Citation
Religious attendance and chronic disease self-management among older adults in the San Luis Valley health and aging study

Material Information

Title:
Religious attendance and chronic disease self-management among older adults in the San Luis Valley health and aging study
Creator:
Ross, Kaile M. ( author )
Language:
English
Physical Description:
1 electronic file (67 pages) : ;

Subjects

Subjects / Keywords:
Self-management (Psychology) ( lcsh )
Medicine and psychology ( lcsh )
Medicine and psychology ( fast )
Self-management (Psychology) ( fast )
Genre:
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Thesis:
(Thesis) M.A.--University of Colorado Denver
Bibliography:
Includes bibliographical references.
System Details:
System requirements: Adobe Reader.
General Note:
Department of Psychology
Statement of Responsibility:
by Kailie M. Ross.

Record Information

Source Institution:
|University of Colorado Denver
Holding Location:
|Auraria Library
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
922008963 ( OCLC )
ocn922008963
Classification:
LD1193.L645 2015m R67 ( lcc )

Downloads

This item has the following downloads:


Full Text
RELIGIOIUS ATTENDANCE AND CHRONIC DISEASE SELF-MANAGEMENT
AMONG OLDER ADULTS IN THE SAN LUIS VALLEY HEALTH AND AGING
STUDY
By
KAILE M. ROSS
B.A., University of Notre Dame, 2007
A thesis submitted to the
Faculty of the Graduate School of the
University of Colorado in partial fulfillment
of the requirements for the degree of
Master of Arts
Clinical Health Psychology Program
2015


2015
KAILE M. ROSS
ALL RIGHTS RESERVED


This thesis for the Master of Arts degree by
Kaile M. Ross
Has been approved for the
Clinical Health Psychology Program
by
Kevin Masters, Chair
Krista Ranby
Jim Grigsby


Ross, Kaile M. (M. A., Clinical Health Psychology)
Religious Attendance and Chronic Disease Self-Management among Older Adults in the
San Luis Valley Health and Aging Study
Thesis directed by Professor Kevin S. Masters.
ABSTRACT
Cardiovascular disease and type-2 diabetes mellitus are two increasingly prevalent
chronic health conditions that require not only medical team oversight but also a high
level of patient involvement to achieve optimal disease management. Regular attendance
at religious services and religious community involvement may help individuals with
cardiovascular disease and diabetes better manage their chronic illness. R/S is predictive
of many positive health related variables, but has yet to be examined in much depth in
relation to self-management for persons with chronic illness. The studys primary aim
was to examine whether and for whom religious attendance is related to self-management
behaviors (physical activity and healthy eating behaviors) and to physical outcome
measures considered to be related to self-management (body mass index and blood
pressure) among elderly Hispanic and Non-Hispanic adults (60 y/o +) with diabetes or
cardiovascular disease from the San Luis Valley Health and Aging Study. The second
aim of the study was to test a proposed model that theorizes that religious attendance
directly and indirectly is associated with disease self-management behaviors and physical
measures of disease self-management through the mechanisms of social support, mood,
and self-regulation. The results showed that in a sample of 468 (54.3% female; 58.5%
Hispanic) individuals with cardiovascular disease or diabetes, the relationship between
religious group attendance and self-management varied by gender, ethnicity, and
diagnosis. The relationship of attendance with physical activity was moderated by gender


{fi = -.122,p= .090) and diagnosis (J3 = ,150,p = .035), with attendance being associated
with more physical activity for men and individuals with cardiovascular disease without
diabetes (vs. individuals with diabetes and co-morbid diagnoses). The relationship of
attendance with blood pressure was moderated by gender (fi = -.178,/) = .007) and
ethnicity (fi = 132, p = .039), with women having lower systolic blood pressure and non-
Hispanic whites (vs. Hispanics) having lower diastolic blood pressure. The structural
equation model results demonstrated mixed support for the proposed model, with the
physical activity model yielding the strongest support. These findings have important
implications for understanding the role that R/S may have in chronic illness disease self-
management.
The form and content of this abstract are approved. I recommend its publication.
Approved: Kevin S. Masters
IV


DEDICATION
This thesis is dedicated to my husband, Patrick Wood, for his continuous love,
support, and encouragement, and to my parents, James and Kathleen Ross, for always
pushing me to accomplish more than I thought possible.
v


ACKNOWLEDGEMENTS
I would like to first acknowledge and thank my advisor and mentor, Kevin S.
Masters, for his guidance and support in this process. I would also like to thank Krista
Ranby for her statistical expertise and her willingness to answer all my questions about
my analyses. I would also like to thank Jim Grigsby for being supportive throughout this
process and allowing me access to the incredible SLVHAS dataset. I would also like to
acknowledge my lab mates, Stephanie Hooker, Megan Grigsby, and Lacey Clement, for
their emotional support and encouragement along the way. Lastly, I would like to thank
my cohort (Jo Vogeli, Shiva Fekri, Tattiana Romo, and Lacey Clement) for the support,
laughter, and tears that helped to make the last few years so memorable and wonderful.
vi


TABLE OF CONTENTS
CHAPTER
I. BACKGROUND
Religion and Health ..............................................4
Religion and Engagement in Health Related Behaviors...............5
R/S and Disease Self-Management...................................6
Pathways from R/S to Disease Self-Management-Social Support, Mood, and Self-
Regulation........................................................7
Model of R/S and Disease Management Behaviors and Physical Measures
Associated with Disease Management...............................10
Purpose of Present Study.........................................14
Hypotheses and Specific Ams......................................15
II. METHOD
Participants.....................................................17
Data Collection .................................................18
Demographics.....................................................18
Measures.........................................................19
Statistical Analyses.............................................22
III. RESULTS
Am 1: Linear Regression Moderated Analyses ......................30
Am 2: SEM Analyses ..............................................37
IV. DISCUSSION.......................................................41
REFERENCES ............................................................47


LIST OF TABLES
TABLE
1. Demographic Data........................................................25
2. Descriptive Data........................................................26
3. Variables with significant differences when compared by gender and ethnicity 27
4. Bivariate Correlations..................................................30
5. Linear Regression Models- Religious Attendance Predicting Outcomes......32
viii


LIST OF FIGURES
FIGURE
1. Oman & Thoresen (2002) Model..........................................11
2. Adapted Model.........................................................12
3. Regression Graph Religious Attendance Predicting Physical Activity
Moderated by Gender...................................................33
4. Regression Graph Religious Attendance Predicting Systolic Blood Pressure
Moderated by Gender...................................................34
5. Regression Graph Religious Attendance Predicting Diastolic Blood Pressure
Moderated by Ethnicity................................................35
6. Regression Graph Religious Attendance Predicting Physical Activity Moderated
by Diagnosis..........................................................36
7. Model with Physical Activity as the Outcome...........................38
8. Model with Healthy Eating Behaviors as the Outcome....................38
9. Model with BMI as the Outcome.........................................39
10. Model with Systolic Blood Pressure as the Outcome....................40
11. Model with Diastolic Blood Pressure as the Outcome...................40
IX


LIST OF ABBREVIATIONS
BMI Body Mass Index
CESD Center for Epidemiologic Studies Depression Scale
CFI Comparative Fit Index
CVD Cardiovascular Disease
DM Diabetes Mellitus
RMSEA Root Mean Square Error of Approximation
R/S Religion and Spirituality
SEM Structural Equation Modeling
SLVHAS San Luis Valley Health and Aging Study
SQRT Square Root Transformed
SRMR Standardized Root Mean Square Residual
T2DM Type-2 Diabetes Mellitus
WHO World Health Organization
x


CHAPTER I
BACKGROUND
The World Health Organization (WHO) defines adherence to long-term therapy
as the extent to which a persons behaviortaking medication, following a diet, and/or
executing lifestyle changescorresponds with agreed recommendations from a health
care provider (WHO, 2003, p. 3). Finding ways to improve long-term therapy self-
management has become a priority for the WHO in recent years as a way to improve
patient health and reduce healthcare costs.
Due to the complex nature of their management, cardiovascular disease and type-
2 diabetes mellitus (T2DM) are often targeted for improving treatment self-management.
Cardiovascular disease, a diagnosis encompassing all diseases of the circulatory system
including high blood pressure, coronary heart disease, heart failure, and stroke, is a major
public health concern affecting an estimated 80 million Americans. Cardiovascular
disease causes over a third of the deaths in the United States as of 2005 (Lloyd-Jones et
al., 2009) and is the leading cause of death worldwide (WHO, 2011). Diabetes is another
major public health concern. As the seventh leading cause of death, diabetes affects
26.9% of US residents age 65 and above (with T2DM accounting for over 90% of
diabetes cases), causing complications such as high blood pressure, heart disease, stroke,
limb amputation, retinopathy, and neuropathy. The risk of diabetes is approximately 66%
higher among Hispanic individuals when compared to non-Hispanics (National Diabetes
Factsheet, 2011).
Proper management of cardiovascular disease and diabetes requires the
collaborative efforts of both a medical team and the patient (Holman & Lorig, 2000).
1


Disease management recommendations for patients with cardiovascular disease and
T2DM are fairly similar, requiring not only medication self-management but also
lifestyle modifications such as changing diet, increasing physical activity, and smoking
cessation. Patients with cardiovascular disease and T2DM often have difficulty
complying with physician recommendations. Adherence to medication is poor among
cardiovascular disease patients, with one study demonstrating that only 50-60% of
patients adhered to prescribed medications during the course of a year (Haynes et al.,
2005). Another study showed that patients begin to adhere less to lifestyle modification
prescriptions than to drug regimens, beginning only one month after acute coronary
syndrome (heart attack or unstable angina) (Chow et al., 2010). Additionally, one
observational study followed patients for 30 months post hospitalization for myocardial
infarction via an outpatient medication reimbursement database and found that only a
quarter of patients adhere to their drug regimens as determined by having less than 80%
of days covered by a filled prescription (Tuppin et al., 2010). For hypertension, 67-77%
of patients report taking their hypertension prescription and 21-27.7% report adhering to
lifestyle modifications, with similar rates of self-management seen across ethnic groups
when comparing non-Hispanic whites, non-Hispanic blacks, and Mexican Americans
(Natarajan, Santa Ana, Liao, Lipsitz, & McGee, 2009). Similarly, patients with diabetes
also struggle with self-management with self-reports of self-management at 78% for
medication, 64% for self-monitoring blood glucose, 37% for diet, and 35% for exercise
(Peyrot et al., 2005). Patients with T2DM, who are female and white, tend to have higher
rates of self-management (Karter, Ackerson, Darbinian, DAgostino, Ferrara, Liu, &
Selby, 2001). The above research suggests that T2DM and cardiovascular disease
2


patients have a more difficult time adhering to lifestyle modification recommendations
such as diet and exercise changes then they do adhering to a medication regimen. Even
with low self-reported adherence rates, it is likely that patients are overestimating their
adherence to lifestyle modification recommendations, as evidenced by a study by Troiano
and colleagues (2008) which found that only 5% of adults in the general US population
achieve recommended levels of physical activity when measured objectively (i.e.
accelerometer).
Patients who do well with self-management often demonstrate better clinical
outcomes (Lorig & Holman, 2003) whereas poor self-management predicts higher rates
of depression (which may lead to further decline in self-management), worse physical
health, increased hospitalization and increased mortality (Krousel-Wood & Frohlich,
2010; Osterberg & Blaschke, 2005). Poor self-management likely played a large role in
the 6 million patient hospitalizations in the United States due to cardiovascular disease
from 1996 to 2006 (Lloyd-Jones et al., 2009). In 2004, of the 609,000 admissions
primarily caused by diabetes, approximately 32% were due to uncontrolled diabetes
conditions (due to poor disease management) (Kim, 2007). As healthcare providers and
the WHO strive to improve quality of life for patients with chronic illness and reduce
healthcare costs, it is important to determine the potential predictors of self-management,
in particular in terms of adherence to lifestyle modifications which patients appear to
struggle with the most, in order to identify what and whom to target in self-management
interventions.
3


Religion and Health
When determining predictors of self-management for individuals with cardiovascular
disease and diabetes, religious and spiritual (R/S) involvement is an area of research that
is relatively unexplored despite the significant role that religion and spirituality have in
many peoples lives. Over 90 percent of Americans believe in God or a higher power, 67
to 75 percent pray on a daily basis, 69 percent are members of a church or synagogue, 40
percent attend a church or synagogue regularly, and 60 percent consider religion to be
very important in their lives (Miller & Thoresen, 2003; Poloma & Pendleton, 1991;
Shuler, Gelberg, & Brown, 1994) with women historically reporting higher rates of R/S
involvement then men (Miller & Hoffmann, 1995).
Over the last few decades, increasing evidence indicates positive relationships
between religious involvement and physical and mental health outcomes (Miller &
Thoresen, 2003). A meta-analysis of over 40 independent samples found that religious
involvement is significantly and positively associated with longevity, with the association
seemingly being greater for women and influenced by sociodemographic and health-
related factors (McCullough, Hoyt, Larson, Koenig, & Thoresen, 2000). Another study
(N=28,080) found that among adults in the U.S., there was a life expectancy gap of over
7 years between persons never attending vs. attending church more than once weekly;
this gap was even more pronounced for African American, who showed a 14 year
difference in life expectancy (Hummer, Rogers, Nam, & Ellison, 1999). A meta-analysis
conducted by Chida and colleagues (2009), showed that R/S was associated with
reduced mortality in a healthy population, independent of behavioral factors such as
smoking, drinking, and exercise; R/S was more strongly associated with reduced
4


mortality in women and older populations (>60 y/o), but not associated with reduced
mortality in a diseased population. Additionally, the meta-analytic results showed a
negative association between R/S and cardiovascular mortality. Similar benefits of R/S
involvement have been found for older Mexican Americans, with one study finding a
32% reduction in mortality risk for weekly church attendees vs. non-attenders (Hill,
Angel, Ellison, & Angel, 2005). In Comstock and Partridges (1972) analysis of over
91,000 people in a Maryland county, those who regularly attended church had a lower
prevalence of cirrhosis, emphysema, suicide, and death from ischemic heart disease.
Several studies have indicated that religious participation and higher religiosity may have
a beneficial effect on blood pressure as well (Armstrong, van Merwyk, & Coates, 1977;
Hixson, Gruchow, & Morgan, 1998; H. G. Koenig et al., 1998; Walsh, 1998), with one
study finding that the relationship was stronger for non-Hispanic whites and blacks, than
for Mexican-American participants (Bell, Bowie, & Thorpe, 2012). These studies
indicate that R/S has a demonstrated benefit on health and longevity; however, the
relationship between R/S and health and longevity, may vary in strength based on
population characteristics, such as gender, age, health status, and ethnicity.
Religion and Engagement in Health Related Behaviors
The positive relationship between religious involvement and health is in part
attributable to avoidance of health risking behaviors and engagement in health enhancing
behaviors. For example, routine practice of religion is associated with physical activity in
older adults (Callaghan, 2006). Similarly, attending religious services is predictive of
engaging in more exercise and less smoking and heavy drinking among the elderly
(Oman & Reed, 1998). One long-term prospective study found that church attendance at
5


47 years of age predicted physical health at 70 years of age in men (via mood and
substance abuse) (Koenig & Vaillant, 2009). Another long-term study conducted by
Strawbridge and colleagues (2001) followed over 2,500 community dwelling adults for
30 years, and found that persons who frequently attended religious groups were more
likely to adopt and maintain positive health behaviors, such as exercising and refraining
from smoking and heavy drinking; however, health benefits were stronger for women
than for men. Another study found that men and women who reported religion as more
important in their lives were more likely to use a variety of preventive services such as
mammograms, flu shots, cholesterol screening, and prostate cancer screening (Reindl
Benjamins & Brown, 2004); the association between religiosity and use of preventive
services was stronger among women. These studies, which demonstrate a positive
relationship between religious involvement and health behaviors, may suggest that a
similar relationship might exist between R/S involvement and disease self-management
health behaviors; however, these studies also indicate that the strength of the relationship
may depend on demographic characteristics.
R/S and Disease Self-Management
Given the literature relating R/S involvement to better health and health
behaviors, it is important to explore possible connections between religious involvement
and self-management behaviors for those with chronic illness. However, the research in
this area remains sparse, inconclusive, correlational, and mostly limited to African
American patients with T2DM. For example, one study examined African American
patients with T2DM and found that church attendance was related to better coping and
adjustment to diabetes, which was related to better self-management behaviors (Samuel-
6


Hodge, Watkins, Rowell, & Hooten, 2008). A similar study, found that among black
women with T2DM, R/S well-being was linked to better glycemic control (Newlin,
Melkus, Tappen, Chyun, & Koenig, 2008). Conversely, one cross-sectional study
conducted with heart failure patients (N=95) found no significant association between
self-reported R/S well-being and self-reported medical compliance (Black, Davis,
Heathcotte, Mitchell, & Sanderson, 2006). The literature relating religion to physical and
mental health suggests that despite the limited work in this area, the possible positive
relationship between R/S and self-management behavior in chronic illness warrants
further investigation. Work is needed exploring whether a relationship between R/S and
chronic illness self-management exists and then exploring potential moderatorsin
particular demographic moderators to determine for whom this relationship is the
strongest, given the ethnic and gender differences that have been demonstrated in the R/S
and health literature.
Pathways from R/S to Disease Self-Management Social Support, Mood, and Self-
Regulation
It is also important to explore potential mechanisms through which R/S may
influence disease self-management behaviors and related physical outcomes. There are
three promising mechanisms that may provide a more in depth understanding of how R/S
may play a role in disease self-management; these three factors are psychological distress
(primarily depression), social support, and self-regulation. Existing research literature
shows that these three mechanisms are associated with both R/S and disease self-
management.
7


Depression, R/S, and Self-Management
R/S involvement has been repeatedly shown to have positive relationships with
mental health (Miller & Thoresen, 2003) and has been specifically demonstrated to be
negatively correlated with depressive symptomatology (regardless of gender or ethnicity)
in a large meta-analysis (147 independent studies) (Smith, McCullough, & Poll, 2003). In
terms of patient adherence to medical recommendations, the negative influence of
depressive symptomatology is particularly strong. A meta-analysis conducted by
DiMatteo and colleagues (2000) found that the odds of noncompliance with medical
treatment recommendations were 3 times higher in depressed vs. non-depressed patients.
Similar reduced rates of medical adherence in patients with depressive symptoms are
seen in patients with T2DM(Lin et al., 2004), heart failure (van der Wal et al., 2006), and
hypertension (Wang et al., 2002).
Social Support, R/S, and Self-Management
R/S involvement, in particular frequent religious service attendance, is associated
with greater social networks and social support (Ellison & George, 1994). In
Strawbridges longitudinal study (1997), people who frequently attended religious
services were more likely to increase their number of social contacts and stay married
throughout the 30 year observation period. Similarly, religious attendance protects
against loneliness in later life (Rote, Hill, & Ellison, 2012). When it comes to chronic
illness self-management, social support may play an important role. A meta-analysis of
122 studies found support for a positive relationship between social support and patient
adherence to medical treatment (DiMatteo, 2004). Social support is also associated with
8


better self-management in patients with diabetes (Toljamo & Hentinen, 2001) and CVD
(Wu et al., 2013).
Self-Regulation, R/S, and Self-Management
The third factor related to both R/S involvement and disease self-management is
self-regulation. Some scientists suggest that the link between R/S, health, and social
behavior may be due to the positive influence religion has on individuals ability to self-
regulate (McCullough & Willoughby, 2009). In particular, a recent study found that
religious service attendance is associated with engagement in more self-regulatory
behavior (Carter, McCullough, & Carver, 2012). Carter and collegues (2012) discovered
that religious invididuals engage in more self-monitoring and frequently believe that a
higher power is watching them (which encourages greater self-monitoring and self-
control). A patients ability to adhere to medical treatment (sticking to a diet and exercise
regimen) is indicative of self-regulation. Specifically self-regulation involves the
initiation of purposeful behavior and inhibition of inappropriate actions (Grigsby, Kaye,
& Robbins, 1992). Without the executive ability to self-regulate and control ones own
behaviors, adhering to physician prescriptions for dietary restrictions or increased
physical activity is challenging. One study by Watkins and colleagues (2000), which
looked at self-regulation via cognitive representations of the disease, found that self-
regulation in patients with diabetes (type 1 and 2) was linked to diabetes self-
management behaviors. Another longitudinal study found that among patients (N=237)
with newly diagnosed T2DM, self-evaluation (a self-regulation behavior) was predictive
of improvement in dietary adherence over the 18 month observation period. Similar
results have been found for self-monitoring (another self-regulation behavior) and
9


improved diabetes control (Karter et al., 2001). Among cardiovascular disease patients,
one intervention demonstrated that improving the self-regulation skills of patients in
coronary rehabilitation served to improve long-term adherence to physical exercise
recommendations (Sniehotta et al., 2005).
Model of R/S and Disease Management Behaviors and Physical Measures
Associated with Disease Management
Existing literature demonstrates not only a connection between R/S and overall
health and health behaviors, but a connection between R/S and less depression (Smith,
McCullough, & Poll, 2003), greater social networks and support (Ellison & George,
1994), and more self-regulatory behavior (Carter, McCullough, & Carver, 2012;
McCullough & Willoughby, 2009), which are associated with disease self-management.
Developing a conceptual model that visually maps out the relationship between R/S,
social support, depression, self-regulation, disease self-management, and physical
measures associated with disease management may be helpful in beginning to understand
the direct and indirect relationship that R/S may have with disease self-management. One
existing conceptual model that looks at the connections between R/S and general health
and disease (Oman & Thoresen, 2002; Figure 1) helps to provide a framework for
understanding the pathways between R/S, disease self-management behaviors and
physical outcomes often related to disease management.
The original model by Oman and Thoresen (2002) takes into account the
biopsychosocial context within which the relationship between R/S and disease self-
management behaviors and physical outcomes occurs, allowing for differing impact of
R/S based on variables such as gender, race/ethnicity, and health status. The original
10


model identifies three mechanisms or pathways through which R/S impacts health:
positive health behaviors, social support, and positive psychological states. In order to
adapt this model for use in individuals with chronic disease, three changes have been
made: 1) positive health behaviors (or disease self-management behaviors) become the
outcome rather than a mechanism, 2) disease self-management behaviors and the physical
outcomes often related to disease self-management, such as blood pressure and body
mass index, are grouped together, and 3) an additional mechanism, self-regulation or
executive control, has been added to the model (Figure 2). Below, the three
mechanisms/mediators are described as they relate to engagement in chronic illness self-
management behaviors and physical measures often associated with disease self-
management for individuals who are involved in a religious community.
Biopsycho-
/ social Context
( (Health status, SES,
( ethnicity, upbringing,
\ stressors, gender,
age, etc.)
Balanced Autonomic'yX -
, _ Nervous System, \ y.
Strengthened Immune [
5IO\ Competence
c
(
Pulmonary
Disease
Liver Disease
)
)
rStomach and Bowel
Disease
Heart Disease,
Blood Pressure,
Stroke
(
c
Cancer
)
Infections

Symbols
+ Positive causal effects
Negative causal effects
--------> Causal direction
--------> Effect modification
Abbreviations for selected cattsal pathways
5S-effects on Social Support from Spirituality/Religion
MB-effects on Health Behaviors from Spirituality/Religion
P -effects on Psychological States from Spirituality/Religion
Fno, effects on Health Behaviors or Disease Detection/Treatment
from Psychological States
P,*, direct effects on immune, endocrine, or other Biological
Systems from Psychological States
Figure 1 Oman & Thoresen (2002)
11


Social Support. In the adapted model (Figure 2), social support serves as a
mediator in the relationship between religious group attendance and self-management
behaviors. For an individual adjusting to a T2DM diagnosis, a religious organization such
as a church provides a social network that can support an individual in making lifestyle
changes. For example, other church members may also have the disease or have
experience with the disease, which may enable provision of empathy and informational
support. Additionally, other church members may be able to provide tangible support
such as transportation to help the person attend his or her medical appointments. Lastly,
the work by Strawbridge and colleagues (2001) indicates that religious persons are more
likely to maintain and increase social support via staying married and increasing social
contacts.
Figure 2
Oman & Thoresens Model Adapted for Disease Managing Health Behaviors and
Physical Measures Associated with Disease Management
Mood (Psychological States). In the adapted model, positive psychological
states serve as a mediator in the relationship between religious group attendance and self-
management behaviors. As previously mentioned, R/S involvement is related to better
12


mental health and less depression. Maintaining mental health and positive mood may be
partially attributed to religious group attendance. Religious group attendance may
facilitate a sense of connectedness to others for individuals and instill a sense of purpose
and meaning to ones life, which may contribute to maintaining a positive mood.
Maintaining a positive mood predicts less depression, which is a risk factor for poor
disease self-management (Park, Hong, Lee, Ha, & Sung, 2004).
Self-Regulation (Executive Control). In the revised model, self-regulation is a
mediator between religious group attendance and self-management behaviors. Practicing
prayer, focusing the mind throughout a religious group activity, and engaging in more
self-monitoring because God may be watching may help the religious practitioner build
a strong capacity for self-regulation (Carter et al., 2012). Self-regulation skills acquired
by R/S involvement may aid the individual in successfully implementing and maintaining
behavioral changes related to disease management. For example, individuals who are
vigilant about not swearing and monitor personal finances (self-monitoring) so as to
donate weekly to their religious organization may be better able to monitor their daily
dietary fat intake. Studies with T2DM and cardiovascular disease patients have shown
improvements in behavioral self-management when patients engage in self-monitoring or
self-evaluation (Karter et al.; Sniehotta et al., 2005; Watkins et al., 2000).
Disease Self-Management Behaviors (Positive health behaviors) and Physical
Outcomes. In this model, positive health behaviors, or chronic illness self-management
behaviors, and physical measures commonly associated with self-management constitute
the predicted outcomes. Based on the model, these behaviors and physical measures are
indirectly predicted by R/S through the three pathways or mediators (social support,
13


positive psychological states, and self-regulation); however, R/S may also directly predict
chronic illness behavioral self-management and physical measures. An example of a
direct effect may be found in the teachings from several religious traditions that exhort
practitioners to treat their body with respect or like a temple (e.g. Buddhism,
Christianity) and discourage the use of harmful substances such as alcohol and tobacco
(e.g. Latter Day Saints, Seventh Day Adventists); however, self-regulation may also
partially mediate this association. Individuals diagnosed with cardiovascular disease who
are part of a religious culture that advises followers to respect and care for their body may
be more likely to follow their physicians advice, or may be more likely to make lifestyle
changes that are in line with how other church members already live (e.g. it is easier to
cut back on alcohol when other church members do not drink). An example of the
indirect effect may be through social support. The church members might be very
supportive of the choice to quit drinking and help the person join an Alcoholics
Anonymous group, or a church-based substance use intervention, to assist the person in
the behavior change.
If a relationship between R/S and disease self-management and physical measures
associated with self-management does exist, the described model may provide a more
sophisticated understanding of the various pathways through which R/S involvement
relates to chronic self-management behaviors and physical measures associated with
disease management for individuals with cardiovascular disease and diabetes.
Purpose of the Present Study
The primary purpose of the current study was to examine whether R/S
involvement (as measured by religious group attendance) is associated with, and for
14


whom it is associated with (biopsychosocial context), engagement in self-management
behaviors and certain physical measures associated with self-management behaviors (i.e.
body mass index (BMI) and blood pressure). This purpose is visually represented by the
top/blue shaded area of figure 2. The secondary purpose was to test the proposed
conceptual model as a way to understand potential mediators of the relationship between
R/S and chronic disease self-management; visually represented by the bottom/gray
shaded area of figure 2.
The objectives of the current study are to determine in a sample of Hispanic and
non-Hispanic community dwelling individuals with diagnosed cardiovascular disease
and/or diabetes: a) if religious group attendance is associated with disease self-
management behaviors (increased physical activity and more healthy eating behaviors)
and physical measures of outcomes often related to self-management (i.e. blood pressure
and BMI), while controlling for age, time since diagnosis, and education, b) if the
relationship between religious group attendance and self-management behaviors and
physical measures associated with self-management is moderated by ethnicity, gender, or
diagnosis, and c) to determine if the proposed model linking R/S to disease self-
management through the pathways/mediators of social support, mood, and self-regulation
is supported by data from the study sample.
Hypotheses and Specific Aims
The following are the specific aims and hypotheses of the current study.
Aiml: To determine whether frequency of religious group attendance is
associated with behavioral self-management (physical activity and eating behaviors) and
physical measures related to self-management (blood pressure and BMI) in individuals
15


with cardiovascular disease and/or diabetes and if this relationship is moderated by
ethnicity, gender, and diagnosis.
Hypothesis 1: For individuals with a diagnosis of diabetes and/or
cardiovascular disease, more frequent religious group attendance will be associated
with disease self-management behaviors and physical measures related to self-
management, including more frequent physical activity, better nutrition behaviors,
lower BMI, and lower systolic and diastolic blood pressure, while controlling for
age, education, and time since initial diagnosis. This relationship will be moderated
by gender, ethnicity and diagnosis, with the relationship being more pronounced for
women, Hispanic elderly, and those with cardiovascular disease.
Aim 2: To determine if there is support for the adapted Oman and Thorsesen
model, which theorizes that religious attendance is both directly associated with disease
self-management behaviors and physical measures associated with disease management
and indirectly associated via the pathways of social support, mood (depression), and self-
regulation.
Hypothesis 2: Structural Equation Model analyses of the data will
demonstrate support of the proposed model. That is, religious group attendance will
be both directly associated with self-management behaviors (e.g. physical activity
and eating behaviors) and physical measures associated with self-management (i.e.
BMI and blood pressure) and indirectly associated through the mediators of social
support, mood, and self-regulation.
16


CHAPTER II
METHOD
Participants
The San Luis Valley Health and Aging Study (SLVHAS) was a population-based
study of health and disability conducted in the early 1990s among Hispanic and non-
Hispanic white residents of two rural southern Colorado counties that make up the San
Luis Valley. The San Luis Valley is a relatively isolated rural region in southern
Colorado where most residents live in small communities or on ranches and farms. The
1990 population of the region was 46 percent Hispanic, 52 percent non-Hispanic White,
and 2 percent other (San Luis Valley Regional Development and Planning Commission,
1992). The majority of Hispanic residents in the San Luis Valley report their ethnicity as
Spanish or other Hispanic as opposed to Mexican American, reflecting the fact that
many Hispanics in this region are not recent immigrants (Bean & Tienda, 1987).
Eligibility criteria for participating in the original study included: (1) residence in
Alamosa or Conejos county; (2) Hispanic or non-Hispanic white ethnicity; and (3) age 60
years or older. The initial study cohort consisted of 1,360 participants who completed a
baseline visit between 1993 and 1995. For the current study, only those participants who
reported a diagnosis of diabetes or cardiovascular disease (i.e. heart disease, heart failure,
myocardial infarction, angina, bypass surgery, or coronary angioplasty) and completed
the study measures without a proxy were included. Participants who reported a less
severe cardiovascular diagnosis (i.e. hypertension), that might not be perceived as
warranting major lifestyle modifications, were not included.
17


Data Collection
The study protocol was approved by the Colorado Multiple Institutional Review
Board. After informed consent was obtained, study personnel conducted a 3-h baseline
interview, either in the participants home or the study research clinic. All interviewers
were bilingual, and Spanish-translated forms were available.
Demographics
The extensive self-report medical history review included: self-report of physician
diagnosed major chronic diseases including diabetes, heart attack, mini-stroke, major
stroke, angina, high blood pressure, heart failure, and procedures indicative of heart
disease, including heart or blood vessel surgery and angioplasty. Participants were also
asked to provide the year when they were diagnosed or received a procedure (e.g. bypass
surgery). The SLVHAS participants who reported having a diagnosis or history of heart
attack, angina, heart failure, coronary angioplasty, or cardiac bypass surgery were
classified has having cardiovascular disease. Participants who reported a history of
diabetes ( Has your doctor ever told you have diabetes (sugar diabetes)?) were
classified as having diabetes. Though individuals were not asked to specify the type of
diabetes, it can be assumed that a minimum of 90% had T2DM (National Diabetes
Factsheet, 2011). Time since diagnosis was calculated from the longest standing
diagnosis or treatment.
Education level was assessed with a single question: What is the highest grade
or year of school that you have completed? Responses were coded based on the year
equivalent of the level of school completed (e.g. 7th grade = 7 years of education).
Individuals, who had completed their GED, were coded as having completed 12 years of
18


school. Age was calculated based on participant provided date of birth and the
documented date of questionnaire completion. Income was assessed with a single item:
About how much was the total income, before taxes, of all your family members, living
in your house, from all sources last year? Ten income ranges were provided as answer
choices. Responses choices ranged from less than 5,000 to 75, 000 or more.
Measures
Predictor. Religious attendance was assessed using a single item asking the
number of times the participant attended a church function in the last month.
Moderators. Participants were asked to identify their sex, with the response
options of male or female. Ethnicity was assessed with a single question: Are you of
Spanish/Hispanic origin or decent? Diagnosis was assessed via extensive self-reported
medical history, see demographics section for more details.
Behavioral Outcomes. Physical activity and healthy eating behaviors were
measures of the behavioral outcomes in this study. To measure physical activity,
participants were questioned about the amount and quality of physical activity they
engaged in currently, including activities for work and leisure, in the past 12 months
using the Coronary Artery Disease Risk Scale. For example, one item asks: In the past
12 months, did you jog, run, hike or do similar activities for at least an hour total during
any month? Other items ask about engaging in a vigorous exercise class or vigorous
dancing, strenuous sports such as skiing, basketball, football or skating, and home
maintenance activities such as snow shoveling, or moving or lifting heavy objects.
Activities are classified into heavy-intensity activities and moderate-intensity activities,
which are translated into metabolic equivalent units (MET); heavy-intensity activity have
19


MET values from 5-8 and moderate-intensity activities are 3 or 4. Total energy
expenditure score is calculated based on activity intensity and months of frequent and
infrequent participation in the activity. Jacob et al. (1989) reported test-retest correlations
of 0.84 for a two week interval.
The nutrition questions on the SLVHAS questionnaire were created for this study
and are similar to questions asked in the first National Health and Nutrition Examination
Survey (NHANES I) (National Center for Health Statistics, 1973) and NHANES II
(National Center for Health Statistics, 1981). The healthy eating behaviors scale was
created from 7 items that asked about eating behaviors that relate to healthy eating, such
as how often do you salt your food at the table? , what do you do with the visible fat
on your meat? and how often do you eat food that is fried? Responses were summed
and averaged for a range of 0-4, with 4 indicating frequent healthy eating behaviors. The
scales internal consistency (a = .54) was similar with that of other brief dietary scales
(Turconi, Celsa, Rezzani, Biino, Sartirana, & Roggi, 2003).
Physical Outcomes. BMI and systolic and diastolic blood pressure were
measured as the physical outcomes in this study. BMI was calculated from body weight
and height, which was measured by research personnel during the interview with the
participant, by using the standard equation for BMI (i.e. BMI = (mass(lb)/height(in)2) x
703). Participants were asked to wear light clothing and no shoes for these measurements.
Seated blood pressure was measured in the right arm by research personnel in triplicate
after 5 min of rest. The last two readings were averaged for study inclusion for both
systolic and diastolic blood pressure.
20


Mediators. This study had three mediators: social support, mood (depression),
and self-regulation. Social support was measured by size of social network. Social
network size was assessed with two questions, 1) How many relatives do you feel close
to? That is, relatives you feel at ease with, can talk to about private matters, and can call
on for help, and 2) How many close friends do you have? That is, people that you feel
at ease with, can talk to about private matters, and can call on for helpThe responses
to the two questions were combined for a total numeric score.
Depressive symptoms were assessed as a measure of mood using the 20-item
Center for Epidemiologic Studies Depression (CES-D) Scale (Radioff, 1977). The scale
asks about feelings and experiences during the last week (e.g. /felt that everything I did
was an effort and /felt depressed) with response options ranging from rarely or none
of the time to most or all of the timed The overall scale reliability is high in the general
population (a = .85) and it similarly high in this sample (a = .89).
The Behavioral Dyscontrol Scale (BDS) (Grigsby & Kaye, 1996), a behavioral
measure of the capacity for behavioral self-regulation, was used to measure executive
control. For this scale, self-regulation is conceptualized as the initiation of purposeful
behavior and the inhibition of inappropriate actions (Grigsby, Kaye, & Robbins, 1992).
The BDS consists of 9 items, the majority of which assess the capacity to control
voluntary motor activity. For example, one item involves having the participant imitate
several hand movements (e.g. placing the left hand on the left ear or pointing with the
right hand to the right eye) demonstrated by the test administrator while facing the
participant. The participant is instructed to use the same hand as the administrator (i.e.
use his or her left hand if the administrator used the left hand) and thereby avoiding the
21


mirroring error (i.e. using their left hand when the administrator used the right hand).
Another item requires the participant to squeeze the administrators hand if the
administrator says the word red but do nothing if the administrator says the word
green. In total, this test takes approximately 15 minutes to administer and has high
internal consistency (a = .87), as well as test-retest (rs = .89 and .86 over 8 weeks and 6
months) and inter-rater reliability (rs = .95- .99) (Grigsby et al., 1992). In this analysis,
the 19-point version of the BDS was utilized and scores can range from 0-19 with higher
scores indicating better behavioral self-regulation. BDS scores have been shown to be
significantly correlated with measures of daily functioning and general cognitive status in
the SLVHAS sample (Grigsby, Kaye, Baxter, Shetterly & Hamman, 1998) and in a
sample of patients presenting at a geriatric outpatient clinic (Kaye, Grigsby, Robbins, &
Korzun, 1990).
Statistical Analysis
The majority of the data analyses were conducted using IBM SPSS Statistics
version 21 (SPSS Inc., 2012); only the structural equation model analyses were
conducted using Mplus (Muthen & Muthen, 2012). Descriptive statistics (e.g., means,
standard deviations) were calculated to describe the sample. Continuous variables were
checked for normal distributions. Variables were transformed in cases for which
transformation approximated a more normal distribution (i.e. bringing skewness and
kurtosis closer to zero) (Tabachnick & Fidell, 2013). Systolic blood pressure was log
transformed. BMI, diastolic blood pressure, depression, physical activity were square root
transformed. These transformed variables were used for the correlational, regression, and
structural equation model analyses. Religious group attendance, self-regulation, healthy
22


eating behaviors, and social support were not transformed. Differences in demographics
and additional variables across ethnicity and gender were assessed by independent t-tests
using all non-transformed variables. Cohens d was calculated for effect size when
significant differences were identified; Cohens d of 0.2 is considered a small effect size,
0.5 is considered a medium effect size, and 0.8 is considered a large effect size.
Hypothesis 1: For individuals with a diagnosis of diabetes and/or
cardiovascular disease, more frequent religious group attendance will be associated
with disease self-management behaviors and physical measures related to self-
management, including more frequent physical activity, better nutrition behaviors,
lower BMI, and lower systolic and diastolic blood pressure, while controlling for
age, education, and time since initial diagnosis. This relationship will be moderated
by gender, ethnicity and diagnosis, with the relationship more pronounced for
women, Hispanic elderly, and those with cardiovascular disease.
Analyses for this hypothesis were tested using linear regression to evaluate the
association of religious group attendance with all outcome variables (i.e. physical
activity, healthy eating behaviors, BMI, and systolic and diastolic blood pressure).
Ethnicity, gender, and diagnosis (cardiovascular disease, diabetes, or both) were used as
moderators in these analyses.
Hypothesis 2: Structural Equation Model analyses of the data will
demonstrate support for the proposed model. That is, religious group attendance
will be both directly associated with self-management behaviors (e.g. physical
activity and eating behaviors) and physical measures associated with self-
management (i.e. BMI and blood pressure) and indirectly associated through the
23


pathways/mediators of social support, mood, and self-regulation. This hypothesis was
tested using Structural Equation Modelling in Mplus, allowing social support, self-
regulation, and mood to correlate. A model was computed for each outcome (i.e. physical
activity, healthy eating, BMI, systolic and diastolic blood pressure). Fit statistics were
examined for each model to assess model fit. Missing data within these models were
handled using full information maximum likelihood estimation within Mplus 6 in order to
utilize all available data (Muthen & Muthen, 2012).
24


CHAPTER III
RESULTS
Out of the one thousand three hundred sixty participants in the main SLVHAS
study, 468 (34.4%) had a qualifying diagnosis of diabetes or cardiovascular disease and
personally completed the survey (rather than having a proxy). This sample had a mean
age of 73 years and contained a slightly greater proportion of women (54.3%) and
Hispanic participants (58.5%) (Table 1). Ethnicity for men and women in the sample was
51.4% and 64.6% Hispanic respectively. This subsample of the SLVHAS study is
similar to the demographics of the overall sample which consisted of 56.8% female and
58.3% Hispanic participants.
Table 1. Demographic data_________________________________________
N (%)
N=468
Gender
Male 214 (45.7)
Female 254 (54.3)
Ethnicity
Hispanic 274 (58.5)
Non-Hispanic White 194 (41.5)
Age M= 73.29 (SD = 7.43)
Education (# of years) M= 10.37 (SD = 3.81)
Income/year (2015 dollar equivalent)*
<7,5000 (7,674) 137 (29.3)
7,500-9,999 (7,674-10,231) 60 (12.8)
10,000- 14,999 (10,232-15,347) 96 (20.5)
15,000 24, 999 (15,348-25,579) 65 (13.9)
25,000 + (25,580+) 58 (12.4)
Diagnoses
Diabetes all 288
Diabetes no CVD 196
CVD-all 269
Heart attach 155
Angina 110
Heart Failure 74
Angioplasty 42
Bypass 32
CVD no DM 178
Both CVD and DM 94
Years since initial diagnosis M= 13.73 (SD = 11.25)
*2015 equivalent based on 2.32% inflation
Note: Cardiovascular disease is abbreviated CVD and diabetes is abbreviated DM.
25


On average, the participants had not completed high school and the majority had
an income of less than $14,999/year (equivalent to $24,819.91 in 2015 based on an
annual inflation of 2.32% over this time period). Among the 1360 original SLVHAS
participants, two hundred and sixty-nine participants (19.8%) reported a history of
cardiovascular disease, with heart attack (N= 155; 11.4%) being the most commonly
reported indicator of cardiovascular disease. These rates are fairly consistent with
national prevalence rates of coronary heart disease (including heart failure, myocardial
infarction, and angina pectoris) (21.1% of men and 10.6% of women for the 60-79 year-
old age group; Mozaffarian, et al. 2015). Atotal of 288 (21.2%) participant reported
having a diagnosis of diabetes, which is consistent with the national prevalence rates
(26.9% among individuals 65 years of age and older; National Diabetes Factsheet, 2011).
Among the participants with cardiovascular disease or diabetes, 94 participants reported
co-morbid diagnoses of diabetes and cardiovascular disease. At the time of data
collection, the average participant had been diagnosed for over a decade (M= 13.7 years;
SD = 11.25).
Table 2. Descriptive data
Mean SD
Religious Group Attendance 2.90 3.11
Social Support 9.26 3.07
Depression 8.19 8.72
Self-Regulation 15.18 3.78
Physical Activity 234.81 208.33
Healthy Eating Behaviors 2.47 0.67
BMI 27.88 4.75
Systolic Blood Pressure 138.51 20.99
Diastolic Blood Pressure 77.19 10.96
Descriptive statistics (mean, and standard deviation) were computed for all
predictor, mediator, and outcome variables (Table 2). Participants in this sample attended
religious functions on average 2.9 times in the past month and had a mean of 9.3 people
26


in their social network. Mean scores for depression were 8.19 (SD = 8.71) indicating
minimal depression symptoms well below the standard cut-off of > 16 (Radioff, 1977).
Means scores for self-regulation were 15.18 (SD = 3.78), indicating little to no
impairments (Grigsby & Kaye, 1992). Mean blood pressure was in the pre-hypertensive
range at 139/77.
Independent samples t-tests were calculated to determine if demographic and
other variables differed by gender or ethnicity in this sample. Variables included in the t-
test analyses were age, education, income, years since diagnosis, religious group
attendance, social network size, depression, self-regulation, physical activity, healthy
eating behaviors, BMI, systolic and diastolic blood pressure. The non-transformed
variables were used for the t-test analyses. Table 3 presents the means for only the
variables for which a statistically significant difference was detected. Cohens d was
calculated to determine the effect size of the observed differences.
Table 3. Variables with significant differences when compared by gender and ethnicity
Mean Mean Cohens d
Male Female
Social Support 9.87 8.87 0.37
Income bracket* 4.41 3.33 0.53
Physical Activity 275.71 195.98 0.39
Depression 6.51 9.61 0.37
Healthy Eating Behaviors 2.30 2.61 0.49
BMI 27.06 28.58 0.33
Systolic Blood Pressure 135. 03 141.46 0.31
Non-Hispanic Hispanic
Age 74.74 71.81 0.42
Education 11.93 8.93 0.06
Social Support 9.68 8.96 0.24
Income bracket* 4.63 3.24 0.53
Self-Regulation 16.37 14.34 0.57
Physical Activity 266.07 211.75 0.26
Healthy Eating Behaviors 2.61 2.37 0.41
BMI 27.27 28.30 0.22
Systolic Blood Pressure 135.57 140.60 0.24
Diastolic Blood Pressure 74.60 t rr,, rd, , 79.03 0.42 r,r> _ .... ,th
*Note: Income is a categorical variable. The 3rcl bracket corresponds to $7,500-$9,000/yearly a the 4th
bracket corresponds to $10,000-$ 14,999/year.
27


When comparing men and women, all significant differences detected were in the
small to moderate effect size range (i.e. Cohens d 0.3-0.5). On average men reported
significantly more people in their social network, higher income bracket, more physical
activity, less depression symptoms, less healthy eating behaviors, lower BMI, and lower
systolic blood pressure than women. When comparing Hispanic and Non-Hispanic white
participants, several significant differences in the small to moderate effect size range
found were found. Non-Hispanic whites on average were older, in a higher income
bracket, had higher self-regulation scores, lower BMI, lower systolic and diastolic blood
pressure, and reported more physical activity, larger social network size, and healthier
eating behaviors than Hispanics.
Bivariate (Pearson) correlations were run between all continuous untransformed
demographic, religious group attendance, mediator (i.e. social support, depression, and
self-regulation), and outcome variables (i.e. physical activity, healthy eating behaviors,
etc.) (Table 4). Religious group attendance was shown to have a significant and negative
correlation with years since diagnosis, depression, and systolic blood pressure and a
positive correlation with social support and self-regulation. Social support (positive) and
depression (negative) were significantly associated with physical activity, but not with
any of the other self-management behaviors or physical outcomes. Self-regulation was
significantly correlated with physical activity (positive), healthy eating behaviors
(positive) and systolic blood pressure (negative).
28


Aim 1 Linear Regression Moderated Analyses
Eighteen participants of the 468 did not have data on frequency of religious group
attendance. Only individuals with frequency of religious group attendance (N=450) could
be included in the linear regression analyses.
All regression analyses were conducted using transformed variables (i.e. BMI,
physical activity, systolic and diastolic blood pressure, and depression). Initial linear
regression analyses were conducted to examine the relationship between religious group
attendance and each outcome (physical activity, healthy eating behaviors, BMI, and
systolic and diastolic blood pressure) when controlling for age, education, and time since
diagnosis and are displayed in Table 5. While controlling for covariates, religious group
attendance was not significantly associated with physical activity, healthy eating
behaviors, BMI, systolic or diastolic blood pressure. Despite the absence of a main effect
of religious group attendance on outcomes, moderators were tested due to the current
29


Table 4. Bivariate correlations
Attendance Age Sex Education Yrs Diagnosed Ethnicity Social Support CESD Self- Regulation Physical Activity Healthy Eating Behavior BMI Systolic Diastolic
Religious Group -.048 Attendance .068 .127** -.nr .071 .194 -.117* .129** .049 .091 .013 -.095* -.066
Age ~ .033 .028 -.008 -208 .058 .052 -215 -224** .068 -.176 .075 -.152**
Sex -.031 .003 .133 -.183" .176** -.024 -.156** 241** .153" .158" -.017
Education .092 -.408** .138** -.041 .485** 236** 270** -.025 -.143** -.079
Years Since Diagnosis -.044 -.010 .028 -.022 -.008 -.095* -.064 .024 -.004
Ethnicity -.115* .013 -265** -.119 -.182 .112* .123* 200
Social Support -219" .166** 208 -.010 -.044 -.043 -.001
CESD -.190** -.199 .015 -.007 .071 .035
Self-Regulation 277 .157 .011 -.138 -.043
Physical Activity .056 .023 -.090 .120*
Healthy Eating Behaviors -- .017 .062 -.034
BMI .074 .105*
Systolic .593
Diastolic
^Correlation is significant at the 0.05 level (2-tailed)
** Correlation is significant at the .01 level (2-tailed)
o


studys a priori hypotheses, which was based on previous R/S literature demonstrating
differences in relationship strength between R/S and health outcomes when moderated by
demographic variables.
Gender did not moderate the relationship between religious attendance and
healthy eating behaviors, BMI or diastolic blood pressure; however, gender did moderate
the relationship between religious attendance and physical activity (fi = -.122; p = .090)
and the relationship between religious attendance and systolic blood pressure (fi = -.178;
p < .01). When examining the scatterplot graph of religious attendance and physical
activity (Figure 3), it appears as though religious group attendance is associated with
more frequent physical activity for men and slightly less or no association with physical
activity for women. The scatterplot graph of religious attendance and systolic blood
pressure indicates that more religious attendance is associated with lower systolic blood
pressure for women but shows no association for men (Figure 4).
31


Table 5. Linear Regression Models-Religious Attendance Predicting Outcomes (controlling for age, education, time since diagnosis)
Without a Gender as moderator Ethnicity as CVD only as DM only as
moderator moderator moderator moderator
Outcome 0* (SE) P P (SE) P P (SE) P P (SE) P P (SE) P
Physical Activity- SQRT J .010 .099 .845 -.122 .200 .090 -.043 .198 .537 .150 .212 .035 -.036 .199 .620
Health Eating Behaviors .040 .010 .392 .061 .019 .326 .091 .020 .146 -.052 .021 .420 .040 .020 .544
BMI- SQRT -.003 .007 .958 -.028 .014 .681 .011 .014 .868 .029 .015 .664 .031 .014 .657
Log Systolic- BP -.038 .001 .439 -.178 .002 .007 -.014 .002 .825 .042 .002 .533 -.065 .002 .342
Diastolic-BP SQRT -.045 .010 .358 -.027 .019 .691 .132 .019 .039 -.007 .021 .917 .039 .019 .566
Note: /?*= standardized beta coefficient; Cardiovascular disease is abbreviated CVD and diabetes is abbreviated DM.
N>


SQRT Physical Activity
Religious Group Attendance
Figure 3. Regression graph religious attendance predicting physical activity moderated by gender
33


Figure 4. Regression graph religious attendance predicting systolic blood pressure moderated by
gender
Ethnicity was added into the regression analyses as a potential moderator of the
relationship between religious attendance and self-management behaviors and indicators.
Ethnicity did not significantly moderate the relationship between religious attendance and
physical activity, healthy eating behaviors, BMI, or systolic blood pressure, but it did
significantly moderate the relationship between attendance and diastolic blood pressure
(fi =.132; p = .039). The regression graph indicated an association between more frequent
religious group attendance and lower diastolic blood pressure for non-Hispanic whites,
but no association for Hispanics (Figure 5).
34


Ethnicity
O Non-Hispanic White
O Hispanic
Non-Hispanic White
Hispanic
'ton-Hispanic White: R2 Linear
= 0.040
Hispanic: R2 Linear = 4.334E-5
Figure 5. Regression graph religious attendance predicting diastolic blood pressure moderated by
ethnicity
Diagnosis was also examined as a potential moderator of the relationship between
religious group attendance and self-management behaviors and physical measures of self-
management. Ninety-four participants had an overlapping diagnosis of cardiovascular
disease and diabetes. Two different interaction terms were created to distinguish between
three disease classifications: 1) cardiovascular disease without diabetes, 2) diabetes
without cardiovascular disease, and 3) co-morbid cardiovascular disease and diabetes
diagnosis. The first diagnosis interaction term compared classification 1 vs. 2 and 3
combined; the second interaction term compared classification 2 vs. 1 and 3 combined.
35


The relationship between religious group was not significantly moderated by diagnosis
for healthy eating behaviors, BMI, or blood pressure; however, the relationship was
moderated by diagnosis at a significant level for physical activity (/? =. 150\p = .035). The
regression graph (figure 6) indicates a positive association between attendance and more
frequent physical activity for participants with only cardiovascular disease diagnosis, no
relationship for participants with only a diabetes diagnosis, and a negative association for
participants with co-morbid cardiovascular disease and diabetes diagnoses.
Figure 6. Regression graph religious attendance predicting physical activity moderated by
diagnosis
36


Aim 2 Adapted Model Analyses
To address aim 2 of this study, five models were estimated based on the two self-
management behaviors and three physical outcomes (Figures 7-11). In each model, the
outcome (i.e. physical activity, healthy eating behaviors, BMI, systolic and diastolic
blood pressure) was predicted both directly by religious attendance and indirectly through
the pathways of religious attendance via social support, mood (depression), and self-
regulation. There were no covariates included in the five models. Of the 468 individuals,
who met criteria for inclusion in this study, 18 individuals had missing data on religious
attendance and were excluded from these models. Therefore, data from 450 individuals
were included.
All five models exhibited adequate model fit with Comparative Fit Index (CFI),
Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean
Square Residual (SRMR) resulting in the same values for all models (i.e., CFI=1.00,
RMSEA=.00, SRMR=.00) and model chi-square ranging from %(w) = 71.83 114.99,p
< .001. All models demonstrated a positive relationship between religious attendance and
social support (p < .001) and self-regulation (p < .01), and an inverse relationship
between religious attendance and depression symptoms (p < .05).
Figure 7 shows the results of the structural equation model examining physical
activity as the outcome as predicted by religious group attendance directly and through
the pathways of social support, depression, and self-regulation. The three mediators (i.e.
social support, depression, and self-regulation) were significantly associated with both
religious attendance and physical activity at thep < .05 level. The direct relationship
from religious group attendance to physical activity was not significant in this model.
37


Figure 7. Model with Physical Activity as Outcome
Figure 8 shows the results of the structural equation model examining healthy eating
behaviors as the outcome as predicted by religious group attendance directly and through
the pathways of social support, depression, and self-regulation. Self-regulation was
shown to be significantly associated (p < .01) with both religious attendance and healthy
eating behaviors, while controlling for social support, depression, and the direct
relationship between attendance and healthy eating behaviors. The direct relationship
between attendance and healthy eating behaviors was marginally significant (p < .10)
when accounting for social support, depression, and self-regulation.
Figure 8.Model with Health Eating Behaviors as Outcome
38


Figure 9 shows the results of the structural equation model examining BMI as the
outcome as predicted directly by religious attendance and through the pathways of social
support, depression, and self-regulation. Neither the direct or indirect pathways from
attendance to BMI were significant in this model.
Figure 9. Model with BMI as outcome
Figures 10 and 11 shows the results of the structural equation model examining
blood pressure (systolic and diastolic respectively) as the outcomes predicted by religious
attendance and through the pathways of social support, depression, and self-regulation. In
the model for systolic blood pressure (Figure 10), religious attendance does not
demonstrate a significant direct relationship with systolic blood pressure, but self-
regulation is demonstrated to be significantly associated with both religious attendance
and systolic blood pressure (p < .01). The model with diastolic blood pressure as the
outcomes (Figure 11), demonstrated that the direct and indirect pathways from attendance
to diastolic blood pressure were not significant.
39


Figure 10. Model with systolic blood pressure as outcome
Figure 11. Model with diastolic blood pressure as outcome
40


CHAPTER IV
DISCUSSION
The primary aim of the currently study was to examine, in a sample of Hispanic
and Non-Hispanic white elderly community members living in the rural San Luis Valley
of Colorado with a diagnosis of cardiovascular disease or diabetes, whether religious
attendance was associated with self-management behaviors and physical measures often
related to self-management and whether gender, ethnicity, and diagnosis moderated these
relationships. This study found that the relationship between religious group attendance
and disease self-management behaviors (physical activity and healthy eating behaviors)
and physical measures of disease self-management (BMI and blood pressure) varied by
gender, ethnicity, and diagnosis. Religious attendance was associated with more frequent
physical activity for men and for individuals with only cardiovascular disease, but was
associated with less physical activity for individuals with co-morbid cardiovascular and
diabetes diagnoses. Frequent religious service attendance was associated with lower
systolic blood pressure among women and lower diastolic blood pressure among non-
Hispanic white participants.
The blood pressure finding are consistent with the existing R/S and health
literature that generally demonstrate greater health benefits of R/S for women (Chida,
Steptoe, & Powell, 2009; McCullough, Hoyt, Larson, Koenig, & Thoresen, 2000).
Additionally the finding that religious service attendance was associated with lower
diastolic blood pressure for non-Hispanic white participants is consistent with the study
by Bell and colleagues (2012) that found a stronger association between R/S and blood
pressure for non-Hispanic whites and blacks than for Hispanic participants. One
41


unexpected finding from this study, given that women typically demonstrate strong R/S-
health associations, was that religious service attendance was associated with reports of
more physical activity among men and not women. Because this effect was only
marginally significant (p = .09), this result may be a type-1 error. Alternatively, it may be
that gender differences have not previously been thoroughly examined in individuals with
diabetes and cardiovascular disease, and religious service attendance may have a greater
relationship with physical activity among men than women for these groups.
Religious attendance did not demonstrate a significant relationship with BMI, nor
were the moderators of gender, ethnicity, or diagnosis significant. These non-significant
findings may potentially be due to the existence of a non-linear relationship. A large
prospective study of more than 1 million adults in the United States, demonstrated a
curvilinear relationship between BMI and risk of death from all causes that is increased
risk of mortality at both the highest and lowest ends of BMI and decreased risk in the
middle (Calle, Thun, Petrelli, Rodriguez, & Health, 1999). Similarly, a study of
individuals (> 65 years of age) found that when compared with normal weight people,
both underweight and obese older adults reported impaired quality of life, particularly
worse physical functioning and physical wellbeing (Yan, et al. 2012). Therefore,
utilizing an analytic approach designed to detect a non-linear relationship may have been
more appropriate. Additionally, use of BMI as a measure of adiposity may lead to
misclassification of weight classification particularly among older women and men (Shah
& Braverman, 2012). Obesity researchers have found that other measures, such as hip-to-
waist circumference ratio, may more accurately measure adiposity (Prentice & Jebb,
2001).
42


Additionally, because R/S effects by disease category have not been previously
examined, the finding that religious attendance is associated with more physical activity
for those with cardiovascular disease and less for those with co-morbid cardiovascular
disease and diabetes is a unique contribution to the literature and open to interpretation.
In regards to the finding that religious attendance was associated with less physical
activity among individuals with co-morbid diagnoses, one interpretation may be that
individuals with comorbid diagnosis likely have extremely poor health and limited
capacity for physical exertion; therefore, they need to be selective in how they expend
their energy and may have to choose between engaging in physical activity and attending
church services.
The secondary aim of this study was to test a model (Figure 2) adapted from
Oman and Thoresens model (2002; Figure 1). The adapted model proposed that religious
attendance has a direct effect on disease self-management behaviors and physical
measures often associated with disease self-management, as well as an indirect effect
through the mechanisms of social support, mood, and self-regulation. The models
(Figures 7-11) showed that, consistent with prior literature, religious group attendance
was significantly associated with more social support, less depression, and more self-
regulation. In terms of finding support for the adapted model as a whole, the results were
mixed. The model examining physical activity as the outcome was the most supported
and consistent with the adapted Oman & Thoresen model, demonstrating support for the
pathways of social support, depression, and self-regulation mediating the relationship
between attendance and physical activity.
43


Self-regulation was related to three out of the five models (i.e. physical activity,
healthy eating behaviors, and systolic blood pressure) indicating that self-regulation may
be a particularly potent mechanism through which religious attendance is linked to
disease self-management. This finding is consistent with work done by McCullough &
Willoughby (2009), which suggests that R/S mental and physical health benefits occur
through the pathway of self-regulation. Their theory posits that R/S strengthens ones
self-regulation muscle through a variety of pathways, such as engaging in frequent
religious practices that frequently and perhaps intensely activate self-monitoring abilities
(e.g. meditating, praying, focusing attention during religious services), and builds self-
control through delayed gratification and encouraging a future-oriented mindset (i.e.
engaging or not engaging in certain behaviors in the present, so as to be rewarded in the
afterlife). R/S leads individuals to build strong generalizable self-regulatory abilities that
may be utilized beyond specific R/S-related practices. These generalizable self-regulatory
abilities allow for engagement in activities that promote social, mental, and physical
health, such as appropriate navigation of social relationships (allowing for increased
social network/social support), regulation of ones emotions (less depression), and
regulation of health behaviors such as choosing healthy rather than unhealthy foods.
Interpreting the model results in light of McCullough & Willoughbys (2009) theory,
suggests that self-regulation may indeed be a crucial mechanism for understanding the
relationship between R/S and disease-self-management.
The models with BMI and diastolic blood pressure as outcomes (Figures 9 & 11)
demonstrated the least support for the studys proposed conceptual model, with no
significant pathways. The non-significant findings may be due to religious attendance not
44


having strong relationship with these outcomes. Alternatively, the relationships may be
non-linear in nature, particularly for BMI. The results of this study provided mixed yet
promising support for the adapted Oman & Thoresen model, particularly for the outcome
of physical activity and for self-regulation as a mechanism, suggesting that further testing
of the model is warranted.
The results from this study add to the growing literature aimed at understanding
the factors involved in facilitating better adherence to lifestyle modification needed to
manage cardiovascular disease and diabetes. The results of this study are also notable
given the uniqueness of the study population. The San Luis Valley of Colorado is a
relatively isolated and poor area of the country. The demographics of the valley are also
unique, with a large proportion of Hispanic community members, originally immigrated
from Spain, and fewer immigrants from Mexico. The large proportion of Hispanic and
non-Hispanic white participants in this sample allowed for important comparisons in
outcomes by ethnicity.
The results add to the rich literature examining the relationship between R/S and
health behaviors and outcomes. Previous work examining the relationship between R/S
and disease self-management had primarily been conducted with small sample sizes and
with African American participants. These results demonstrated that in a relatively large
sample of Hispanic and non-Hispanic white community dwelling elderly, that attending
religious services may have a positive relationship with self-management behaviors for
some individuals with diabetes and cardiovascular disease.
The results of this study have limitations given the cross-sectional nature of the
data. It is possible that the direction of the relationship between religious group
45


attendance and self-management behaviors could be reversed (i.e. men who are
physically active are more capable of attending church function and women with lower
blood pressure feel healthy enough to attend church). Similarly, the direction of the
relationships tested in the structural equation models may be reversed in direction or be
bidirectional (i.e. individuals with a high BMI may be more isolated and have less social
support and therefore may have no one to drive them to church, feeling isolated from a
church community may then lead to compensatory eating behaviors and higher BMI).
Additionally, there are other potential third factors that were not examined in this study
that could also impact the relationship between religious group attendance and self-
management, such as access to medical care, access to healthy food options in an isolated
community, the religious communities relationship with food (e.g. does partaking in the
religious community culture involve frequent potlucks with unhealthy foods), and racial
discrimination. One outcome that was not measured that could have important
implications for the blood pressure outcomes was adherence to medication. Given that
many individuals with cardiovascular disease and diabetes also have a diagnosis of
hypertension, religious group attendance may influence adherence to hypertension
medications, partially explaining the lower blood pressure outcomes demonstrated for
women and non-Hispanic white participants.
Further research is needed examining the relationship and possible impact of R/S
variables on self-management behaviors and long-term health outcomes related to R/S. In
particular, longitudinal research is needed to determine if being part of a religious
community or being highly spiritual is associated with changes in health behaviors from
pre to post-diagnosis.
46


REFERENCES
Armstrong, B., van Merwyk, A. J., & Coates, H. (1977). Blood pressure in Seventh-day
Adventist vegetarians. American Journal of Epidemiology, 105, 444-449.
Bean, F.D., Tienda, M. (1987). The Hispanic population of the United States. New York,
NY: Russell Sage Foundation.
Bell, C. N., Bowie, J. V., & Thorpe, R. J. (2012). The interrelationship between
hypertension and blood pressure, attendance at religious services and
race/ethnicity. Journal of Religion and Health, 51, 310-311.
Black, G., Davis, B. A., Heathcotte, K., Mitchell, N., & Sanderson, C. (2006). The
relationship between spirituality and compliance in patients with heart failure.
Progress in Cardiovascular Nursing, 21, 128-133.
Callaghan, D. (2006). The influence of basic conditioning factors on healthy behaviors,
self-efficacy, and self-care in adults. Journal of Holistic Nursing, 24, 178-185.
Calle, E. E., Thun, M. J., Petrelli, J. M., Rodriguez, C., & Heath, C.W. (1999). Body-
Mass Index and Mortality in a Prospective Cohort of U.S. Adults. New England
Journal of Medicine, 341, 1097-1105 DOI: 10.1056/NEJM199910073411501
Carter, E. C., McCullough, M. E., & Carver, C. S. (2012). The mediating role of
monitoring in the association of religion with self-control. Social Psychological
and Personality Science, 5(6), 691-697.
Chida, Y., Steptoe, A., & Powell, L. H. (2009). Religiosity/spirituality and mortality.
Psychotherapy andPsychosomatics, 78, 81-90.
Chow, C. K., Jolly, S., Rao-Melacini, P., Fox, K. A., Anand, S. S., & Yusuf, S. (2010).
Association of diet, exercise, and smoking modification with risk of early
47


cardiovascular events after acute coronary syndromes. Circulation, 121, 750-758.
doi: 10.1161/CIRCULATIONAHA. 109.891523
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, New
Jersey: Lawrence Ehrlbaum Associates.
Comstock, G. W., & Partridge, K. B. (1972). Church attendance and health. Journal of
Chronic Diseases, 25, 665-672.
DiMatteo, M. R. (2004). Social support and patient self-management to medical
treatment: a meta-analysis. Health Psychology, 23, 207-218. doi: 10.1037/0278-
6133.23.2.207
DiMatteo, M. R., Lepper, H. S., & Croghan, T. W. (2000). Depression is a risk factor for
noncompliance with medical treatment: meta-analysis of the effects of anxiety
and depression on patient self-management. Archives of Internal Medicine, 160,
2101-2107.
Ellison, C. G., & George, L. K. (1994). Religious involvement, social ties, and social
support in a southeastern community. Journal for the Scientific Study of Religion,
33, 46-61.
Grigsby, J., & Kaye, K. (1996). Behavioral dyscontrol scale: Manual (2nd ed.). Ward,
Colorado.
Grigsby, J., Kaye, K., Baxter, J., Shetterly, S. M., & Hamman, R. F. (1998). Executive
cognitive abilities and functional status among community-dwelling oder person
in the San Luis Valley Health and Aging Study. Journal of the American
Geriatric Society, 44, 590-596.
48


Grigsby, J., Kaye, K., & Robbins, L. J. (1992). Reliabilities, norms and factor structure of
the Behavioral Dyscontrol Scale. Perceptual and Motor Skills, 74(3 Pt 1), 883-
892.
Haynes, R. B., Yao, X., Degani, A., Kripalani, S., Garg, A., & McDonald, H. P. (2005).
Interventions to enhance medication self-management. Cochrane Database of
Systematic Reviews (A), CD000011. doi: 10.1002/14651858.CD000011.pub2
Hill, T. D., Angel, J. L., Ellison, C. G., & Angel, R. J. (2005). Religious attendance and
mortality: An 8-year-follow-up of Older Mexican Americans. Journal of
Gerontology: Social Sciences, 60, 102-109. doi: 10.1093/geronb/60.2.S102
Hixson, K. A., Gruchow, H. W., & Morgan, D. W. (1998). The relation between
religiosity, selected health behaviors, and blood pressure among adult females.
Preventive Medicine, 27, 545-552. doi: 10.1006/pmed. 1998.0321
Holman, H, & Lorig, K. (2000). Patients as partners in managing chronic disease.
Partnership is a prerequisite for effective and efficient health care. British Journal
of Medicine, 320, 526-527.
Hummer, R. A., Rogers, R. G., Nam, C. B., & Ellison, C. G. (1999). Religious
involvement and U.S. adult mortality. Demography, 36, 273-285.
Jacobs, D. R., Jr., Hahn, L. P., Haskell, W. L., Pirie, P., & Sidney, S. (1989). Validity and
reliability of short physical activity history: CARDIA and the Minnesota Heart
Health Program. Journal of Cardiopulmonary Rehabilitation, 9, 448-459.
Karter, A. J., Ackerson, L. M., Darbinian, J. A., DAgostino, R. B., Jr., Ferrara, A., Liu,
J., & Selby, J. V. (2001). Self-monitoring of blood glucose levels and glycemic
49


control: the Northern California Kaiser Permanente Diabetes registry. American
Journal of Medicine, 111, 1-9.
Kaye, K., Girgsby, J., Robbins, L. J., & Korzun, B. (1990). Prediction of independent
functioning and behavior problems in geriaric patients. Journal of the American
Geriatric Society, 38, 1304-1310.
Kim, S. (2007). Burden of hospitalizations primarily due to uncontrolled diabetes:
implications of inadequate primary health care in the United States. Diabetes
Care, 30, 1281-1282. doi: 10.2337/dc06-2070
Kim, K. H., & Sobal, J. (2004). Religion, social support, fat intake and physical activity.
Public Health Nutrition, 7, 773-781. doi: 10.1079/PHN2004601
Koenig, H. G., George, L. K., Hays, J. C., Larson, D. B., Cohen, H. J., & Blazer, D. G.
(1998). The relationship between religious activities and blood pressure in older
adults. International Journal of Psychiatry in Medicine, 28, 189-213.
Koenig, L. B., & Vaillant, G. E. (2009). A prospective study of church attendance and
health over the lifespan. Health Psychology, 28, 117-124. doi: 10.1037/a0012984
Krousel-Wood, M. A., & Frohlich, E. D. (2010). Hypertension and depression: coexisting
barriers to medication self-management. Journal of Clinical Hypertension, 12,
481-486. doi: 10.111 l/j,1751-7176.2010.00302.x
Lin, E. H, Katon, W., Von Korff, M., Rutter, C., Simon, G. E., Oliver, M., . Young, B.
(2004). Relationship of depression and diabetes self-care, medication self-
management, and preventive care. Diabetes Care, 27, 2154-2160.
Lloyd-Jones, D., Adams, R., Carnethon, M., De Simone, G., Ferguson, T. B., Flegal, K., .
. Stroke Statistics, Subcommittee. (2009). Heart disease and stroke statistics
50


2009 update: a report from the American Heart Association Statistics Committee
and Stroke Statistics Subcommittee. Circulation, 119, e21-181. doi:
10.1161/CIRCULATIONAHA. 108.191261
Lorig, K. R., & Holman, H. (2003). Self-management education: history, definition,
outcomes, and mechanisms. Annals of Behavioral Medicine, 26, 1-7.
McCullough, M. E., Hoyt, W. T., Larson, D. B., Koenig, H. G., & Thoresen, C. (2000).
Religious involvement and mortality: a meta-analytic review. Health Psychology,
19, 211-222.
McCullough, M. E., & Willoughby, B. L. B. (2009). Religion, self-regulation, and self
control: Associations, explanations, and implications. Psychological Bulletin, 135,
69-93. doi: 10.1037/a0014213
Miller, A. S., & Hoffmann, J. P. (1995). Risk and religion: An explanation of gender
differences in religiosity. Journal for the Scientific Study of Religion, 34\ 63-75.
Miller, W. R., & Thoresen, C. E. (2003). Spirituality, religion, and health. An emerging
research field. American Psychologist, 58, 24-35.
Mozaffarian, D., Benjamin, E. J., Go, A. S., Arnette, D. K., Blaha, M. J., Cushman, M.,
... Stroke Statistics Subcommittee. (2015). Heart disease and stroke statistics -
2015 update: a report from the American Heart Association. Circulation, 131,
e29-e322. doi: 10.1161/CIR.0000000000000152.
Muthen, L. & Muthen, B. (2012). Mplus version 6. [Computer software],
Natarajan, S., Sana Ana, E. J., Liao, Y., Lipsitz, S. R., & McGee, D. L (2009). Effect of
treatment and self-management on ethnic differences in blood pressure control
among adults with hyptertension. Annals of Epidemiology, 19, 172-179.
51


National Center for Health Statistics (1973). Plan and Operation of the Health and
Nutrition Examination Survey, 1971-73. Washington, DC: DHHS (PHS)
publication.
National Center for Health Statistics (1981). Plan and Operation of the Second National
Examination Survey, 1976-80. Washington, DC: DHHS (PHS) publication.
Newlin, K., Melkus, G. D., Tappen, R., Chyun, D., & Koenig, H. G. (2008).
Relationships of religion and spirituality to glycemic control in Black women
with type 2 diabetes. Nursing Research, 57, 331-339. doi:
10.1097/01 .NNR.0000313497.10154.66
Oman, D., & Reed, D. (1998). Religion and mortality among the community-dwelling
elderly. American Journal of Public Health, 88, 1469-1475.
Oman, D., & Thoresen, C. E. (2002). Does religion cause health?': differing
interpretations and diverse meanings. Health Psychol, 7, 365-380. doi:
10.1177/1359105302007004326
Osterberg, L., & Blaschke, T. (2005). Self-management to medication. New England
Journal of Medicine, 353, 487-497. doi: 10.1056/NEJMra050100
Park, H, Hong, Y., Lee, H, Ha, E., & Sung, Y. (2004). Individuals with type 2 diabetes
and depressive symptoms exhibited lower self-management with self-care.
Journal of Clinical Epidemiology, 57, 978-984.
Patrick, D. L., Danis, M., Southerland, L. I., & Hong, G. (1988). Quality of life following
intensive care. Journal of General Internal Medince, 3, 218-223.
Peyrot, M., Rubin, R. R., Lauritzen, T., Snoek, F. J., Matthews, D. R., & Skovlund, S. E.
(2005). Psychosocial problems and barriers to improved diabetes management:
52


results of the Cross-National Diabetes Attitudes, Wishes and Needs (DAWN)
Study. Diabetic Medicine, 22, 1379-1385. doi: 10.1111/j. 1464-
5491.2005.01644.x
Prentice, A. M., & Jebb, S. A. (2001). Beyond body mass index. Obesity Reviews, 2, 141-
147.
Poloma, M., & Pendleton, B. (1991). The effects of prayer and prayer experience on
measures of general well being. Journal of Psychology and Theology, 1, 71-83.
Radloff, L. S. (1977). The CES-D scale: A self-report depression scale for research in the
general popultation. Applied Psychological Measurement, 1, 385-401.
Reindl Benjamins, M., & Brown, C. (2004). Religion and preventative health care
utilization among the elderly. Social Science and Medicine, 58, 109-118.
Rote, S., Hill, T. D., & Ellison, C. G. (2012). Religious attendance and loneliness in later
life. The Gerontologist, 53, 39-50.
Samuel-Hodge, C. D., Watkins, D. C., Rowell, K. L., & Hooten, E. G. (2008). Coping
styles, well-being, and self-care behaviors among African Americans with type 2
diabetes. Diabetes Educator, 34, 501-510. doi: 10.1177/0145721708316946
San Luis Valley Regional Development and Planning Commission (1992). San Luis
Valley Overall Economic Development Program. Alamosa, CO: San Luis Valley
Regional Development and Planning Commission.
Shah, N. R., & Braverman, E. R. (2012). Measuring adiposity in patients: the utility of
body mass index (BMI), percent body fat, and leptin. PloS One, 7, e33308.
53


Shuler, P. A., Gelberg, L., & Brown, M. (1994). The effects of spiritual/religious
practices on psychological well-being among inner city homeless women. Nurse
Practitioner Forum, 5, 106-113.
Smith, T. B., McCullough, M. E., & Poll, J. (2003). Religiousness and depression:
evidence for a main effect and the moderating influence of stressful life events.
Psychological Bulletin, 129, 614-636.
Sniehotta, F. F., Scholz, U., Schwarzer, R., Fuhrmann, B., Kiwus, U., & Voller, H.
(2005). Long-term effects of two psychological interventions on physical exercise
and self-regulation following coronary rehabilitation. International Journal of
Behavioral Medicine, 12, 244-255. doi: 10.1207/sl5327558ijbml204_5
SPSS Inc. (2012). Statistical package for the social sciences v. 21 [Computer software],
Strawbridge, W. J., Shema, S. J., Cohen, R. D., & Kaplan, G. A. (2001). Religious
attendance increases survival by improving and maintaining good health
behaviors, mental health, and social relationships. Annals of Behavioral Medicine,
23, 68-74. doi: 10.1207/S15324796ABM2301_1
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statitiscs (6th ed.). Pearson
Education, Inc.
Toljamo, M., & Hentinen, M. (2001). Self-management to self-care and social support.
Journal of Clinical Nursing, 10, 618-627.
Troiano, R. P., Berrigan, D., Dodd, K. W., Masse, L. C., Tilert, T., & McDowell, M.
(2008). Physical activity in the United States measured by accelerometer.
Medicine & Science in Sports & Exercise, 40, 181-188. doi:
10.1249/mss.0b013e31815a51b3
54


Tuppin, P., Neumann, A., Danchin, N., de Peretti, C., Weill, A., Ricordeau, P., &
Allemand, H. (2010). Evidence-based pharmacotherapy after myocardial
infarction in France: self-management-associated factors and relationship with 30-
month mortality and rehospitalization. Archives of Cardiovascular Disease, 103,
363-375. doi: 10.1016/j.acvd.2010.05.003
Turconi, G., Celsa, M., Rezzani, C. Biino, G., Sartirana, M. A., & Roggi, C. (2003).
Reliability of a deitary questionnaire on food habits, eating behaviour and
nutritional knowledge of adolescents. European Journal of Clinical Nutrition, 57,
753-763. doi: 10.1038/sj.ejcn.l601607
van der Wal, M. H., Jaarsma, T., Moser, D. K., Veeger, N. J., van Gilst, W. H., & van
Veldhuisen, D. J. (2006). Compliance in heart failure patients: the importance of
knowledge and beliefs. European Heart Journal, 27, 434-440. doi:
10.1093/eurheartj/ehi603
Walsh, A. (1998). Religion and hypertension: testing alternative explanations among
immigrants. Behavioral Medicine, 24, 122-130. doi:
10.1080/08964289809596390
Wang, P. S., Bohn, R. L., Knight, E., Glynn, R. J., Mogun, EL, & Avom, J. (2002).
Noncompliance with antihypertensive medications: the impact of depressive
symptoms and psychosocial factors. Journal of General Internal Medicine, 17,
504-511.
Watkins, K. W., Connell, C. M., Fitzgerald, J. T., Klem, L., fiickey, T., & Ingersoll-
Dayton, B. (2000). Effect of adults' self-regulation of diabetes on quality-of-life
outcomes. Diabetes Care, 23, 1511-1515.
55


World Health Organization (WHO), (2003). Self-management to Long-Term Therapies:
Evidence for Action. Geneva, Switzerland.
World Health Organization (WHO), (2011). Global status report on noncommunicable
diseases 2010. Geneva, Switzerland.
Wu, J. R., Frazier, S. K., Rayens, M. K., Lennie, T. A., Chung, M. L., & Moser, D. K.
(2013). Medication self-management, social support, and event-free survival in
patients with heart failure. Health Psychology, 32, 637-646. doi:
10.1037/a0028527
Yan, L. L., Daviglus, M. L., Liu, K., Pirzada, A., Garside, D. B., Schiffer, L., Dyer, A.
R., & Greenland, P. (2004). BMI and health-related quality of life in adults 65
years and older. Obesity Research, 12, 69-76. DOI: 10.1038/oby.2004.10
56


Full Text

PAGE 1

RELIGIOIUS ATTENDANCE AND CHRONIC DISEASE SELF MANAGEMENT AMONG OLDER ADULTS IN THE SAN LUIS VALLEY HEALTH AND AGING STUDY By KAILE M. ROSS B.A., University of Notre Dame, 2007 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Master of Arts Clinical Health Psychology Program 2015

PAGE 2

2015 KAILE M. ROSS ALL RIGHTS RESERVED

PAGE 3

ii This thesis for the Master of Arts degree by Kaile M. Ross Has been approved for the Clinical Health Psychology Program b y Kevin Masters Chair Krista Ranby Jim Grigsby July 17 2015

PAGE 4

iii Ross, Kaile M. (M.A., Clinical Health Psychology) Religious Attendance and Chronic Disease Self Management among Older Adults in the San Luis Valley Health and Aging Study Thesis directed by Professor Kevin S. Masters ABSTRACT Cardiovascular disease and t ype 2 d iabetes m ellitus are two increasingly prevalent chronic health conditions that require not only medical team oversight but also a high level of patient involvement to achieve optimal disease management. Regular attendance at religious services and religious community involvement may help individuals with cardiovascular disease and diabetes better manage their chronic illness. R/S is predictive of many positive healt h related variables but has yet to be examine d in much depth in relation to self management for persons with chronic illness. T he was to examine whether and for whom religious attendance is related to self management behaviors (physic al activity and healthy eating behaviors) and to physical outcome measures considered to be related to self management ( body mass index and blood pressure) among elderly Hispanic and Non Hispanic adults (60 y/o +) with diabetes or cardiovascular disease from the San Luis Valley Health and Aging Study The second aim of the study was to test a proposed model that theorizes that religious attendance directly and indirectly is associated with disease self management behaviors and physical measures of diseas e self management through the mechanisms of social support, mood, and self regulation. The results showed that in a sample of 468 (54.3% female; 58.5% Hispanic) individuals with cardiovascular disease or diabetes, the relationship between religious group a ttendance and self management varied by gender, ethnicity, and diagnosis. The relationship of attendance with physical activity was moderated by gender

PAGE 5

iv ( = .122, p = .090) and diagnosis ( = .150, p = .035) with attendance being associated with more phy sical activity for men and individuals with cardiovascular disease without diabetes (vs. individuals with diabetes and co morbid diagnoses) The relationship of attendance with blood pressure was moderated by gender ( = .178, p = .007) and ethnicity ( = .132, p = .039), with women having lower systolic blood pressure and non Hispanic whites (vs. Hispanics) having lower diastolic blood pressure. The structural equation model results demonstrated mixed support for the proposed model, with the physical acti vity model yielding the strongest support. These findings have important implications for understanding the role that R/S may have in chronic illness disease self management. The form and content of this abstract are approved. I recommend its publication Approved: Kevin S. Masters

PAGE 6

v DEDICATION This thesis is dedicated to my husband, Patrick Wood fo r his continuous love, support and encouragement and to my parents, James and Kathleen Ross, for always pushing me to accomplish more than I thought possible

PAGE 7

vi ACKNOWLEDGEMENTS I would like to first acknowledge and thank my advisor and mentor, Kevin S. Masters, for his guidance and support in this process. I would also like to thank Krista Ranby for her statistical expertise and her willingness to answer all my questions about my analyses. I would also like to thank Jim Grigsby for being supportive throughout this process and allowing me access to the incredible SLVHAS dataset. I would also like to acknowledge my lab mates, Stephanie Hooker, Megan Grigsby, and Lacey Clement, for their emotional support and encouragement along the way. Lastly, I would like to thank my cohort (Jo Vogeli, Shiva Fekri, Tattiana Romo, and Lacey Clement) for the support, laughter and tears that helped to make the last fe w years so memorable and wonderful.

PAGE 8

vii TABLE OF CONTENTS CHAPTER I. BACKGROUND Religion and Health ................................ ................................ ................................ 4 Religion and Engagement in Health Related Behaviors ................................ .......... 5 R/S and Disease Self Management ................................ ................................ .......... 6 Pathways from R/S to Disease Self Management Social Support, Mood, and Self Regulation ................................ ................................ ................................ ................ 7 Model of R/S and Disease Management Behaviors and Physical Measures Associated with Disease Management ................................ ................................ ... 10 Purpose of Present Study ................................ ................................ ....................... 14 Hypotheses and Spe cific Aims ................................ ................................ .............. 15 II. METHOD Participants ................................ ................................ ................................ ........... 17 Data Collection ................................ ................................ ................................ .... 18 Demographics ................................ ................................ ................................ ....... 18 Measures ................................ ................................ ................................ ............... 19 Statistical Analyses ................................ ................................ ............................... 22 III RESULTS Aim 1: Linear Regression Moderated Analyses ................................ ................... 30 Aim 2 : SEM Analyses ................................ ................................ .......................... 37 I V. DISCUSSION ................................ ................................ ................................ ....... 41 REFERENCES ................................ ................................ ................................ ................. 47

PAGE 9

viii LIST OF TABLES TABLE 1 Demographic Data ................................ ................................ ................................ 25 2 De scriptive Data ................................ ................................ ................................ .... 26 3 Variables with significant differences when compared by gender and ethnicity 27 4 Bivariate C orrelations ................................ ................................ ........................... 30 5 Linear Regression Models Religious Attendance Predicting Outcomes ............. 32

PAGE 10

ix LIST OF FIGURES FIGURE 1 Oman & Thor e sen (2002) Model ................................ ................................ ......... 11 2. Adapted Model ................................ ................................ ................................ .... 12 3. Regression Graph Religious Attendance Predicting Physical Activity Moderated by Gender ................................ ................................ ........................... 33 4 Regression Graph Religious Attendance Predicting Systolic Blood Pressure Moderate d by Gender ................................ ................................ ........................... 34 5 Regression Graph Religious Attendance Predicting Diastolic Blood Pressure Moderated by Ethnicity ................................ ................................ ......................... 35 6 Regression Graph Religious Attendance Predicting Physical Activity Moderated by Diagnosis ................................ ................................ ................................ .......... 36 7 Model w ith Physical Activity as the Outcome ................................ .................... 38 8 Model with Healthy Eating Behaviors as the Outcome ................................ ....... 38 9 Model with BMI as the Outcome ................................ ................................ ......... 39 10. Model with Systolic Blood Pressure as the Outcome ................................ ......... 40 11. Model with Diastolic Blo od Pressure as the Outcome ................................ ....... 40

PAGE 11

x LIST OF ABBREVIATIONS BMI Body Mass Index CESD Center for Epidemiologic Studies Depression Scale CFI Comparative Fit Index CVD Cardiovascular Disease DM Diabetes Mellitus RMSEA Root Mean Square Error of Approximation R/S Religion and Spirituality SEM Structural Equation Modeling SLVHAS San Luis Valley Health and Aging Study SQRT Square Root Transformed SRMR Standardized Root Mean Square Residual T2DM Type 2 Diabetes Mellit us WHO World Health Organization

PAGE 12

1 CHAPTER I BACKGROUND The World Health Organization (WHO) defines adherence to long term therapy taking medication, following a diet, and/or executing lifestyle changes corresponds with agreed recommendations from a health (WHO, 2003, p. 3) Finding ways to improve long t erm therapy self management has become a priority for the WHO in recent years as a way to improve patient health and reduce healthcare costs. Due to the complex nature of their management, cardiovascular disease and type 2 diabetes mellitus (T2DM) are often targeted for improving treatment self management. Cardiovascular disease, a diagnosis encompassing all diseases of the circulatory system in cluding high blood pressure, coronary heart disease, heart failure, and stroke, is a major public health concern affecting an estimated 80 million Americans. Cardiovascular disease causes over a third of the deaths in the United States as of 2005 (Lloyd Jo nes et al., 2009) and is the leading cause of death worldwide (WHO, 2011) Diabetes is another major public health concern. As the seventh leading cause of death, diabetes affects 26.9% of US residents age 65 and above (with T2DM accounting for over 90% of diabetes cases), causing complications such as high blood pressure, heart disease, stroke, limb amputation, retinopathy, and neuropathy. The risk of diabetes is approximately 66% higher among Hispanic individuals when compared to non Hispanics (National D iabetes Factsheet, 2011). Proper management of cardiovascular disease and diabetes requires the collaborative efforts of both a medical team and the patient (Holman & Lorig, 2000)

PAGE 13

2 Disease management recommendations for patients with cardiovascular disea se and T2DM are fairly similar, requiring not only medication self management but also lifestyle modifications such as changing diet, increasing physical activity, and smoking cessation. Patients with cardiovascular disease and T2DM often have difficulty c omplying with physician recommendations. Adherence to medication is poor among cardiovascular disease patients, with one study demonstrating that only 50 60% of patients adhered to prescribed medications during the course of a year (Haynes et al., 2005) A nother study showed that patients begin to adhere less to lifestyle modification prescriptions than to drug regimens, beginning only one month after acute coronary syndrome (heart attack or unstable angina) (Chow et al., 2010) Additionally, one observatio nal study followed patients for 30 months post hospitalization for myocardial infarction via an outpatient medication reimbursement database and found that only a quarter of patients adhere to their drug regimens as determined by having less than 80% of da ys covered by a filled prescription (Tuppin et al., 2010) For hypertension, 67 77% of patients report taking their hypertension prescription and 21 27.7% report adhering to lifestyle modifications, with similar rates of self management seen across e thnic groups when comparing non Hispanic whites, non Hispanic blacks, and Mexican Americans (Natarajan, Santa Ana, Liao, Lipsitz, & McGee, 2009). Similarly, patients with diabetes also struggle with self management with self reports of self management at 78% fo r medication, 64% for self monitoring blood glucose, 37% for diet, and 35% for exercise (Peyrot et al., 2005) Patients with T2DM, who are female and white, tend to have higher rates of self management & Selby, 2001). The above research suggests that T2DM and cardiovascular disease

PAGE 14

3 patients have a more difficult time adhering to lifestyle modification recommendations such as diet and exercise changes then they do adhering to a medication regimen. Even with low self reported adherence rates, it is likely that patients are overestimating their adherence to lifestyle modification recommendations, as evidenced by a study by Troiano and colleagues (2008) which found that only 5% of adults in the general US p opulation achieve recommended levels of physical activity when measured objectively (i.e. accelerometer). Patients who do well with self management often demonstrate better clinical outcomes (Lorig & Holman, 2003) whereas poor self management predicts hig her rates of depression (which may lead to further decline in self management) worse physical health, increased hospitalization and increased mortality (Krousel Wood & Frohlich, 2010; Osterberg & Blaschke, 2005) Poor self management likely played a larg e role in the 6 million patient hospitalizations in the United States due to cardiovascular disease from 1996 to 2006 (Lloyd Jones et al., 2009) In 2004, of the 609,000 admissions primarily caused by diabetes, approximately 32% were due to uncontrolled di abetes conditions (due to poor disease management) (Kim, 2007) As healthcare providers and the WHO strive to improve quality of life for patients with chronic illness and reduce healthcare costs, it is important to determine the potential predictors of se lf management in particular in terms of adherence to lifestyle modifications which patients appear to struggle with the most, in order to identify what and whom to target in self management interventions.

PAGE 15

4 Religion and Health When determining predicto rs of self management for individuals with cardiovascular disease and diabetes religious and spiritual (R/S) involvement is an area of research that is relatively unexplored despite the significant role that religion and spirituality have in lives. Over 90 percent of Americans believe in God or a higher power, 67 to 75 percent pray on a daily basis, 69 percent are members of a church or synagogue, 40 percent attend a church or synagogue regularly, and 60 percent consider religion to be very i mportant in their lives (Miller & Thoresen, 2003; Poloma & Pendleton, 1991; Shuler, Gelberg, & Brown, 1994) with women historically reporting higher rates of R/S involvement then men (Miller & Hoffmann, 1995). Over the last few decades, increasing evidenc e indicates positive relationships between religious involvement and physical and mental health outcomes (Miller & Thoresen, 2003) A meta analysis of over 40 independent samples found that religious involvement is significantly and positively associated with longevity with the association seemingly being greater for women and influenced by sociodemographic and health related factors (McCullough, Hoyt, Larson, Koenig, & Thoresen, 2000) Another study ( N=28,08 0) found that among adults in the U.S., there w as a life expectancy gap of over 7 years between persons never attending vs. attending church more than once weekly; this gap was even more pronounced for African American, who showed a 14 year difference in life expectancy (Hummer, Rogers, Nam, & Ellison, 1999) A meta analysis conducted by Chida and colleagues (2009), showed that R/S was associated with reduced mortality in a healthy population, independent of behavioral factors such as smoking, drinking, and exercise; R/S was more strongly associated wit h reduced

PAGE 16

5 mortality in a diseased population. Additionally, the meta analytic results showed a negative association between R/S and cardiovascular mortality. Similar benefi ts of R/S involvement have been found for older Mexican Americans, with one study finding a 32% reduction in mortality risk for weekly church attendee s v s non attenders ( Hill, Angel, Ellison, & Angel, 2005 ). over 91,000 people in a Maryland county, those who regularly attended church had a lower prevalence of cirrhosis, emphysema, suicide, and death from ischemic heart disease. Several studies have indicated that religious participation and higher religiosity may have a beneficial effect on blood pressure as well (Armstrong, van Merwyk, & Coates, 1977; Hixson, Gruchow, & Morgan, 1998; H. G. Koenig et al., 1998; Walsh, 1998) with one study finding that the relationship was stronger for non Hispanic whites and b lacks, than for Mexican American participants (Bell, Bowie, & Thorpe, 2012). These studies indicate that R/S has a demonstrated benefit on health and longevity; however, the relationship between R/S and health and longevity, may vary in strength based on p opulation characteristics, such as gender, age, health status, and ethnicity. Religion and Engagement in Health Related Behaviors The positive relationship between religious involvement and health is in part attributable to avoidance of health risking behaviors and engagement in health enhancing behaviors For example, r outine practice of religion is associated with physical activity in older adults (Callaghan, 2006) Similarly, a ttending religious services is predictive of engaging in more exercise and less smoking and heavy drinking among the elderly (Oman & Reed, 1998) One long term prospective study found that church attendance at

PAGE 17

6 47 years of age predicted physical health at 70 years of age in men (via mood and substance abuse) (Koenig & Vaillant, 2 009) Another long term study conducted by Strawbridge and colleagues (2001) followed over 2,500 community dwelling adults for 30 years, and found that persons who frequently attended religious groups were more likely to adopt and maintain positive health behaviors, such as exercising and refraining from smoking and heavy drinking; however, health benefits were stronger for women than for men. Another study found that men and women who reported religion as more important in their lives were more likely to use a variety of preventive services such as mammograms, flu shots, cholesterol screening, and prostate cancer screening ( Reindl Benjamins & Brown, 2004 ); the association between religiosity and use of preventive services was stronger among women These studies, which demonstrate a positive relationship between religious involvement and health behaviors, may suggest that a similar relationship might exist between R/S involvement and disease self management health behaviors; however, these studies also indicate that the strength of the relationship may depend on demographic characteristics. R/S and Disease Self Management Given the literature relating R/S involvement to better health and health behaviors, it is important to explore possible connecti ons between religious involvement and self management behaviors for those with chronic illness. However, the research in this area remains sparse, inconclusive, correlational, and mostly limited to African American patients with T2DM For example, one stud y examined African American patients with T2DM and found that church attendance was related to better coping and adjustment to diabetes, which was related to better self management behaviors (Samuel

PAGE 18

7 Hodge, Watkins, Rowell, & Hooten, 2008) A similar study found that among black women with T2DM R/S well being was linked to better glycemic control (Newlin, Melkus, Tappen, Chyun, & Koenig, 2008) Conversely, one cross sectional study conducted with heart failure patients (N=95) found no significant association between self reported R/S well being and self reported medical compliance (Black, Davis, Heathcotte, Mitchell, & Sanderson, 2006) The li terature relating religion to physical and mental health suggests that despite the limited work in this area, the possible positive relationship between R/S and self management behavior in chronic illness warrants further investigation. Work is needed expl oring whether a relationship between R/S and chronic illness self management exists and then exploring potential moderators in particular demographic moderators to determin e for whom this relationship is the strongest, given the ethnic and gender differen ces that have been demonstrated in the R/S and health literature. Pathways from R/S to Disease Self Management -Social Support, Mood, and Self Regulation It is also important to explore potential mechanisms through which R/S may influence disease self m anagement behaviors and related physical outcomes. There are three promising mechanisms that may provide a more in depth understanding of how R/S may play a role in disease self management; these three factors are psychological distress (primarily depressi on), social support, and self regulation Existing research literature shows that these three mechanisms are associated with both R/S and disease self management.

PAGE 19

8 Depression, R/S, and Self Management R/S involvement has been repeatedly shown to have posi tive relationships with mental health ( Miller & Thoresen, 2003) and has been specifically demonstrated to be negatively correlated with depressive symptomatology (regardless of gender or ethnicity) in a large meta analysis (147 independent studies) (Smith, McCullough, & Poll, 2003) In terms of patient adherence to medical recommendations, the negative influence of depressive symptomatology is particularly strong. A meta analysis conducted by DiMatteo and colleagues (2000) found that the odds of noncomplian ce with medical treatment recommendations were 3 times higher in depressed vs. non depressed patients. Similar reduced rates of medical adherence in patients with depressive symptoms are seen in patients with T2DM (Lin et al., 2004) heart failure (van de r Wal et al., 2006) and hypertension (Wang et al., 2002) Social Support R/S, and Self Management R/S involvement, in particular frequent religious service attendance, is associated with greater social networks and social support (Ellison & George, 1994 ) In services were more likely to increase their number of social contacts and stay married throughout the 30 year observation period. Similarly, religious attendance protec ts against loneliness in later life (Rote, Hill, & Ellison, 2012). When it comes to chronic illness self management social support may play an important role A meta analysis of 122 studies found support for a positive relationship between social support and patient adherence to medical treatment (DiMatteo, 2004) Social support is also associated with

PAGE 20

9 better self management in patients with diabetes (Toljamo & Hentinen, 2001 ) and CVD ( Wu et al., 2013 ) Self Regulation R/S, and Self Management The third factor relat ed to both R/S involvement and disease self management is self regulation. Some scientists suggest that the link between R/S, health, and social regulat e (McCullough & Willoughby, 2009). In particular, a recent study found that religious service attendance is associated with engagement in more self regulatory behavior (Carter, McCullough, & Carver, 2012). Carter and collegues (2012) discovered that religi ous invididuals engage in more self monitoring and frequently believe that a higher power is watching them (which encourages greater self monitoring and self control). reg imen) is indicative of self regulation. Specifically self regulation involves the initiation of purposeful behavior and inhibition of inappropriate actions (Grigsby, Kaye, & Robbins, 1992). Without the executive ability to self own behaviors, adhering to physician prescriptions for dietary restrictions or increased physical activity is challenging. One study by Watkins and colleagues (2000), which looked at self regulation via cognitive representations of the disease, found that self regulation in patients with diabetes (type 1 and 2) was linked to diabetes self management behaviors. Another longitudinal study found that among patients (N=237) with newly diagnosed T2DM self evaluation (a self regulation behavior) was predictive of improvement in dietary adherence over the 18 month observation period. Similar results have been found for self monitoring (another self regulation behavior) and

PAGE 21

10 improved diabetes control (Karter et al., 2001) Among cardiovascular disease patients, on e intervention demonstrated that improving the self regulation skills of patients in coronary rehabilitation served to improve long term adherence to physical exercise recommendations (Sniehotta et al., 2005) Model of R/S and Disease Management Behaviors and Physical Measures Associated with Disease Management Existing literature demonstrates not only a connection between R/S and overall health and health behaviors, but a connection between R/S and less depression (Smith, McCullough, & Poll, 2003) greater social networks and support (Ellison & George, 1994) and more self regulatory behavior (Carter, McCullough, & Carver, 2012; McCullough & Willoughby, 2009) which are associated with disease self management. Developing a conceptual model that vi sually maps out the relationship between R/S, social support, depression, self regulation, disease self management, and physical measures associated with disease management may be helpful in beginning to understand the direct and indirect relationship that R/S may have with disease self management. One existing conceptual model that looks at the connections between R/S and general health and disease (Oman & Thoresen, 2002; Figure 1) helps to provide a framework for understanding the pathways between R/S, di sease self management behaviors and physical outcomes often related to disease management. The original model by Oman and Thoresen (2002) takes into account the biopsychosocial context within which the relationship between R/S and disease self management behaviors and physical outcomes occurs, allowing for differing impact of R/S based on variables such as gender, race/ethnicity, and health status. The original

PAGE 22

11 model identifies three mechanisms or pathways through which R/S impacts health: positive health behaviors, social support, and positive psychological states. In order to adapt this model for use in individuals with chronic disease, three changes have been made: 1) positive health behaviors (or disease self management behaviors) become the outcome rat her than a mechanism, 2) disease self management behaviors and the physical outcomes often related to disease self management, such as blood pressure and body mass index, are grouped together, and 3) an additional mechanism, self regulation or executive co ntrol, has been added to the model (Figure 2) Below, the three mechanis ms/mediators are described as they relate to engagement in chronic illness self management behaviors and physical measures often associated with disease self management for individuals who are involved in a religious community Figure 1 Oman & Thor e sen (2002)

PAGE 23

12 Social Support. In the adapted model (F igure 2), social support serves as a mediator in the relationship between religious group attendance and self management behaviors. For an individual adjusting to a T2DM diagnosis, a religious organization such as a church provides a social network that can support an individual in making lifestyle changes. For example, other church members may also have the disease or have experience with the disease, which may enable provision of empathy and informational support. Additionally, other church members may be able to provide tangible support such as transportation to help the person attend his or her medical appointments. Lastly, the work by Strawbridge and colleagues (2001) indicates that religious persons are more likely to maintain and increase social support via staying married and increasing social contacts. Figure 2 viors and Physical Measures Associated with Disease Management Mood ( Psychological States ) In the adapted model, positive psychological states serve as a mediator in the relationship between religious group attendance and self management behaviors. As previously mentioned, R/S involvement is related to better

PAGE 24

13 mental health and less depression. Maintaining mental health and positive mood may be partially attributed to religious group attendance. Religious group attendance may facilitate a sense of connec tedness to others for individuals and instill a sense of purpose Maintaining a positive mood predicts less depression, which is a risk factor for poor disease self management ( Park, Hong, Lee, Ha, & Sung, 2004) Self Regulation (Executive Control). In the revised model, self regulation is a mediator between religious group attendance and self management behaviors. Practicing prayer, focusing the mind throughout a religious gro up activity, and engaging in more self a strong capacity for self regulation (Carter et al., 2012) Self regulation skills acquired by R/S involvement may aid the individua l in successfully implementing and maintaining behavioral changes related to disease management. For example, individuals who are vigilant about not swear ing and monitor personal finances (self monitoring) so as to donate weekly to their religious organization may be better able to monitor their daily dietary fat intake. S tudies with T2DM and cardiovascular disease patients have shown improvements in behavioral self management when patients engage in self monitoring or self evaluation (Ka rter et al.; Sniehotta et al., 2005; Watkins et al., 2000). Disease Self Management Behaviors ( Positive health behaviors ) and Physical Outcomes In this model positive health behaviors, or chronic illness self management behaviors, and physical measures commonly associated with self management constitute the predicted outcomes. Based on the model, these behaviors and physical measures are indirectly predicted by R/S through the three pathways or mediators (social support,

PAGE 25

14 positive psychological states, an d self regulation); however, R/S may also directly predict chronic illness behavioral self management and physical measures An example of a direct effect may be found in the teachings from several religious traditions that exhort practitioners to treat th Christianity) and discourage the use of harmful substances such as alcohol and tobacco (e.g. Latter Day Saints, Seventh Day Adventists) ; however, self regulation may also partially mediate this assoc iation Individuals diagnosed with cardiovascular disease who are part of a religious culture that advises followers to respect and care for their body may changes t hat are in line with how other churc h members already live (e.g. it i s easier to cut back on alcohol when other church members do not drink). An example of the indirect effect may be through social support. The church members might be very supportive of th Anonymous group, or a church based substance use intervention, to assist the person in the behavior change. If a relationship between R/S and disease self management and physical measures a ssociated with self management does exist, the described model may provide a more sophisticated understanding of the various pathways through which R/S involvement relates to chronic self management behaviors and physical measures associated with disease m anagement for individuals with cardiovascular disease and diabetes. Purpose of the Present S tudy The primary purpose of the current study was to examine whether R/S involvement (as measured by religious group attendance) is associated with, and for

PAGE 26

15 whom it is associated with (biopsychosocial context), engagement in self management behaviors and certain physical measures associated with self management behaviors (i.e. body mass index (BMI) and blood pressure). This purpose is visually represented by t he top/blue shaded area of figure 2. The secondary purpose was to test the proposed conceptual model as a way to understand potential mediators of the relationship between R/S and chronic disease self management; visually represented by the bottom/gray sha ded area of figure 2. The objectives of the current study are to determine in a sample of Hispanic and non Hispanic community dwelling individuals with diagnosed cardiovascular disease and /or diabetes: a) if religious group attendance is associated with di sease self management behavio rs (increased physical activity and more healthy eating behaviors ) and physical measures of outcomes often related to self management (i.e. blood pressure and BMI), while controlling for age, time since diagnosis, and education b ) if the relationship between religious group attendance and self management behaviors and physical measures associated with self management is moderated by ethnicity gender or diagnosis, and c ) to determine if the proposed model linking R/S to disea se self management through the pathways/mediators of social support, mood, and self regulation is supported by data from the study sample. Hypotheses and Specific A ims The following are the specific aims and hypotheses of the current study. Aim1: To determ ine whether frequency of religious group attendance is associated with behavioral self management (physical activity and eating behaviors ) and physical measures related to self management (blood pressure and BMI) in individuals

PAGE 27

16 with cardiovascular disease and/or diabetes and if this relationship is moderated by ethnicity, gender, and diagnosis. Hypothesis 1: For individuals with a diagnosis of diabetes and/or cardiovascular disease, more frequent religious group attendance will be associated with disease s elf management behaviors and physical measures related to self management, including more frequent physical activity, better nutrition behaviors, lower BMI, and lower systolic and diastolic blood pressure, while controlling for age, education, and time sin ce initial diagnosis. This relationship will be moderated by gender, ethnicity and diagnosis, with the relationship being more pronounced for women, Hispanic elderly, and those with cardiovascular disease. Aim 2: To determine if there is support for the adapted Oman and Thorsesen model, which theorizes that religious attendance is both directly associated with disease self management behaviors and physical measures associated with disease management and indirectly associated via the pathways of social support, mood (depression), and self regulation. Hypothesis 2: Structural Equation Model analyses of the data will demonstrate support of the proposed model. That is, religious group attendance will be both directly a ssociated with self management behaviors (e.g. physical activity and eating behaviors) and physical measures associated with self management (i.e. BMI and blood pressure) and indirectly associated through the mediators of social support, mood, and self reg ulation.

PAGE 28

17 CHAPTER II METHOD Participants The San Luis Valley Health and Aging Study (SLVHAS) was a population based study of health and disability among Hispanic and non Hispanic white residents of two rural southern Color ado counties that make up the San Luis Valley The San Luis Valley is a relatively isolated rural region in southern Colorado where most residents live in small communities or on ranches and farms. The 1990 population of the region was 46 percent Hispanic, 52 percent non Hispanic White, and 2 percent other (San Luis Valley Regional Development and Planning Commission, 1992). The majority of Hispanic residents in the San Luis Valley report their ethnicity as n American, reflecting the fact that many Hispanics in this region are not recent immigrants (Bean & Tienda, 1987). Eligibility criteria for participating in the original study included: (1) residence in Alamosa or Conejos county; (2) Hispanic or non Hisp anic white ethnicity; and (3) age 60 years or older. The initial study cohort consisted of 1,360 participants who completed a baseline visit between 1993 and 1995. For the current study, only those participants who reported a diagnosis of diabetes or cardiovascular disease ( i.e. heart disease, heart failure, myocardial infarction, angina, bypass surgery, or coronary angioplasty ) and completed the study measures without a proxy were included. Participants who reported a less severe cardiovascular diagno sis (i.e. hypertension), that might not be perceived as warranting major lifestyle modifications, were not included.

PAGE 29

18 Data Collection The study protocol was approved by the Colorado Multiple Institutional Review Board. After informed consent was obtained, study personnel conducted a 3 h baseline were bilingual, and Spanish translated forms were available. Demographics The extensive self report medical history review included: self report of physician diagnosed major chronic diseases including diabetes, heart attack, mini stroke, major stroke, angina, high blood pressure, heart failure, and procedures indicative of heart disease, including heart or blood v essel surgery and angioplasty. Participants were also asked to provide the year when they were diagnosed or received a procedure (e.g. bypass surgery). The SLVHAS participants who reported having a diagnosis or history of heart attack, angina, heart failure, coronary angioplasty, or cardiac bypass surgery were classified has having cardiovascular d isease. Participant s who reported a history of Has your doctor ever told you have diabetes (sugar diabetes)? were classified as having diabetes. Though individu als were not asked to specify the type of diabetes it can be assumed that a minimum of 90% ha d T2DM (National Diabetes Factsheet, 2011) Time since diagnosis was calculated from the longest standing diagnosis or treatment. Education level was assessed wit h a single question: Responses were coded b ased on the year equivalent of the level of school completed (e.g. 7 th grade = 7 years of education). Individuals, who had completed their GED, were coded as having completed 12 years of

PAGE 30

19 school. Age was calculated based on participant provided date of birth and the documented date of questionnaire completion. Income was ass essed with a single item: m e, before taxes, of all your family members, living Ten income ranges were provided as answer choices. Responses choices ranged from to Measures Predictor. Religious attendance was assessed using a single item asking the Moderators. Participants were asked to identify their sex, with t he response options of male or female. Ethnicity was assessed with a single question: Diagnosis was assessed via extensive self reported medical history, see demographics section for more details. Behavioral Outcomes. Physical activity and healthy eating behaviors were measures of the behavioral outcomes in this study. To measure physical activity, participants were questioned about the amount and quality of physical activity they engaged in currently, inclu ding activities for work and leisure, in the past 12 months using the Coronary Artery Disease Risk Scale For example, one item asks: 12 months, did you jog, run, hike or do similar activities for at least an hour total during vigorous exercise class or vigorous dancing strenuous sports such as skiing, basketball, football or skating snow shoveling, or moving or lifting heavy objects Activitie s are classified into heavy intensity activities and moderate intensity activities, which are translated into metabolic equivalent units (MET); heavy intensity activity have

PAGE 31

20 MET values from 5 8 and moderate intensity activities are 3 or 4. Total energy exp enditure score is calculated based on activity intensity and months of frequent and infrequent participation in the activity. Jacob et al. (1989) reported test retest correlations of 0.84 for a two week interval. The nutrition questions on the SLVHAS quest ionnaire were created for this study and are similar to questions asked in the first National Health and Nutrition Examination Survey (NHANES I) ( National Center for Health Statistics, 1973 ) and NHANES II ( National Center for Health Statistics, 1981). Th e healthy eating behaviors scale was created from 7 items that asked about eating behaviors that relate to healthy eating, such how often do you salt your food at the table ? and Responses were summed and averaged for a range of 0 4, with 4 indicating frequent healthy eating behaviors. The (Turconi, Celsa, Rezzani, Biin o, Sartirana, & Roggi, 2003). Physical O utcomes. BMI and systolic and diastolic blood pressure were measured as the physical outcomes in this study BMI was calculated from b ody weight and height, which was measured by research personnel during the interview with the participant by using the standard equation for BMI (i.e. BMI = (mass( lb) /height( in) 2 ) x 703). Participants were asked to wear light clothing and no shoes for these measurements Seated blood pressure was measured in the right arm by res earch personnel in triplicate after 5 min of rest T he last two readings were averaged for study inclusion for both systolic and diastolic blood pressure

PAGE 32

21 Mediators This study had three mediators: social support, mood (depression), and self regulation. S ocial support was measured by size of social network. Social How many relatives do you feel close to? That is, relatives you feel at ease with, can talk to about private matters, and can call on for help How many close friends do you have? That is, people that you feel at ease with, can talk to about private matters, and can call on for help. The responses to the two questions were combined for a total numeric score Depressive symptoms were assessed as a measure of mood using the 20 item Center for Epidemiologic Studies Depression (CES D) Scale (Radloff, 1977). The scale I felt that everything I did was an effort I felt de pressed rarely or none of the time most or all of the time. ). The Behavioral Dyscontrol Sca le (BDS) (Grigsby & Kaye, 1996) a behavioral measure of the capacity for behavioral self regulation, was used to measure executive control. For this scale, self regulation is conceptualized as the initiation of purposeful behavior and the inhibition of in appropriate actions (Grigsby, Kaye, & Robbins, 1992) The BDS consists of 9 items, the majority of which assess the capacity to control voluntary motor activity. For example, one item involves having the participant imitate several hand movements (e.g. pla cing the left hand on the left ear or pointing with the right hand to the right eye) demonstrated by the test administrator while facing the participant. The participant is instructed to use the same hand as the administrator (i.e. use his or her left hand if the administrator used the left hand) and thereby avoiding the

PAGE 33

22 mirroring error (i.e. using their left hand when the administrator used the right hand). administrator says his test takes approximately 15 minutes to administer and has high retest ( rs = .89 and .86 over 8 weeks and 6 months) and inter rater reliability ( rs = .95 .99) (Grigsby et al., 1992) In this analysis, the 19 point version of the BDS was utilized and scores can range from 0 19 with higher scores indicating better behavioral self regulation BDS scores have been shown to be significantly correlated with measures of daily functioning and general cognitive status in the SLVHAS sample (Grigsby, Kaye, Baxter, Shetterly & Hamman, 1998) and in a sample of patients presenting at a geriatric outpatient clinic (Kaye, Grigsby, Robbins & Korzun, 1990). Statistical Analysis The majority of the data analyses were conducted using IBM SPSS Statistics version 21 (SPSS Inc., 2012 ); only the structural equation model analyses were conducted using Mplus ( Muthn & Muthn 2012) Descriptive statistics (e .g., means, standard deviations ) were calculated to describe the sample. Continuous variables were checked for normal distributions Variables were transformed in cases for which transformation approximated a more normal distribution (i.e. br inging skewness and kurtosis closer to zero) (Tabachnick & Fidell, 2013). Systolic blood pressure was log transformed. BMI diastolic blood pressure, depression, physical activity were square root transformed. These transformed variables were used for the correlational, regression, and structural equation model analyses. Religious group attendance, self regulation, healthy

PAGE 34

23 eating behaviors, and social support were not transform ed. Differences in demographics and additional variables across ethnicit y and ge nder were assessed by independent t tests using all non d was calculated for effect size when d of 0.2 is considered a small effect size, 0.5 is considered a medium effect size and 0.8 is considered a large effect size Hypothesis 1: For individuals with a diagnosis of diabetes and/or cardiovascular disease, more frequent religious group attendance will be associated with disease self management behaviors and physical measure s related to self management, including more frequent physical activity, better nutrition behaviors, lower BMI, and lower systolic and diastolic blood pressure, while controlling for age, education, and time since initial diagnosis. This relationship will be moderated by gender, ethnicity and diagnosis, with the relationship more pronounced for women, Hispanic elderly, and those with cardiovascular disease. Analyse s for this hypothesis were tested using linear regression to evaluate the association of relig ious group attendance with all outcome variables (i.e. physical activity, healthy eating behaviors BMI, and systolic and diastolic blood pressure). E thnicit y, gender, and diagnosis ( cardiovascular disease diabetes or both ) were used as moderators in these analyses Hypothesis 2: Structural Equation Model analyses of the data will demonstrate support for the proposed model. That is, religious group attendance will be both directly associated with self management behaviors (e.g. physical activity a nd eating behaviors) and physical measures associated with self management (i.e. BMI and blood pressure) and indirectly associated through the

PAGE 35

24 pathways /mediators of social support, mood, and self regulation This hypothesis was tested using Structural Equa tion Modelling in Mplus, allowing social support, self regulation, and mood to correlate. A model was computed for each outcome (i.e. physical activity, healthy eating, BMI, systolic and diastolic blood pressure). Fit statistics were examined for each mode l to assess model fit. Missing data within these models w ere handled using full information maximum likelihood estimation within Mplus 6 in order to utilize all available data ( Muthn & Muthn 2012)

PAGE 36

25 CHAPTER III RESULTS Out of the o ne thousand three hundred sixty participants in the main SLVHAS study, 468 ( 34.4% ) had a qualifying diagnosis of diabetes or cardiovascular disease and personally completed the survey (rather than having a proxy). This sample had a mean age of 73 years and contained a slig htly greater proportion of women (54.3%) and Hispanic participants (58.5%) (Table 1) Ethnicity for men and women in the sample was 51.4% and 64.6% Hispanic respectively. This subsample of the SLVHAS study is similar to the demographics of the overall sa mple which consisted of 56.8% female and 58.3% Hispanic participants Table 1 Demographic d ata N (%) N=468 Gender Male 214 (45.7) Female 254 (54.3) Ethnicity Hispanic 274 (58.5) Non Hispanic White 194 (41.5) Age M = 73.29 ( SD = 7.43) Education (# of years) M = 10.37 ( SD = 3.81) Income/year (2015 dollar equivalent)* < 7,5000 (7,674) 137 (29.3) 7,500 9,999 (7,674 10,231) 60 (12.8) 10,000 14,999 (10,232 15,347) 96 (20.5) 15,000 24, 999 (15,348 25,579) 65 (13.9) 25,000 + (25,580+) 58 (12.4) Diagnoses Diabetes all 288 Diabetes no CVD 196 CVD all Heart attach Angina Heart Failure Angioplasty Bypass 269 155 110 74 42 32 CVD no DM 178 Both CVD and DM 94 Years since initial diagnosis M = 13.73 ( SD = 11.25) *2015 equivalent based on 2.32% inflation Note: Cardiovascular disease is abbreviated CVD and diabetes is abbreviated DM.

PAGE 37

26 On average, the participants had no t completed high school and the majority had an income of less than $14,999/year (equivalent to $24,819.91 in 2015 based on an annual inflation of 2.32% over this time period). Among the 1360 original SLVHAS participants, t wo hundred and sixty nine partici pants (19.8%) reported a history of cardiovascular disease, with heart attack (N= 155 ; 11.4% ) being the most commonly reported indicator of cardiovascular disease These rates are fairly consistent with national prevalence rates of coronary heart disease (including heart failure, myocardial infarction, and angina pectoris) (21.1% of men and 10.6% of women for the 60 79 year old age group; Mozaffarian, et al. 2015) A total of 288 (21.2%) participant reported having a diagnosis of diabetes which is consis tent with the national prevalence rates (26.9% among individuals 65 years of age and older; National Diabetes Factsheet, 2011). Among the participants with cardiovascular disease or diabetes, 94 participants reported co morbid diagnoses of diabetes and car diovascular disease. At the time of data collection, the average participant had been diagnosed for over a decade ( M = 13.7 years; SD = 11.25). Tab le 2. Descriptive d ata Mean SD Religious Group Attendance 2.90 3.11 Social Support 9.26 3.07 Depression 8.19 8.72 Self Regulation 15.18 3.78 Physical Activity 234.81 208.33 Healthy Eating Behaviors 2.47 0.67 BMI 27.88 4.75 Systolic Blood Pressure 138.51 20.99 Diastolic Blood Pressure 77.19 10.96 Descriptive statistics (mean, and standard deviation) were computed for all predictor, mediator, and outcome variables (Table 2). Participants in this sample attended religious functions on average 2.9 times in the past month and had a mean of 9.3 people

PAGE 38

27 i n their social network. Mean scores for depression were 8.19 (SD = 8.71) indicating minimal depression symptoms well below the standard cut Means scores for self regulation were 15.18 (SD = 3.78), indicating little to no impair ments (Grigsby & Kaye, 1992). Mean blood pressure was in the pre hypertensive range at 139/77. Independent samples t tests were calculated to determine if demographic and other variables differed by gender or ethnicity in this sample. Variables included in the t test analyses were age, education, income, years since diagnosis, religious group attendance, social network size, depression, self regulation, physical activity, healthy eating behaviors, BMI, systolic and diastolic blood pressure. The non transfo rmed variables were used for the t test analyses Table 3 presents the means for only the d was calculated to determine the effect size of the observed differences. Table 3 Variables with significant differences when compared by gender and ethnicity Mean Mean d Male Female Social Support 9.87 8.87 0.37 Income bracket* 4.41 3.33 0.53 Physical Activity 275.71 195.98 0.39 Depression 6.51 9.61 0.37 Health y Eating Behaviors 2.30 2.61 0.49 BMI 27.06 28.58 0.33 Systolic Blood Pressure 135. 03 141.46 0.31 Non Hispanic Hispanic Age 74.74 71.81 0.42 Education 11.93 8.93 0.06 Social Support 9.68 8.96 0.24 Income bracket* 4.63 3.24 0.53 Self Regulation 16.37 14.34 0.57 Physical Activity 266.07 211.75 0.26 Health y Eating Behaviors 2.61 2.37 0.41 BMI 27.27 28.30 0.22 Systolic Blood Pressure 135.57 140.60 0.24 Diastolic Blood Pressure 74.60 79.03 0.42 *Note: Income is a categorical variable. The 3 rd bracket corresponds to $7,500 $9,000/yearly a the 4 th bracket corresponds to $10,000 $14,999/year.

PAGE 39

28 Wh en comparing men and women, all significant differences detected were in the d 0.3 0.5). On average m en reported sig nificantly more people in their social network, higher income bracket, more physical activity, less depression symptoms less healthy eating behaviors, lower BMI, and lower systolic blood pressure than women When comparing Hispanic and Non H ispanic white participants, several significant differences in the small to moderate effect size range found were found. Non Hispanic whites on average were older in a higher income bracket, had higher self reg ulation scores, lower BMI, lower systolic and diastolic blood pressure, and reported more physical activity, larger social network size, and healthier eating behaviors than Hispanics Bivariate (P earson) correlations were run between all continuous untransformed demographic, religious group attendan ce, mediator (i.e. social support, depression, and self regulation), and outcome variables (i.e. physical activity, healthy eating behaviors, etc.) (Table 4). Religious group attendance was shown to have a significant and negative correlation with years si nce diagnosis, depression, and systolic blood pressure and a positive correlation with social support and self regulation. Social support (positive) and depression (negative) were significantly associated with physical activity, but not with any of the oth er self management behaviors or physical outcomes. Self regulation was significantly correlated with physical activity (positive), healthy eating behaviors (positive) and systolic blood pressure (negative).

PAGE 40

29 Aim 1 Linear Regression Moderated Analyses Eighteen participants of the 468 did not have data on frequency of religious group attendance. Only individuals with frequency of religious group attendance (N=450) could be included in the linear regression analyses. All regression analyses were conducted using transformed variables (i.e. BMI, physical activity, systolic and diastolic blood pressure, and depression). Initial linear regression analyses were conducted to examine the relationship between religious group attendance and each outcome (physical a ctivity, healthy eating behaviors, BMI, and systolic and diastolic blood pressure) when controlling for age, education, and time since diagnosis and are displayed in Table 5. While controlling for covariates, religious group attendance was not significant ly associated with physical activity, healthy eating behaviors, BMI, systolic or diastolic blood pressure. Despite the absence of a main effect of religious group attendance on outcomes, moderators were tested due to the current

PAGE 41

30

PAGE 42

31 e s, which was based on previous R/S literature demonstrating differences in relationship strength between R/S and health outcomes when moderated by demographic variables. Gender did not moderate the relationship betw een religious attendance and healthy eating behaviors, BMI or diastolic blood pressure; however, gender did moderate the relationship between religious attendance and physical activity ( = .122; p = .090) and the relationship between religious attendance and systolic blood pressure ( = .178; p < .01). When examining the scatterplot graph of religious attendance and physical activity (Figure 3), it appears as though religious group attendance is associated with more frequent physical activity for men and slightly less or no association with physical activity for women. The scatterplot graph of religious attendance and systolic blood pressure indicates that more religious attendance is associated with lower systolic blood pressure for women but shows no a ssociation for men (Figure 4).

PAGE 43

32

PAGE 44

33 Figure 3. R egression g raph religious attendance predicting physical a ctivity m oderated by gender

PAGE 45

34 Figure 4. Regression graph r eligious attendance predicting systolic blood p ressure moderated by g ender Ethnicity was added into the regression analyses as a potential moderator of the relationship between religious attendance and self management behaviors and indicators. Ethnicity did not significantly moderate the relationship between religious attendance and physical activity, healthy eating behaviors, BMI, or systolic blood pressure, but it did significantly moderate the relationship between attendance and diastolic blood pressure ( =.132; p = .039). The regression graph indi cated an association between more frequent religious group attendance and lower diastolic blood pressure for non Hispanic whites, but no association for Hispanics (Figure 5).

PAGE 46

35 Figure 5. Regression graph r elig ious attendance predicting d iastol ic blood p ressure moderated by ethnicity Diagnosis was also examined as a potential moderator of the relationship between religious group attendance and self management behaviors and physical measures of self management. Ninety four participants had an overlapping diagnosis of cardiovascular disease and diabetes. Two different interaction terms were created to distinguish between three disease classifications: 1) cardiovascular disease without diabetes, 2) diabetes without cardiovascular disease, and 3) co morbid c ardiovascular disease and diabetes diagnosis. The first diagnosis interaction term compared classification 1 vs. 2 and 3 combined; the second interaction term compared classification 2 vs. 1 and 3 combined.

PAGE 47

36 The relationship between religious group was not significantly moderated by diagnosis for healthy eating behaviors, BMI, or blood pressure; however, the relationship was moderated by diagnosis at a significant level for physical activity ( =.150; p = .035). The regression graph (figure 6) indicates a p ositive association between attendance and more frequent physical activity for participants with only cardiovascular disease diagnosis, no relationship for participants with only a diabetes diagnosis, and a negative association for participants with co mor bid cardiovascular disease and diabetes diagnoses. Figure 6. Regression graph religious attendance predicting physical activity moderated by diagnosis

PAGE 48

37 Aim 2 Adapted Model Analyses To address aim 2 of this study, five models were estimated based on the two self management behaviors and three physical outcomes (Figures 7 11). In each model, the outcome (i.e. physical activity, healthy eating behaviors, BMI, systolic and diastolic b lood pressure) was predicted both directly by religious attendance and indirectly through the pathways of religious attendance via social support, mood (depression), and self regulation. There were no covariates included in the five models. Of the 468 indi viduals, who met criteria for inclusion in this study, 18 individuals had missing data on religious attendance and were excluded from these models. Therefore, data from 450 individuals were included. All five models exhibited adequate model fit with Comp arative Fit Index ( CFI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR) resulting in the same values for all models (i.e., CFI=1.00, RMSEA=.00, SRMR=.00) and model chi 2 (10 ) = 71.8 3 114.99, p < .001. All models demonstrated a positive relationship between religious attendance and social support ( p < .001) and self regulation ( p < .01), and an inverse relationship between religious attendance and depression symptoms ( p < .05). Fig ure 7 shows the results of the structural equation model examining physical activity as the outcome as predicted by religious group attendance directly and through the pathways of social support, depression, and self regulation. The three mediators (i.e. s ocial support, depression, and self regulation) were significant ly associated with both religious attendance and physical activity at the p < .05 level. The direct relationship from religious group attendance to physical activity was not significant in th is model.

PAGE 49

38 Figure 7. Model with Physical Activity as Outcome Figure 8 shows the results of the structural equation model examining healthy eating behaviors as the outcome as predicted by religious group attendance directly and through the pathways of social support, depression, and self regulation. Self regulation was shown to be significant ly associated ( p < .01) with both religious attendance and healthy eating behaviors, while controlling for social support, depression, and the direct relationship b etween attendance and healthy eating behaviors. The direct relationship between attendance and healthy eating behaviors was marginally significant ( p < .10) when accounting for social support, depression, and self regulation. Figure 8 Model w ith Health Eating Behaviors as O utcome

PAGE 50

39 Figure 9 shows the results of the structural equation model examining BMI as the outcome as predicted directly by religious attendance and through the pathways of social support, depression, and self regulation. Neither the di rect or indirect pathways from attendance to BMI were significant in this model. Figure 9. Model with BMI as o utcome Figures 10 and 11 shows the results of the structural equation model examining blood pressure (systolic and diastolic respectively) as the outcomes predicted by religious attendance and through the pathways of social support, depression, and self regulation. In the model for systolic blood pressure (Figure 10), religious attendance does not demonstrate a significant direct relationship wi th systolic blood pressure, but self regulation is demonstrated to be significant ly associated with both religious attendance and systolic blood pressure ( p < .01). The model with diastolic blood pressure as the outcomes (Figure 11), demonstrated that the direct and indirect pathways from attendance to diastolic blood pressure were not significant.

PAGE 51

40 Figure 10. Model with s ys tolic blood pressure as o utcome Figure 11. Model wi th diastolic blood pressure as o utcome

PAGE 52

41 CHAPTER IV DISCUSSION The primary aim of the currently study was to examine, in a sample of Hispanic and Non Hispanic white elderly community members living in the rural San Luis Valley of Colorado with a diagnosis of cardiovascular disease or diabetes, whether religious attend ance was associated with self management behaviors and physical measures often related to self management and whether gender, ethnicity, and diagnosis moderated these relationships. This study found that the relationship between religious group attendance and disease self management behaviors (physical activity and healthy eating behaviors) and physical measures of disease self management (BMI and blood pressure) varied by gender, ethnicity, and diagnosis. Religious attendance was associated with more frequ ent physical activity for men and for individuals with only cardiovascular disease, but was associated with less physical activity for individuals with co morbid cardiovascular and diabetes diagnoses. Frequent religious service attendance was associated w ith lower systolic blood pressure among women and lower diastolic blood pressure among non Hispanic white participants. The blood pressure finding are consistent with the existing R/S and health literature that generally demonstrate greater health benefit s of R/S for women (Chida, Steptoe, & Powell, 2009; McCullough, Hoyt, Larson, Koenig, & Thoresen, 2000). Additionally the finding that religious service attendance was associated with lower diastolic blood pressure for non Hispanic white participants is co nsistent with the study by Bell and colleagues (2012) that found a stronger association between R/S and blood pressure for non Hispanic whites and blacks than for Hispanic participants. One

PAGE 53

42 unexpected finding from this study, given that women typically de monstrate strong R/S health associations, was that religious service attendance was associated with reports of more physical activity among men and not women. Because this effect was only marginally significant (p = .09), this result may be a type 1 error. Alternatively, it may be that gender differences have not previously been thoroughly examined in individuals with diabetes and cardiovascular disease, and religious service attendance may have a greater relationship with physical activity among men than w omen for these groups. Religious attendance did not demonstrate a significant relationship with BMI, nor were the moderators of gender, ethnicity, or diagnosis significant. These non significant findings may potentially be due to the existence of a non linear relationship. A large prospective study of more than 1 million adults in the United States, demonstrated a curvilinear relationship between BMI and risk of death from all causes that is increased risk of mortality at both the highest and lowest en ds of BMI and decreased risk in the middle ( Call e, Thun, Petrelli, Rodriguez, & Health, 1999 ). S imilarly, a study of individuals ( ) found that when compared with normal weight people, both underweight and obese older adults reported impair ed quality of life, particularly worse physical functioning and physical wellbeing (Yan, et al. 2012). Therefore, utilizing an analytic approach designed to detect a non lin ea r relationship may have been more appropriate. Additionally, use of BMI as a mea sure of adiposity may lead to misclassification of weight classification particularly among older women and men (Shah & Braverman, 2012). Obesity researchers have found that other measures, such as hip to waist circumference ratio, may more accurately meas ure adiposity (Prentice & Jebb, 2001).

PAGE 54

43 Additionally, because R/S effects by disease category have not been previously examined, the finding that religious attendance is associated with more physical activity for those with cardiovascular disease and less for those with co morbid cardiovascular disease and diabetes is a unique contribution to the literature and open to interpretation. In regards to the finding that religious attendance was associated with less physical activity among individuals with co mo rbid diagnoses, one interpretation may be that individuals with comorbid diagnosis likely have extremely poor health and limited capacity for physical exertion; therefore, they need to be selective in how they expend their energy and may have to choose bet ween engaging in physical activity and attending church services. The secondary aim of this study was to test a model (Figure 2) adapted from (2002; Figure 1). The adapted model proposed that religious attendance has a direct ef fect on disease self management behaviors and physical measures often associated with disease self management, as well as an indirect effect through the mechanisms of social support, mood, and self regulation. The models (Figures 7 11) showed that, consist ent with prior literature, religious group attendance was significantly associated with more social support, less depression, and more self regulation. In terms of finding support for the adapted model as a whole, the results were mixed. The model examinin g physical activity as the outcome was the most supported and consistent with the adapted Oman & Thoresen model, demonstrating support for the pathways of social support, depression, and self regulation mediating the relationship between attendance and phy sical activity.

PAGE 55

44 Self regulation was related to three out of the five models (i.e. physical activity, healthy eating behaviors, and systolic blood pressure) indicating that self regulation may be a particularly potent mechanism through which religious att endance is linked to disease self management. This finding is consistent with work done by McCullough & Willoughby (2009), which suggest s that R/S mental and physical health benefits occur through the pathway of self self religious practices that frequen tly and perhaps intensely activate self monitoring abilities (e.g. meditating, praying, focusing attention during religious services), and builds self control through delayed gratification and encouraging a future oriented mindset (i.e. engaging or not eng aging in certain behaviors in the present, so as to be rewarded in the afterlife). R/S leads individuals to build strong generalizable self regulatory abilities that may be utilized beyond specific R/S related practices. These generalizable self regulatory abilities allow for engagement in activities that promote social, mental, and physical health, such as appropriate navigation of social relationships (allowing for increased nd regulation of health behaviors such as choosing healthy rather than unhealthy foods. suggests that self regulation may indeed be a crucial mechanism for understanding th e relationship between R/S and disease self management. The models with BMI and diastolic blood pressure as outcomes (Figures 9 & 11) significant pathways. The non signific ant findings may be due to religious attendance not

PAGE 56

45 having strong relationship with these outcomes. Alternatively, the relationships may be non linear in nature, particularly for BMI. The results of this study provided mixed yet promising support for the a dapted Oman & Thoresen model, particularly for the outcome of physical activity and for self regulation as a mechanism, suggesting that further testing of the model is warranted. The results from this study add to the growing literature aimed at understa nding the factors involved in facilitating better adherence to lifestyle modification needed to manage cardiovascular disease and diabetes. The results of this study are also notable given the uniqueness of the study population. The San Luis Valley of Colo rado is a relatively isolated and poor area of the country. The demographics of the valley are also unique, with a large proportion of Hispanic community members, originally immigrated from Spain, and fewer immigrants from Mexico. The large proportion of Hispanic and non Hispanic white participants in this sample allowed for important comparisons in outcomes by ethnicity. The results add to the rich literature examining the relationship between R/S and health behaviors and outcomes. Previous work examining the relationship between R/S and disease self management had primarily been conducted with small sample sizes and with African American participants. These results demonstrated that in a relatively large sample of Hispanic and non Hispanic white community dwelling elderly, that attending religious services may have a positive relationship with self management behaviors for some individuals with diabetes and cardiovascular disease. The results of this study have limitations given the cross sectional nature of the data. It is possible that the direction of the relationship between religious group

PAGE 57

46 attendance and self management behaviors could be reversed (i.e. men who are physically active are more capable of attending church function and women with lower bl ood pressure feel healthy enough to attend church). Similarly, the direction of the relationships tested in the structural equation models may be reversed in direction or be bidirectional (i.e. individuals with a high BMI may be more isolated and have less social support and therefore may have no one to drive them to church, feeling isolated from a church community may then lead to compensatory eating behaviors and higher BMI). Additionally, there are other potential third factors that were not examined in this study that could also impact the relationship between religious group attendance and self management, such as access to medical care, access to healthy food options in an isolated partaking in the religious community culture involve frequent potlucks with unhealthy foods), and racial discrimination. One outcome that was not measured that could have important implications for the blood pressure outcomes was adherence to medication. Given that many individuals with cardiovascular disease and diabetes also have a diagnosis of hypertension, religious group attendance may influence adherence to hypertension medications, partially explaining the lower blood pressure outcomes demonstrated for women and non Hispanic white participants. Further research is needed examining the relationship and possible impact of R/S variables on self management behaviors and long term health outcomes related to R/S. In particular, longitudinal research is needed to determine if being part of a religious community or being highly spiritual is associated with changes in health behaviors from pre to post diagnosis.

PAGE 58

47 REFERENCES Armstrong, B., van Merwyk, A. J., & Coates, H. (1977). Blood pressure in Seventh day Adventist vegetarians. Am erican J ournal of Epidemiol ogy 105 444 449. Bean F D Tienda M. (1987). The Hispanic population of the United States New York, NY: Russell Sage Foundation. Bell, C. N., Bowie, J. V., & Thorpe, R. J. (2012). The interrelationship between hypertension and blood pressure, attendance at religious services and race/ethnicity. Journal of Religion and Health, 51, 310 311. Black, G., Davis, B. A., Heathcotte, K., Mitchell, N., & Sanderson, C. (2006). The relationship between spirituality and compliance in patients with heart failure. Prog ress in Cardiovasc ular Nurs ing 21 128 133. Callaghan, D. (2006). The influence of basic conditioning factors on healthy behaviors, self effic acy, and self care in adults. J ournal of Holist ic Nurs ing 24 178 185. Calle, E. E., Thun, M. J., Petrelli, J. M., Rodriguez, C., & Heath, C.W. (1999). Body Mass Index and Mortality in a Prospective Cohort of U.S. Adults. New England Journal of Medicine, 341, 1097 1105 DOI: 10.1056/NEJM199910073411501 Carter, E. C., McCullough, M. E., & Carver, C. S. (2012). The mediating role of monitoring in the association of religion with self control. Social Psychol ogical and Personality Science, 3 (6), 691 697. Chida, Y., Steptoe, A., & Powell, L. H. (2009). Religiosity/spirituality and mortality. Psychotherapy and Psychosomatics, 78, 81 90. Chow, C. K., Jolly, S., Rao Melacini, P., Fox, K. A., Anand, S. S., & Yusuf S. (2010). Association of diet, exercise, and smoking modification with risk of early

PAGE 59

48 cardiovascular events after acute coronary syndromes. Circulation, 121 750 758. doi: 10.1161/CIRCULATIONAHA.109.891523 Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, New Jersey: Lawrence Ehrlbaum Associates. Comstock, G. W., & Partridge, K. B. (1972). Church attendance and health. J ournal of Chronic Dis eases 25 665 672. DiMatteo, M. R. (2004). Social support and patient self mana gement to medical treatment: a meta analysis. Health Psychol ogy 23 207 218. doi: 10.1037/0278 6133.23.2.207 DiMatteo, M. R., Lepper, H. S., & Croghan, T. W. (2000). Depression is a risk factor for noncompliance with medical treatment: meta analysis of the effects of anxiety and depression on patient self management Arch ives of Intern al Med icine 160 2101 2107. Ellison, C. G., & George, L. K. (1994). Religious involvement, social ties, and social support in a southeastern community. Journal for the Scientific Study of Religion, 33 46 61. Grigsby, J., & Kaye, K. (1996). Behavioral dyscontrol scale: Manual (2nd ed.). Ward, Colorado. Grigsby, J ., Kaye, K., Baxter, J., Shetterly, S. M., & Hamman, R. F. (1998). Executive cognitive abilities and functional status among community dwelling oder person in the San Luis Valley Health and Aging Study. Journal of the American Geriatric Society, 44, 590 59 6.

PAGE 60

49 Grigsby, J., Kaye, K., & Robbins, L. J. (1992). Reliabilities, norms and factor structure of the Behavioral Dyscontrol Scale. Percept ual and Mot or Skills, 74 (3 Pt 1), 883 892. Haynes, R. B., Yao, X., Degani, A., Kripalani, S., Garg, A., & McDonald, H. P. (2005). Interventions to enhance medication self management Cochrane Database of Syst ematic Rev iews (4), CD000011. doi: 10.1002/14651858.CD000011.pub2 Hill, T. D., Angel, J. L., Ellison, C. G., & Angel, R. J. (2005). Religious attendance and mortality: An 8 year follow up of Older Mexican Americans Journal of Gerontology: Social Sciences, 60 102 109. doi: 10.1093/geronb/60.2.S102 Hixson, K. A., Gruchow, H. W., & Morgan, D. W. (1998). The relation between religiosity, selected health behaviors, and blood pressure among adult females. Prev entive Med icine 27 545 552. doi: 10.1006/pmed.1998.0321 Holman, H, & Lorig, K. (2000). Patients as partners in managing chroni c disease. Partnership is a prerequisite for effective and efficient health care. British Journal of Medicine, 320 526 527. Hummer, R. A., Rogers, R. G., Nam, C. B., & Ellison, C. G. (1999). Religious involvement and U.S. adult mortality. Demography, 36 273 285. Jacobs, D. R., Jr., Hahn, L. P., Haskell, W. L., Pirie, P., & Sidney, S. (1989). Validity and reliability of short physical activity history: CARDIA and the Minnesota Heart Health Program. Journal of Cardiopulmonary Rehabilitation, 9 448 459. Karter, A. J., Ackerson, L. M., Darbinian, J. A., D'Agostino, R. B., Jr., Ferrara, A., Liu, J., & Selby, J. V. (2001). Self monitoring of blood glucose levels and glycemic

PAGE 61

50 control: the Northern California Kaiser Permanente Diabetes registry. Am erican J ourn al of Med icine 111 1 9. Kaye, K., Girgsby, J., Robbins, L. J., & Korzun, B. (1990). Prediction of independent functioning and behavior problems in geriaric patients. Journal of the American Geriatric Society, 38, 1304 1310. Kim, S. (2007). Burden of hos pitalizations primarily due to uncontrolled diabetes: implications of inadequate primary health care in the United States. Diabetes Care, 30 1281 1282. doi: 10.2337/dc06 2070 Kim, K. H., & Sobal, J. (2004). Religion, social support, fat intake and physica l activity. Public Health Nutrition, 7, 773 781. doi: 10.1079/PHN2004601 Koenig, H. G., George, L. K., Hays, J. C., Larson, D. B., Cohen, H. J., & Blazer, D. G. (1998). The relationship between religious activities and blood pressure in older adults. Int ernational J ournal of Psychiatry in Med icine 28 189 213. Koenig, L. B., & Vaillant, G. E. (2009). A prospective study of church attendance and health over the lifespan. Health Psychol ogy 28 117 124. doi: 10.1037/a0012984 Krousel Wood, M. A., & Frohlich, E. D. (2010). Hypertension and depression: coexisting barriers to medication self management J ournal of Clin ical Hypertens ion 12 481 486. doi: 10.1111/j.1751 7176.2010.00302.x Lin, E. H., Katon, W., Von Korff, M., Rutter, C., Simon, G. E., Ol iver, M., . Young, B. (2004). Relationship of depression and diabetes self care, medication self management and preventive care. Diabetes Care, 27 2154 2160. Lloyd Jones, D., Adams, R., Carnethon, M., De Simone, G., Ferguson, T. B., Flegal, K., . Stroke Statistics, Subcommittee. (2009). Heart disease and stroke statistics --

PAGE 62

51 2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation, 119 e21 181. doi: 10.1161/CIRCULATIONAHA.108.19 1261 Lorig, K. R., & Holman, H. (2003). Self management education: history, definition, outcomes, and mechanisms. Ann als of Behav ioral Med icine 26 1 7. McCullough, M. E., Hoyt, W. T., Larson, D. B., Koenig, H. G., & Thoresen, C. (2000). Religious involv ement and mortality: a meta analytic review. Health Psychol ogy 19 211 222. McCullough, M. E., & Willoughby, B. L. B. (2009). Religion, self regulation, and self control: Associations, explanations, and implications. Psychological Bulletin, 135, 69 93. d oi: 10.1037/a0014213 Miller, A. S., & Hoffmann, J. P. (1995). Risk and religion: An explanation of gender differences in religiosity. Journal for the Scientific Study of Religion, 34 : 63 75. Miller, W. R., & Thoresen, C. E. (2003). Spirituality, religion, and health. An emerging research field. Am erican Psychol ogist 58 24 35. Mozaffarian, D., Benjamin, E. J., Go, A. S., Arnette, D. K., Blaha, M. J., Cushman, M. 2015 update: a report from the American Heart Association. Circulation, 131, e29 e322. doi: 1 0.1161/CIR.0000000000000152 Muthn, L. & Muthn, B (201 2 ). Mplus version 6. [Com puter software]. Natarajan, S., Sana Ana, E. J., Liao, Y., Lipsitz, S. R., & McGee, D. L (2009). Effect of treatment and self management on ethnic differences in blood pressure control among adults with hyptertension. Annals of Epidemiology, 19, 172 179.

PAGE 63

52 N ational Center for Health Statistics (1973) Plan and Operation of the Health and Nutrition Examination Survey, 1971 73 Washington, DC: DHHS (PHS) publication. National Center for Health Statistics (1981) Plan and Operation of the Second National Examination Survey, 1976 80 Washington, DC: DHHS (PHS) publication. Newlin, K., Melkus, G. D., Tappen, R., Chyun, D., & Koenig, H. G. (2008). Relationships of religion and spirituality to glycemic control in Black women with type 2 diabetes. Nurs ing Res e arch 57 331 339. doi: 10.1097/01.NNR.0000313497.10154.66 Oman, D., & Reed, D. (1998). Religion and mortality among the community dwelling elderly. Am erican J ournal of Public Health, 88 1469 1475. Oman, D., & Thoresen, C. E. (2002). 'Does religion cause health?': differing interpretations and diverse meanings. Health Psychol, 7 365 380. doi: 10.1177/1359105302007004326 Osterberg, L., & Blaschke, T. (2005). Self management to medication. N ew Engl and J ournal of Med icine 353 487 497. doi: 10.1056/NEJMra0 50100 Park, H., Hong, Y., Lee, H., Ha, E., & Sung, Y. (2004). Individuals with type 2 diabetes and depressive symptoms exhibited lower self management with self care. J ournal of Clin ical Epidemiol ogy 57 978 984. Patrick, D. L., Danis, M., Southerland, L I., & Hong, G. (1988). Quality of life following intensive care. Journal of General Internal Medince, 3 218 223. Peyrot, M., Rubin, R. R., Lauritzen, T., Snoek, F. J., Matthews, D. R., & Skovlund, S. E. (2005). Psychosocial problems and barriers to impr oved diabetes management:

PAGE 64

53 results of the Cross National Diabetes Attitudes, Wishes and Needs (DAWN) Study. Diabet ic Med icine 22 1379 1385. doi: 10.1111/j.1464 5491.2005.01644.x Prentice, A. M., & Jebb, S. A. (2001). Beyond body mass index. Obesity Reviews, 2, 141 147. Poloma, M., & Pendleton, B. (1991). The effects of prayer and prayer experience on measures of general well being. Journa l of Psychology and Theology, 1, 71 83. Radloff, L. S. (1977). The CES D scale: A self report depression scale for research in the general popultation. Applied Psychological M easurement, 1, 385 401. Reindl Benjamins, M., & Brown, C. (2004). Religion and preventative health care utilization among the elderly. Soc ial Sci ence and Med icine 58 109 118. Rote, S., Hill, T. D., & Ellison, C. G. (2012). Religious attendance and loneliness in later life. The Gerontologist, 53, 39 50. Samuel Hodge, C. D., Watkins, D. C., Rowell, K. L., & Hooten, E. G. (2 008). Coping styles, well being, and self care behaviors among African Americans with type 2 diabetes. Diabetes Educ ator 34 501 510. doi: 10.1177/0145721708316946 San Luis Valley Regional Development and Planning Commission (1992). San Luis Valley Overal l Economic Development Program. Alamosa, CO: San Luis Valley Regional Development and Planning Commission. Shah, N. R., & Braverman, E. R. (2012 ). Measuring adiposity in p at ients: the utility of body mass i ndex (BMI), p ercent b ody f at, and l eptin PloS One, 7, e33308.

PAGE 65

54 Shuler, P. A., Gelberg, L., & Brown, M. (1994). The effects of spiritual/religious practices on psychological well being among inner city homeless women. Nurse Pract itioner Forum, 5 106 113. Smith, T. B., McCullough, M. E., & Poll, J. (2 003). Religiousness and depression: evidence for a main effect and the moderating influence of stressful life events. Psychol ogical Bul letin 129 614 636. Sniehotta, F. F., Scholz, U., Schwarzer, R., Fuhrmann, B., Kiwus, U., & Voller, H. (2005). Long term effects of two psychological interventions on physical exercise and self regulation following coronary rehabilitation. Int ernational J ournal of Behav ioral Med icine 12 244 255. doi: 10.1207/s15327558ijbm1204_5 SPSS Inc. (20 12 ). Statistical pack age for the social sciences v. 21 [Computer software]. Strawbridge, W. J., Shema, S. J., Cohen, R. D., & Kaplan, G. A. (2001). Religious attendance increases survival by improving and maintaining good health behaviors, mental health, and social relationships. Ann als of Behav ioral Med icine 23 68 74. doi: 10.1207/S15324796ABM2301_1 Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statitiscs (6 th ed.). Pearson Education, Inc. Toljamo, M., & Hentinen, M. (2001). Self management to self care and social support. J ournal of Clinical Nurs ing 10 618 627. Troiano R P Berrigan D Dodd K W Masse L C Tilert T & McDowell M. (2008). Physical activity in the United States measured by accelerometer. Medicine & Science in Sports & Exercise, 40, 181 18 8. doi: 10.1249/mss.0b013e31815a51b3

PAGE 66

55 Tuppin, P., Neumann, A., Danchin, N., de Peretti, C., Weill, A., Ricordeau, P., & Allemand, H. (2010). Evidence based pharmacotherapy after myocardial infarction in France: self management associated factors and relationship with 30 month mortality and rehospitalization. Arch ives of Cardiovasc ular Dis ease 103 363 375. doi: 10.1016/j.acvd.2010.05.003 Turconi, G., Celsa, M., Rezzani, C. Biino, G., Sartirana, M. A., & Roggi, C. (2003). Reliab ility of a deitary questionnaire on food habits, eating behaviour and nutritional knowledge of adolescents. European Journal of Clinical Nutrition, 57, 753 763. doi: 10.1038/sj.ejcn.1601607 van der Wal, M. H., Jaarsma, T., Moser, D. K., Veeger, N. J., van Gilst, W. H., & van Veldhuisen, D. J. (2006). Compliance in heart failure patients: the importance of knowledge and beliefs. Eur opean Heart J ournal 27 434 440. doi: 10.1093/eurheartj/ehi603 Walsh, A. (1998). Religion and hypertension: testing alternative explanations among immigrants. Behav ioral Med icine 24 122 130. doi: 10.1080/08964289809596390 Wang, P. S., Bohn, R. L., Knight, E., Glynn, R. J., Mogun, H., & Avorn, J. (2002). Noncompliance with antihypertensive medications: the impact of depressive symptoms and psychosocial factors. J ournal of Gen eral Intern al Med icine 17 504 511. Watkins, K. W., Connell, C. M., Fitzgerald, J. T., Klem, L., Hickey, T., & I ngersoll Dayton, B. (2000). Effect of adults' self regulation of diabetes on quality of life outcomes. Diabetes Care, 23 1511 1515.

PAGE 67

56 World Health Organization ( WHO ), (2003). Self management to Long Term Therapies: Evidence for Action Geneva, Switzerland. World Health Organization ( WHO ), (2011). Global status report on noncommunicable diseases 2010 Geneva, Switzerland. Wu, J. R., Frazier, S. K., Rayens, M. K., Lennie, T. A., Chung, M. L., & Moser, D. K. (2013). Medication self management social support, and event free survival in patients with heart failure. Health Psychol ogy 32 637 646. doi: 10.1037/a0028527 Yan, L. L., Daviglus, M. L., Liu, K., Pirzada, A., Garside, D. B., Schiffer, L., Dyer, A. R., & Greenland, P. (2004). BMI and he alth related quality of life in adults 65 years and older. Obesity Research, 12, 69 76. DOI: 10.1038/oby.2004.10