see Tables 6-8).

53

Table 6. Correlation Among Support Variables, Gender, and Self-Management Outcome Variables for African Americans, Asian Americans, Native Americans, and Asian Pacific Islanders
with Type 2 Diabetes
Emotional Support Diet
Unsupportive Behavior
Diet Emotional Support PA
Instrumental Support
PA Appraisal Support PA
Unsupportive Behavior
PA
Gender

-0.23*

-0.15

0.03

-0.03

0.002

0.06

Fat Consumption
NCI Estimated total fat(g)
-0.02

-0.09

-0.22*

-0.19

-0.15

-0.05

Physical Activity
IPAQ Physical Activity
0.06

0.17

0.17

0.16

0.20^

-0.18

Diabetes Distress
Mean of 17 items, range 0-
5
-0.20^

-0.51***

-0.08

-0.11

-0.10

0.11

BMI

-0.28**

-0.17

-0.004

-0.18

-0.11

0.14

HbA1c

0.01

0.06

-0.008

0.06

0.08

-0.13

* p<.05; ** p<.01; ***p<.001
^ Marginally significant p=.07

54

Table 7. Correlation Among Support Variables, Gender, and Self-Management Outcomes for Whites with Type 2 Diabetes
Emotional Support Diet
Unsupportive Behavior
Diet Emotional Support PA Instrumental Support PA Appraisal Support PA
Unsupportive Behavior
PA
Gender

-0.25**

-0.02

0.16

0.09

0.12

-0.10

Fat Consumption
NCI Estimated total fat(g)
0.09

-0.01

-0.15

-0.02

-0.18^

0.06
Physical Activity
IPAQ Physical Activity
0.07

0.09

0.17

0.09

0.14

0.17

Diabetes Distress
Mean of 17 items, range 0-
5
-0.08

-0.33**

-0.05

0.11
-0.06

-0.09

BMI

0.04

-0.24*

-0.03

0.02

-0.14

0.01

HbA1c

-0.05

0.01

-0.02

-0.06

0.03

-0.21*

p<.05; ** p<.01; ***p<.001
^ Marginally significant p=.07

55

Table 8. Correlation Among Support Variables, Gender, and Self-Management Outcomes for Hispanics with Type 2 Diabetes

Emotional Support
Diet
Unsupportive
Behavior Diet
Emotional Support
PA
Instrumental
Support PA
Appraisal Support
PA
Unsupportive
Behavior PA
Gender

-0.25**

-0.12

-0.11

-0.05

-0.02

0.04

Fat Consumption
NCI Estimated total
fat(g)
0.19*

-0.01

0.03

0.03

0.01

-0.19*

Physical Activity
IPAQ Physical
Activity
0.07

0.003

0.10

0.08

0.13

0.02

Diabetes Distress
Mean of 17 items,
range 0-5
-0.18*

-0.17*

-0.06

-0.004

-0.03

-0.06

BMI

-0.05

-0.05

0.01

-0.02

-0.12

-0.01

HbA1c

-0.13

-0.13

-0.11
-0.09

-0.11

0.01

* p<.05; ** p<.01; ***p<.001
^ Marginally significant p=.07

56
Multivariate Regression Analyses
Table 9 reports the results of multivariate regression models of spousal support as
a correlate of saturated fat consumption. Controlling for covariates (gender, race, age,
acculturation, BMI), neither emotional support for diet, nor unsupportive behavior for
diet were found to be significantly associated with fat consumption (β = 1.04, p = 0.51; β
= -1.81, p=0.33, respectively). Covariates in the model that were related to fat
consumption included gender (β = -29.05, p <0.0001), acculturation (β = -4.96, p =
0.004), and BMI (β = 0.62, p = 0.007). Specifically, females in our sample consumed less
saturated fat than males. Higher levels of acculturation were associated with lower levels
of saturated fat consumption. Further, increased BMI levels were associated with an
increase in saturated fat consumption.

57

Table 9. Associations Between Spousal Support and Saturated Fat Consumption
Dependent Variable:
Total Saturated Fat Intake (g)
Independent Variables Std. β SE p-value
Emotional Support for Diet 1.04 1.57 0.51
Unsupportive Behavior for
Diet
-1.81 1.85 0.33
Gender -29.05 3.17 <0.0001
Other 3.34 4.11 0.42
Hispanic 0.30 3.82 0.94
White Reference Reference Reference
Acculturation -4.96 1.72 0.004
Age -0.35 0.20 0.08
BMI 0.62 0.23 0.007
Note. F=11.92 r2=0.26; p<.0001 for overall model

58
Table 10 reports the results of the final multivariate regression model of spousal
support as a correlate of moderate physical activity. Higher levels of instrumental
support for physical activity and unsupportive behavior for physical activity were
marginally associated with lower levels of moderate physical activity (β = -15.94, p
=0.06; β = -117.93, p =0.06, respectively). However, controlling for covariates, emotional
support for physical activity (β = 9.69, p=0.25) and appraisal support for physical activity
(β =6.73, 0.56) were not found to be significantly associated with moderate physical
activity. Women reported participating in significantly less moderate physical activity
than men (β = -32.42, p =0.01).

59

Table 10. Associations between spousal support and light physical activity
Dependent Variable:
Light Physical Activity
Independent Variables Std. β SE p-value
Emotional Support for
Physical Activity
0.28 9.03 0.98
Instrumental Support for
Physical Activity
-8.83 9.10 0.33
Appraisal Support for
Physical Activity
38.96 19.74 0.05
Unsupportive Behavior for
Physical Activity
-2.14 11.85 0.86
Gender -32.74 14.26 0.02
Other 2.08 18.82 0.91
Hispanic 6.11 17.75 0.73
White Reference Reference Reference
Age -1.32 0.90 0.15
Acculturation 27.90 10.81 0.01
Interaction between
Acculturation and Appraisal
Support for Physical Activity
-22.02 9.53 0.02
Note. F=1.71 r2=0.06; p=0.08 for overall model

60
Exploratory tests for additional moderators revealed there was a significant
interaction between BMI and unsupportive behavior for physical activity (β = 3.52, p
=0.05). Individuals with higher BMI levels who reported receiving more criticizing and
complaining about being physically active had lower levels of moderate physical activity,
while those with higher BMI levels who reported receiving less criticizing and
complaining about being physically active had higher levels of moderate physical
activity. On the other hand, individuals with lower BMI levels who reported receiving
more criticizing and complaining about being physically active had higher levels of
moderate physical activity, while those with lower BMI levels who reported receiving
less criticizing and complaining about being physically active had lower levels of
moderate physical activity (see Figure 4).

61
Figure 4. The Interaction between Appraisal support for Physical Activity and Acculturation
0
50
100
150
200
250
Low Appraisal Support High Appraisal Support
Light Physical Activity
Low Acculturation
High Acculturation

62
Although findings between instrumental support and moderate physical activity
were encouraging, exploratory analyses were conducted to determine if types of support
would be associated with light and vigorous physical activity. Table 11 reports the results
of a multivariate regression model of spousal support as a correlate of light physical
activity. Controlling for covariates, emotional support for physical activity (β = 0.28,
p=0.98), instrumental support for physical activity (β = -8.83, p=0.33), and unsupportive
behavior for physical activity (β = -2.14, p=0.86), were not associated with light physical
activity. However, higher levels of appraisal support for physical activity (β = 38.96,
p=0.05) were significantly associated with light physical activity. Additionally, women
reported significantly less light physical activity than men (β = -32.74, p =0.02), Higher
levels of acculturation were significantly associated with higher levels of light physical
activity (β = -27.90, p =0.01). Moreover, there was a significant interaction between
acculturation and appraisal support for physical activity (β = -22.02, p =0.02). Individuals
with higher levels of acculturation and lower levels of appraisal support reported higher
levels of light physical activity, while those with higher levels of acculturation and higher
levels of appraisal reported lower levels of light physical activity. On the other hand,
individuals with lower levels of acculturation and lower levels of appraisal support
reported lower levels of light physical activity, while individuals who had lower levels of
acculturation and reported higher levels of appraisal support had higher levels of light
physical activity (See Figure 5).

63

Table 11. Associations between spousal support and moderate physical activity
Dependent Variable:
Moderate Physical Activity
Independent Variables Std. β SE p-value
Emotional Support for
Physical Activity
9.69 8.45 0.25
Instrumental Support
for Physical Activity
-15.94 8.48 0.06
Appraisal Support for
Physical Activity
6.73 11.42 0.56
Unsupportive Behavior
for Physical Activity
-117.93 61.32 0.06
Gender -32.42 13.10 0.01
Other -7.41 17.36 0.67
Hispanic 6.57 16.32 0.69
White Reference Reference Reference
Age -0.69 0.84 0.41
Acculturation 0.78 7.1 0.91
BMI -15.59 6.78 0.22
Interaction between
BMI and unsupportive
for Physical Activity
3.52 1.77 0.05
Note. F=1.97 r2=0.07; p=0.03 for overall model

64
Figure 5. Interaction between Unsupportive Behavior for Physical Activity and Body Mass Index (BMI)
0
20
40
60
80
100
120
140
160
180
Low Unsupportive
Behavior for Physical
Activity
High Unsupportive
Behavior for Physical
Activity
Moderate Physical Activity
Low BMI
High BMI

65
Table 12 reports the results of the final multivariate regression model of spousal
support as a correlate of vigorous physical activity. Controlling for covariates, emotional
support for physical activity (β = 0.19, p=0.98), instrumental support (β = -9.24, p=0.26),
appraisal support (β =10.10, 0.37), and unsupportive behavior for physical activity (β =
4.87, p=0.65) were not found to be significantly associated with vigorous physical
activity. However, increased barriers to physical activity were significantly associated
with lower levels of vigorous physical activity (β = -32.99, p=0.001).
Table 12. Associations between spousal support and vigorous physical activity
Dependent Variable:
Vigorous Physical Activity
Independent Variables Std. β SE p-value
Emotional Support for
Physical Activity
0.19 8.08 0.98
Instrumental Support
for Physical Activity
-9.24 8.21 0.26
Appraisal Support for
Physical Activity
10.10 11.18 0.37
Unsupportive for
Physical Activity
4.87 10.63 0.65
Gender -16.76 13.06 0.20
Other -3.32 16.65 0.84
Hispanic 12.09 15.85 0.45
White Reference Reference Reference
Age -1.12 0.79 0.16
Acculturation 0.68 6.91 0.92
Barriers to Physical
Activity
-32.99 9.92 0.001
Note. F=2.19 r2=0.08; p=.02 for overall model

66
This study was designed to explore the association between spousal support
variables and behavioral outcomes for self-management, such as diet and physical
activity. When controlling for gender, age, acculturation and race, emotional support for
diet and unsupportive behavior for diet was not found to be associated with saturated fat
consumption, contrary to the hypotheses. These findings may indicate that there are other
factors that may influence ones dietary behaviors. For example, emotional support for
diet and unsupportive behavior for diet were the support measures for diet available in the
data. It is possible that other types of support may have a more important influence on
saturated fat consumption. Appraisal support for diet may provide a positive feedback
loop to individuals that would reinforce healthier food choices. It is also possible that
additional variables, such as quality of relationship and perceived control over food,
combined with support are also necessary to effect dietary change (Beverly, Miller &
Wray, 2008). Additional studies that include other types of support, such as quality of
relationship, self-efficacy, and perceived control over food, in addition to other variables
that may be related to diet are warranted.
Discussion
Despite no significant associations for types of support and saturated fat
consumption, there were noteworthy findings between types of support and physical
activity levels. Instrumental support for physical activity and unsupportive behavior for
physical activity was marginally associated with moderate physical activity. Contrary to
my hypothesis, increased levels of instrumental support were marginally associated with
lower levels of moderate physical activity. These findings have been supported in the

67
literature. In a study among women diagnosed with osteoarthritis, researchers found that
higher levels of instrumental support from spouses were associated with feeling
powerless. Further, those who had increased feelings of powerlessness engaged in fewer
self-care behaviors, including exercising less (Martire et al., 2002). Thrasher and
colleagues reported that instrumental support was associated with physical activity levels
only when self-efficacy levels of their participants were high (Thrasher et al., 2004).
Thus, it appears that when a person is already functionally independent, the receipt of
instrumental support may lessen feelings of independence and control. Although
perceived independence was not measured in this study, it is an interesting moderator to
consider for future research. Future research on negative outcomes associated with
support are warranted in order to advise family-based interventions. Additionally,
interventions aimed at increasing spousal support in couples facing chronic illness should
be context specific, and tailored to the needs of the individual. Researchers and clinicians
can educate spousal couples not only about specific types of support that could be more
helpful, but effective ways to offer specific types of support. Further, these data also
indicated that increased negative actions such as complaining or criticizing a spouse for
exercising may affect ones efforts to participate in moderate physical activity, particularly
if individuals were overweight. Thus, interventions should also focus on teaching ways to
minimize negative actions and words that could impact physical activity levels.
Results showed that participants with high BMI levels who perceived high levels
of unsupportive behavior from their spouse reported lower levels of moderate physical
activity. In contrast, those with lower BMI levels who reported high levels of

68
unsupportive behavior from their spouse had higher levels of moderate physical activity.
These findings were notable, as 95% if our sample were categorized as overweight (mean
BMI = 34.24). It is possible that negative interactions, such as criticizing and
complaining about being physically active, might be more influential among overweight
and obese individuals. There are a few possible explanations that could account for these
findings. First, a negative evaluation from one’s spouse can lead to an increased amount
of psychological distress (Rook, 2001). Higher levels of psychological distress could lead
to decreased levels of physical activity. Second, it is also possible that criticism from
one’s spouse can lower one’s self-efficacy, which in turn could lower one’s physical
activity levels (Beverly et al, 2008). Further, overweight individuals may be especially
vulnerable to spousal criticism due to an already present stigma about their weight
(Vartanian & Shaprow, 2008; Puhl & Heuer, 2009). Clearly, additional research is
warranted in order to further elucidate these findings. Existing literature has shown that
the risk of developing Type 2 diabetes is associated with being overweight (Hossain et
al., 2007, Kopelman, 2000). Thus, more information about the effects of negative social
interaction, particularly among those who are overweight, could have important
implications for diabetes management research.
There were no significant associations between emotional and appraisal support
for physical activity and moderate physical activity. It is possible that encouragement and
positive affirmations may not be enough to promote moderate levels of physical activity
for this population. It is also possible that the social context – the needs of the individual,

69
the severity of the illness, the stage of the illness, etc. – can determine the type of support
solicited and provided (Sullivan & Davilla, 2010; Cutrona et al., 2000).
Results from regression analyses examining types of social support and moderate
physical activity led to an exploration of the association between types of spousal support
and light and vigorous physical activity levels. Interestingly, higher levels of appraisal
support were positively associated with higher levels of light physical activity. Also,
individuals who were less acculturated who reported receiving higher levels of appraisal
support had increased light physical activity. Although there is not a great deal of
research on race, acculturation and appraisal support, these findings are encouraging.
Previous studies have shown that individuals who are less acculturated were less likely to
be physically active than individuals who are more acculturated (Evenson, Sarmiento, &
Ayala, 2004; Masel, Rudkin, & Peek, 2006). Further, low physical activity levels among
less acculturated groups could be due to many factors, including personal and
environmental barriers, and cultural beliefs (Kumanyika, 1993; Higgins & Learn, 1999;
Albright et al., 2005; Bull et al., 2009). Our findings may indicate that for our sample of
less acculturated individuals, where collectivism is highly valued, appraisal support may
have increased light activity levels by shifting to a person-centered approach, allowing
the focus to be placed on the diabetic spouse. This data supports the idea that positive
feedback from a spouse, through words or actions, may temporarily reduce the focus on
personal and environmental barriers, and may increase some self- management behaviors,
such as walking. Further research on appraisal support as a moderator between
acculturation and physical activity are warranted, especially since minority populations

70
are disproportionately affected by negative health outcomes related to diet and physical
activity. This research will also add to the literature on social support, and the influence
of race, ethnicity and culture.
There were no significant associations between types of support and vigorous
physical activity. It is possible that other variables are important for patients with Type 2
diabetes, in order for vigorous levels of physical activity to occur. Additionally, neither
gender nor race moderated the association between types of support and diet and physical
activity. It is possible that the measures used in this study limited the ability to fully
capture the effects of gender and race differences on support. Since only two types of
support for diet (emotional, unsupportive behavior) were included, it is possible that other
types of support, such as instrumental support for diet, would have captured gender and
race differences in fat consumption, For example, due to gender roles, women are more
likely to prepare meals for the household compared to men. Additionally, the physical
activity measure used in this study did not fully explicate occupational and household
chores, and provided mainly examples of “traditional” physical activity. Previous studies
have indicated that minorities participate in higher levels of occupational related physical
activity (Crespo et al., 2001). It is also possible that there were differences in the cultural
interpretation of physical activity related questions, which could have impacted the
responses of minority participants.
This study had several limitations. First, this study utilized cross-sectional data,
which limited the ability to draw conclusions about the causal influence of spousal
support on saturated fat consumption and physical activity levels. For example, it is

71
possible that individuals who have higher levels of physical activity may seek higher
levels of support, or that initial physical activity levels of a spouse may elicit
unsupportive behavior. Also, the use of self-report data may be subject to reporting bias.
For example, this could have affected our physical activity findings, particularly if the
participants over-reported physical activity levels, a problem that has been mentioned
with our physical activity measure (Rzewnicki, Auweele & Bourdeaudhuij, 2002). There
was no assessment of marital quality, which has been found to impact perceived levels of
support (Trief et al., 2004; Trief et al., 2006). For example, poor marital quality often
includes lack of communication. Lack of communication would not only affect support
provided, but also the support perceived (Cutrona, 1996). The participants who have
agreed to participate in the study may have more supportive relationships, thus the results
may not be generalizable to the full population of married couples where one partner has
Type 2 diabetes. Other limitations included the use of support measures. The use of only
emotional and unsupportive behavior for diet may have impacted the results. Further, it is
important to note that our use of a cross sectional design does not fully capture the
concept that support can be helpful or harmful to an individual depending on several
factors, including the stage and severity of one’s illness. A longitudinal design that
assesses various time points and exposures to different stressors among individuals with
Type 2 diabetes should be utilized to further clarify the positive or negative effects of
spousal support on Type 2 diabetes self-management.
Overall, the findings from this study were encouraging and highlight the
complexities of social support research. This research demonstrates that measuring types

72
of social support lends a valuable perspective in determining the mechanisms that explain
how support effects health outcomes. Prior to the development of interventions, the
findings indicate that more work should be done that examines the positive and negative
impact of the types of social support, rather than a composite measure of support, on the
health behaviors of individuals with Type 2 diabetes. Further, particular attention should
be focused on the impact of gender, race and acculturation on types of social support.
73
CHAPTER FOUR: THE ASSOCIATION BETWEEN SPOUSAL SUPPORT AND
DIABETES DISTRESS

The purpose of this study was a cross-sectional study designed to test the association
between types of spousal support and diabetes distress. This examination also included
whether the effects of spousal support were conditional based on race and gender.

Specific Hypotheses for Study 2 were:

Hypothesis 1: Higher levels of emotional support for diet will be will be
associated with lower levels of diabetes distress.
Hypothesis 2: Higher levels of and emotional, instrumental and appraisal support
for physical activity will be will be associated with lower levels of diabetes
distress.
Hypothesis 3: Higher levels of unsupportive behavior for diet will be associated
with higher levels of diabetes distress.
Hypothesis 4: Higher levels of unsupportive behavior for physical activity will be
associated with higher levels of diabetes distress.
Hypothesis 5: The observed effect of emotional support for diet and emotional
support for physical activity on diabetes distress will be more pronounced among
men than women and among Whites than Hispanics.

74
Hypothesis 6: The observed effect of instrumental and appraisal support for
physical activity on diabetes distress will be more pronounced among women
than men and among Hispanics than Whites.
Hypothesis 7: The observed effect of unsupportive behavior for diet and
unsupportive behavior for physical activity on diabetes distress will be more
pronounced among women than men and among Hispanics than Whites.

Results
Multivariate Regression Analyses

Table 13 reports the results of multivariate regression models of spousal support
for diet as a correlate of diabetes distress. Controlling for covariates, emotional support
for diet and unsupportive behavior for diet were not found to be significantly associated
with diabetes distress (β = -0.02, p = 0.64; β = -0.09, p=0.22, respectively). Covariates in
the model that were related to diabetes distress included gender (β = -0.34, p =0.000),
being Hispanic (β = 0.22, p =0.06), and BMI (β = 0.02, p = 0.01). Specifically, Hispanics
in our sample reported having moderately significantly higher levels of diabetes distress
compared to Whites. Females in our sample reported having higher levels of diabetes
distress than males. Higher BMI levels were also associated with higher levels of diabetes
distress.

75
Table 13. Associations between spousal support for diet and diabetes distress
Dependent Variable:
Diabetes Distress
Independent Variables Std. β SE p-value
Emotional Support for
Diet
-0.02 0.05 0.64
Unsupportive Behavior
for Diet
-0.09 0.08 0.22
Gender -0.34 0.10 0.000
Other 0.08 0.13 0.51
Hispanic 0.22 0.12 0.06
White Reference Reference Reference
Acculturation 0.03 0.05 0.62
Age -0.009 0.006 0.17
BMI 0.02 0.007 .01
Health of Spouse 0.03 0.02 0.08
Interaction between
Health of Spouse and
Unsupportive Behavior
for Diet
-0.01 0.006 0.04
Barriers for Diet 0.55 0.08 <0.0001
Note. F=13.51 r2=0.35; p <0.0001 for overall model

Exploratory tests for additional moderators revealed that there was also a
significant interaction between the health of the non-diabetic spouse and unsupportive
behavior for diet (β = 0.55, p <0.0001). Participants who reported having a spouse who
was more unsupportive reported higher levels of diabetes distress. However, diabetes
distress levels were much higher when the health of the non-diabetic spouse was good,
compared to spouses with poorer health. On the other hand, participants who reported
having a spouse who was more supportive had lower levels of diabetes distress,
regardless of the health of the non-diabetic spouse. However, diabetes distress levels

76
were lower when the health of the spouse was good, compared to when the spouse
reported having poorer health (see Figure 6). Interaction analyses were performed for
support variables to explore race and gender differences, however results showed no
interaction effects.

77

Figure 6. The Interaction Between Unsupportive Behavior for Diet and Health of Spouse
-1
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
Low Unsupportive
Behavior for Diet
High Unsupportive
Behavior for Diet
Diabetes Distress
Low Health of Spouse
High Health of Spouse

78
Tables 14 and 15 report the results of the final multivariate regression model of
spousal support for physical activity as a correlate of diabetes distress. Interaction
analyses were performed for emotional support, appraisal support, instrumental support
and unsupportive behavior to explore gender differences in these associations. Although
no significant interactions were found when the interaction between emotional support,
appraisal support, and unsupportive behavior and gender were added to the model, results
showed a marginally significant interaction between gender and instrumental support for
physical activity (p=0.06; see Figure 7). Thus, stratified multilevel model regression
analyses were performed. Results showed that increased barriers to physical activity
were associated with diabetes distress in men (β = 0.58, p < 0.001; See Table 14), while
high levels of instrumental support (β = 0.27 p = 0.03), being a race in the other group (β
= 0.62, p = 0.01), being Hispanic (β = 0.70, p = 0.006), and increased barriers to physical
activity (β = 0.74, p <0.0001), were all related to high levels of diabetes distress in
women (see Table 15). Interaction analyses were performed for support variables to
explore race differences, however results showed no interaction effects.

79

Figure 7. The Interaction Between Instrumental Support for Physical Activity and Gender
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
Low Instrumental Support for
Physical Activity
High Instrumental Support for
Physical Activity
Diabetes Distress
Men
Women

80

Table 14. Associations between spousal support for physical activity and diabetes distress for men
Dependent Variable:
Diabetes Distress
Independent Variables Std. β SE p-value
Emotional Support for
Physical Activity
-0.037 0.69 0.59
Appraisal Support for
Physical Activity
-0.06 0.09 0.52
Instrumental Support for
Physical Activity
0.02 0.07 0.74
Unsupportive Behavior
for Diet
-0.13 0.09 0.13
Other 0.065 0.15 0.66
Hispanic 0.20 0.13 0.14
White Reference Reference Reference
Acculturation 0.020 0.06 0.74
Age -0.010 0.007 0.13
BMI 0.009 0.008 0.23
Barriers to Physical
Activity
0.58 0.09 <0.0001
Note. F= 8.65 r2=0.33; p<0.0001 for overall model

81

Table 15. Associations between spousal support for physical activity and diabetes distress for women
Dependent Variable:
Diabetes Distress
Independent Variables Std. β SE p-value
Emotional Support for
Physical Activity
-0.09 0.13 0.46
Appraisal Support for
Physical Activity
0.02 0.17 0.90
Instrumental Support for
Physical Activity
0.27 0.12 0.03
Unsupportive Behavior
for Physical Activity
0.23 0.18 0.22
Other 0.62 0.24 0.01
Hispanic 0.70 0.25 0.006
White Reference Reference Reference
Acculturation -0.09 0.10 0.40
Age 0.008 0.01 0.50
BMI 0.03 0.02 0.09
Barriers to Physical
Activity
0.74 0.15 <0.0001
Note. F= 4.72 r2=0.35; p<0.0001 for overall model

82
An exploration of the association between spousal support and diabetes distress
revealed that types of support are important to consider when examining the
psychological health of individuals with Type 2 diabetes. Results indicated that emotional
support for diet was not significantly associated with diabetes distress, contrary to my
hypothesis. Previous studies have indicated that emotional support has been associated
with lower levels of stress and depression (Cohen, 1984). However, it is important to note
that among these studies, many used a composite scale of social support and did not
measure types of support. Measures of stress also varied from study to study, making it
difficult to draw adequate conclusions. Secondly, our measure of spousal support for diet
only included two types of support - emotional support for diet and unsupportive
behavior for diet. It is possible that other types of support for diet are related to diabetes
distress, and that for our sample, other types of support, such as instrumental support for
diet (preparing food and shopping for healthier food), were more important to decrease
levels of stress related to diabetes. Our findings may also reflect that the type of support
that is perceived, although intended to be helpful, may not be the type of support that is
needed to decrease levels of stress. Previous literature has indicated that support may
only be helpful if it is needed by the recipient at the right place and at the right time
(Maisel & Gable, 2009; Rafaeli & Gleason, 2009; Cutrona, 2000).
Discussion
Results showed partial support for the association between unsupportive behavior
and diabetes distress. Although there was not a statistically significant independent
association between unsupportive behavior for diet and diabetes stress, there was a

83
significant interaction between unsupportive behavior for diet and the health of the non-
diabetic spouse. Compared to lower levels of unsupportive behavior for diet, higher
levels of unsupportive behavior for diet were associated with high levels of diabetes
distress. However, levels of diabetes distress were higher when the non-diabetic spouse
had better health. On the other hand, when the diabetic spouse perceived lower levels of
unsupportive behavior, the diabetic spouse reported lower levels of diabetes distress
when their spouse was in good health. In contrast, levels of diabetes distress were higher
when poorer health was reported. There are several reasons that could account for these
findings. First, it is possible that when the spouse in poorer health, the diabetic spouse
may be less likely to expect higher levels of support, and also more likely to minimize
negative social interactions initiated by the spouse, compared to if the spouse had better
health. Second, having a spouse with a chronic illness may incite a great deal of stress
(Ell, 1996; Mann, 1998; Revenson, 2003), and pressure to take on more responsibilities
in the marriage, particularly for the healthy spouse. The spouse with a chronic illness may
also be more likely to rely on the healthy spouse, and may rely more heavily on their
partner’s actions and words as an aid to appraise their illness. When the healthy spouse
demonstrates unsupportive behavior, the patient may be more likely to appraise their
illness as stressful and burdensome, causing higher levels of diabetes distress.
Conversely, a healthy spouse may also minimize the severity of an illness, and expect
their unhealthy spouse to do more to manage their illness, such as eat healthier or
exercise more (Berg & Upchurch, 2007; Heijmans, deRidder, and Bensing, 1999). Our
findings also indicate that the health of the spouse is important to consider when

84
researching the impact on spousal support in individuals with chronic illnesses. The
health of the spouse can affect the support provided and perceived (Revenson &
Majerovitz, 1991; Berg & Upchurch, 2007) Thus, although there have been many studies
that have concluded spousal support is related to positive health outcomes, further
research on the effects of unsupportive behavior, in addition to the impact of the health of
the spouse on self- management is warranted. This research is needed to guide the design
of family-based interventions intended to improve self-management behaviors and
minimize the negative impacts of coping with a chronic illness.
Although spousal support for physical activity and diabetes distress were not
associated for men, findings suggested that for women, higher levels of instrumental
support were associated with higher levels of diabetes distress. These findings add to
existing literature that states not all types of support are helpful to individuals, even if it is
well intended (Cutrona, 1992; Franks & Stevens, 1996, Martire et al., 2002, Cutrona,
2000). It is possible that for women, exercising with a spouse, or having a spouse offer to
exercise might decrease one’s sense of control, leading to more stress. This can occur
especially if she is already a functionally independent person (Martire et al., 2002). The
work of Trief and colleagues has concluded that compared to men, women may be less
comfortable being helped by their spouses, due to traditionally being the nurtures of their
households (Trief et al., 2001). However, other studies have indicated that women are
more likely to seek support from others, such as friends, compared to men (Edwards,
Nazroo, & Brown, 1998). Thus, it is also possible that women may benefit from
instrumental support for physical activity from other sources rather than spouses. It must

85
also be noted that we did not measure marital quality or marital satisfaction. Previous
studies have indicated that low marital satisfaction is associated with higher levels of
distress, and is an indication of poorer adjustment to chronic illness (Revenson &
Majerovitz, 1991). Prior studies have also indicated that for women, poor marital quality
has a greater effect on marital outcomes compared to men. Thus, for women, if a marital
relationship is poor, exercising with one’s spouse may lead to higher levels of diabetes
distress. Clearly, further research on the effects of instrumental spousal support on
diabetes distress is needed. What is clear is that not only does the support have to be
utilized at the right place and the right time, the provider of the support is also important.
Further, one’s relationship with the provider is also an important element that may effect
the perception of support. Knowledge of gender differences on spousal appraisal, support
and coping styles will help provide insight on how to tailor interventions based on
gender.
Several limitations of this study should be noted. First, this study utilized cross-
sectional data, which limited the ability to draw conclusions about the causal influence of
spousal support on levels of diabetes distress. For example, it is possible that individuals
who have higher levels of diabetes distress may be more withdrawn and utilize less
support from their spouses. Second, the use of self-report data may be subject to reporting
bias. Participants may have been less likely to report poorer self-management behaviors
and higher levels of emotional distress. Third, the measures of support were designed
specifically to assess support for diet or physical activity. This distinction may have
minimized other support (for example, support for household chores or medication

86
adherence) and thus reduced the ability to detect associations. Fourth, it has been
documented that the perception of support is influenced by many factors, including the
stage and severity of illness. This study did not measure items such as years since
diabetes diagnosis, or medication regimen – items that would have provided some
indication of the stage and severity of illness. For example, Polonsky and colleagues
found that patients who were insulin users had higher levels of diabetes distress
(Polonsky et al., 2005). Fifth, because it is more challenging to recruit two members
from the family compared to one, this study could have represented couples that were
healthier than typical diabetic couples, or who were more likely to seek help from the
medical community.
Day to day management of Type 2 diabetes can be overwhelming and stressful.
This study aimed to assess whether or not support from one’s spouse helped to minimize
emotional distress related to Type 2 diabetes. Findings from this study supported and
expanded the literature on the effects of spousal support on diabetes distress. This
research highlights the importance of examining the impact of negative interactions and
spousal health on the management of Type 2 diabetes. Future studies should include a
longitudinal assessment of couples coping with type 2 diabetes in order to further
understand factors such as marital quality, health of the support provider, unsupportive
behavior, and the severity of illness on the perception and providance of support.

87
CHAPTER FIVE: THE ASSOCIATION BETWEEN SPOUSAL SUPPORT, BMI AND
HBA1C

Study 3 will examine the association between spousal support and biological
indicators of type 2 diabetes self –management, including HbA1c and BMI. This
examination will also include whether the effects of spousal support vary based on race
and gender.
Specific hypotheses that will be tested in Study 3 are:
Hypothesis 1: Higher levels of emotional support for diet will be associated with
lower levels of HbA1c, and lower BMI values.
Hypothesis 2: Higher levels of emotional, instrumental and appraisal support for
physical activity will be associated with lower levels of HbA1c, and lower BMI
values.
Hypothesis 3: Higher levels of unsupportive behaviors for diet will be associated
with higher BMI and HbA1c values.
Hypothesis 4: Higher levels of unsupportive behaviors for physical activity will
be associated with higher BMI and HbA1c values.
Hypothesis 5: The observed effect of emotional support for diet and emotional
support for physical activity on HbA1c and BMI will be more pronounced among
men than women and among Whites than Hispanics.

88
Hypothesis 6: The observed effect of instrumental and appraisal support for
physical activity on HbA1c and BMI will be more pronounced among women
than men and among Hispanics than Whites.
Hypothesis 7: The observed effect of unsupportive behavior for diet and
unsupportive behavior for physical activity on HbA1c and BMI will be more
pronounced among women than men and among Hispanics than Whites.
Multivariate Regression Analyses
Results
Tables 16, 17, and 18 report the results of the final multivariate regression model
of spousal support for diet as a correlate of BMI. Interaction analyses were performed for
emotional support for diet and unsupportive behavior for diet to explore differences in
race and gender among these associations. No significant interactions were found when
the interaction between emotional support and unsupportive behavior for diet and gender
were added to the model, in addition to unsupportive behavior for diet and race.
However, results showed a significant interaction between race and emotional support for
diet (p=0.04; see Figure 8). Thus, multilevel model regression analyses were conducted
where the model was stratified by race. Table 16 reports the results of a multivariate
regression model of spousal support for diet as a correlate of BMI among races that
comprise the other group. Controlling for covariates, emotional support for diet and
unsupportive behavior for diet were not found to be significantly associated with BMI (β
= -0.94, p = 0.12; β = -0.11, p=0.89, respectively). Covariates in the model that were
significantly associated with BMI included acculturation (β = -1.62, p =0.007) and

89
education (β = -0.63, p =0.02). Specifically, participants who were less acculturated had
higher BMI levels. Further, participants who had lower levels of education had higher
BMI levels.

90
Figure 8. The Interaction Between Race and Emotional Support for Diet
28
29
30
31
32
33
34
35
36
1.2 2 2.8
Emotional Support for Diet
Other
Hispanic
White

91

Table 16. Associations between spousal support for diet and body mass index among African Americans,
Asian Americans and Native Americans
Dependent Variable:
BMI
Independent Variables Std. β SE p-value
Emotional Support for
Diet
-0.94 0.61 0.12
Unsupportive Behavior
for Diet
-0.11 0.79 0.89
Gender 1.29 1.37 0.35
Acculturation -1.62 0.58 0.007
Age -0.05 0.08 0.52
Diabetes Distress 1.41 0.88 .11
Education -0.63 0.26 0.02
Note. F=5.84 r2=0.37; p <0.0001 for overall model

92

Table 17 shows the results of a multivariate regression model of spousal support
for diet as a correlate of BMI among Hispanics. Controlling for covariates, emotional
support for diet and unsupportive behavior for diet were both not found to be
significantly associated with BMI (β = 0.38, p = 0.96; β = 0.02, p=0.98, respectively).
Among Hispanics, younger participants had marginally significantly higher BMI levels
compared to older participants (β = -0.14, p = 0.07).

93

Table 17. Associations between spousal support for diet and body mass index among Hispanics
Dependent Variable:
BMI
Independent Variables Std. β SE p-value
Emotional Support for
Diet
0.38 0.70 0.96
Unsupportive Behavior
for Diet
0.02 0.83 0.98
Gender 0.24 1.48 0.87
Acculturation -0.49 0.70 0.48
Age -0.14 0.08 0.07
Diabetes Distress 1.20 0.69 0.08
Education 0.17 0.23 0.44
Note. F=1.43 r2=0.08; p=0.20 for overall model

Table 18 shows the results of a multivariate regression model of spousal support
for diet as a correlate of BMI among Whites. Controlling for covariates, emotional
support for diet and unsupportive behavior for diet were both not found to be
significantly associated with BMI (β = 1.22, p = 0.65; β = -1.46, p=0.79, respectively).
However, among Whites, participants who were less acculturated had significantly higher
BMI levels compared to participants who were more acculturated (β = -4.46, p=0.008).
Younger participants also had significantly higher BMI levels than older participants (β =
-0.39, p<0.0001). Higher levels of diabetes distress were significantly associated with
higher BMI levels (β = 1.96, p=0.02). Finally, lower levels of education were
significantly associated with higher BMI levels (β = -0.47, p=0.02).

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Table 18. Associations between spousal support for diet and body mass index among Whites
Dependent Variable:
BMI
Independent Variables Std. β SE p-value
Emotional Support for
Diet
1.22 0.65 0.07
Unsupportive Behavior
for Diet
-1.46 0.83 0.08
Gender -1.44 1.29 0.27
Acculturation -4.46 1.65 0.008
Age -0.39 0.09 <0.0001
Diabetes Distress 1.96 0.79 0.02
Education -0.47 0.19 0.02
Note. F=6.91 r2=0.36; p <0.0001 for overall model

Table 19 reports the results of a multivariate regression model of spousal support
for physical activity as a correlate of BMI. Controlling for covariates, instrumental
support for physical activity, and unsupportive behavior for physical activity were not
found to be significantly associated with BMI (β = 0.10, p = 0.85; β = 0.48, p=0.48,
respectively). However, higher levels of emotional support for physical activity were
found to be associated with higher BMI levels (β = 1.10, p = 0.03), and lower levels of
appraisal support for physical activity were found to be associated with higher BMI
levels (β = -1.57, p = 0.02). Several covariates were associated with BMI, including
acculturation, age, diabetes distress, barriers to physical activity, and education.
Specifically, participants who were younger and less acculturated had higher BMI levels
(β = -0.14, p = 0.006; β = -1.20, p = 0.006, respectively) compared to those who were

95
older and more acculturated. Higher levels of diabetes distress were also associated with
higher BMI levels (β = 0.00, p = 0.05). Increased barriers to physical activity were
associated with higher BMI levels (β = 2.42, p < 0.0001). Lastly, less education was
associated with higher BMI levels (β = -0.26, p = 0.04).

Table 19. Associations between spousal support for physical activity and body mass index
Dependent Variable:
BMI
Independent Variables Std. β SE p-value
Emotional Support for
Physical Activity
1.10 0.51 0.03
Appraisal Support for
Physical Activity
-1.57 0.70 0.02
Instrumental Support
for Physical Activity
0.10 0.52 0.85
Unsupportive Behavior
for Physical Activity
0.48 0.68 0.48
Gender -0.72 0.83 0.39
Other -0.90 1.06 0.40
Hispanic 1.12 1.03 0.28
White Reference Reference Reference
Acculturation -1.20 0.44 0.006
Age -0.14 0.05 0.006
Diabetes Distress 0.99 0.50 0.05
Barriers to Physical
Activity
2.42 0.71 < 0.0001
Education -0.26 0.13 0.04
Note. F=6.62 r2=0.23; p <0.0001 for overall model

Tables 20 and 21 report the results of the final multivariate regression model of
spousal support for diet as a correlate of HbA1c. Interaction analyses were performed for
emotional support for diet and unsupportive behavior for diet to explore differences in

96
race and gender among these associations. Although no significant interactions were
found for race when the interaction between emotional support and unsupportive
behavior for diet and race were added to the model, results showed a significant
interaction between gender and unsupportive behavior for diet (p=0.001; see Figure 9).
Controlling for covariates, higher levels of unsupportive behavior for diet was
significantly associated with higher HbA1c levels among women (β = 0.47, p = 0.01).
Further, Hispanic women had significantly higher HbA1c levels compared to Whites ((β
= 1.07, p = 0.01; See Table 20). Among men, lower levels of unsupportive behavior for
diet were associated with higher HbA1c levels (β = -0.29, p = 0.04). Younger men had
higher HbA1c levels compared to older men (β = -0.03, p = 0.01). In addition, higher
barriers to diet among men were significantly associated with higher HbA1c levels (β =
0.37, p = 0.03; See Table 21).

97
97

Figure 9. The Interaction Between Gender and Unsupportive Behavior for Diet
5.8
5.9
6
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
Low Unsupportive Behavior for
Diet
High Unsupportive Behavior for
Diet
HBA1c
Men
Women

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Table 20. Associations between spousal support for diet and HbA1c among women
Dependent Variable:
HbA1c
Independent Variables Std. β SE p-value
Emotional Support for
Diet
-0.09 0.16 0.58
Unsupportive Behavior
for Diet
0.47 0.18 0.01
Other 0.38 0.40 0.34
Hispanic 1.07 0.41 0.01
White Reference Reference Reference
Acculturation -0.19 0.17 0.27
Age -0.006 0.02 0.77
Barriers to Diet 0.51 0.29 0.08
Note. F=2.13 r2=0.14; p=0.05 for overall model

Table 21. Associations between spousal support for diet and HbA1c among men
Dependent Variable:
HbA1c
Independent Variables Std. β SE p-value
Emotional Support for
Diet
-0.05 0.11 0.61
Unsupportive Behavior
for Diet
-0.29 0.14 0.04
Other 0.25 0.28 0.37
Hispanic 0.29 0.25 0.24
White Reference Reference Reference
Acculturation 0.01 0.12 0.91
Age -0.03 0.01 0.01
Barriers to Diet 0.37 0.17 0.03
Note. F=3.69 r2=0.12; p=0.0009 for overall model

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Table 22 displays the results of the final multivariate regression model of spousal
support for physical activity as a correlate of HbA1c. Interaction analyses were
performed for emotional, appraisal, instrumental support, and unsupportive behavior for
physical activity with gender and race. There were no significant interactions found for
race and gender and the support variables for physical activity. Controlling for covariates,
results showed that there was not a significant association between emotional, appraisal
and instrumental support for physical activity and HbA1c (β = -0.08, p = 0.51; β = 0.02, p
= 0.87; β = -0.05, p = 0.66, respectively). However, lower levels of unsupportive
behavior for physical activity were marginally associated with higher HbA1c levels (β = -
0.28, p = 0.07). Younger participants had marginally significantly higher HbA1c levels
compared to older participants (β = -0.02, p = 0.06). Additionally, higher levels of
diabetes distress were significantly associated with higher HbA1c levels (β = 0.21, p =
0.04).

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Table 22. Associations between spousal support for physical activity and HbA1c
Dependent Variable:
HbA1c
Independent Variables Std. β SE p-value
Emotional Support for
Physical Activity
-0.08 0.12 0.51
Appraisal Support for
Physical Activity
0.02 0.16 0.87
Instrumental Support
for Physical Activity
-0.05 0.12 0.66
Unsupportive Behavior
for Physical Activity
-0.28 0.15 0.07
Gender -0.14 0.19 0.44
Other 0.08 0.24 0.75
Hispanic 0.29 0.23 0.21
White Reference Reference Reference
Acculturation -0.05 0.10 0.60
Age -0.02 0.01 0.06
Diabetes Distress 0.21 0.10 0.04
Note. F=2.05 r2=0.07; p=0.02 for overall model

This study aimed to assess the effects of spousal support on two biological
indicators of Type 2 diabetes self-management, BMI and HbA1c. Research on the
strength of these associations among spousal support and BMI and HbA1c has been
limited. Findings on the association between spousal support for diet and BMI suggest
the need for further research on race and cultural differences in the perception and
providance of support. Although there were no independent associations between
emotional support for diet and unsupportive behavior for diet and BMI, there was a
Discussion

101
significant interaction between emotional support for diet and race. Further, while there
was no significant association between emotional support for diet and BMI for the races
in the other category and Hispanics, there was a marginally significant association for
whites between emotional support for diet and BMI.
Despite the dearth of research in this area, extant literature has indicated that there
are underlying differences in the way various cultural groups view emotional support,
and that cultural groups may evaluate and respond differently to emotional support
(Wellisch et al., 1999; Burleson et al., 2006; Burleson & Mortenson, 2003; Burleson,
2003; Burleson & Hanasono, 2010). Consistent with the literature, results indicated that
among whites, higher levels of emotional support for diet were associated with higher
levels of BMI. Although findings are not consistent with the earlier hypothesis that the
association between emotional support and BMI would be more pronounced among
Hispanics compared to whites, they are notable. Previous literature suggests that
European Americans may have a greater preference for emotional support compared to
other cultures (Burleson, 2003). Although this was not exactly the case in our data, it may
help to explain why there were no associations between emotional support for diet and
BMI among the other groups. Coming from a more person-centered, individualistic
culture may influence one’s assessment and perceptions of support. Additional research
should occur to provide insight into how culture influences support processes, in addition
to the role of acculturation in this process.
Similar to emotional support being associated with higher levels of BMI in
Whites, data also revealed an association between emotional support for physical activity

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and BMI. Among participants, higher levels of emotional support for physical activity
were associated with higher levels of BMI. Although previous research has indicated that
emotional support is important in relationships (Cutrona, 2000; Gottlieb & Wagner,
1991), the findings nevertheless suggest that even though the provider is well intended
when providing support, it may not be appropriate for the recipient. The work of Cutrona
and colleagues acknowledges that support is helpful only under various conditions,
including the context in which support is provided, the nature of the relationship, and the
controllability of the stressor among others (Cutrona, 2000; Cutrona & Russel, 1990;
Cutrona & Suhr, 1992). Although previous work of Cutrona and colleagues have found
emotional support to be helpful regardless of the controllability of the stressor (Cutrona,
2000), my argument contends that for our sample, emotional support was not helpful due
the perceived lack of control over diet and exercise behaviors. Thus, it is possible that
higher levels of emotional support could lead to lower levels of physical activity, and less
healthy eating habits, leading to higher levels of BMI. For example, if a mother has child
care responsibilities, or does not feel that she has time in her schedule to exercise, no
matter how often her spouse provides helpful reminders to exercise or talk about how
often he likes to exercise, her feelings of control over her ability to exercise are not going
to increase. In fact, her levels of distress may actually increase due to feeling that her
spouse does not understand her perceived barriers.
Higher levels of appraisal support for physical activity was found to be associated
with lower BMI levels. For our sample, a spouse who provided positive affirmations
about exercise, either through actions or words, had a more positive impact on BMI

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levels than emotional support for physical activity. Appraisal support provided a positive
feedback loop to the individual that in turn helped to minimize the “threat” or barriers to
exercise (Cohen & McKay, 1984). Thus, the data suggests that for our sample,
reassurance and rewards provided by the spouse was more effective than encouragement
in lowering BMI levels. Additional research on the effects of emotional and appraisal
support on diet, exercise, and BMI are needed. Although there is quite a bit of research
on the positive effects of emotional support, research is needed to determine the factors
associated with negative outcomes associated with emotional support. These findings will
help to further tailor interventions for spouses coping with chronic illnesses such as Type
2 diabetes.
Although there were no significant associations found between emotional support
for diet and physical activity and HbA1c, results indicated significant associations
between unsupportive behavior for diet and physical activity and HbA1c. Findings
showed that gender moderated the relationship between unsupportive behavior for diet
and HbA1c, such that for women, lower levels of unsupportive behavior for diet were
significantly associated with higher levels of HbA1c, and for men, higher levels of
unsupportive behavior for diet were significantly associated with higher levels of HbA1c.
The findings provide support for existing literature, which shows that women and men
may have different perceptions of support, largely influenced by gender roles and cultural
norms (Schwarzer and Gutierrez-Dona, 2005; Xu & Burleson, 2001; Burleson, 2003).
Although there are several variables that have been shown to affect HbA1c levels, diet
has been documented as a contributing factor (Gannon & Nuttall, 2004). Traditional

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social roles dictate that women are largely responsible for the preparation of meals. Thus,
high levels of unsupportive behavior for diet from a female spouse – bringing home
unhealthy foods and offering unhealthy foods- may be more detrimental to men, as men
may be more dependant on their wives for the preparation of food. In contrast, because a
woman is the primary preparer of meals, she may be less dependent on her husband for
the preparation of food. Although there is not a great deal of research on types of spousal
support and HbA1c, there is a large amount of literature on marital quality and HbA1c,
many with mixed results. For example, Olson and colleagues found that relationship
functioning was associated with HbA1c levels (Olson et al., 2010). On the other hand,
Peyrot, McMurry and Kruger found that being married was associated with better
glycemic control (Peyrot, McMurray, & Kruger, 1999). There is also a growing amount
of research that focuses on the “marital support gap”, where women are thought to
provide higher levels of support than men, and of better quality (Xu & Burleson, 2001).
Despite not measuring marital quality in this study, it is possible that marital quality is
also related to HbA1c levels for this data set. Perhaps women also have higher HbA1c
levels due to low levels of marital satisfaction. It is also possible that participants in this
study may have been depressed, which has also been found to be associated with HbA1c
levels (Musselman et al., 2003; Harris, 2003). Our findings that higher levels of
unsupportive behavior for physical activity are marginally associated with higher HbA1c
values may also indicate that increased amounts of criticizing and complaining about a
spouse not being physically active is a symptom of poor marital quality (i.e. poor
communication), and high levels of depression. Findings emphasize the need for further

105
research on types of spousal support and its impact on HbA1c levels, in addition to
potential variables that may mediate or moderate the relationship between the two, such
as gender, marital quality and depression. The effects of unsupportive behavior on
HbA1c levels also deserve further attention, as the focus of most social support literature
is primarily on positive social interactions.
Despite the study findings, several limitations need to be considered. First, this
study utilized cross-sectional data, which limited the ability to draw conclusions about
the causal influence of spousal support on BMI and HbA1c levels. For example, it is
quite plausible that individuals who are overweight may be more or less likely to see help
from their spouse. Second, the use of self-report data may be subject to reporting bias,
thereby affecting the validity of the study findings. For example, participants may have
been less likely to report poorer self-management behaviors and poorer support behaviors
from their spouse. Third, measures of support were designed specifically to assess
support for diet or physical activity. This distinction may have minimized other support
(for example, support for household chores or medication adherence) and thus reduced
the ability to detect associations. Fourth, because it is more challenging to recruit two
members from the family compared to one, this study could have represented couples that
were healthier than typical diabetic couples, or who were more likely to seek help from
the medical community.
Despite these limitations, results from this study suggest the potential importance
of the spouse in the management of Type 2 diabetes, and the impact of race and gender
on this relationship. However, spousal support research should consider not only the type

106
of support perceived, but the context in which it is provided. Both spousal support and
criticism can impact self-management behaviors. Further, even if it is well intended,
spousal support can have a negative impact on health behaviors.

107
CHAPTER SIX: CONCLUSION

Management of Type 2 Diabetes encompasses a challenging regimen requiring
adhering to medication, monitoring blood sugar, following a healthy diet and exercise
plan, and maintaining regular visits to the doctor. Not only can this regimen be an
overwhelming task, it can also cause a great deal of emotional distress (Fisher et al.,
2002). Previous literature has indicated that support from one’s spouse can have a
positive impact on health outcomes, including assisting with the management of a
chronic illness (Uchino, 2004; Barrera, 2000; Trief et al., 2002). However, several
previous studies have included global measures of spousal support, and did not examine
the effects of gender and race on the perceptions of spousal support for Type 2 diabetes
patients. Further, there has been little on the effects of unsupportive behavior on Type 2
diabetes self-management in married couples. This research aimed to explore the effects
of types of spousal support, including unsupportive behavior, on Type 2 diabetes self-
management, while examining the effects on gender and race on these relationships. The
first study provided information on the effects of types of spousal support on diet and
physical activity behaviors. The second study investigated the relationship between types
of spousal support and diabetes related distress. Finally, the third study assessed the
association between types of spousal support and HbA1c and BMI. Although results of
these studies only partially supported a priori hypotheses, results highlight the importance
of the spouse in the management of Type 2 diabetes.

108
In the assessment of types of spousal support on diet and physical activity levels,
findings revealed that emotional support for diet and unsupportive behavior for diet had
no effect on saturated fat consumption among patients with Type 2 diabetes. However,
some interesting outcomes were observed for types of spousal support and physical
activity levels. Higher levels of instrumental support for physical activity were
marginally associated with lower levels of moderate physical activity. Further, the data
also indicated that BMI moderated the relationship between unsupportive behavior and
moderate physical activity. Patients with higher BMI levels who perceived higher levels
of unsupportive behavior from their spouse had lower levels of moderate physical
activity. Also, higher levels of appraisal support were associated with higher levels of
light physical activity. There were no gender and race differences found in these
analyses. However, findings also showed that individuals who were less acculturated but
who reported receiving higher levels of appraisal support had higher levels of light
physical activity compared to patients with low levels of acculturation that reported
receiving lower levels of appraisal support for physical activity.
Summary
The examination of types of spousal support and diabetes distress revealed that
there were no significant main effects between emotional support for diet and
unsupportive behavior for diet on diabetes distress. However, patients who reported
receiving higher levels of unsupportive behavior for diet had higher levels of diabetes
distress than when their spouse had better health, compared to patients whose spouse was
in poorer health. Although there were no differences in race, further exploration also

109
showed gender differences in these analyses. For women, higher levels of instrumental
support for physical activity were associated with higher levels of diabetes distress. This
relationship was not found for men.
Findings also showed that race moderated the relationship between emotional
support for diet and BMI. For whites, higher levels of emotional support for diet were
associated with increased BMI levels. There were no significant associations between
emotional support for diet and BMI among the races that comprise the other group and
among Hispanics. Additionally, findings also illustrated that higher levels of emotional
support for physical activity were associated with higher BMI levels. On the other hand,
higher levels of appraisal support for physical activity were found to be associated with
lower BMI levels. Although there were no main effects between emotional support for
diet and HbA1c, results indicated that the relationship between unsupportive behavior for
diet and HbA1c varied by gender. For women, lower levels of unsupportive behavior for
diet were associated with higher levels of HbA1c. For men, higher levels of unsupportive
behavior for diet were associated with higher HbA1c levels. Finally, higher levels of
unsupportive behavior for physical activity were also found to be marginally associated
with higher HbA1c levels.
In testing the relationship between emotional support and Type 2 diabetes self-
management, it was expected that emotional support would be positively correlated with
positive health outcomes, such as decreased saturated fat consumption, increased
Implications for Types of Support
Emotional Support

110
moderate physical activity levels, and decreased BMI. Although there were no
associations between emotional support and behavioral and psychological outcomes,
findings revealed that emotional support for diet was associated with increased BMI
levels among whites, and that emotional support for physical activity was associated with
an increase in BMI levels. Thus, findings revealed that increased amounts of emotional
support were associated with negative outcomes. This was an interesting finding, as
emotional support has been viewed as important in relationships, and has been linked to
positive health outcomes (Gottlieb & Wagner, 1991; Cutrona, 2000). However, one must
also note that there have been many mixed outcomes in the social support literature
regarding emotional support. Although it has been generally assumed that emotional
support can lead to positive health outcomes, these results may reflect that emotional
support is not always helpful to the recipient, even if the provider means well (Cutrona,
2000; Gallant, 2003; Rosland & Piete, 2010). In a study where focus groups were
conducted among older adults with chronic illnesses, participants acknowledged
receiving emotional support in the form of encouragement to take their medication and
exercise. However, the emotional support perceived resulted in negative outcomes due to
the support being interpreted as nagging or strict behavior (Gallant, Spitze & Prohaska,
2007). Thus, it is possible that participants in our study responded negatively to
emotional support due to feeling their spouse was being overprotective. It is also possible
that for our sample, encouragement to exercise may express the non-diabetic spouse’s
concern with the health of the diabetic spouse, leading to a negative appraisal of
situational factors relating to the illness of the diabetic spouse.

111

Instrumental Support
It was expected that instrumental support would be positively associated with
better behavioral, psychological and biological outcomes. However, results indicated that
increased amounts of instrumental support were associated with lower levels of physical
activity. Further, for women, increased amounts of instrumental support were associated
with higher levels of diabetes distress. Similar to emotional support, although well-
intended, higher levels of instrumental support may lead to lower levels of perceived
independence (Seeman, Bruce & McAvay, 1996; Martire et al., 2002). The diagnosis and
management of Type 2 diabetes can be demanding and stressful, particularly for one who
has led a primarily independent life (Gallant, 2003; Samuel-Hodge et al., 2000; Glasgow,
Toobert and Gillette, 2001). Traditionally, women are the primary preparers of family
meals, and serve as the major caregiver in the home (Whitmore, Melkus, Grey& 2005).
Thus, for women, a spouse offering to assist with exercise might decrease one’s sense of
control, leading to higher levels of stress (Seeman, Bruce & McAvay, 1996; Martire et
al., 2002). Further, when a spouse offers to exercise, this may be misinterpreted as a way
for the spouse to keep a more vigilant eye on the diabetic spouse, or for the healthier
spouse to make sure that an accident does not occur (Seeman, Bruce & McAvay, 1996;
Martire et al., 2002). Instrumental support could also be interpreted as nagging or strict
behavior, leading to negative outcomes such as decreases in physical activity and
increased levels of diabetes distress (Martire et al., 2002; Rosland et al., 2010). It would
be interesting to see if similar results would occur if additional sources of support were to

112
be assessed. For example, one study reported that support from family was associated
with increased nagging and unsupportive behavior compared to support from friends
(Gallant, Spitze, & Prohaska, 2007), which was found to be more beneficial.
Appraisal Support
Supporting the hypotheses, appraisal support was found to be associated with
positive health outcomes, such as increased light physical activity and decreased BMI
levels. Moreover, for individuals who were less acculturated, higher levels of appraisal
support from a spouse led to increased light activity. Unlike emotional and instrumental
support, one can argue that appraisal support is more person-centered, and contributes
feedback that one is valued and respected (Cohen, 1984; Cohen and McKay, 2004). For
example, seeking advice on effective ways to exercise may demonstrate to one’s spouse
not only are their opinions respected and valued, but that they are also doing a good job
with their exercise regimen, and their spouse has faith in their ability to exercise. Further,
appraisal support can also allow an individual to appraise their situation as being less
stressful (Cohen, 1984; Cohen and McKay, 2004). For example, when a spouse provides
rewards for exercise, this may offer more immediate, tangible, positive feedback that the
diabetic spouse is able and effectively meeting his or her physical activity goals, and that
this task is not insurmountable. The effect of appraisal support on acculturation was a
notable finding. Previous studies have shown that less acculturated individuals are less
likely to be physically active compared to individuals who are more acculturated
(Evenson, Sarmiento, & Ayala, 2004; Masel, Rudkin, Peek, & 2006). Additionally, lower
physical activity levels among less acculturated groups could be due to many factors,

113
including personal and environmental barriers, and cultural beliefs (Kumanyika, 1993;
Higgins & Learn, 1999; Albright et al., 2005; Bull et al., 2009). It is possible that for our
sample of less acculturated individuals, where collectivism is highly valued, appraisal
support may have increased light activity levels by shifting to a person-centered
approach, giving more focus to the diabetic spouse. This data supports the idea that
positive feedback from a spouse, through words or actions, may temporarily reduce the
focus on personal and environmental barriers, and may increase some self- management
behaviors, such as walking.
Unsupportive Behavior
In line with hypotheses, results indicated that unsupportive behavior, such as
criticizing and complaining, was associated with negative health outcomes. Findings
revealed that unlike other types of support, unsupportive behavior was negatively
associated with all self-management outcomes, including behavioral, psychological and
biological. Further, findings support and expand previous literature, which has found
negative behaviors to diminish positive health outcomes (Manne et al., 1999; Beverly et
al., 2008; Whiffen & Aube, 1999). Due to the cross-sectional design of the study, what is
unclear is whether individuals who have higher BMI levels, higher levels of diabetes
distress, and higher HbA1c levels also have a poorer relationship with their spouse, or are
also depressed. These factors can lead to individuals who are more likely to perceive
higher levels of unsupportive behavior from their spouse. Further, since HbA1c and BMI
are a function of many variables, including diet, physical activity and adherence to
medication (Heisler et al., 2005; Lerman et al., 2004), it may be difficult to fully interpret

114
exactly why unsupportive behaviors were associated with HbA1c and BMI. Nevertheless,
results are encouraging and show support for more research about the effects of
unsupportive behavior from spouses. This is especially important, as much of the support
literature focuses on the positive effects of supportive interactions. Increased knowledge
about spousal unsupportive interactions will help to inform researchers on family
interventions designed to improve chronic disease management. Further, because
individuals with chronic illness may be more likely to report perceiving unsupportive
behavior from family compared to friends, informing families on types of unsupportive
behaviors and how to effectively minimize them should be an important goal when
designing family interventions (Gallant, Spitze & Prohaska, 2007).
Despite its notable findings, this dissertation had several limitations. There were
several issues relating to measurement. Because the data came from a study with a
primary interest on preventing and managing Type 2 diabetes among married couples, the
spousal support measures had constraints. Although types of spousal support were
measured (emotional, instrumental, appraisal, unsupportive), only spousal support for
diet and physical activity were examined. Further, only emotional and unsupportive
behaviors were examined for diet. This distinction may have minimized other support
(for example, support for household chores or medication adherence) and thus reduced
the ability to detect associations. All three studies utilized cross-sectional data, which
limited the ability to draw conclusions about the causal influence of spousal support on
indicators of Type 2 diabetes self-management. For example, it is possible that
Limitations

115
individuals who have higher levels of diabetes distress may seek or receive higher levels
of support, or that initial BMI levels of a spouse may elicit unsupportive behavior. Also,
the use of self-report data may be subject to reporting bias. Although all measures of
spousal support were self-reported, reporting bias may have a greater effect on findings
from Study 1 and 2, due to the outcome measures being self-reported (diet, physical
activity and diabetes distress). Study 3 includes biological outcome measures, therefore
findings would less likely be influenced by reporting biases. Additionally, all three
studies measured perceived support. Although there has been a large amount of research
that indicates that one’s perception of support is a stronger indicator of health outcomes
than one’s actual receipt of support, existing literature also indicates that perceived
support can be influenced by many factors, including gender, one’s upbringing, and
personality traits (Wethington and Kessler, 1986; Shrout, Herman and Bolger, 2006;
Cutrona, 1996; Uchino, 2004;). There is some support for the inclusion of measures of
received support, as it may be more dependent on external factors and provide a
chronological glimpse into the effects of the stages and severity of a chronic illness on
support provided (Uchino, 2004; Shrout, Herman and Bolger, 2006; Gleason et al., 2003;
Cranford et al., 2006). Multi-modal outcome assessments, including perceived support
and daily diary studies, are needed in future studies to order to provide additional insight
on the influences of internal and external factors on the providance, receipt and
perception of support.
There was no assessment of marital quality, which has been found to impact
perceived levels of support (Trief et al., 2004; Trief et al., 2006). For example, poor

116
marital quality often includes lack of communication. Lack of communication would not
only affect support provided, but also the support perceived (Cutrona, 1996). The
participants who have agreed to participate in the study may have more supportive
relationships, thus the results may not be generalizable to the full population of married
couples where one partner has type 2 diabetes.
Further, it is important to note that our use of a cross sectional design does not
fully capture the concept that support can be helpful or harmful to an individual
depending on several factors, including the perceived controllability of a stressor, and
perceived independence levels. Additionally, although our overall sample size was
adequate, sub-group analyses resulted in less participants in each group, which may have
limited the ability to detect significant associations. Thus, a longitudinal design that
assesses various time points and exposures to different stressors among a large sample of
diverse individuals with Type 2 diabetes is recommended to further clarify the positive or
negative effects of spousal support. It has also been documented that the perception of
support is influenced by many factors, including the stage and severity of illness
(Polonsky et al., 2005; Cutrona, 2000). This study did not measure items such as years
since diabetes diagnosis, or medication regimen – items that would have provided some
indication of the stage and severity of illness. For example, Polonsky and colleagues
found that patients who were insulin users had higher levels of diabetes distress
(Polonsky et al., 2005). Higher levels of diabetes distress can affect levels of support
provided and perceived. It is a challenging task to recruit two members from a family
compared to one. Thus, this study could have represented couples that were healthier than

117
typical diabetic couples, or who were more likely to seek help from the medical
community.
This study was limited to measuring spousal perceptions of support. It is possible
that for our sample, other sources of support were more important than support provided
by a spouse, such as from friends or children. Previous research has indicated that women
are more likely to seek outside sources of support compared to men (Edwards, Nazroo,
Brown, 1998; Kristofferzon, Lofmark & Carlsson, 2003), and that support from friends
and children may also be important in assisting with self-management behaviors (Gallant,
2003; Stoller, 1998). Further, this study measured only functional aspects of support. It is
possible that structural measures, such as one’s social network, may be especially
influential in influencing health outcomes (Sarkadi and Rosenqvist, 2002; Israel, 1982).
Finally, this study included a sample of participants who were members of Kaiser
Permanente. Thus, findings were only generalizable to individuals who have access to
care, and who were relatively middle class. For example, individuals without access to
care with lower incomes may have poorer outcome measures and different support
structures, which may vary their perceptions of support (Schultz et al., 2006).
Findings from this study were noteworthy, and highlighted the complexities of
spousal support. It explored various types of spousal support, including unsupportive
behavior and examined the effects of this support on Type 2 diabetes self-management.
Although there is an abundance of literature on social support and spousal support, there
is a dearth of literature about the effects of types of spousal support on Type 2 diabetes
Further Implications and Future Directions

118
self-management. Rather, spousal support is usually assessed as a composite measure of
support. Further, based on the literature there have been mixed effects about whether or
not support is helpful to individuals. Is support helpful all of the time, or only in the face
of a stressor? Findings from this study helped to elucidate findings regarding the effects
of social support, revealing that not all support is helpful and wanted to an individual,
even if well intended. There are several factors that should be taken into account, such as
the stage and severity of illness, perceived independence levels, acculturation, gender,
race, BMI, and marital quality (Polonsky et al., 2005; Cutrona, 2004; Trief et al., 2004;
Seeman, Bruce & McAvay, 1996; Martire et al., 2002). Additionally, depending on the
individual, certain types of support are more helpful than others (Gallant, 2003; Cutrona,
2004; Cutrona et al., 2007). This could account for some of the mixed results in social
support literature. Findings support the need for further research on the impact of these
situational factors on the perception of support. This can provide guidance to clinicians
and researchers in the field on how to design programs and interventions to assist spousal
couples in managing chronic illnesses such as Type 2 diabetes. These results suggest that
programs and interventions should be tailored based on contextual factors relating to the
individual. Further, interventions should aim for educating spousal couples on effective
ways of communication (Cutrona, 2004; Trief et al, 2004). For example, it is important
for the diabetic spouse to articulate what type of support he or she will need from a
spouse. This may prevent unwanted support, and may lessen distress associated with
unwanted support (Trief et al., 2002; Trief et al., 2004; Trief et al., 2007; Cutrona, 2004;
Cutrona et al., 2007). Researchers and clinicians can work with spousal couples on ways

119
to handle issues relating to diabetes self-management based on contextual factors. For
example, if the diabetic spouse is female, perhaps an intervention should focus on
increasing communication between the couple on helpful ways to assist the diabetic
spouse in the kitchen during the preparation of meals, in addition to modeling ways to
effectively increase appraisal support in the home. If the diabetic spouse already has an
exercise routine in place, the spouse can increase appraisal support by helping to plan the
family schedule around the exercise routine. Thus, interventions should be created in
order to maximize the autonomy and confidence of the diabetic spouse. Marital quality is
also important to assess and take into account (Trief et al., 2002; Trief et al., 2004; Trief
et al., 2007; Cutrona, 1994). The diagnosis of a chronic illness can be very draining on a
married couple, especially if their marriage already suffers from having low levels of
communication (Trief et al., 2002; Trief et al., 2004; Trief et al., 2007; Cutrona, 2002).
Thus, further programs should also be developed in order to assist couples where poor
marital quality and satisfaction are reported. Ideally, programs should be developed that
target multiple outcomes in addition to self-management, including marital quality and
the physical and mental health of the non-diabetic spouse.
Further research is also necessary to assess the impact of unsupportive behavior,
especially since much of extant literature explores positive aspects of support. Findings
of this study suggest that unsupportive behavior may have a significant impact on Type 2
diabetes self- management. Thus, intervention attempts should be focused on increased
efforts to minimize criticizing and complaining within spousal couples, by relaying the
message that it can be harmful to the health of the diabetic spouse. Interventions that will

120
increase communication and goal setting are suggested in order to decrease unsupportive
behavior in couples (Gallant, 2003; Cutrona, 2004; Manne et al., 1999; Beverly et al.,
2008; Whiffen & Aube, 1999).
Further research is also warranted in order to assess the impact of race on spousal
support. This study was notable in that it was able to assess some racial differences.
However, it was not able to fully assess how the perception of spousal support from
African Americans, Native Americans and Asian Americans impact Type 2 diabetes self-
management behaviors. Thus, an ideal future study would have a longitudinal design in
order to measure the impact of spousal support on different stages of illness, and would
include larger representation from several different ethnic groups. Due to the increasing
trend of individuals diagnosed with Type 2 diabetes, and the increased burden of
managing the illness in the home, a thorough understanding of the role of the spouse is
vital. Thus, continued investigation of the impact of types of spousal support on Type 2
diabetes self-management, including the role of contextual factors such as race and
gender are necessary in order to improve the health and quality of life of families affected
by Type 2 diabetes.

121
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137

APPENDIX

Spousal Support for Physical Activity

How often has your spouse/partner said or done what is described, during the last
three months?
Please check  one answer for each question

Never Rarely
A few
times
Often
Very
often
Does
not
apply
Emotional Support

Gave me helpful reminders to
exercise (“Are you going to
exercise tonight?”)

0

1

2

3

4

(8)

Gave me encouragement to stick
with my exercise program

0

1

2

3

4

(8)

Discussed exercise with me

0

1

2

3

4

(8)

Talked about how much they
like to exercise

0

1

2

3

4

(8)

Instrumental Support

138
How often has your spouse/partner said or done what is described, during the last
three months?
Please check  one answer for each question

Never Rarely
A few
times
Often
Very
often
Does
not
apply
Exercised with me

0

1

2

3

4

(8)

Offered to exercise with me

0

1

2

3

4

(8)

Changed their schedule so we
could exercise together

0

1

2

3

4

(8)

Appraisal Support

Gave me rewards for exercising
(bought me something or gave
me something I like) 
0

1

2

3

4

(8)

139
How often has your spouse/partner said or done what is described, during the last
three months?
Please check  one answer for each question

Never Rarely
A few
times
Often
Very
often
Does
not
apply
Planned for exercise on
recreational outings

0

1

2

3

4

(8)

Helped plan activities around my
exercise

0

1

2

3

4

(8)

Asked me for ideas on how they
can get more exercise

0

1

2

3

4

(8)

Unsupportive Behavior

Complained about the time I
spend exercising

0

1

2

3

4

(8)

Criticized me or made fun of me
for exercising

0

1

2

3

4

(8)

140

Spousal Support for Diet

How often has your spouse/partner said or done what is described, during the last
three months?
Please check  one answer for each question

Never Rarely
A few
times
Often
Very
often
Does
not
apply
Emotional Support

Encouraged me not to eat
"unhealthy foods" (cake,
salted chips) when I'm
tempted to do so

0

1

2

3

4

(8)

Discussed my eating habit
changes with me (asked
me how I'm doing with
my eating changes)

0

1

2

3

4

(8)

Reminded me not to eat
high fat, high salt foods

0

1

2

3

4

(8)

Complimented me on
changing my eating habits
("Keep it up", "We are
proud of you")

0

1

2

3

4

(8)

Commented if I went back
to my old eating habits 
0

1

2

3

4

(8)

Unsupportive Behavior

141
How often has your spouse/partner said or done what is described, during the last
three months?
Please check  one answer for each question

Never Rarely
A few
times
Often
Very
often
Does
not
apply
Ate high fat or high salt
foods in front of me

0

1

2

3

4

(8)

Refused to eat the same
foods I eat

0

1

2

3

4

(8)

Brought home foods I'm
trying not to eat

0

1

2

3

4

(8)

Got angry when I
encouraged them to eat
low salt, low fat foods

0

1

2

3

4

(8)

Offered me food I'm
trying not to eat

0

1

2

3

4

(8)

Abstract (if available)

Abstract The present study sought to explore the effects of spousal support on Type 2 diabetes self-management. Cross-sectional data from 305 spousal pairs, ages 30-70, were collected from a tailored self-management intervention study for type 2 diabetes entitled Prevention and Control of Diabetes in Families (PCDF). Three separate studies were conducted in order to examine spousal support effects on three different self-management domains: behavioral (diet, physical activity), psychological (diabetes distress), and biological (BMI, HbA1c). The studies were also designed to assess gender and race as moderators of the relationship between spousal support and self-management measures. Findings from Study 1 revealed that increased instrumental support and unsupportive behavior for physical activity was associated with decreased moderate physical activity levels. Additionally, increased appraisal support for physical activity was associated with higher levels of light activity. Individuals who had lower levels of acculturation and perceived higher levels of appraisal support reported higher levels of light physical activity. Findings from Study 2 revealed that for women, increased levels of instrumental support were associated with higher levels of diabetes distress. Further, higher levels of perceived unsupportive behavior for diet were associated with increased levels of diabetes distress, particularly when the non-diabetic spouse was healthier. Finally, findings from Study 3 revealed that increased unsupportive behavior for diet and emotional support for diet was associated with negative health outcomes, including increased BMI among Whites, and increased HbA1c levels. Findings support and expand prior research on spousal support literature, particularly regarding the negative impacts of support. Results indicate that support can have negative health outcomes, even if the provider has well-meaning intentions.

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Asset Metadata

Creator Partlow, Keosha R. (author)

Core Title An examination of the association between spousal support and type 2 diabetes self-management

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Preventive Medicine (Health Behavior)

Publication Date 05/04/2011

Defense Date 03/23/2011

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag appraisal support,emotional support,Marriage,OAI-PMH Harvest,self-management,social support,spousal support,type 2 diabetes

Place Name California (states), Los Angeles (city or populated place), USA (countries)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Sussman, Steven Y. (committee chair), Baezconde-Garbanati, Lourdes (committee member), Lincoln, Karen (committee member), Reynolds, Kim D. (committee member), Xiang, Anny Hui (committee member)

Creator Email krpartlo@usc.edu,krpartlow@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m3895

Unique identifier UC1228182

Identifier etd-Partlow-4433 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-471927 (legacy record id),usctheses-m3895 (legacy record id)

Legacy Identifier etd-Partlow-4433.pdf

Dmrecord 471927

Document Type Dissertation

Rights Partlow, Keosha R.

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu