Occupational stress, dietary self-efficacy, eating habits and

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Occupational stress, dietary self-efficacy, eating habits and
OCCUPATIONAL STRESS, DIETARY SELF-EFFICACY, EATING HABITS AND
BODY COMPOSITION IN POLICE OFFICERS
by
REBECCA WITTEN GRIZZLE
KATHLEEN C. BROWN, COMMITTEE CHAIR
DIANE GRIMLEY
M. GAIL HILL
ELIZABETH MAPLES
MICHAEL T. WEAVER
A DISSERTATION
Submitted to the graduate faculty of The University of Alabama at Birmingham,
In partial fulfillment of the requirements for the degree of
Doctor of Philosophy
BIRMINGHAM, ALABAMA
2009
Copyright by
Rebecca Witten Grizzle
2009
ii
OCCUPATIONAL STRESS, DIETARY SELF-EFFICACY, EATING HABITS AND
BODY COMPOSITION IN POLICE OFFICERS
REBECCA W. GRIZZLE
SCHOOL OF NURSING
ABSTRACT
Obesity is a paramount public health threat in the U.S. as approximately twothirds of the adult population is overweight. Police officers have an even higher
prevalence of overweight and obesity and other cardiovascular risk factors. Police
officers often attribute their above average cardiovascular disease risk to shift work, jobrelated stress, and poor dietary habits while working. Occupational stress is a major
concern among police officers. Therefore, it is important to understand the relationships
among occupational stress, obesity, and diet habits. However, few research studies have
characterized these relationships in police officers.
The specific aims of this study were to determine the associations of: 1) personal
factors (gender, age, race/ethnicity, marital status, and shift assignment) and cognitive
factors (benefits of healthy eating, barriers to healthy eating, and dietary self-efficacy)
with occupational stress, 2) personal and cognitive factors with fat-related diet habits, and
3) personal factors, fat-related diet habits, and occupational stress with body composition,
comprised of BMI and waist circumference.
This nonexperimental, correlational study was guided by the revised Health
Promotion Model. A convenience sample of 289 sworn police officers completed the Job
iii
Stress Survey, Diet Habits Questionnaire, Eating Habits Confidence Survey, and Healthy
Eating Benefit/Barriers Scale instruments.
Among the personal factors, only race/ethnicity was significantly associated with
occupational stress. Black police officers had a significantly lower mean occupational
stress level when compared to White police officers. Dietary self-efficacy was inversely
associated with occupational stress; whereas, barriers to healthy eating was positively
correlated to occupational stress level. No relationship was found between occupational
stress and fat-related diet habits. The model containing race/ethnicity, dietary selfefficacy, and barriers to healthy eating explained 26.4% of the variance in fat-related diet
habits. Black police officers tended to have higher fat-related diet habits than White
police officers. Gender, race/ethnicity, and the interaction between gender and
race/ethnicity were significantly associated with body composition. Neither fat-related
habits nor occupational stress were significantly related to body composition. The
findings from this study can be used in planning dietary and occupational stress
interventions among police officers. Future research is needed to determine other
predictors of diet habits and body composition.
Keywords: occupational stress, diet habits, obesity, police officers, dietary self-efficacy,
race/ethnicity
iv
DEDICATION
This dissertation is dedicated in honor of the memory of my maternal
grandmother, Margaret Bethune White Garrison, who passed away on March 19, 2009.
Grandmama Garrison always reminded me to “put your best foot forward.” As a member
of the PEO Sisterhood, Chapter S, Virginia, she promoted educational opportunities for
women. She continued to support and encourage me up until the week of my dissertation
defense.
v
ACKNOWLEDGEMENTS
I would like to express gratitude to my longtime graduate advisor and doctoral
chairperson, Dr. Kathleen C. Brown, for her steadfast support, sincere encouragement,
unfailing commitment and wise counsel. I thank her especially for the many years she has
contributed to my professional development as I have progressed through graduate school
and the doctoral program.
I am thankful for the expertise and encouragement of my doctoral committee
members, Drs. Michael T. Weaver, Gail Hill, Elizabeth Maples and Diane Grimley. I am
especially grateful for the knowledge they have bestowed upon me during my doctoral
program and their time and assistance in completing this dissertation.
I would also like to thank the City of Birmingham Good Health Program staff
members, Thomas Kekes-Szabo, Pam Carver, and Tracy Faircloth for assisting me during
the data collection phase of this research project.
My sincere appreciation also goes to Dr. Andrea Berndt, of the University of
Texas Health Sciences Center at San Antonio, for her informative statistical consultation.
I am truly grateful for the National Institute of Occupational Safety and Health
fellowship award that has supported me during my doctoral work.
I would like to thank Dr. Mantana Damrongsak-Brown, my dear friend and fellow
doctoral student, for her tremendous support and encouragement during the doctoral
vi
program. I would not have completed my coursework without your help in taking care of
me while I was in town.
To my extended family, I appreciate all of your love and encouragement. To my
parents and grandparents: thank you for instilling in me a love of nursing and of lifelong
learning.
To my children, Sophia Noelle and Alexander Garrison, thank you for being such
wonderful children while Mommy was doing schoolwork. In addition, I am grateful to
Barbara Fisher, for her love, support, and especially for taking care of my children while
I worked.
Finally, my deepest appreciation goes to my husband, Scott Grizzle, for his
constant support, understanding, encouragement, and above all, for his love. Without
you, I would never have fulfilled my dreams. Thank you for always convincing me to do
my best.
vii
TABLE OF CONTENTS
Page
COPYRIGHT .................................................................................................................... ii
ABSTRACT ..................................................................................................................... iii
DEDICATION ...................................................................................................................v
ACKNOWLEDGMENTS ................................................................................................ vi
LIST OF TABLES ............................................................................................................. xi
LIST OF FIGURES .......................................................................................................... xii
LIST OF ABBREVIATIONS .......................................................................................... xiii
CHAPTER
1 INTRODUCTION ..........................................................................................................1
Statement of the Problem ..............................................................................................1
Background of the Problem ..........................................................................................5
Purposes of the Study....................................................................................................7
Significance of the Study ..............................................................................................8
Research Questions .......................................................................................................9
Theoretical Framework .................................................................................................9
Definition of Terms.....................................................................................................13
Assumptions................................................................................................................14
2 LITERATURE REVIEW .............................................................................................16
Obesity ........................................................................................................................16
Cardiovascular Disease Risk Reduction .....................................................................18
Personal, Situational and Cognitive Factors ...............................................................18
Personal Factors .....................................................................................................19
Occupational Stress ................................................................................................21
Cognitive Factors ...................................................................................................23
Dietary Habits Intervention Studies ............................................................................26
Summary .....................................................................................................................30
viii
TABLE OF CONTENTS (Continued)
Page
3 METHODOLOGY .......................................................................................................31
Design .........................................................................................................................31
Sample.........................................................................................................................32
Characteristics of the Sample.................................................................................32
Sample Size and Power Analysis ...........................................................................32
Ethical Considerations ............................................................................................... 33
Protection of Human Subjects .............................................................................. 34
Data Collection Procedure ..........................................................................................35
Measurement ...............................................................................................................36
Demographic Survey .............................................................................................37
Body Composition .................................................................................................37
Job Stress Survey ...................................................................................................38
Healthy Eating Benefits/Barriers Scale .................................................................39
Eating Habits Confidence Survey ..........................................................................40
Fat-Related Dietary Habits Questionnaire .............................................................40
Data Management and Analyses .................................................................................42
Data Management ..................................................................................................42
Statistical Analyses ................................................................................................44
Limitations of the Study..............................................................................................46
4 FINDINGS ....................................................................................................................48
Description of the Sample ..........................................................................................48
Instrument Reliability ................................................................................................51
Descriptive Analyses of the Study Variables.............................................................52
Findings Related to Research Questions....................................................................54
Research Question 1 .............................................................................................54
Research Question 2 .............................................................................................57
Research Question 3 .............................................................................................58
Research Question 4 .............................................................................................59
Research Question 5 .............................................................................................59
Research Question 6 .............................................................................................61
Research Question 7 .............................................................................................62
Research Question 8 .............................................................................................63
5 DISCUSSION, CONCLUSIONS, IMPLICATIONS, AND
RECOMMENDATIONS ..............................................................................................65
Discussion ..................................................................................................................65
Occupational Stress ...............................................................................................65
Personal Factors and Occupational Stress ............................................................67
ix
TABLE OF CONTENTS (Continued)
Page
Cognitive Factors and Occupational Stress ..........................................................68
Fat-Related Diet Habits and Occupational Stress .................................................69
Fat-Related Diet Habits and Personal and Cognitive Factors...............................70
Personal Factors and Body Composition ..............................................................71
Fat-Related Diet Habits, Occupational Stress and Body Composition.................72
The Conceptual Model ..........................................................................................73
Conclusions ................................................................................................................74
Implications................................................................................................................75
Implications for Nursing Education ......................................................................75
Implications for Nursing Practice .........................................................................76
Recommendations for Future Research .....................................................................77
REFERENCES ..................................................................................................................79
APPENDIX
A
REVISED HEALTH PROMOTION MODEL .....................................................89
B
POWER ANALYSIS FORMULAE .....................................................................91
C INSTITUTIONAL REVIEW BOARD FOR HUMAN USE APPROVAL .........94
D LETTER OF INTRODUCTION ..........................................................................96
E PERMISSION TO REPRINT MODEL AND USE QUESTIONNAIRES ..........98
F
RESEARCH INSTRUMENTS ...........................................................................104
x
LIST OF TABLES
Table
Page
1
Demographic Characteristics of the Sample by Gender .........................................49
2
Number of Items and Cronbach’s Alpha Coefficients for the Instruments and
Subscales .................................................................................................................51
3
Body Mass Index (kg/m²) Classification for Police Participants ............................52
4
Waist Circumference Risk Classification for Police Participants by Gender .........53
5
Range of Possible Scores, Observed Ranges, Means, Medians and Standard
Deviations of Dietary Study Variables ...................................................................53
6
Range of Possible Scores, Observed Ranges, Means, and Standard Deviations
of the Occupational Stress Scale and Subscales .....................................................55
7
Means and Standard Deviations for Job Stress Item Index Scores, Severity
Ratings and Frequency Ratings for Police Officers ................................................56
8
Means and Standard Deviations of Occupational Stress By Gender, Race/
Ethnicity, and Marital Status....................................................................................58
9
Bivariate Correlations of Benefits of Healthy Eating, Barriers to Healthy
Eating, and Dietary Self-Efficacy with Occupational Stress ..................................59
10
Regression Weights of Race/Ethnicity, Dietary Self-Efficacy and Barriers to
Healthy Eating for Fat-Related Diet Habits after Removing Non-significant
Variables .................................................................................................................61
11
Regression Weights Describing Personal Factor Relationships with Body
Composition after Removing Non-significant Variables ......................................62
12
Regression Weights Describing Fat-Related Diet Habits Relationships with
Body Composition, Controlling for Age ...............................................................63
13
Regression Weights Describing Occupational Stress Relationships with
Body Composition, Controlling for Age ...............................................................64
xi
LIST OF FIGURES
Figure
1
Page
The Conceptual Model for the Study .....................................................................13
xii
LIST OF ABBREVIATIONS
ANOVA
Analysis of Variance
BMI
Body Mass Index
BRFSS
Behavioral Risk Factor Surveillance System
CDC
Centers for Disease Control and Prevention
CVD
Cardiovascular Disease
DHQ
Dietary Habits Questionnaire
FHQ
Food Habits Questionnaire
GLM
General Linear Model
HEBBS
Healthy Eating Benefits/Barriers Scale
HPM
Health Promotion Model
IRB
Institutional Review Board
JSS
Job Stress Survey
MRC
Multiple Regression/Correlation
NHANES
National Health and Nutrition Examination Survey
SPSS
Statistical Product and Service Solutions
SC
Set Correlation
UAB
University of Alabama at Birmingham
USDA
United States Department of Agriculture
WC
Waist Circumference
xiii
1
CHAPTER 1
INTRODUCTION
Police work can be a stressful and dangerous occupation (Bureau of Labor and
Statistics, U.S. Department of Labor, 2007). Since the terrorist attacks of September 11,
2001, concern for the health and safety of the professionals who have chosen to protect
the public has heightened (Ramey, 2003). Because public safety workers, such as police
officers, must be in good health to meet the demands of their jobs, health care research is
needed to study health promotion issues among these employees. One approach to
promote the health of police officers is to prevent cardiovascular disease, the primary
cause of death in the United States (Centers for Disease Control and Prevention [CDC],
2004). Research suggests that police officers have above average prevalence of risk
factors for cardiovascular disease (CVD) such as obesity, smoking, hypertension, and
hypercholesterolemia (Ramey, 2003; Williams, Petratis, Baechle, Ryschon, Campain, &
Sketch, 1987). In addition, perceived stress potentiates several cardiovascular disease risk
factors in police officers (Franke, Ramey, & Shelly, 2002).
Statement of Problem
Obesity is the primary public health concern in the U.S. according to a national
survey of nutrition researchers (Myers, Beyer, & Geiger, 2003). From 1991 to 2001, the
percentage of adults who were overweight increased from 45% to 58% of the population.
These figures include the obesity category in which prevalence rates had increased from
2
12% to 21% of adults (Mokdad, Ford, Bowman, Dietz, Vinicor, Bales, et al., 2003). The
most recent analysis from the National Health and Nutrition Examination Survey
(NHANES) estimates that 66% of adults are overweight, of which 32% are classified as
obese (Ogden, Carroll, Curtin, McDowell, Tabak, & Flegal, 2006). The rising obesity
epidemic is threatening to increase the incidence of weight-related chronic health
problems such as diabetes mellitus, cardiovascular disease, osteoarthritis, and some forms
of cancer (Calle, Rodriquez, Walker-Thurmond, & Thun, 2003; Mokdad et al., 2003).
Furthermore, poor diet and physical inactivity are poised to overtake tobacco use as the
primary actual cause of death in the U.S. (Mokdad, Marks, Stroup, & Gerberding, 2004;
2005).
Obesity and overweight exact a tremendous burden on society in terms of
economic costs. In the U.S., it is estimated that 9.1% of national health care expenditures
are related to obesity and overweight (Finkelstein, Fiebelkorn, & Wang, 2003). The direct
costs of health care for obese individuals are significantly higher due to increased
prescription medication usage, outpatient visits, and hospitalizations when compared to
normal weight individuals (Raebel, Malone, Conner, Xu, Porter, & Lanty, 2004). Selfinsured employers bear the burden of direct (medical claims) and indirect costs (paid time
off) which are significantly greater for overweight and obese employees (Durden, Huse,
Ben-Joseph, & Chu, 2008).
Weight status is assessed by calculating body mass index (BMI), the ratio of
weight in kilograms to height in meters squared (kg/m²). Being overweight is defined as
having a BMI of 25 to 29.9 kg/m² and obesity is defined as a BMI equal to 30 kg/m² or
greater. Obesity is also manifested centrally as excessive fatness around the midsection of
3
the body. The best measure of abdominal fat content is waist circumference (WC). In
men, a WC greater than 40 inches, and in women, a WC greater than 35 inches is
indicative of central obesity and higher CVD risk (National Institutes of Health, 1998;
2000). Body composition is the assessment of total body fatness taking into consideration
both BMI and WC. The combined measures of BMI and WC are more predictive of CVD
risk outcomes than either measure alone (Zhu et al., 2004).
Although approximately two-thirds of the adult population is overweight, police
officers tend to have an even higher prevalence of overweight and obesity. Because
police officers must be fit for duty, the City of Birmingham, Alabama Good Health
Program annually screens members of the police force for health risks (Brown, Hilyer,
Artz, Glasscock, & Weaver, 1995; Forrester, Weaver, Brown, Phillips, & Hilyer, 1996;
Weaver, Forrester, Brown, Phillips, Hilyer, & Capulouto, 1998). The rate of overweight
and obesity among this group of police officers is 38% and 49%, respectively, totaling
87%, which far exceeds national and state statistics of overweight and obesity for the
general population (Good Health Program, 2005a).
Furthermore, retired police officers have a higher incidence of CVD compared to
the age-referenced general population (31.5% vs. 18.4%). When surveyed on how their
positions increased their cardiovascular risks, former police officers cited job-related
stress, poor dietary habits while at work, and shift work rotation as the most common
reasons (Franke, Collins, & Hinz, 1998). Occupational stress is a major concern among
police officers due to exposure to critical incidents, poor cooperation between units,
excessive workload, and inadequate supervisor and peer support (Collins & Gibbs, 2003;
Gershon, Lin, & Li, 2002; Ramey, Downing, & Knoblauch, 2008). The best predictors of
4
CVD among police officers were perceived stress, time in profession, and hypertension
(Franke et al., 2002). Additionally, occupational stress has been linked to greater health
care claims and costs (Manning, Jackson, & Fusilier, 1996). Consequently, police work is
a high-risk profession for which health promotion research is needed.
Regarding diet, fat-related diet habits are a major concern in the development of
obesity. Among the macronutrients, fat contains the most calories per gram. Thus, the
trend towards higher fat intake is disconcerting. Research suggests that less than 15% of
American respondents consumed fat intake at 30% or less of energy and only 16% met
the goal of reduced saturated fat intake to less than 10% of energy (Patterson, Haines, &
Popkin, 1994). Higher fat intake combined with a sedentary lifestyle, creates an energy
imbalance resulting in excess weight gain.
Generally, police work is considered sedentary in nature; therefore, physical
activity outside of work is important to maintain healthy waist and fitness levels
(Williams et al., 1987). In a study of 470 police officers, 68% were categorized as
nonexercisers (Franke & Anderson, 1994). Overweight police officers stated that the
primary reasons for the excess weight were “poor dietary habits due to varying work
schedules with no scheduled meal times, lack of nutrition knowledge, and lack of an
organized exercise program” (Demling & DeSanti, 2000, p. 23). Furthermore, police
officers suggested that irregular work hours, lack of routine, fatigue, and unpredictable
events impeded their efforts to establish healthy diet and exercise habits (Ramey et al.,
2008).
Among males in a large prospective study, lower saturated fat intake and
increased fruit intake were significantly related to a reduced risk of CVD (McCullough et
5
al., 2000). Men and women with the highest healthy eating scores had respectively a 39%
and 28% lower risk of CVD compared to subjects with the lowest scores (McCullough et
al., 2002). Another study examined the influences of prudent (i.e. heart healthy) versus
Western patterned diets on coronary heart disease (CHD) risk in men. A prudent diet was
found to lower risk for fatal CHD and nonfatal myocardial infarction, independent of
other lifestyle factors (Hu, Rimm, Stampfer, Aschero, Spiegelman, & Willett, 2000).
Because fat intake can significantly impact CVD risks and obesity rates, it is
important to examine the factors that influence both fat-related diet habits and body
composition among police officers. Unfortunately, there is a paucity of scientific data on
the variables that influence these health outcomes. Little is known about which
characteristics of police officers are significantly associated with their diet habits and
body composition, or what role occupational stress may play. Therefore, more research is
needed to examine the relationships among variables, such as personal and cognitive
factors that contribute to diet habits and body composition in this population. Personal
factors include gender, age, race/ethnicity, marital status, and shift work. Cognitive
factors include benefits and barriers to healthy eating and dietary self-efficacy.
Background of the Problem
Personal factors such as demographic variables influence diet habits through
various mechanisms. Several studies have found that the demographic variables of
gender, age, ethnicity, income, and educational level are significantly correlated with
healthy dietary intake (Foote, Murphy, Wilkens, Basiotis, & Carlson, 2004; Guo,
Warden, Paeratakul, & Bray, 2004; Hann, Rock, King, & Drewnowski, 2001; Thompson,
6
Mistune, Subaru, McNell, Berrigan, & Kipnis, 2005; Trudeau, Kristal, Li, & Patterson,
1998). Findings suggest that male gender, younger age, Black race, lower income, and
lower education levels are associated with unhealthy diet habits.
Another personal factor relevant to the police profession that has been associated
with health outcomes is shift work. Ely and Mostardi (1986) found that police officers
who worked rotating shifts had significantly elevated norepinephrine levels, which may
contribute to the development of hypertension. Night shift work has been found to be
associated with a significantly larger mean weight gain than day shift work (Geliebter,
Gluck, Tanowitz, Aronoff, & Zammit, 2000). In a qualitative study, the factors that
influenced diet habits among night shift nurses were social interaction among colleagues
and circadian rhythm dysfunction (Persson & Mårtensson, 2006).
Cognitive variables related to engaging in health-promoting behaviors include
self-efficacy, perceived barriers, and perceived benefits (Pender, Murdaugh, & Parsons,
2002). These factors have been found to be correlated to diet habits in several studies
(Fowles & Feucht, 2004; Glanz, Kristal, Tilley, & Hirst, 1998; Wilson, Friend, Teasley,
Green, Reaves, & Sica, 2002; Zunft et al., 1997). Perceived barriers represent the
difficulties in performing a certain behavior (Pender, 1996). Specific barriers to healthy
eating are unavailability, expense, difficulty, inconvenience and loss of satisfaction of
healthy food options (Fowles & Feucht, 2004). The term healthy eating refers to
behavioral eating patterns that comprise a high quality diet, as defined by the dietary
guidelines for Americans (U.S. Department of Health and Human Services and U.S.
Department of Agriculture, 2005). Perceived benefits are the anticipated positive
outcomes of that behavior (Pender, 1996). Benefits to healthy eating include weight
7
control, disease prevention, increased fitness, staying healthy, living longer, and looking
attractive (Strolla, Gans, & Risicia, 2006; Zunft et al., 1997).
Perceived self-efficacy is defined as the belief in one’s ability to perform a
particular task (Bandura, 1977). In terms of health functioning, “efficacy beliefs largely
determine whether people consider changing their health habits and whether they succeed
in making and maintaining the change” (Bandura, 1997, p. 6). Specifically, dietary selfefficacy is the confidence one has to eat a healthy, balanced diet under challenging
circumstances, such as adhering to a diet or choosing healthier food options at social
functions (Sallis, Pinski, Grossman, Patterson, & Nader, 1988).
Yet to be investigated are factors that influence diet habits in at risk populations
such as ethnic minorities and highly stressed workers such as police officers. In
conclusion, more research is needed to analyze the relationships among the personal,
situational, and cognitive factors that may contribute to diet habits and body composition
in the police population.
Purposes of the Study
The overall goal of the study is to examine the interrelationships among personal
and cognitive factors, occupational stress, fat-related diet habits, and body composition in
police officers. The specific aims of this study are to determine the associations of: 1)
personal and cognitive factors with occupational stress, 2) personal and cognitive factors
and fat-related diet habits, and 3) personal factors, occupational stress, and fat-related diet
habits with body composition. Understanding these relationships is the first step in
8
developing effective police health promotion interventions that target nutritional and
stress-related health outcomes.
Significance of the Study
Very little nutrition research is available on specific at-risk worker populations,
such as police officers. The long-term objectives of this research study are to understand
the factors that are associated with diet habits and body composition in order to develop
nutrition and stress-related health promotion programs for police officers. Effective
nutrition intervention strategies that influence the most pertinent factors need to be
developed. It is hoped that by improving fat-related diet habits in the police population,
health risks would be attenuated and the prevalence of obesity-related diseases would
decrease. Therefore, it is important for health professionals to understand the
relationships among factors that contribute to diet habits and body composition in this
group of public safety workers.
Findings from this study may assist health professionals in identifying
characteristics of police officers that can be incorporated in the development of more
effective fat-related dietary interventions for these workers. There is a renewed need to
focus on behavioral strategies for the primary prevention of cardiovascular disease and
obesity. While individuals’ awareness and motivation are necessary to successfully
implement positive health behaviors, those factors are insufficient to maintain change.
Therefore, other cognitive and situational variables that influence health behavior need to
be explored.
9
Research Questions
The research questions in this study are as follows:
1. What are the levels of occupational stress in police officers?
2. What are the bivariate relationships between the personal factors (gender, age,
race/ethnicity, marital status, and shift assignment) and occupational stress?
3. What are the bivariate relationships between benefits of healthy eating, barriers to
healthy eating, dietary self-efficacy, and occupational stress?
4. Is there a relationship between occupational stress and fat-related diet habits?
5. What is the best model from the set of personal factors, benefits of healthy eating,
barriers to healthy eating, and dietary self-efficacy that explain the variability in
fat-related diet habits?
6. What are the relationships among the personal factors and body composition
(comprised of BMI and waist circumference), controlling for age?
7. Is there a relationship between fat-related diet habits and body composition
(comprised of BMI and waist circumference), controlling for age?
8. Is there a relationship between occupational stress and body composition
(comprised of BMI and waist circumference), controlling for age?
Theoretical Framework
The study is guided by the revised Health Promotion Model (HPM) which was
originally developed by Nola Pender in the early 1980s to explain lifestyle patterns that
predict health-promoting behaviors among individuals (see Appendix A). After years of
research, it was amended to its current form (Pender, 1996). The model is derived from
10
social cognitive theory which broadly explains the complexities of human health
behavior through the reciprocal interaction between person, behavior, and environment
(Bandura, 2004). The revised HPM assumes that individuals are complex
biopsychosocial beings who regulate their own behavior. It assumes individuals interact
with the environment resulting in changes to health behavior (Pender et al., 2002).
The revised HPM consists of ten determinants of behavior organized into three
major components: individual characteristics and experiences, behavior-specific
cognitions and affect, and behavioral outcome. Individual characteristics and experiences
are proposed to directly and/or indirectly determine the occurrence of health-promoting
behaviors. Behavior-specific cognitions and affect are the factors considered the primary
motivators that directly influence whether an individual will commit to and engage in a
particular health-promoting behavior. The behavioral outcome is the action that achieves
a higher level of health and wellness for the individual (Pender et al., 2002).
The individual characteristics and experiences are divided into prior related
behavior and personal factors. Prior related behavior is previous engagement in health
promotion activities, including knowledge and skills acquisition. Because past behavior is
assumed to predict future behavior, these factors are thought to affect health-promoting
behaviors directly through habit formation or indirectly by influencing the perceptions of
benefits, barriers, self-efficacy, and activity-related effect.
Personal factors are described as biological, psychological, and socio-cultural.
Biological factors include the demographic characteristics of age and gender, as well as,
physical characteristics such as strength and developmental status. Examples of
psychological variables are motivation, perceived health status and self-esteem. Socio-
11
cultural factors include variables such as race, ethnicity, educational level, and financial
status. These personal factors are proposed to influence behavior either directly or
indirectly.
The model’s primary mechanism for influencing health promotion is the
behavior-specific cognitions and affect component. The perceptual variables identified in
this category include (1) perceived benefits of action, (2) perceived barriers to action, (3)
perceived self-efficacy, (4) activity-related affect, (5) interpersonal influences, and (6)
situational influences. Perceived benefits of action relate to the anticipated rewards for
the health behavior, such as weight loss due to dieting. Perceived barriers to action are
the expected difficulties in undertaking a specific behavior, such as monetary and time
constraints. Perceived self-efficacy is defined as a “judgment of personal capability to
organize and carry out a particular course of action” (Pender, Murdaugh, & Parsons,
2006, p. 53). Activity-related affect is the subjective feeling, either positive or negative,
associated with a particular health behavior. The model proposes that activity-related
affect influences perceived self-efficacy, which in turn affects perceived barriers to
action. Perceived benefits, barriers, self-efficacy, and activity-related affect directly
influence health-promoting behaviors or indirectly through increasing or decreasing
commitment to action, which indicates level of readiness.
The components of interpersonal factors are norms, support, and models, which
take into consideration the “wishes, examples, and praise” of family, co-workers, health
professionals, and significant others” (Pender et al., 2002, p. 55). Types of situational
influences include options, demand characteristics, and aesthetic features of the
environment, providing context for the desired health behavior. Both interpersonal and
12
situational influences effect health-promoting behaviors directly and indirectly through
reinforcing the commitment to a plan of action, which is synonymous with intentionality.
The final component of the revised HPM is the behavioral outcome. Healthpromoting behavior is directed by the commitment to a plan of action, which is
determined by the six behavior-specific cognitions and affect. The last modifying element
is immediate competing demands and preferences. Competing demands are influences
such as family responsibilities and multiple priorities over which one has little control.
Competing preferences are those strong desires for alternative behaviors over which a
person has more control, such as choosing a tasty, high-fat food instead of a healthier
option. These factors may hinder an individual’s ability to engage in health-promoting
behaviors. Health-promoting behaviors are defined as ongoing activities integrated into
one’s lifestyle that ultimately lead to improved health, functional status, and quality of
life (Pender et al., 2006). Examples of these behaviors are physical activity, healthy
dietary habits, and stress management.
In this study of diet habits, body composition, and occupational stress in police
officers, the revised HPM is a useful conceptual framework that guides the research. The
model directs the selection of research questions, study variables, and behavioral
outcome. The conceptually relevant personal factors of police officers in this study are
gender, age, race/ethnicity, marital status, and shift assignment. These variables were
hypothesized to explain the variability in diet habits and body composition.
Dietary self-efficacy, perceived barriers to healthy eating, and perceived benefits
of healthy eating are the specific cognitive-perceptual variables thought to influence diet
habits. For example, police officers face barriers such as limited time to prepare a healthy
13
bag lunch. Possible benefits of healthy diet habits are increased fitness or reduced
incidence of chronic diseases.
Occupational stress is a demand characteristic. This is a specific situational
influence that may be related to variables in the study.
The final component adapted from the revised HPM is the behavioral outcome.
As seen in the conceptual model in Figure 1, the specific health-promoting behavioral
and health outcomes are fat-related diet habits and body composition which consists of
BMI and waist circumference.
Personal
Factors
- Gender
-Age
-Race/
Ethnicity
-Marital
Status
-Shift
Assignment
Cognitive
Factors
Behavioral
Outcome
Health
Outcome
FatRelated
Diet
Habits
Body
Composition
-BMI & WC
Benefits of
Healthy Eating
Barriers to
Healthy Eating
Dietary
Self-Efficacy
Occupational
Stress
Figure 1. The conceptual model for the study.
Definition of Terms
The following definitions are used in this research study:
14
Personal factors refer to the sociodemographic variables of gender, age,
race/ethnicity, marital status and shift assignment. Self-report data was obtained from the
Good Health Questionnaire (Good Health Program, 2005b).
Barriers to healthy eating refer to the perception of anticipated hurdles in
adherence to a healthy diet and were measured by the Healthy Eating Benefits/Barriers
Scale (HEBBS) developed by Walker, Pullen, Hertzog, Broekner, and Hageman (2006).
Benefits of healthy eating refer to the positive outcomes of adherence to a healthy
diet and were measured by the HEBBS (Walker et al., 2006).
Dietary self-efficacy is defined as an individual’s confidence in his or her ability
to maintain a healthy diet and was measured by the Eating Habits Confidence Survey
(Sallis et al., 1988).
Occupational stress refers to perceived job-related stress and was measured by
the Job Stress Survey (JSS) developed by Spielberger and Reheiser (1994a).
Diet habits are the overall tendencies toward adopting lower fat options when
choosing and preparing foods to eat. This variable was measured by the Fat-Related
Dietary Habits Questionnaire (DHQ) developed by Kristal, Shattuck, and Henry (1990).
Body composition is the assessment of total body fatness taking into consideration
both body mass index and waist circumference. These data were obtained from height,
weight, and waist circumference measurements collected during the Good Health
Program police screen by trained data collectors.
Assumptions
This research study was also based on the following assumptions:
15
1. Police officers must be fit for duty.
2. Individuals are complex biopsychosocial beings who seek to regulate their own
health behavior.
3. Benefits and barriers to healthy eating, dietary self-efficacy, occupational stress,
and fat-related diet habits can be accurately measured in participants.
4. Individual cognitions and behaviors can be modified to create healthy lifestyles.
16
CHAPTER 2
LITERATURE REVIEW
The aim of this study was to examine the interrelationships among personal and
cognitive factors, occupational stress, fat-related diet habits, and body composition in
police officers. Few studies have explored factors contributing to overweight, obesity,
and diet habits in police officers. This chapter contains a review of research literature
related to this topic.
Obesity
One must understand the increasing obesity epidemic in order to grasp the
magnitude of the cardiovascular morbidity that faces the nation. Using data from the
Behavioral Risk Factor Surveillance System (BRFSS), a nationwide randomized
telephone survey of over 184,000 adults, researchers investigated trends in the prevalence
of overweight (BMI greater than 25 kg/m²) and obesity (BMI greater than 30 kg/m²)
(Mokdad, Bowman, Ford, Vinicor, Marks, & Koplan, 2001). From 1991 to 2001, the
prevalence of obesity had increased from 12% to 20.9% of adults. The percentage of
overweight adults increased from 45% to 58%. The co-morbidities significantly
associated with being overweight and obese are diabetes, high blood pressure, high
cholesterol, asthma, and arthritis (Mokdad et al., 2003).
Prevalence rates from BRFSS data have underestimated the overweight and
obesity burden compared to data from NHANES III, a cross-sectional nationally
17
representative examination survey of 4415 adults over 25 years old. After adjusting the
NHANES data for age, the prevalence of obesity was 29.5% and the prevalence of
overweight was 63.3% (Yun, Zhu, Black, & Brownson, 2006). Using NHANES III data,
there was a graded increase in prevalence ratio of multiple obesity-related co-morbidities
including diabetes, gallbladder disease, hypertension, and osteoarthritis (Must, Spadano,
Coakley, Field, Colditz, & Diez, 1999).
Federal agencies have recommended dietary guidelines and several interventions
for weight loss including nutrition, exercise, medication, and surgery (U.S. Preventive
Services Task Force, 2003). Regarding nutrition, recommendations are that proper
proportions and amounts of protein, carbohydrates, and fat need to be consumed in order
to prevent caloric excess. A high quality diet, consistent with the Dietary Guidelines for
Americans 2005 put forth by the United States Department of Agriculture (USDA),
emphasizes the consumption of a variety of nutrient-dense foods. Based on a reference
2,000-calorie intake, individuals should consume two cups of fruits, two and a half cups
of vegetables, three cups of low-fat or fat-free milk, and three ounces of whole grains per
day. Additionally, individuals should consume only 20% to 35% of calories from total
fat, mostly polyunsaturated and monounsaturated fats while consuming less than 10% of
calories from saturated fat and keeping trans fatty acid intake and cholesterol intake (no
more than 300 milligrams daily) to a minimum. Lastly, less than one teaspoon of salt
(2,300 mg of sodium) should be consumed daily (U.S. Department of Health and Human
Services and USDA, 2005). The above recommended amounts would be higher or lower
depending on an individual’s body composition and daily caloric requirements.
18
Cardiovascular Disease Risk Reduction
One approach to promote health is to target the primary cause of death in the
United States, which is cardiovascular disease (CDC, 2004). McCullough and colleagues
(2002) in large prospective study found that males with higher diet quality scores had
significantly lower relative risk for CVD (RR = 0.72 and 0.66, respectively). Generally, a
high fat diet is defined as dietary intake of more than 30% of calories from fat which has
nine calories per gram and is found in animal and plant sources. Combined with a
sedentary lifestyle, excessive dietary fat intake creates an imbalance in energy intake
leading to weight gain. Research suggests that less than 15% of American respondents of
the 1987-1988 Nationwide Food Consumption Survey kept fat intake at 30% or less of
energy and only 16% met the goal of reduced saturated fat intake to less than 10% of
energy (Patterson et al., 1994). Areas of fat-related dietary habits that could be modified
include adding fat to food as a condiment, cooking with fat, choosing high-fat food
options, and not replacing high-fat foods with low-fat food options (Kristal et al., 1990).
Because police officers may be at elevated risk for CVD and diet is an important
component of controlling the disease, improving diet habits among police officers is an
important area for intervention by occupational health professionals and further research
is needed to examine the correlates of healthy eating habits.
Personal, Situational and Cognitive Factors
Higher overall diet quality is related to sociodemographic variables such as race,
ethnicity, age, income, and education level (Variyam, Blaylock, Smallwood, & Basiotis,
1998). However, little is known about which characteristics of police officers are
19
associated with their diet habits, specifically the variables that influence fat-related diet
habits. Therefore, more research is needed to examine the relationships among variables
such as personal and cognitive factors that contribute to diet habits in this population.
Police officers have an above-average risk for CVD and retired and current officers
typically attribute their health risks to shift work, irregular work hours, job-related stress,
fatigue, lack of routine, and poor dietary habits while working (Franke et al., 1998;
Ramey et al., 2008). Because total fat, saturated fat and trans fat intake can significantly
impact CVD risks, it is important to study the fat-related diet habits among this group of
workers.
Personal Factors
Personal factors, such as demographic variables, influence healthy diet habits
through various mechanisms. Several studies have found that the demographic variables
of gender, age, ethnicity, income, and educational level are significantly correlated with
healthy dietary behaviors. Findings suggested that male gender, younger age, Black race,
lower income, and lower education levels are associated with poorer diet habits (Guo et
al., 2004; Hann et al., 2001; Thompson et al., 2005; Trudeau et al., 1998; Variyam,
Blaylock, Smallwood, & Basiotis, 1998). Females, older persons, and those individuals
with higher educational levels are more likely to make self-initiated, healthy dietary
changes such as increasing fruit and vegetable intake and reducing dietary fat
consumption (Kristal, Hedderson, Patterson, & Neuhauser, 2001). Age, education level,
and BMI only accounted for 4% of the variability in general nutritional adequacy among
a nationwide sample of 9,769 adults (Foote et al., 2004). Therefore, more research is
20
needed to determine other factors which may be associated with healthy dietary intake
and specific macronutrients such as fat.
The healthy eating habits of male health professionals and other predominately
White populations have been studied (Hann et al, 2001; Kristal et al., 2001; McCullough
et al., 2000; Trudeau et al., 1998). Sociodemographic variables were found to be related
to healthy eating. However, generalizability of the findings to other types of workers and
ethnic minorities is not known. The current research study explored personal factors in a
predominantly Black police force which had limited variability in educational and
income level within the same department. Therefore, education and income were
relatively controlled for in the study.
A personal factor specific to police office work that has been found to be
correlated with poor health outcomes is rotating shift work. Ely and Mostardi (1986)
found that police officers who worked rotating shifts had significantly elevated
norepinephrine levels, a possible contributor to the development of hypertension.
Although limited research has reported correlations between shift work and specific diet
habits, it is plausible that working during non-daytime hours is stressful and food options
may be limited which in turn may affect eating behaviors and other health outcomes. For
instance, nurses who were prone to unhealthy eating during the night shift, reported
eating certain unhealthy foods because of cravings or to keep awake (Persson &
Mårtensson, 2006). Night shift work has been found to be associated with a significantly
larger mean weight gain than day shift work (Geliebter et al., 2000). However, little is
known about the associations between specific dietary habits such as fat intake and shift
work or the role of work-related stress in this area. Therefore, further research is needed
21
to describe the relationships among shift work, occupational stress, diet habits and body
composition.
Occupational Stress
The situational factor of perceived stress has also been associated with diet and
obesity. Researchers propose that glucocorticoid release caused by chronic stress
potentiates pleasurable or compulsive behaviors such as the ingestion of high-fat, highcarbohydrate “comfort foods” which leads to abdominal obesity. (Dallman et al., 2003).
Quasi-experimental research has shown that increased intake of sweet high-fat foods
occurred under stressful conditions among emotional eaters (Oliver, Wardle, & Gibson,
2000).
The relationships between job-related stress and obesity have previously been
explored (Overgaard, Gyntelberg & Heitmann, 2004). Results of a large prospective
study found a dose-response relationship between chronic work stress and general and
central obesity, as measured by BMI and waist circumference, in predominately White
Europeans (Brunner, Chandola, & Marmot, 2007). A weak association between work
stress and BMI was found among Finnish public sector workers, after controlling for age,
marital status, smoking, alcohol use, physical activity, and negative affectivity
(Kouvonen, Kivimäki, Cox, Cox, & Vahtera, 2005). Findings were mixed in that lower
job control, higher job strain, and higher effort-reward imbalance were correlated with a
higher BMI among both genders, but job demands were associated with a lower BMI in
men. However, these studies did not measure diet habits which are possibly an important
mediator in the relationship between job-related stress and obesity. In another study, BMI
22
was determined to be related to work stress in 1971 clerical/professional females
(Hellerstedt & Jeffery, 1997). Future studies that examine the relationships between
occupational stress and body composition should explore obesity measures in addition to
BMI (Ramey et al., 2008), in order to gain further information, such as the association
with central obesity. Overgaard and colleagues (2004) also recommend that future studies
examine the association between chronic work stress and eating habits. Therefore, the
current research initiative measured fat-related diet habits and included waist
circumference in the multivariate construct of body composition.
Studies have typically operationalized work stress using a tool based on Karasek’s
demand-control model (1979, as cited in Vagg & Spielberger, 1998), which has been
criticized for not measuring the perceived severity of work-related stressors and for not
establishing the psychometric properties of the instrument (Spielberger & Vagg, 1999).
The Job Stress Survey (JSS) was designed to address the shortcomings of previous job
stress instruments and was based on common items from the Police Stress Survey
(Spielberger, Grier, & Pate, 1980 as cited in Spielberger & Vagg, 1999) and the Teacher
Stress Survey (Grier, 1982, as cited in Spielberger & Vagg, 1999). The scale has
demonstrated high internal consistency and acceptable validity among both men and
women in a variety of job categories (Spielberger, Reheiser, Reheiser, & Vagg, 2000).
Spielberger and Reheiser (1994a) did not find any significant gender-related differences
in occupational stress level among professional, clerical, and maintenance workers.
However, significant main effects were found among university, corporate, and military
employees, with female military personnel reporting substantially higher occupational
stress levels (Spielberger & Reheiser, 1994b). More investigation is needed to determine
23
if there are differences in occupational stress level as a function of gender and
race/ethnicity in other types of employee groups such as police officers.
Few studies have examined the relationship between occupational stress and diet
habits. Job strain has been found to be positively associated with calorie intake from high
fat food among 1872 professional male employees in Minnesota (Hellerstedt & Jeffery,
1997). Consuming less fruits and vegetables has been found to be significantly associated
with work stress among British civil servants (Chandola et al., 2008). Yet to be
investigated is the relationship between diet and occupational stress among police
officers working in the South.
Cognitive Factors
Cognitive variables related to engaging in health-promoting behaviors include
self-efficacy, perceived barriers, and perceived benefits (Pender et al., 2002). These
factors have been found to be correlated to healthy eating in several studies (Fowles &
Feucht, 2004; Glanz, Kristal et al., 1998; Wilson et al., 2002).
Perceived barriers represent the difficulties in performing a certain behavior
(Pender, 1996). Examples of specific barriers to healthy eating are preferences, taste,
cost, unavailability, difficulty, inconvenience, and lack of satisfaction with healthy food
options (Fowles & Feucht, 2004; Strolla, et al., 2006). Fewer perceived barriers to
healthy eating have been associated with healthy diet habits such as dietary fat reduction,
general healthy eating behaviors, adherence to dietary guidelines, and fruit and vegetable
intake (Fowles & Feucht, 2004; Sporny & Contento, 1995; Walker et al., 2006). On the
contrary, perceived barriers to eating a low fat diet and eating more fruits and vegetables
24
were not associated with dietary fat reduction or fruit and vegetable consumption in a
prospective, population-based study of adults (Kristal et al., 2001). Research on barriers
to healthy eating has been conducted in predominantly White female adult samples.
Therefore, studies are needed in more diverse samples which include individuals of
different ethnicities, working adults, and males.
Perceived benefits are the anticipated positive outcomes of a behavior (Pender,
1996). Benefits to healthy eating include weight control, disease prevention, fitness,
higher energy level, staying healthy, looking more attractive, feeling better and living
longer (Stolla et al., 2006; Zunft et al., 1997). Greater perceived benefits to healthy eating
have been significantly associated with healthy dietary behaviors among older rural
women, White individuals, male auto workers, and other blue collar workers (Glanz, et
al., 1998; Kristal et al., 1995; Walker et al., 2006). It is not known if the benefits to
healthy eating will be important determinants of diet habits among a more highly
educated and ethnically diverse worker group such as police officers.
Perceived self-efficacy is defined as the belief in one’s ability to perform a
particular task (Bandura, 1977). In terms of health functioning, one’s beliefs about
whether personal health habit change is possible may determine whether there is a
successful outcome (Bandura, 1997). Therefore, self-efficacy is an important construct in
numerous theory-based studies that examine health behavior changes, including diet and
exercise to lower the risk of developing hypertension and obesity. Specifically, dietary
self-efficacy is the confidence one has to eat a healthy, balanced diet under challenging
circumstances, such as adhering to a diet or choosing healthier food options at social
functions (Sallis et al., 1988). Self-efficacy for eating habits has been found to be an
25
important determinant of healthy eating behaviors among older rural women, Black
adolescents, and government employees (Sporny & Contento, 1995; Walker et al., 2006;
Wilson et al., 2002). More research is needed regarding perceived dietary self-efficacy
among individuals in high stress occupations. Therefore, self-efficacy for healthy eating
was included in this study of predominantly Black male police officers.
Conflicting evidence exists as to whether self-efficacy, benefits, or barriers are
better predictors of healthy eating. Kristal and colleagues (1995) studied psychosocial
factors related to healthful dietary intake in a large, worksite-based study. A total of
16,287 workers were assessed for predisposing factors (beliefs, benefits, and motivation)
and enabling factors (barriers, norms, and social support) related to diet. The
predisposing factors were better predictors of the outcomes of dietary practices,
intentions, and self-efficacy than the enabling factors. These findings were confirmed in
another study of 2764 male auto workers (Glanz et al., 1998). Unfortunately, the
summary scales for the predisposing and enabling factors lacked adequate internal
consistency, with Cronbach’s alpha coefficients only in the range of .57 to .65. Another
cross-sectional study had mixed findings regarding the utility of cognitive-perceptual
variables in the prediction of healthy diet habits, as defined by adequate fruit and
vegetable intake. For instance, Trudeau et al. (1998) assessed beliefs, intrinsic motives
(benefits), barriers, attitudes, and stage of dietary change. These factors were more
strongly related to fruit intake than vegetable intake. Therefore, research initiatives using
more reliable instruments are needed to provide evidence for cognitive factors that best
predict diet habits.
26
Dietary Habits Intervention Studies
A number of worksite intervention studies that utilized multi-component health
promotion and cardiovascular risk reduction approaches to influence outcomes have been
reported. Gomel and colleagues compared the effects of four different intervention
strategies on multiple risk factor reduction among participants at 28 randomly selected
ambulance stations near Sydney, Australia. No changes were found in percentage of body
fat, cholesterol, or aerobic activity at 12 months, and BMI rose significantly in all
intervention groups. However, they found behavioral counseling to be a slightly more
effective strategy in lowering one CVD risk factor, mean blood pressure, than health risk
assessment, risk factor education, or behavioral counseling plus incentives. Overall, the
results were disappointing for that study; the researchers did not include interventions to
change the cognitive factors of stress, benefits and barriers and self-efficacy, and the
authors concluded that it is very difficult to achieve long-term CVD risk factor reduction
(Gomel, Oldenburg, Simpson, & Owen, 1993).
In a study of 24 industrial worksites in Massachusetts, researchers tested the
effects of a worksite environmental modification and health education intervention on
dietary and smoking habits among blue-collar workers. The integrated program positively
affected fat, fiber, fruit, and vegetable consumption among treatment versus control
worksites. The findings suggest that worksite programs influencing individual behaviors
must also be expanded to encourage employer policies that promote health (Sorensen et
al., 1998). However, the study was not well described, and it is unclear what personal or
cognitive factors significantly influenced the dietary behavior changes of these
employees.
27
The Food Habits Questionnaire (FHQ) was adapted as one of the outcome
measures in research on the effectiveness of a revised cardiovascular health promotion
program to modify nutrition and smoking behaviors among employees at various Oregon
worksites (Take Heart II). In this quasi-experimental study, 11 intervention worksites
from the Take Heart I study were matched with 11 other worksites, which served as the
comparison group. Worksites were from both the public and private sectors, comprised of
government, service and manufacturing companies with an average of 215 employees.
Workers who participated were not well characterized. The majority were White males in
a variety of unskilled, craft, technical, clerical, managerial, and professional positions
(Glasgow, Terborg, Strycker, Boles, & Hollis, 1997).
This health promotion intervention featured employee steering committees, 20
minute assessment sessions of cholesterol and heart disease risk levels, and periodic
nutrition-related activities. Using worksite as the unit of analysis, findings indicated that
the Take Heart II program was modestly effective in reducing fat intake behaviors among
subjects in the intervention worksites versus the comparison worksites at the two year
follow up assessment. There were statistically significant improvements in the FHQ
scales and a fat intake screening questionnaire in the cohort data from the intervention
sites. However, there were no significant changes in total cholesterol or smoking rates.
The overall disappointing results suggest that it is very challenging to produce long-term
health behavior changes across worksites with a low-intensity program (Glasgow et al.,
1997).
The FHQ is an earlier version of the Diet Habits Questionnaire, which was used
in the current study. It is not known if diet habits, as measured by this instrument, is
28
related to body composition. Before an instrument is used to measure diet habits as an
outcome variable in an intervention study, a determination should be made as to whether
it is significantly correlated with obesity and overweight if these are the health issues of
concern.
Another low intensity worksite study tested the Heart At Work program for
cardiovascular wellness promotion among factory workers. The intervention included a
health fair and four modules on exercise, low-fat diets, and risk factor education.
Knowledge of blood pressure management, cardiovascular risk factors, and nutrition
were significantly improved among intervention participants. No significant changes in
physical activity, weight loss, cholesterol, or blood pressure levels were observed at one
year. The results suggest overall favorable impact on health knowledge and awareness,
but no effects on outcomes of interest such as health behavior or physical measures
(Pegus, Bazzarre, Brown, & Menzin, 2002). Again, the method of measuring dietary
habits must be linked to behaviorally-based health outcomes, such as a change in body
composition, which did not occur even after one year. Before intervention studies of
workers are conducted, a careful examination of the interrelationships among the
sociodemographic, dietary habits, and obesity measures must be explored to determine
what factors are salient to planning the intervention.
In general, there have been numerous low-intensity intervention studies on
worksite based populations which focused on cardiovascular risk reduction, health
education, and behavioral counseling. However, very little research is available on police
officers and only one dietary intervention study was located. The Austin, Texas Police
Department noted that increased prevalence of obesity, chronic disease, and other
29
nutrition-related problems were impacting the department’s absenteeism rates and poor
public image of the unfit police officers. Therefore the administration developed a
wellness program to target health issues (Briley, Montgomery, & Blewitt, 1990).
Briley and associates studied the effects of a year-long nutrition education pilot
program on 28 police department employees’ cholesterol levels and body weight. Police
participants attended six 30-minute seminars on nutrition and eating behaviors and met
with a registered dietician for six 30-minute individual counseling sessions. Results
suggested that this moderate to high intensity intervention was successful in reducing
body weight and total cholesterol levels (Briley, Montgomery, & Blewett, 1992). At a
five-year follow up of the original cohort, intake of fat as a percent of energy was
significantly lower at 72 months than at baseline (42.4% versus 33.3%, respectively). The
authors concluded that the worksite nutrition program was successful in producing longterm dietary changes in police department employees (Montgomery & Briley, 1995). The
researchers did not control for age, which could be a possible confounder in dietary
behavior studies. There is a possible curvilinear relationship between age and weight
which could have explained the results. Also, the researchers did not measure body
composition and were unable to determine if cardiovascular risks (other than cholesterol
level) were still present. For instance, police participants may lose body weight, and
thereby reduce BMI. However, if central adiposity does not change, as reflected in the
waist circumference, then there is still an increased risk for heart disease and diabetes due
to central obesity. Therefore, it is important to measure both BMI and waist
circumference as outcome measures in nutrition intervention studies. Furthermore, an
30
understanding the relationships among personal factors and anthropometric
measurements would be important for interpreting the intervention study’s results.
Summary
Although many intervention studies have been conducted to influence healthy
diets, few of these studies included cognitive factors that may contribute to successful
implementation of healthy diets sufficient to maintain positive change. More correlational
studies are needed to explore the increased cardiovascular risks, high BMIs, and poor
dietary habits of police officers. The majority of research on promoting healthy diets has
focused on women, Whites, Europeans, and non-police employee groups. A need exists
for research that specifically evaluates the factors contributing to obesity and related poor
dietary habits among police officers, to inform employers about strategies to adopt for
improving the health status of police officers.
31
CHAPTER 3
METHODOLOGY
This study utilized existing data from the City of Birmingham Health Screen as
well as data on occupational stress, diet habits, dietary self-efficacy, and benefits and
barriers to healthy eating. This chapter describes the methodological procedures used to
conduct this study. Strategies for research design, sampling, power analysis,
measurement, data collection, statistical analyses, and protection of human subjects are
detailed.
Design
The study employed a non-experimental, correlational design to elucidate the
relationships among variables. The independent variables included personal factors
consisting of gender, age, race/ethnicity, marital status, and shift assignment.
Additionally, the cognitive factors of dietary self-efficacy, barriers to healthy eating,
benefits to healthy eating, and occupational stress were examined as independent
variables. Fat-related diet habits and the multivariate construct of body composition were
studied as the dependent variables.
A correlational design was appropriate for the purposes of the study because
critical relationships among selected variables were explored. This type of design is
utilized to refine the associations among variables in a theoretical framework (Brink &
Wood, 1998). The strengths of a correlational design include efficiency, practicality, and
32
realism (Polit & Beck, 2004). This study explored the nature of relationships among
factors based on the revised Health Promotion Model. The study also built upon prior
knowledge of demographic correlates such as age, race, and educational level and
enhanced the understanding of the predictors of diet habits and body composition in
police officers.
Sample
Characteristics of the Sample
The accessible population was police officers employed by the municipal
government of Birmingham, Alabama. The convenience sample was police officers who
attended the 2006 annual Good Health Program police screen and volunteered to
participate in the research study. Inclusion criteria included employment as a sworn City
of Birmingham police officer for at least 6 months, between the ages of 22 and 65 years,
and able to read and write English language. A wide age range was selected to allow for
adequate variability in the sample. Although the majority of the female police employees
are not sworn officers, the researcher has decided to include female police officers in the
study.
Sample Size and Power Analysis
In calculating the required sample size for this study, the researcher determined
three components: the significance criterion, the population effect size, and power (Polit
& Beck, 2004). For this study, the significance criterion was set at .05 and the power was
set at the conventional standard of .80. The population effect size was estimated from
33
prior research in the same area of study. Past research on predictor variables from the
Health Promotion Model have accounted for 10 to 59% of the variance in behaviorspecific outcome measures (Pender et al., 2006). However, very little research was found
to have examined nutritional outcomes. Therefore, existing literature was not used to
estimate the effect size for this proposed study. A population R² of .20 is typical of
behavioral studies (Cohen, Cohen, West, & Aiken, 2003), and was used as the estimated
effect size.
The required sample size of 260 participants for this study was determined by the
power analysis formulae for the multiple regression/correlation (MRC) analyses with
eight predictor variables (Cohen et al., 2003). Using power analysis formulae for set
correlation (SC) and multivariate methods from Cohen (1988) to determine sample size
for the multivariate research questions, a required sample size of 214 participants was
calculated (see Appendix B). Therefore, the larger sample size of 260 sworn police
officers was set for this study. A total of 289 police officers participated in the study.
Ethical Considerations
Because workers can be considered a vulnerable research population, worksite
studies must maintain confidentiality of participants and ensure no coercion or job
repercussions can occur (Rose & Pietri, 2002). Questionnaires include items about
personal health habits and beliefs, job stress perceptions, and other sensitive information.
Therefore, police supervisors and other city administrative officials may want to know
individual police officer responses and physical measures for employment purposes.
Access to data is an important issue because police participants may also be concerned
34
that their results could affect job performance, rank, promotion, or compensation.
Consequently, in conducting sound ethical research, the researcher must protect the
confidentiality of the data collected by maintaining anonymity of the participants from
the individual responses they provide.
An equally important consideration is that of the participants’ rights of autonomy,
including self-determination and voluntary consent. Self-determination is the freedom to
control one’s own life and to choose one’s destiny. The researcher should ensure that the
study is free from coercion and includes a discussion at the time of recruitment of the
risks, benefits, alternatives, and the option to withdraw at any time without penalty.
Persuasion to be involved in the study should not include peer or supervisory pressure
(Burns & Grove, 2001). Additionally, police officers should not feel that nonparticipation
will adversely affect their job status.
Protection of Human Subjects
The study strictly adhered to the guidelines implemented by the Institutional
Review Board (IRB) for protection of human subjects at the University of Alabama at
Birmingham (UAB). The Good Health Program has previously addressed confidentiality
concerns by having employee and supervisor advisory committees meet to discuss these
issues. Also, data collectors were trained to maintain strict confidentiality of health
information. City officials did not have access to any raw data and results were only
reported as aggregate data. An application for expedited review, describing the use of
Good Health Program existing data and supplemental instruments to measure
occupational stress, dietary self-efficacy, and barriers and benefits to healthy eating was
35
approved by the UAB IRB (see Appendix C). Regarding the protection of raw data,
numbered identifiers were used to keep the participants’ responses confidential. Data
from the supplemental questionnaires was matched to the participant’s health screen
information and the dataset was deidentified by the primary study’s principal
investigator. Statistical analyses were performed off site and raw data was stored at the
UAB School of Nursing in a locked office. The raw data will be destroyed after five
years according to the Good Health Program policies and procedures. To protect the
police participants’ privacy, the findings of the study will be reported as aggregate data
only.
The study was free, confidential, had low potential for harm, and had possible
benefits of raising individuals’ awareness about health and contributing to police health
promotion research. Participants were able to withdraw from the study at any time
without any personal or career-related repercussions. They also had the right to ask for
clarification from the researcher. In the event that future questions arise, the researcher’s
contact information was provided at the time of recruitment via a letter of introduction
(see Appendix D).
Data Collection Procedure
Police officers in the City of Birmingham participate annually in the Good Health
Screen. Self-administered questionnaires in Teleform® format were distributed to police
stations in labeled envelopes two weeks prior to the Good Health Screen. In this study,
the instruments measuring occupational stress, eating habits, self-efficacy, and benefits
and barriers to healthy eating comprised additional questionnaires that were also included
36
in the packet. Physical data were measured and recorded by trained data collectors.
Return of the questionnaire and participation in the physical measurements at the Good
Health Screen constituted willingness to participate in the study.
Data collection forms were checked by the researcher and/or health screen staff
for completeness and legibility prior to the participant leaving the site. Verbal
clarification was elicited as needed and participants were asked to answer any items or
sections that may have been missing. Participants who were unable to complete the
questionnaires at the time of the screen were provided confidential envelopes
preaddressed to the Good Health Program for return via interdepartmental mail. Police
participants were encouraged to take a tape measure key chain bearing the Good Health
Program logo to keep track of waist circumference. However, this token gift was
provided to all employees at the health screen regardless of participation in the study.
Measurement
The instruments in this study included the Good Health Program Questionnaire
(Good Health Program, 2005b), the Job Stress Survey (Spielberger & Reheiser, 1994a),
the Fat-Related Dietary Habits Questionnaire (Kristal et al., 1990), the Eating Habits
Confidence Survey (Sallis, et al., 1988), and the Healthy Eating Benefits/Barriers Scale
(Walker et al., 2006). These instruments were selected because of established reliability
and validity to measure the variables of interest based on the conceptual framework of the
study. The researcher obtained permission to use these questionnaires from the primary
author of each instrument (see Appendix E).
37
Demographic Survey
The Good Health Program Health Questionnaire was developed by investigators
at the School of Nursing at the UAB in 1991. This self-report survey consists of 84
questions regarding demographic data, health habits, and personal and family medical
history (Good Health Program, 2005b). The study only analyzed those items pertaining to
gender, age, race/ethnicity, marital status, and shift assignment which comprise the
personal factors in the conceptual model (see Appendix F). Additional demographic
information was used for descriptive purposes only. One item was added to determine the
length of time in years and months that a participant had been a sworn police officer.
Body Composition
The Good Health Program also collects data on the body measurements of height,
weight, and waist circumference during the annual police screen. The methods for
measuring body composition in this study via body mass index (BMI) and waist
circumference (WC) are described below. The body composition measurements of BMI
and WC are considered very good predictors of selected chronic diseases and all-cause
mortality among populations in industrialized nations (Seidell, Kahn, Williamson,
Lissner, & Valdez, 2001).
Body mass index. This measure of body composition which indicates total body
adiposity is derived from the ratio of weight to height. Weight was measured to the
nearest ¼ pound without shoes, gun belts, bullet-proof vests, or other gear on via a
calibrated digital scale. Height was measured to the nearest ¼ inch while the police
officer is standing barefoot with heels against the standard measuring device and the chin
38
is level. Weight and height measurements were converted to the metric scale and BMI
was calculated by dividing kilograms of body weight by height in meters squared
(kg/m²).
Waist circumference. This is the measurement of abdominal girth or central
adiposity in inches using a Gulick II tape measure. WC was measured to the nearest ¼
inch at the level of the umbilicus while standing with arms perpendicular to the body
during a normal exhalation phase. Across genders and ethnicities, there is a very strong
correlation between BMI and WC (r from 0.88 to 0.92) indicating high validity for these
two variables (Zhu, Heymsfield, Toyoshima, Wang, Pietrobelli, & Heshka, 2005).
Job Stress Survey
The Job Stress Survey (JSS), developed by Spielberger and Reheiser (1994a), is
based on common items from the Police Stress Survey (Spielberger, Grier, & Pate, 1980,
as cited in Vagg & Spielberger, 1998) and the Teacher Stress Survey (Grier, 1982, as
cited in Vagg & Spielberger, 1998). The JSS is a 30-item instrument that measures the
perceived intensity and frequency of job-related stress in a variety of occupations over the
past 6 months (see Appendix F). The scale focuses on the work situations which result in
psychological strain. Each item is rated twice: first, on a scale of 1 to 9 for the perceived
severity of each stressor and second, on a scale of 0 to 9 or more times to indicate the
frequency of the occurrence of each stressor. An overall job stress index is determined by
the sum of the cross-products of the stress severity and frequency scores. The index score
ranges from 0 to 79.8 and higher scores denote an increased level of occupational stress
(Spielberger et al., 2000; Vagg & Spielberger, 1998). The five stressors with the highest
39
item index scores should be evaluated carefully to interpret reasons for those results
(Spielberger & Vagg, 1999).
Principal components factor analyses of the instrument have determined there are
two factors: job pressure and lack of organizational support. The factor structure is
remarkably stable across large samples of different genders, worksites, and occupational
levels. For the overall index and nine subscales, the Cronbach’s alpha coefficients range
from .80 to .93, indicating very good internal consistency reliability (Spielberger &
Reheiser, 1994; Spielberger et al., 2000). According to Polit and Beck (2004), a
reliability coefficient above 0.70 is satisfactory. In summary, the JSS is a reliable and
valid generic measure of occupational stress.
Healthy Eating Benefits/Barriers Scale
The Healthy Eating Benefits/Barriers Scale (HEBBS) was developed by Walker
and colleagues (2006). This instrument includes 18 items which require the participant to
rate the degree of agreement with the statements regarding one’s ideas about healthy
eating on a 4-point Likert-type scale from strongly agree to strongly disagree (see
Appendix F). Nine items are considered barriers to healthy eating such as, inconvenience,
unappetizing, expensive, time consuming, and limiting. Nine items are considered
benefits to healthy eating such as, staying healthy, being fit, reducing risk of cancer and
heart disease, and losing weight.
Construct validity of the tools was evaluated in Walker’s study using data from
220 rural women aged 50 to 69 years. Principal axis factor analysis and varimax rotation
analysis resulted in a single benefits factor and a single barriers factor that explained 33%
40
of the variance. The Cronbach’s coefficient alpha for both scales were found to be .80,
indicating acceptable internal consistency (S. Walker, personal communication,
September 29, 2005). Furthermore, Fowles and Feucht (2004) tested the healthy eating
barriers scale among 247 pregnant women to establish reliability and validity.
Exploratory factor analysis revealed a scale of five barrier factors that explained 73% of
the variance. Test-retest reliability was .79. Alpha coefficients ranged from .73 to .77. In
summary, this tool appears to be a reliable and valid instrument to assess barriers and
benefits to healthy eating, especially among females.
Eating Habits Confidence Survey
Dietary self-efficacy is measured using the Eating Habits Confidence Survey
developed by Sallis et al. (1988). This instrument consists of 20 items that include four
subscales: preventing relapse, reducing fat, reducing salt, and reducing calories (see
Appendix F). It measures the confidence that one has in really motivating oneself to
consistently maintain a healthy diet in certain situations during the last six months. The
responses are on a 5-point Likert-type scale that adds up to a composite score of up to
100 points. The original scales have established reliability and validity with a Cronbach’s
alpha of .85 to .93 and five factors explaining 44% of variance (Sallis et al., 1988). The
scale’s primary author recommends using the revised 20-item scale (J. Sallis, personal
communication, November 1, 2005).
Fat-Related Dietary Habits Questionnaire
41
The Fat-Related Dietary Habits Questionnaire (DHQ) was developed by Kristal
and others (1990) as an overall behavioral measure of the tendencies toward adopting
lower fat options when choosing and preparing foods to eat (see Appendix F). This
instrument assesses fat-related diet habits over the past month via a self-administered,
self-report survey. A total of 22 items representing five subscales are rated on a 4-point
Likert-type scale as follows: 1- usually or always, 2- often, 3- sometimes, and 4- rarely or
never. “Not applicable” is also an option if a respondent does not eat that particular food.
Five items are negatively worded and are reverse scored. The scores range from 1.0
indicating low-fat diet habits to 4.0 indicating high-fat diet habits. Any item with a
response indicated as not applicable is not included in the calculation of scores (i.e.
ignored in the numerator and denominator). The five subscale scores are determined by
summing the nonmissing item scores and dividing by the number of nonmissing items.
The mean of subscale scores are averaged to mean summary score.
In the original development of the DHQ scale in a sample of 97 well-educated,
middle-aged female enrollees of a large health maintenance organization, the test-retest
reliability coefficient was .87 using a 3-month interval (Kristal et al., 1990). Among 42
primarily male skilled workers in a control group of an intervention study, the test-retest
correlation coefficient was acceptable at 0.74 after nine months (Spoon et al., 2002).
Kristal, Beresford, and Lazovich (1994) reported that the one year test-retest reliability
coefficient was .75 among 957 adult control subjects of a tailored self-help intervention
study. The scale’s internal consistency reliability is also satisfactory. The Cronbach’s
alpha coefficient was .83 among the entire sample of 178 workers in a multi-site Nevada
industrial company (Spoon et al., 2002). Among 894 mostly affluent white women, the
42
internal consistency for the summary scale was .77 (Kristal et al., 1992). These results are
supported by the findings of Birkett and Boulet (1995), who determined that the
Cronbach’s alpha for the DHQ scale was .73 among Canadian male manual laborers. In
general, the subscale scores have not performed as well with a range of .47 to .76 among
several studies (Birkett & Boulet, 1995; Kristal et al., 1990; Spoon et al., 2002).
The validity of the instrument has been established in different samples, including
1022 blue collar workers and middle-aged diabetics enrolled in multi-faceted intervention
studies by Glasgow and associates. Concurrent validity of the DHQ was established from
moderate to strong correlations with criterion dietary assessment measures including:
BMI (r = 0.41), percent of calories from fat (r = 0.59) and a brief fat intake screening
questionnaire (r = 0.66) (Glasgow et al., 1997; Glasgow, Perry, Toobert, & Hollis, 1996).
In a women’s health study, there was a strong correlation between the total scale and
percentage of energy from fat (r = 0.68, p <.001). Additionally, factor analyses confirmed
the five dimensions of fat-related behavior thus establishing adequate construct validity
(Kristal et al., 1992). In conclusion, the composite score of the DHQ has adequate
reliability and validity to measure the variable of overall fat-related diet habits.
Data Management and Analyses
Data Management
Labels were assigned to each item in the questionnaire and placed in a code book
with instructions. Data entry was performed by manually scanning data collection forms
into a computer database in batches of three. Data verification entailed comparing data
input for each individual case to the corresponding raw data form thus ensuring data
43
accuracy. Occupational stress and cognitive-perceptual data were linked to demographic
data from the police screen via numbered identifiers and data were deidentified before the
researcher received the dataset from the Good Health Program principal investigator.
Data from ineligible participants (i.e., non sworn personnel) were deleted from the
dataset prior to data analysis.
In handling missing interval data, case mean substitution is appropriate if the
extent of missing data is relatively small (i.e., less than 10% to 20% of items per variable)
(Fox-Wasylyshyn & El-Masri, 2005). For example, this procedure is acceptable for the
JSS if less than 6 items are missing per survey (C.D. Spielberger, personal
communication, August 16, 2006). Therefore, JSS forms with six or more missing items
were deleted from the dataset. For the DHQ measure, case mean substitution was also
used for missing data. Given that the researcher was interested only in a summary score
and not the subscale scores, the DHQ index measure was calculated by determining the
mean of the nonmissing items scores, instead of the mean of the subscale scores. For all
scales, missing data were replaced with the mean of the remaining items in that scale if
the extent of missingness was less than or equal to 20%. When missing data exceeded
20%, then data from that particular measure was deleted from the database prior to data
analysis. All other complete data from participants were included in the analyses.
Interval level variables, except body composition, were tested for violations of the
assumptions of normality, linearity, homoscedasticity, and independence. Body
composition was evaluated for the corresponding violations of multivariate assumptions.
Regression diagnostics were performed to evaluate influential outliers for multiple
regression analyses. If an outlier observation existed due to participant error or was
44
influential, it was deleted from analysis. Collinearity diagnostics were performed to
determine if there was a high correlation among study variables. If the above assumptions
were not met, then data transformations were considered.
The benefits of healthy eating and barriers of healthy eating variable were
reverse-coded to ease interpretation of scores and subsequent correlations. Therefore,
higher scores reflected a higher level of that construct. In addition, the nominal variable
of marital status was recoded into married and not married (including single, separated,
divorced, and widowed) categories to avoid unequal cell size numbers. Due to the small
numbers of Hispanic, Native American, and other ethnicities, these groups were excluded
from analyses that examined race as a predictor variable. The race variable was recoded
as 0 for Black and 1 for White to facilitate interpretation of regression coefficients.
Statistical Analyses
Data were analyzed using SPSS version 16.0 (SPSS Inc., Chicago, IL). Analyses
were interpreted at a significance level of .05. Cronbach’s alpha coefficients were
calculated for the Job Stress Survey, the Fat-Related Dietary Habits Questionnaire, the
Eating Habits Confidence Survey, and the Healthy Eating Benefits/Barriers Scale to
evaluate the internal consistency reliability of these instruments and their subscales, if
indicated, in this police sample.
Initial descriptive statistics were calculated to describe the study sample. The
body composition variables were described separately by gender. The distribution of each
variable was appropriately evaluated based on the measurement level (i.e. nominal,
45
ordinal, or interval). Descriptive statistics using measures of central tendency were
utilized to answer research question one.
The relationships between the personal factors and occupational stress were
explored using independent t-tests to compare means for the categorical independent
variables. Analysis of variance (ANOVA) was used to explore the relationship between
shift assignment and occupational stress. Bivariate correlation using Pearson’s r was used
to examine the association between age and occupational stress.
Research questions three and four employed bivariate correlation analysis using
Pearson’s r to test the strength and direction of relationships between the interval level
variables of age, benefits to healthy eating, barriers to healthy eating, dietary self-efficacy
and occupational stress. A t-test of ρ=0 was used to determine if there was a significant
relationship between these variables.
Multiple regression was used to determine the strength and direction of
relationships in research question five. F-tests of the null hypothesis that the explained
variance is equal to zero (H0: R² = 0) were used to analyze the relationships between the
personal and cognitive factors and diet habits. Backwards elimination technique was used
for the sake of parsimony to delete non-significant variables from the model (Tabachnick,
& Fidell, 2007). The results of these analyses determined the set of predictor variables
that produced the most explained variance in the outcome variable.
Finally, multivariate regression methods using the general linear model (GLM)
test was utilized to answer research questions six through eight. A backwards elimination
procedure was also used for model reduction in research question six. Multivariate
regression analyses determined the nature of the relationships among the personal factors,
46
fat-related diet habits, occupational stress and the multivariate construct of body
composition, which is comprised of BMI and waist circumference.
Limitations of the Study
The limitations derived from use of a convenience sample, potential selection
bias, and self-report data. Generalizability is limited due to convenience sampling. A
cross-sectional design is not as strong as a prospective cohort design in determining
predictors of an outcome. Thus, cause and effect relationships cannot be determined from
this study. Another limitation to this research study related to the fact that police officers
who volunteered were agreeing to expend extra time at the Good Health Screen to
complete the questionnaires. Those who chose to volunteer may be different than
nonparticipating police officers. These issues could have resulted in unrepresentative
sample characteristics, thus limiting external validity.
The scoring of the DHQ measure may have also created problems with reliability
and validity. The use of a not applicable response option resulted in missing data. The
scale scores were then calculated as the mean of the remaining, non-missing items.
Therefore, the missing item score was in essence, the mean of the other scores which
could be biased or misleading (Birkett & Boulet, 1995). Case mean substitution is an
applicable technique for handling missing data if items on a scale are closely correlated to
one another (Fox-Wasylyshyn & El-Masri, 2005). This technique has even been found to
be robust when 20% of items were missing for data with both random and systematic
patterns of missingness (Roth, Switzer, and Switzer, 1999 as cited in Fox-Wasylyshyn &
El-Masri, 2005).
47
Spoon et al. (2002) reported that the fish item was reported as not eaten by 21%
of their sample. Unfortunately, vegetarianism was not surveyed among police participants
and vegetarians could not be excluded from analyses to limit missing data. In this study,
DHQ data from 36 participants had to be deleted due to greater than four items with
missing data. The rate of missing data among DHQ items was 0 to 5%, except in items
18, 19, and 22 which inquired about eating habits pertaining to the consumption of homebaked goods, frozen desserts and mayonnaise spread. The missing rates for these items
were 16%, 10%, and 12%, respectively, indicating that in this sample, a proportion of
participants did not eat those types of foods. Therefore, the study results may not be
generalizable to other individuals and situations.
Lastly, inherent limitations exist in the SPSS software for regression analyses
because this statistical package does not have an all subsets regression routine, which is
preferable for exploratory model building. A backward elimination procedure was used
for the purpose of parsimony, however, this stepwise technique has been criticized
because it capitalizes on chance and may not be interpretable based on theory
(Kleinbaum, Kupper, Muller, & Nizam, 1998). Findings using this technique should be
interpreted with caution.
48
CHAPTER 4
FINDINGS
This chapter outlines the results of data analyses pertinent to the research
questions and the demographic characteristics of the police participants. The first section
provides a description of sample characteristics by gender including age, race/ethnicity,
marital status, education, rank and shift assignment. In the second section, the reliability
of the research instruments and relevant subscales are provided. Descriptive analyses of
the study variables are presented in the third section of this chapter. The fourth section
consists of the findings from analyses of the research questions.
Description of the Sample
A total of 710 City of Birmingham police officers attended the health screen and
were invited to participate in the study. Of the 308 questionnaires that were collected, 19
(6.2%) questionnaires were excluded from analyses due to ineligibility. The reasons for
exclusion were as follows: 11 were non-sworn personnel, 2 were over 65 years of age, 3
had less than 6 months experience, and 3 had extensive missing data. The final sample
size consisted of 289 police participants who volunteered to participate with a response
rate of 40.7%.
Demographic characteristics of the sample are displayed in Table 1. Female
police officers comprised 15.6% of the study sample. The average age of the participants
was 41.7 years (SD = 7.85), with a range of 24 to 63 years. Male participants had a mean
49
age of 41.6 (SD = 7.97, range 24-63 years) and female participants had a mean age of
42.7 (SD = 6.99, range 26-56 years). The majority of participants were Black (58.7%),
married (59.4 %), and had attended college (45.5%) or obtained a college degree (35%).
Female participants were less ethnically diverse and were more likely to be single, older,
and have a higher educational level than their male counterparts. The number of years as
a sworn police officer ranged from 9 months to 37 years, with a mean of 13.3 years (SD =
6.98). The average number of years in this capacity for male sworn police officers was
13.2 years (SD = 6.96) with a median of 13.1 years, while females were sworn a mean of
14.3 years (SD = 6.89) with a median of 15.6 years. Most participants (82.7%) held the
rank of police officer, while the remainder of the sample consisted of police sergeants,
lieutenants, or captains. The shift assignment with the highest frequency was day shift
(45.5%), followed by evening shift (20.3%). On average, females were sworn longer and
less likely to serve on evening or night shifts.
Table 1
Demographic Characteristics of the Sample by Gender
Male
(N= 244)
%
N
Characteristic
Age groupa
Female
(N= 45)
%
N
Total
(N= 289)
%
N
22-29 years
17
6.97
2
4.44
19
6.57
30-39 years
40-49 years
96
94
39.34
38.53
12
25
26.67
55.56
108
119
37.37
41.18
50-59 years
60-65 years
34
3
13.93
1.23
6
0
13.33
0.00
40
3
13.87
1.01
50
Table 1 (Continued)
Male
(N= 244)
%
N
Characteristic
Race/ethnicity
Black
White
Hispanic
Native American
Other
134
90
4
4
7
Marital status
Married
Single
Divorced
Separated
Widowed
Education
High school
Some college
College degree
Graduate degree
156
40
38
7
0
64.73
16.60
15.77
2.90
0.00
17.28
45.68
33.33
3.70
42
111
81
9
56.07
37.66
1.67
1.67
2.93
32
12
0
0
0
72.73
27.27
0.00
0.00
0.00
31.11
40.00
24.44
0.00
4.44
6.67
44.44
44.44
4.44
13.33
17.78
22.22
37.78
8.89
35
6
1
3
77.78
13.13
2.22
6.67
23
5
5
12
51.11
11.11
11.11
26.67
14
18
11
0
2
3
20
20
2
Years sworn
> 5 years
5-10 years
11-15 years
16-20 years
21+ years
Rank
Police officer
Police sergeant
Police lieutenant
Police captain
15.16
24.18
24.59
23.77
12.30
204
26
9
5
83.61
10.66
3.69
2.05
107
53
42
39
44.40
21.99
17.43
16.17
37
59
60
58
30
Shift assignment
Day
Evening
Night
Other
6
8
10
17
4
Total
(N= 289)
%
N
b
Female
(N= 45)
%
N
Note: Not all percentages add up to 100% due to rounding error.
a
M = 41.7; SD = ± 7.85
b
M = 13.3; SD = ± 6.98
166
102
4
4
7
58.66
36.05
1.41
1.41
2.47
170
58
49
7
2
59.44
20.28
17.13
2.45
0.70
45
131
101
11
15.63
45.49
35.07
3.82
45
65
70
66
43
15.57
22.49
24.22
25.96
11.76
239
32
10
8
82.70
11.07
3.46
2.77
130
58
47
51
45.46
20.28
16.43
17.83
51
Instrument Reliability
Cronbach’s alpha coefficients were used to assess the internal consistency of the
Healthy Eating Benefits/Barriers Scale, Eating Habits Confidence Survey, Fat-Related
Dietary Habits Questionnaire, and Job Stress Survey and their subscales, if indicated. As
seen in Table 2, the reliability estimates ranged from .80 for the Barriers to Healthy
Eating Subscale to .96 for the Job Stress Severity Subscale. These coefficients suggest an
acceptable internal consistency in the instruments used in the study.
Table 2
Number of Items and Cronbach’s Alpha Coefficients for the Instruments and
Subscales
Instrument
Number of
Cronbach’s
items
alpha
coefficient
Healthy Eating Benefits/Barriers Scale (HEBBS)
18
0.80
Benefits of Healthy Eating Subscale
9
0.82
Barriers to Healthy Eating Subscale
9
0.80
Eating Habits Confidence Survey
20
0.94
Fat-Related Dietary Habits Questionnaire (DHQ)
22
0.83
Job Stress Survey (JSS) Index
30
0.95
Job Stress Frequency Subscale
30
0.95
Job Stress Severity Subscale
30
0.96
Job Pressure Index Subscale
10
0.90
Lack of Organizational Support Index Subscale
10
0.91
52
Descriptive Analyses of the Study Variables
The average participant was categorized as obese with a BMI of 30.2 kg/m2. Male
police officers had a mean BMI of 30.5 kg/m2 and female police officers had a mean
BMI of 28.5 kg/m2. Males’ mean waist circumference was 38.9 inches and females’
mean waist circumference was 33.2 inches. These are in the higher ranges of normal. The
percentage of participants in each BMI category is presented in Table 3.
Table 3
Body Mass Index (kg/m²) Classification For Police Participants (N=286)
Principal BMI
cut-off points
Police Participants
N
Underweight
Normal range
Overweight
Obese
Obese class I
Obese class II
Obese class III
<18.50
18.50 - 24.99
≥25.00
≥30.00
30.00 - 34-99
35.00 - 39.99
≥40.00
%
0
29
117
140
101
28
11
0
10.1
40.9
49.0
35.4
9.8
3.8
Source: Adapted from the World Health Organization (2006).
In terms of weight classification based on BMI, only 10% of police participants
were in the normal range, whereas 40.6% were overweight and another 49.3 % were
considered obese. As presented in Table 4, 37.6% of male police officers and 29.5% of
female police officers were at high risk for Type II diabetes, hypertension, and
cardiovascular disease due to large waist circumference (greater than 40 inches for males
and 35 inches for females).
53
Table 4
Waist Circumference Risk Classification For Police Participants by
Gender(N=281)
Male
(N= 237)
Female
(N= 44)
Inches
≤40
>40*
≤35
>35*
Number
148
89
31
13
62.4%
37.6%
70.5%
29.5%
Percentage
* Increased disease risk for Type II diabetes, hypertension, and CVD.
Descriptive statistics, including range, means, medians, and standard deviations of
the dietary variables are described in Table 5.
Table 5
Range of Possible Scores, Observed Ranges, Means, Medians and Standard Deviations
of the Dietary Study Variables
Study variable
Range of
possible
scores
Range of
sample
scores
Benefits of healthy eating
9-36
9-36
27.40
27.00
3.95
Barriers to healthy eating
9-36
9-36
22.37
22.00
4.32
Dietary self-efficacy
20-100
20-100
74.83
77.00
16.97
Fat-related diet habits
1-4
1.5-4.0
2.75
2.82
0.47
Mean
Median
SD
As shown in Table 5, police participants had lower mean scores for barriers to
healthy eating (M = 22.37, SD = 4.32) than for benefits of healthy eating (M = 27.40, SD
= 3.95). The mean value for dietary self-efficacy was in the high range at 74.83 (SD =
54
16.97). Mean score for fat-related diet habits for the entire sample was in the mid- to
high-range (M = 2.75, SD = 0.47). All dietary variables met the assumption of normality
with acceptable skewness and kurtosis values.
Findings Related to Research Questions
This section presents the findings of the study organized by research question.
The first research question was answered using descriptive statistics. Bivariate
correlations were used to explore research questions two through four. Univariate
multiple regression analyses were utilized to explore research question five. Multivariate
regression methods were employed to examine research questions six through eight.
Research Question 1
What are the levels of occupational stress in police officers?
As seen in Table 6, the means and standard deviations were calculated for the
overall level of occupational stress, as well as, the severity and frequency ratings and the
levels of job pressure and lack of organizational support. The mean level of occupational
stress was moderate at 23.01 (SD = 16.13). Police officers had a higher mean score for
lack of organizational support subscale at 25.17 (SD = 20.11). The mean score for job
pressure subscale was lower at 20.88 (SD = 16.84). This indicated that police officers had
a higher perceived lack of organizational support; whereas, job pressure did not
contribute as much to the job stress index. The average severity rating was 4.47 on a scale
of 1 to 9 and the average frequency rating was 3.95 on a scale of 0 to 9+. Stressors, or
average, occurred less frequently than 4 times every 6 months at a moderate intensity.
55
Table 6
Range of Possible Scores, Observed Ranges, Means, and Standard Deviations of the
Occupational Stress Scale and Subscales (n=286)
Scale or Subscale
Range of
Range of
M
SD
possible
sample
scores
scores
Occupational Stress
0-79.8
0-79.8
23.01
16.13
Job Stress Severity
1-9
1.13-8.87
4.47
1.62
Job Stress Frequency
0-9
0-9
3.95
2.14
Job Pressure
0-81
0-81
20.88
16.84
Lack of Organizational Support
0-81
0-81
25.17
20.11
Means and standard deviations for individual occupational stress item index
scores and severity and frequency ratings are presented in Table 7. The five highest mean
item index scores were identified as inadequate salary (52.60), insufficient personnel to
handle an assignment (36.66), poorly motivated coworkers (35.44), fellow workers not
doing their job (33.60), and inadequate or poor quality equipment (33.56). These five
statements were also rated the highest for job stress severity (range 5.26 to 6.75) and job
stress frequency (range 5.10 to 7.02) among police officers. Inadequate salary was
consistently the highest occupational stressor in terms of perceived severity and
frequency. Three of the top five stressors were items from the lack of organizational
support subscale (poorly motivated coworkers, fellow workers not doing their job, and
inadequate or poor quality equipment).
56
Table 7
Means and Standard Deviations for Job Stress Item Index Scores, Severity Ratings, and
Frequency Ratings for Police Officers (n=286)
Item
Statement
Job Stress
Job Stress
Job Stress
Index
Severity
Frequency
1 Assignment of disagreeable
duties
2 Working overtime
3 Lack of opportunity for
advancement
4 Assignment of new or
unfamiliar duties
5 Fellow workers not doing
their job
6 Inadequate support by
supervisor
7 Dealing with crisis situations
8 Lack of recognition for good
work
9 Performing tasks not in job
description
10 Inadequate or poor quality
equipment
11 Assignment of increased
responsibility
12 Periods of inactivity
13 Difficulty getting with
supervisor
14 Experiencing negative
attitudes toward the
organization
15 Insufficient personnel to
handle an assignment
16 Making critical on-the-spot
decisions
17 Personal insult from
customer/consumer/colleague
18 Lack of participation in policy
making decisions
M
SD
M
SD
M
SD
13.58
22.93
14.49
23.5
4.99
4
0.24
2.48
2.72
4.88
2.9
3.49
18.16
25.25
4.79
2.59
2.88
3.33
10.21
15.85
3.59
2.23
2.28
2.75
33.6
29.18
5.3
2.55
5.18
3.55
24.47
25.08
29.4
23.43
5.22
4.77
2.69
2.32
3.41
4.61
3.48
3.32
25.02
28.56
4.92
2.64
3.86
3.53
19.1
23.49
4.27
2.46
3.57
3.38
33.56
28.73
5.49
2.43
5.1
3.52
21.41
11.39
23.4
14.44
4.49
3.09
2.34
1.82
3.8
3.18
3.21
3.15
11.36
19.75
3.48
2.35
2.06
2.79
28.79
28.04
4.75
2.57
4.71
3.55
36.66
30.19
5.67
2.63
5.31
3.49
25.9
24.08
4.57
2.47
4.86
3.32
21.37
23.93
4.06
2.45
4.16
3.46
20.72
25.06
4.32
2.5
3.67
3.54
57
Table 7 (Continued)
Item
Statement
19 Inadequate salary
20 Competition of advancement
21 Poor or inadequate
supervision
22 Noisy work area
23 Frequent interruptions
24 Frequent changes from boring
to demanding activities
25 Excessive paperwork
26 Meeting deadlines
27 Insufficient personal time
(e.g. coffee breaks, lunch)
28 Covering work for another
employee
29 Poorly motivated coworkers
30 Conflicts with other
departments
Total
Job Stress
Index
Job Stress
Severity
Job Stress
Frequency
M
SD
M
SD
M
SD
52.6
18.71
30.23
24.39
6.75
4.63
2.61
2.47
7.02
3.08
3.2
3.27
20.6
13.18
20.34
27.34
20.01
23.53
4.41
3.1
3.77
2.63
2.19
2.41
3.2
2.78
4.08
3.5
3.2
3.5
20.2
30.54
17.81
22.2
27.96
21.57
3.93
4.81
3.79
2.3
2.59
2.27
4.22
4.98
3.55
3.56
3.5
3.32
18.17
24.34
3.84
2.42
3.22
3.38
26.84
35.44
27.51
28.85
4.5
5.26
2.66
2.62
4.57
5.49
3.6
3.46
12.68
23.01
21.97
16.13
3.5
4.47
2.39
1.62
2.15
3.95
2.95
2.14
Research Question 2
What are the bivariate relationships between the personal factors (gender, age,
race/ethnicity, marital status, and shift assignment) and occupational stress?
Results for this research question are shown in Table 8. The associations of
gender, race/ethnicity, and marital status with occupational stress were analyzed using
two sample t-tests for the means. The correlation between age and occupational stress
was evaluated by Pearson’s r. The relationship of shift assignment and occupational
stress was evaluated by ANOVA. There was no difference in occupational stress levels as
a function of shift assignment (F = 1.04; df = 3, 275; p = .376). Additionally, no
differences in occupational stress emerged as a function of gender or marital status.
58
However, occupational stress level was significantly different as a function of
race/ethnicity. Black police officers had a significantly lower mean occupational stress
level (M = 18.84, SD = 15.15) compared to White police officers (M = 28.71, SD =
15.29). Age of the police officer was not found to be significantly correlated with
occupational stress level (r = -.050, p = .398).
Table 8
Means and Standard Deviations of Occupational Stress By Gender, Race/Ethnicity,
and Marital Status
Male
Female
(N= 242)
(N= 44)
M
SD
M
SD
df
t
p
22.81
15.68
Black
(N= 165)
M
SD
18.84
15.15
Married
(N= 169)
M
SD
23.33
15.14
24.13
18.58
White
(N= 102)
M
SD
28.71
15.29
Not married
(N= 114)
M
SD
22.18
16.89
284
-0.498
0.619
df
t
p
265
-5.153
<.0001
df
t
p
281
0.600
0.549
Research Question 3
What are the bivariate relationships between benefits of healthy eating, barriers to
healthy eating, dietary self-efficacy, and occupational stress?
Bivariate correlations were examined between barriers to healthy eating, benefits
of healthy eating, dietary self-efficacy, and occupational stress. Results are described in
59
Table 9. A modest, but statistically significant, negative relationship was found between
dietary self-efficacy and occupational stress (r = -0.198, df = 282, p = .0008).
Additionally, the association between barriers to healthy eating and occupational stress
was significantly positively correlated with a Pearson’s r = 0.229 (df = 281, p = <.001). A
high barriers to healthy eating score corresponded to a high level of perceived
occupational stress level. However, no relationship was demonstrated between benefits of
healthy eating and occupational stress (r = -0.024; p = 0.689).
Table 9
Bivariate Correlations of Benefits of Healthy Eating, Barriers to Healthy Eating, and
Dietary Self-efficacy with Occupational Stress
Variables
p value
N
r
Benefits of healthy eating
281
-0.024
0.689
Barriers to healthy eating
281
0.229
<0.0001
Dietary self-efficacy
282
-0.198
0.0008
Research Question 4
Is there a relationship between occupational stress and fat-related diet habits?
The bivariate association between occupational stress and fat-related diet habits
was tested using Pearson’s r. No relationship was found between occupational stress and
fat- related diet habits (r = -0.056, df = 249, p = .375).
Research Question 5
What is the best model from the set of personal factors, benefits of healthy eating,
60
barriers to healthy eating, and dietary self-efficacy that explain the variability in fatrelated diet habits?
Univariate multiple regression was used to assess the ability of gender, age,
race/ethnicity, marital status, shift assignment, benefits of healthy eating, barriers to
healthy eating, and dietary self-efficacy to predict fat-related diet habits. Preliminary
analyses were conducted to ensure that no violation of the assumptions of normality,
linearity, multicollinearity, homoscedasticity, and independence of errors occurred. The
overall test for the model was significant (F= 8.739, df= 8, 215, p < .0001), explaining
24.5% of the variance in fat-related diet habits. Because multiple regression analyses are
sensitive to outliers, potential influential outliers were examined using the studentized
residual, Mahalanobis distance, and Cook’s distance scores (Tabachnick & Fidell, 2007).
One case exceeded the critical values and response set bias was identified. Therefore, the
case was deleted and regression analyses were reevaluated to determine if there was an
improvement in R square greater than .02. The overall test for the model omitting the
influential outlier was significant (F= 10.471, df= 8, 214, p < .0001), with 28.1% of the
variance in fat-related diet habits explained by all eight predictor variables.
Using the rules of parsimony, non-significant variables were deleted from the
model until the least number of significant variables that explained the most variance.
The regression model for race/ethnicity, dietary self-efficacy, barriers to healthy eating
on fat-related diet habits is presented in Table 10. The first negative coefficient
demonstrates there is difference of 0.282 in the mean fat-related diet habits scores
between Black and White police officers, controlling for all other effects in the model.
This indicated that Black police officers tended to have higher fat-related diet habits
61
scores than White police officers. The second negative coefficient illustrates that as
dietary self-efficacy decreases, there is a tendency toward higher fat-related diet habits,
after controlling for all other effects in the model. The independent relationship between
barriers to healthy eating and fat-related diet habits was significant (p = 0.049) after
controlling for all other effects in the model. In summary, the model containing
race/ethnicity, dietary self-efficacy, and barriers to healthy eating explained 26.4% of
variance in fat-related diet habits among police officers.
Table 10
Regression Weights of Race/Ethnicity, Dietary Self-Efficacy and Barriers to Healthy
Eating for Fat-Related Diet Habits after Removing Non-significant Variables (N = 230)
Variables
βˆ
SE
t value
p value
Intercept
3.470
0.22
15.795
<.0001
Race/ethnicity
-0.282
0.054
-5.204
<.0001
Dietary self-efficacy
-0.012
0.002
-6.938
<.0001
Barriers to healthy eating
0.013
0.007
1.975
0.049
Note: Overall model: F (3, 226) = 27.087, p-value <.0001, R-Sq = 0.264
Research Question 6
What are the relationships among the personal factors and body composition
(comprised of BMI and waist circumference), controlling for age?
The relationships for the personal factors with body composition were examined
using multivariate multiple regression analysis. Age of the police officer was entered into
the model as a covariate. Analyses were conducted to ensure that multivariate
assumptions, including equality of covariance matrices were met. Outliers and potential
62
influential cases were examined using Cook’s and Mahalanobis distance scores. Two
cases exceeded the critical values and were deleted from the dataset prior to analysis
because of undue influence. Marital status and shift assignment were not statistically
significant independent predictors of body composition and therefore, these variables
were deleted from the model. The regression results are presented in Table 11.
Table 11
Regression Weights Describing Personal Factor Relationships with Body Composition
after Removing Non-significant Variables (N = 259)
Personal Factor
βBMI
βWC
Intercept
24.653
26.031
0.383
203.741 (2, 253) <.0001
0.048
0.133
0.892
15.312 (2, 253) <.0001
3.86
8.417
0.668
62.982 (2, 253) <.0001
2.638
2.077
0.966
4.496 (2, 253)
0.012
-2.434
-3.695
0.976
3.127 (2, 253)
0.046
Age
Gender
Race/ethnicity
Gender x Race/ethnicity
Wilk’s λ
F (df1, df2)
p
Age, gender, race/ethnicity, and the interaction of gender and race/ethnicity were
statistically significant independent predictors of body composition, composed of BMI
and waist circumference.
Research Question 7
Is there a relationship between fat-related diet habits and body composition
(comprised of BMI and waist circumference), controlling for age?
The relationship between fat-related diet habits and body composition, controlling
for age, was tested using multivariate multiple regression. As seen in Table 12, the
63
relationship between fat-related diet habits and body composition was non-significant (p=
0.441) when controlling for age.
Table 12
Regression Weights Describing Fat-Related Diet Habits Relationships with Body
Composition, Controlling for Age (N = 244)
Wilk’s
F (df1, df2)
p
βBMI
βWC
λ
Intercept
24.963
29.501
0.635 68.975 (2, 240)
<.0001
Age
0.098
0.154
0.934
8.462 (2, 240)
<.0001
Fat-related diet habits
0.456
0.809
0.993
0.822 (2, 240)
0.441
Research Question 8
Is there a relationship between occupational stress and body composition
(comprised of BMI and waist circumference), controlling for age?
The relationship between occupational stress and body composition, controlling
for age, was tested using multivariate multiple regression. As shown in Table 13, no
relationship was found between occupational stress and body composition (p= 0.162)
after controlling for age.
64
Table 13
Regression Weights Describing Occupational Stress Relationships with Body
Composition, Controlling for Age (n=276)
Wilk’s
F (df1, df2)
p
βBMI
βWC
λ
Intercept
26.42
31.33
0.43
179.80 (2, 272)
<.0001
Age
0.092
0.151
0.94
8.74 (2, 272)
<.0001
-0.005
0.014
0.99
1.84 (2, 272)
0.162
Occupational stress
65
CHAPTER 5
DISCUSSION, CONCLUSIONS, IMPLICATIONS,
AND RECOMMENDATIONS
A nonexperimental, correlational design was employed to explore the
relationships among occupational stress, personal factors, cognitive factors, diet habits
and body composition in police officers. This chapter presents the discussion,
conclusions, implications, and recommendations based on the results. The first section
provides a discussion of the key findings and their strengths, limitations, and consistency
with previous research, as well as the applicability of the conceptual model. Relevant
interpretations and possible explanations are offered. In the second section, a summary of
the conclusions is outlined. The implications for nursing education, practice, and research
are presented in the third section of this chapter. The fourth section consists of the
recommendations for future research.
Discussion
Occupational Stress
This study found that the average occupational stress level of this sample of
police officers was low to moderate at 23.01 out of 79.8. By contrast, other occupational
groups surveyed using the same JSS measure had even lower mean levels, including
managerial/professional (M=20.19), clerical/skilled maintenance (M=19.65) and senior
military (M=20.81) (Spielberger & Vagg, 1999). The item with the highest occupational
66
stress level was inadequate salary which police officers rated at 52.6. The high rating on
the occupational stress level for salaries may be attributed to the level of pay for police
officers employed by this municipality in comparison to other police officers employed
by cities in the Birmingham, Alabama area.
The low to moderate occupational stress level contrasts to the low average
occupational stress level identified in firefighters employed by the same city using the
same instrument. Damrongsak (2008) found that the average occupational stress level
was only 15.91 among firefighters in the same municipality. The occupational stress level
of firefighters related to inadequate salary was lower, at 45.23; however, firefighters were
surveyed prior to a salary dispute. Another possible reason that police officers had higher
occupational stress levels than firefighters is due to the nature of the work. Police officers
are often exposed to excessive paperwork, personal insults, and overtime work (Bureau
of Labor Statistics, U.S. Department of Labor, 2007). Typically, firefighters have two
days off between 24-hour shifts and they stay in close knit work groups, whereas police
officers work alone or with a partner and in 8-hour shifts, which do not allow for as many
days off to attend to personal needs.
Additionally, police officers reported a higher level of perceived lack of
organizational support (25.17) and a lower level of job pressure (20.88). A possible
reason is that police officers are accustomed to the pressure of responding to crisis
situations and are not required to adhere to deadlines. However, police officers may be
distressed by organizational factors such as negative coworker behavior, working shortstaffed, and the unavailability of proper equipment.
67
Personal Factors and Occupational Stress
Although some studies have reported that demographic and other personal factors
are associated with occupational stress, only race/ethnicity category was significantly
related to occupational stress in this study. Black police officers had significantly lower
occupational stress levels (M= 18.84) when compared to White police officers (M=
28.71). One possible reason may be that the majority of the police force is Black and 75%
of residents of this municipality are Black, whereas only 23% of residents are White
(U.S. Census Bureau, 2008). Black officers may perceive less occupational stress by
having a majority status within their workplace. It is unclear why White police officers
had significantly higher occupational stress levels. Relevant results from the literature
cannot be referenced because previous studies have examined occupational stress in
predominantly White American or European samples and workplace minorities have not
been studied.
No differences in occupational stress levels were found as a function of gender,
marital status or shift assignment after controlling for the other variables in the model.
Some of these findings are consistent with other research studies. Spielberger and
Reheiser (1994a) did not find any significant gender main effects for occupational stress
level. Night shift pattern was not associated with increased stress, although female gender
and being divorced or separated, but not single were related to higher stress levels
(Collins & Gibbs, 2003). The police chief at the time of the current study was a Black
woman who had risen in the ranks of the police department over several years. Having a
female chief as an advocate and role model and the fact that the majority of female police
officers (73%) were Black, may explain why female police officers had comparable
68
occupational stress levels to male police officers. However, a lack of power to detect
differences between the genders could be a possible explanation.
A potential reason for the lack of relationship between marital status and
occupational stress, after controlling for all other variables in the model, is that the five
marital status groups were recoded into married and not married categories. This assumes
that single, separated, divorced, and widowed persons have more in common with one
another than persons who are legally married. However, legal married status may not be a
proxy for social support in this sample, and the recoding may have resulted in the loss of
opportunity to detect a relationship. In addition, legal married status does not take into
account other types of committed relationships such as those among unmarried
heterosexuals, lesbians, gay men, or bisexual persons.
Occupational stress was not associated with age in this study, after controlling for
all other variables in the model. This result may possibly be explained because police
officers gain valuable experience and the ability to adapt to work-related stressors over
time. Therefore, perceptions of stress may be minimized with experience. However, it
may be useful to test the relationship between the number of years as a sworn officer and
occupational stress, controlling for age, in order to verify this possible explanation in
future police studies.
Cognitive Factors and Occupational Stress
Regarding cognitive factors, it is interesting to note that there were significant
correlations between occupational stress and barriers to healthy eating and dietary selfefficacy. A moderate positive relationship was found between barriers to healthy eating
69
and occupational stress (r = .23) indicating that as occupational stress increases, the
perceived barriers to healthy eating also increase. Job-related stress may amplify the
expected difficulties that an officer may encounter when trying to maintain healthy eating
habits.
No significant relationship was found between benefits to healthy eating and
occupational stress. Possible reasons for a lack of association are the limited variability in
the benefits to healthy eating measure and the rather high scores indicating a possible
social desirability bias in participants’ responses. Findings of this study cannot be
compared to other investigations because previous studies have not examined the
interrelationships among occupational stress and these cognitive-perceptual dietary
constructs.
Fat-Related Diet Habits and Occupational Stress
Results from this study did not demonstrate a significant relationship between
occupational stress and fat-related diet habits. This was an unexpected finding because
situational influences, such as occupational stress, are proposed to influence healthpromoting behavior in the revised HPM. Other studies have determined that stress is
related to unhealthy dietary habits such as eating increased amounts of high-fat foods or
consuming less fruits and vegetables (Oliver et al., 2000; Chandola et al., 2008). In this
study, a potential explanation for the lack of a significant relationship between stress and
fat related diet habits is that, in this sample of police officers, stress-related eating habits
may be a more complex issue. It is possible that occupational stress results in a
diminished appetite in some and overeating in others, which may have led to a finding of
70
no relationship between occupational stress and fat-related diet habits among police
officers. In addition, males and females may behave differently with regard to their diet
habits in response to occupational stressors.
Another possible reason for the lack of relationship is that the fat-related diet
habits tool that did not inquire about high-fat coffee drinking habits that police officers
may engage in. Additionally, the fat-related diet habits measure had considerable missing
data due to the use of the not applicable response and 10% to 16% of participants did not
consume home-baked goods, frozen desserts or mayonnaise spread. Therefore, case mean
substitution was used to make the dataset more complete. This limited the variability in
the fat-related diet habits measure which may have impaired the exploration of this
variable’s relationship to occupational stress. Police officers may also engage in other
forms of unhealthy behaviors, such as alcohol misuse and abuse, to deal with stressful
work situations.
Fat-Related Diet Habits and Personal and Cognitive Factors
This study found that the set of factors including race/ethnicity, dietary selfefficacy, and barriers to healthy eating predicted 26.4% of the variance in fat-related diet
habits among police officers. Sporny and Contento (1995) also found that perceived
difficulties to eating lower fat diets (barriers) and the confidence (self-efficacy) in one’s
ability to reduce dietary fat intake were important factors in explaining healthy diet
behaviors.
Black police officers tended to have higher fat-related diet habits. This finding is
supported by a dietary intake study which found that Black respondents had consumed
71
the highest percentage of energy from fat (Thompson et al., 2005). The variables of age,
gender, marital status, shift assignment, and benefits to health eating were not found to be
significant predictors of fat-related diet habits in police officers. These findings are
surprising because previous studies have found that older age, female gender, and
married individuals tended to have healthier diet habits (Kristal et al., 2001; Thompson et
al., 2005; Trudeau et al., 1998). Because the sample consisted primarily of middle-aged,
married, male, day shift police officers, there may not have been sufficient variability in
demographic characteristics to detect significant independent relationships among the
predictor and outcome variables.
The current study’s finding of barriers to healthy eating as a significant predictor
of healthy eating contradicts a previous study which found that barriers to healthy eating
were not significantly associated with dietary fat reduction or fruit and vegetable intake
(Kristal et al., 2001). However, the previous study assessed barriers to eating a healthful
diet by asking two questions without established psychometric properties; whereas, the
current study measured barriers to healthy eating using a well-developed scale with a
reliability coefficient of .80. Additionally, the benefits to healthy eating measure
exhibited limited variability and a bias toward social desirable responses that may have
impeded the examination of the relationship between this measure and the moderate to
high fat dietary habits found among police officers.
Personal Factors and Body Composition
In this study, age, gender, race/ethnicity, and the interaction of gender and
race/ethnicity were statistically significant predictors of body composition, which is made
72
of up BMI and waist circumference. These results are supported by literature which
suggested that the relationship between BMI and body fat content varies somewhat with
age, gender, and possibly race/ethnicity because of differences in factors such as
composition of lean muscle mass, adipose distribution, and hydration status (Gallagher,
Visser, Sepulveda, Pierson, Harris, & Heymsfield, 1996).
Fat-Related Diet Habits, Occupational Stress, and Body Composition
A surprising finding from this study was the non-significant relationship between
fat-related diet habits and body composition, which is made up of BMI and waist
circumference. A plausible explanation is that police officers may have a larger average
BMI due to increased lean muscle mass. BMI does not take into account the weight of
muscle mass versus the weight of fat which may confound this measure of total body
fatness (U.S. Preventive Task Force, 2003). According to the World Health Organization
(2006), BMI may not correlate to the same degree of body fatness in different people due
to the differences in body proportions. Additionally, the popularity of high-fat, highprotein, low-carbohydrate diet patterns, such as the Atkins diet, may increase the level of
fat-related diet habits score, while decreasing total body and central adiposity. Not only
can a high-fat diet lead to obesity and overweight, but a high-carbohydrate diet can also
contribute to a caloric excess if physical activity is minimal. Lastly, the low variability in
both fat-related diet habits measure and body composition may have potentially hindered
the examination of multivariate relationships among these constructs.
The lack of a statistically significant relationship between occupational stress and
body composition in police officers was also unexpected. Findings from a large
73
prospective study revealed that chronic work stress predicted both BMI and waist
circumference (Brunner et al., 2007). However, the researchers in that study could not
generalize to “non-European ethnic groups” (p. 834). Furthermore, chronic work stress
was based on measures from the job strain model in the Whitehall II study, in contrast to
the occupational stress measure used in this study (Kuper & Marmot, 2003).
Another cross-sectional study also found a weak, but significant relationship
between work stress and BMI among Finnish employees, after controlling for age,
marital status, and smoking, alcohol intake, and physical activity (Kouvonen et al., 2005).
The current study did not control for these possible confounders. Because physical
activity level is known to be related to body composition and exercise is a potent stress
reliever, these variables should be included in future studies of job-related stress and
body composition. This study did not control for time in profession, but it is possible that
police officers may have become accustomed to higher occupational stress levels.
Additionally, the variance and range of the occupational stress variable was limited and
the sampling relative to body composition was majority overweight and obese with a
high risk waist circumference. Finally, significant relationships may have been difficult
to detect because overweight and obesity status is a complex health outcome with
multiple related factors including genetic predisposition, environmental influences,
activity level, and dietary intake.
The Conceptual Model
Results from this study provided support for the revised Health Promotion Model
used to guide this research. The individual characteristics and experiences were
74
represented by the selected personal factors. The behavior-specific cognitions and affect
corresponded to the benefits of healthy eating, the barriers to healthy eating, and dietary
self-efficacy. Occupational stress represented the situational influences. The selected
health-promoting behavior was fat-related diet habits and body composition was
conceptualized as the overall behavioral outcome. The findings from this study were
consistent with model expectations.
Results from research question five suggest that the individual characteristic of
race/ethnicity and behavior-specific cognitions of barriers to healthy eating and dietary
self-efficacy are significant predictors of the behavioral health outcome of fat-related diet
habits. The behavior-specific cognitions were conceptualized as the primary motivators
of the health promoting behavior. The individual characteristics both indirectly and
directly influence the health promoting behavior. Results from this study suggest that
personal factors such as gender, age, and race/ethnicity influence both the health
promoting behavior and the behavioral outcome. Furthermore, there is support for
interrelationships among the cognitive and situational factors (i.e. barriers to healthy
eating, dietary self-efficacy, and occupational stress).
Conclusions
Based on the findings of this study, the following conclusions about the
relationships among occupational stress, personal factors, cognitive factors, diet habits,
and body composition in police officers were ascertained:
75
1. The most frequently reported occupational stressors for police officers were
inadequate salary, insufficient personnel, and poorly motivated coworkers, contributing
to a higher perceived lack of organizational support.
2. Black police officers had a lower level of occupational stress than White police
officers.
3. Dietary self-efficacy was significantly negatively correlated with occupational
stress and barriers to healthy eating were significantly positively correlated with
occupational stress.
4. No significant relationship was found between occupational stress and fatrelated diet habits.
5. The model containing race/ethnicity, dietary self-efficacy, and barriers to
healthy eating significantly predicted 26.4% of the variance in fat-related diet habits.
6. Black police officers tended to have higher fat-related diet habits than White
police officers.
7. The model consisting of age, gender, and race/ethnicity was significantly
associated with body composition, consisting of BMI and waist circumference.
8. There was no significant relationship between fat-related diet habits or
occupational stress and body composition, consisting of BMI and waist circumference.
Implications
Implications for Nursing Education
Findings from this study support the need for undergraduate and graduate nursing
programs to include course content on health promotion among employee groups, such as
76
police officers, to encourage healthy eating habits and body weight maintenance. Nursing
school curricula should incorporate content in adult and community health nursing
courses regarding the importance of assessing overweight and obesity status and
occupational stress levels among adult workers. Students need to learn that workers who
report high occupational stress may have low perceptions of self-efficacy for eating
healthy diets and may perceive that there are many barriers to healthy eating.
When planning health intervention projects, nursing students should be
encouraged to adopt a relevant conceptual framework, such as the revised Health
Promotion Model, which takes into account the person, behavior, and environment.
Therefore, pertinent components that influence health behavior can be included in
nursing interventions. Moreover, nursing students should learn that promoting healthy
fat-related diet habits in police officers requires a careful review of their dietary-self
efficacy and attention to strategies for overcoming barriers to healthy eating.
Implications for Nursing Practice
Occupational health nurses should alert police department administrators that
police officers have occupational stress levels higher than the norm-referenced
population, most notably a higher perceived lack of organizational support. Potential
stressors such as inadequate salary, insufficient personnel to handle an assignment,
poorly motivated coworkers, fellow workers not doing their job, and inadequate or poor
quality equipment should be discussed in health and safety meetings so that specific
programs and policies can be developed to modify stress levels of police officers.
77
Because the average BMI and waist circumference among police officers indicate
a propensity towards obesity, occupational health nurses should screen for obesity and
counsel police officers regarding ways to maintain a healthy weight. Nurses should assess
the perceptions of dietary self-efficacy and perceived barriers to healthy eating among
police officers in order to improve fat-related diet habits in this employee group.
Recommendations for Future Research
Based on the findings, conclusions, and implications, the following
recommendations are made:
1. In future studies of police officers, differences in the diet habits and personal,
cognitive, and situational factors should be tested between the overweight and
obese groups.
2. Other situational factors, such as food options, which may be more important in
influencing dietary habits in police officers, should be included in future studies.
3. Interpersonal influences, such as social support for healthy eating, need to be
investigated.
4. The relationships between occupational stress and other variables not included in
this study, such as caloric intake and alcohol use, should be explored.
5. When studying the predictors of body composition, researchers should control for
physical activity level/exercise and other health habits.
6. Because of the extent of missing data decreasing the sample size, a different
measure for fat-related diet habits should be considered in future studies.
78
7. A worksite dietary intervention addressing dietary self-efficacy and the perceived
barriers to healthy eating should be tested to determine its influence on fat-related
diet habits.
79
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89
APPENDIX A
REVISED HEALTH PROMOTION MODEL
90
91
APPENDIX B
POWER ANALYSIS FORMULAE
92
The following MRC formulae from Cohen et al. (2003) were used to calculate the
required sample size for multiple regression/correlation (MRC) analyses:
f ² = sri ²
1- R²
n* = L + k + 1
f²
Whereas:
n*
=
the required sample size
L
=
7.85 (value read off from Table E.2 (p. 651) with α = .05, a power
of .80 and kB = 1 because the source of variance is a single Xi)
k
=
8 (the maximum # of independent variables in research question)
f²
=
effect size for the semipartial correlation coefficient of a single Xi
R²
=
.20 (the estimated small effect size for the entire study by
convention)
sri ²
=
.025 (the unique contribution to R² or sri ² = R² / k = .20 / 8 = .025)
The values for f ² and n* where calculated as follows:
f ² = .025 = .022 = .03125
1-.20
.80
n* = 7.85 + 8 + 1 = 260
.0313
Therefore, the required sample size for the proposed study is 260 police participants.
93
The following SC formulae from Cohen (1998) were used to calculate the required
sample size for set correlation (SC) analyses and multivariate methods:
N = 1 (v + u − 1) + (kY + kX + 3) + max (kC, kA + kG)
s
2
2
v = λ −u−1
f²
λ = λL – 1/vL – 1/v (λL - λU)
1/vL – 1/vU
Whereas:
N = the required sample size
s = 2 (value read off of Table 10.2.1 where kx = 10 and ky = 2)
v = 413 (denominator df of F ratio using the linearly interpolated value of λ)
u = 10 (numerator df of F ratio which is kYkX or 2*5)
kY = 2 (number in a set of dependent variables in research question)
kX = 5 (number of independent variables in research question)
kC = 0 (number of variables in the set that is being partialed)
kA = 0 (number of variables in the set that is being partialed)
kG = 0 (set of variables used for Model 2 error reduction)
λ = 17.4 (value read off of Table 9.4.2, a = .05, u = 20, v =120, and power =.80)
f ² = .04 (estimated small effect size in the population, which implies a R² of .08)
The values for λ, v, and N were calculated as follows:
v = 17.4/.04 – 10 – 1 = 424
λ = 17.4 − 1/120 − 1/424 (17.4−16.8) = 16.97
1/120 − 0
v = 16.7/.04 – 10 – 1 = 413.25
N = 1 (413 + 10 – 1) + (2 + 10 + 3) + 0 = 214
2
2
2
Therefore, the required sample size for the proposed study is 214 police participants.
94
APPENDIX C
INSTITUTIONAL REVIEW BOARD FOR HUMAN USE APPROVAL
95
96
APPENDIX D
LETTER OF INTRODUCTION
97
September 12, 2006
Dear Police Officer:
I am a doctoral student in occupational health nursing at the School of Nursing,
University of Alabama at Birmingham and I have assisted with Good Health Program
activities over the last four years. Currently, I am collecting data for my research that will
examine how personal factors, health beliefs, and job stress are related to eating habits
and body composition. I need your help in returning the attached survey. In completing
the survey, you will contribute to your profession and understanding of stress and
nutrition in police officers.
The survey takes about 20 minutes to complete. Participation is voluntary and your
answers will remain strictly confidential. Your employer will not have access to this
information. Also, your employment will in no way be influenced by your decision to
participate or not. To ensure confidentiality, the surveys will be placed in a locked file
and I will be the only person able to access them. All information will be reported only
as group data. Consent for participating in the study will be indicated by returning the
completed survey to the check-in desk at the police health screen.
Please feel free to ask me any questions either before, during, or after you complete the
survey. I can be reached by phone at 704-770-8751 or email [email protected] If you
have any questions regarding your rights as a research participant, please contact Ms.
Sheila Moore, Office of the Institutional Review Board for Human Use (IRB), at 1-800822-8816. Press the option for an operator/attendant and ask for extension 4-3789. You
may call Monday through Friday, 8:00 a.m. – 5:00 p.m. (Central Standard Time). Thank
you in advance for your time and participation.
Sincerely,
Rebecca Grizzle, MSN, RN
Occupational Health Nursing Doctoral Student
University of Alabama at Birmingham
UAB School of Nursing
1701 University Blvd.
Birmingham, AL 35294
98
APPENDIX E
PERMISSION TO REPRINT MODEL AND USE QUESTIONNAIRES
99
Nola Pender <[email protected]> wrote:
Dear Rebecca:
You have my permission to reprint the revised Health Promotion Model as
a conceptual framework for your study in the Appendix of your
dissertation. I am pleased that you found the model helpful. You are
welcome to use the version of the HPM that appears on my website.
Wishing you good health,
Nola Pender
Quoting "Grizzle, Rebecca" <[email protected]>:
>
>
>
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>
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>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
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>
Dear Dr. Pender,
I am a University of Alabama at Birmingham School of Nursing doctoral
candidate. I have utilized the revised Health Promotion Model as the
conceptual framework for my dissertation study. The title of my
dissertation is "Occupational Stress, Dietary Self-Efficacy, Eating
Habits and Body Composition in Police Officers." Could I please have
your permission to reprint the revised HPM in the appendix of my
dissertation? I have found that it is most useful when describing the
interrelationships among the variables in my research study.
I have an electronic copy of the revised HPM from a link on your
faculty website page:
http://www.nursing.umich.edu/faculty/pender/chart.gif which I would
prefer to use over the depictions in several editions of your Health
Promotion in Nursing Practice textbook.
Thank you so much for considering this matter.
Warmest regards,
Beckie Grizzle, MSN, RN
University of Alabama at Birmingham
School of Nursing
100
August 16, 2006
Rebecca Grizzle, MSN, RN
1225 Reading Blvd.
Wyomissing, PA 19610
Dear Ms. Grizzle:
In response to your recent request, I am very pleased to give you permission to reproduce
and use the Job Stress Survey (JSS) in your dissertation research project, entitled:
Occupational Stress, Dietary Self Efficacy, Eating Habits and
Body Composition in Police Officers
It is my understanding that your research will be carried out with sworn police officers at
the:
Boutwell Auditorium, Birmingham, Alabama
This permission is contingent on your agreement to share your research findings with us.
I look forward to receiving further details about your procedures and the results of your
study as such information becomes available.
Best wishes on your research project.
Sincerely,
Charles D. Spielberger, Ph.D., ABPP
Distinguished Research Professor of Psychology
Director, Center for Research in Behavioral
Medicine and Health Psychology
Phone (813) 974-2342; Fax (813) 974-4617
101
Alan Kristal <[email protected]> wrote:
Hello Rebecca Grizzle
You most certainly may use and improve the questionnaire in any way you see fit.
The latest I have on the instrument (I’ve not worked in this area for many years) is at
http://ffq.fhcrc.org/ffq_forms.aspx?ffqmenu=o
Alan Kristal
From: Rebecca Grizzles [mailto:[email protected]]
Sent: Tuesday, January 10, 2006 1:25 PM
To: Kristal, Alan R
Subject: 1990 Kristal food habits questionnaire
Dear Dr. Kristal,
Hi, I am a doctoral student at the University of Alabama at Birmingham, studying for a
PhD in nursing. My research interest is promoting healthy dietary changes among police
officers. I have been reading about different measures to assess diet quality in the area of
fat intake. Specifically, I am interested in using your 18-item Food Habits Questionnaire
(regarding low-fat eating patterns) for my dissertation research.
Could I please adopt this tool for use in my dissertation research of police officers and
other municipal workers? I would like to pilot test it soon with my population of
interest. Also, have there been any modifications to the questionnaire since the original
psychometric testing in 1990 and/or is there a coding manual? I noticed that other
researchers (Glasgow et al., 1997) have used a 21-item Kristal Food Habits Questionnaire
in worksite intervention studies.
Thank you very much in advance for your assistance.
Sincerely,
Rebecca Grizzle, MSN, RN
UAB School of Nursing
102
Jim Sallis <[email protected]> wrote:
Rebecca,
Thanks for your interest in this scale. I still recommend use of the shorter scale,
but we do not have separate validity data on it. You are welcome to adapt so it
fits your use.
LeeAnn, please mail her a copy of this paper.
Sallis, J.F., Pinski, R.B., Grossman, R.M., Patterson, T.L., and Nader, P.R.
(1988). The development of self-efficacy scales for health-related diet and
exercise behaviors. Health Education Research, 3, 283-292.
Thanks for passing along the greetings from Dr. Grimley. Please give her my
regards.
JSallis
James F. Sallis, Ph.D.
Professor of Psychology, San Diego State University
Director, Active Living Research Program
3900 Fifth Avenue, Suite 310, San Diego, CA 92103 USA
phone 619-260-5535; fax 619-260-1510
email <[email protected]>
At 10:23 AM 11/4/2005, you wrote:
Dear Dr. Sallis,
I am a doctoral student at the University of Alabama at Birmingham (PhD
program in Nursing) and I am interested in the dietary self-efficacy scale that you
and your colleagues developed. In your cover letter posted on the SDSU
website, you mentioned that this 20-item scale was more practical than the
original one reported in the 1988 Health Education Research article.
With your permission, I would like to adopt this tool for use in my proposed
dissertation research of police officers and other municipal workers. I would like
to pilot test it soon with my population of interest. By the way, a UAB professor
of mine, Dr. Diane Grimley, fondly remembers you from your days in Rhode
Island.
Sincerely,
Rebecca Grizzle, MSN, RN
University of Alabama at Birmingham
School of Nursing
103
Susan Noble Walker <[email protected]> wrote:
Hello, Rebecca
I apologize for my delay in getting back to you and hope this information will still be useful. It took
me some time to locate the information you requested. I am attaching the Healthy Eating
Benefits/Barriers Scales that Dr. Carol Pullen and I developed for use in our research with midlife
and older women, as well as a brief summary of the available psychometrics from baseline of our
current R01. If the instrument is useful for you, you are welcome to adopt it for use in your study.
With best wishes, Susan
Susan Noble Walker, EdD, RN, FAAN
Professor and Dorothy Hodges Olson Chair in Nursing
University of Nebraska Medical Center, College of Nursing
985330 Nebraska Medical Center
Omaha, NE 68198-5330
Phone: (402) 559-6561 Fax: (402) 559-6379
E-mail: [email protected]
At 09/20/2005 03:01 p.m. you wrote:
Hi Drs. Walker and Pullen,
I'm Rebecca Grizzle, a doctoral student in nursing at the University of Alabama at Birmingham.
From your abstract for a Sigma Theta Tau 2003 presentation, I read about your study of the
determinants of healthy eating in rural women. I'm very interested in locating the barriers and
benefits to healthy eating scales and the healthy eating self-efficacy scale. Nutrition and disease
prevention are also research interests of mine and I was hoping to find these particular tools,
given that they are valid and reliable. Would you please respond at your earliest convenience
regarding the possibility of obtaining these instruments?
Sincerely,
Rebecca Grizzle, MSN, RN
University of Alabama at Birmingham
School of Nursing
104
APPENDIX F
RESEARCH INSTRUMENTS
105
GOOD HEALTH PROGRAM QUESTIONNAIRE
INSTRUCTIONS: Please complete the questions below.
1. How long have you been working as a sworn police officer?
_________ Year(s) _________ Month(s)
2. What is your age today (in years)?
3. Sex
[ ] Male
__________
[ ] Female
4. What is the highest grade you completed in school?
[ ] Grade School or less
[ ] Some college
[ ] Some high school
[ ] College Graduate
[ ] High school graduate
[ ] Post Graduate Degree
5. What is your race?
[ ] Aleutian, Alaska Native, Eskimo, or American Indian
[ ] White
[ ] Asian
[ ] Other
[ ] Black
[ ] Don’t Know
[ ] Pacific Islander
6. Are you of Hispanic origin, such as Mexican-American, Puerto Rican, or Cuban?
[ ] Yes
[ ] No
7. Are you currently?
[ ] Married
[ ] Never married
[ ] Divorced
[ ] Separated
8. What is your current shift work assignment?
[ ] Day
[ ] Evening
[ ] Night
9. Height ___________
10. Weight __________
11. Waist Circumference ____________
THANK YOU VERY MUCH
[ ] Other
[ ] Widowed
106
JOB STRESS SURVEY
Part A. Instructions: For job-related events judged to produce approximately the same amount of
stress as the standard (number 5). For those events that you feel are more stressful than the standard,
circle a number proportionately larger than “5.” If you feel an event is less than stressful than the
standard, circle a number proportionately lower than “5.”
Stressful job-related events
1. Assignment of disagreeable duties
2. Working overtime
3. Lack of opportunity for advancement
4. Assignment of new or unfamiliar duties
5. Fellow workers not doing their job
6. Inadequate support by supervisor
7. Dealing with crisis situations
8. Lack of recognition for good work
9. Performing tasks not in job description
10. Inadequate or poor quality equipment
11. Assignment of increased responsibility
12. Periods of inactivity
13. Difficulty getting along with supervisor
14. Experiencing negative attitudes toward the
organization
15. Insufficient personnel to handle an assignment
16. Making critical on-the-spot decisions
17. Personal insult from customer /consumer/
colleague
18. Lack of participation in policy-making
decisions
19. Inadequate salary
20. Competition for advancement
21. Poor or inadequate supervision
22. Noisy work area
23. Frequent interruptions
24. Frequent changes from boring to demanding
activities
25. Excessive paperwork
26. Meeting deadlines
27. Insufficient personal time (e.g. coffee breaks,
lunch)
28. Covering work for another employee
29. Poorly motivated coworkers
30. Conflicts with other departments
Amount of Stress
Moderate
4
5
6
4
5
6
4
5
6
4
5
6
4
5
6
4
5
6
4
5
6
4
5
6
4
5
6
4
5
6
4
5
6
4
5
6
4
5
6
Low
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
3
3
1
2
3
4
5
1
1
2
2
3
3
4
4
1
2
3
1
2
1
1
1
1
1
High
9
9
9
9
9
9
9
9
9
9
9
9
9
7
7
7
7
7
7
7
7
7
7
7
7
7
8
8
8
8
8
8
8
8
8
8
8
8
8
6
7
8
9
5
5
6
6
7
7
8
8
9
9
4
5
6
7
8
9
3
4
5
6
7
8
9
2
2
2
2
2
3
3
3
3
3
4
4
4
4
4
5
5
5
5
5
6
6
6
6
6
7
7
7
7
7
8
8
8
8
8
9
9
9
9
9
1
2
3
4
5
6
7
8
9
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
1
2
3
4
5
6
7
8
9
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
6
6
6
7
7
7
8
8
8
9
9
9
107
Part B. Instruction: For each of the job-related events listed, please indicate the appropriate number
of days during the past 6 months on which you have personally experienced this event. Circle “0” if
the event did not occur; circle the number “9+” for each event that you experienced personally on 9
or more days during the past 6 months.
Stressful job-related events
1. Assignment of disagreement duties
2. Working overtime
3. Lack of opportunity for advancement
4. Assignment of new or unfamiliar duties
5. Fellow workers not doing their job
6. Inadequate support by supervisor
7. Dealing with crisis situations
8. Lack of recognition for good work
9. Performing tasks not in job description
10. Inadequate or poor quality equipment
11. Assignment of increased responsibility
12. Periods of inactivity
13. Difficulty getting along with supervisor
14. Experiencing negative attitudes toward the
organization
15. Insufficient personnel to handle an
assignment
16. Making critical on-the-spot decisions
17. Personal insult form customer /consumer/
colleague
18. Lack of participation in policy-making
decisions
19. Inadequate salary
20. Competition for advancement
21. Poor or inadequate supervision
22. Noisy work area
23. Frequent interruptions
24. Frequent changes from boring to demanding
activities
25. Excessive paperwork
26. Meeting deadlines
27. Insufficient personal time (e.g. coffee breaks,
lunch)
28. Covering work for another employee
29. Poorly motivated coworkers
30. Conflicts with other departments
0
0
0
0
0
0
0
0
0
0
0
0
0
Number of Days on Which the Event
Occurred during the past 6 months
1
2 3
4 5
6 7 8
1
2 3
4 5
6 7 8
1
2 3
4 5
6 7 8
1
2 3
4 5
6 7 8
1
2 3
4 5
6 7 8
1
2 3
4 5
6 7 8
1
2 3
4 5
6 7 8
1
2 3
4 5
6 7 8
1
2 3
4 5
6 7 8
1
2 3
4 5
6 7 8
1
2 3
4 5
6 7 8
1
2 3
4 5
6 7 8
1
2 3
4 5
6 7 8
9+
9+
9+
9+
9+
9+
9+
9+
9+
9+
9+
9+
9+
0
1
2
3
4
5
6
7
8
9+
0
1
2
3
4
5
6
7
8
9+
0
1
2
3
4
5
6
7
8
9+
0
1
2
3
4
5
6
7
8
9+
0
1
2
3
4
5
6
7
8
9+
0
0
0
0
0
1
1
1
1
1
2
2
2
2
2
3
3
3
3
3
4
4
4
4
4
5
5
5
5
5
6
6
6
6
6
7
7
7
7
7
8
8
8
8
8
9+
9+
9+
9+
9+
0
1
2
3
4
5
6
7
8
9+
0
0
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9+
9+
0
1
2
3
4
5
6
7
8
9+
0
0
0
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
6
6
6
7
7
7
8
8
8
9+
9+
9+
108
DIET HABITS QUESTIONNAIRE
Please consider you food choices of the past MONTH. Important note: If the question
does not apply to the way you eat, circle N/A for “not applicable”. For example, if you
do not eat chicken, circle N/A to “remove the skin off chicken”.
IN THE PAST MONTH…
1
2
3
4
5
6
7
8
9
10
11
12
13
14
When you ate chicken, how often
was it fried?
How often did you remove the skin?
When you ate red meat, how often
did you trim all the visible fat?
When you ate ground meat, how
often was it extra lean?
When you ate fish, how often was it
fried?
How often did you have a
vegetarian dinner (main meal
without meat, fish, eggs, or cheese)?
When you ate spaghetti or noodles,
how often were they plain, or with a
red or tomato sauce without meat?
When you ate cooked vegetables,
how often did you add butter,
margarine, or other fat?
How often were they fried?
When you ate potatoes, how often
were they fried, like French fries or
hash browns?
When you ate baked or broiled
potatoes, how often did you eat
them without any butter, margarine,
or sour cream?
When you ate green salads, how
often did you use no dressing?
When you ate green salads, how
often did you use low-fat or nonfat
dressing?
When you ate bread, rolls, or
muffins, how often did you eat them
without butter or margarine?
Usually
or
Always
Often
Sometimes
Rarely
or
Never
Not
Applicable
1
2
3
4
N/A
1
2
3
4
N/A
1
2
3
4
N/A
1
2
3
4
N/A
1
2
3
4
N/A
1
2
3
4
N/A
1
2
3
4
N/A
1
2
3
4
N/A
1
2
3
4
N/A
1
2
3
4
N/A
1
2
3
4
N/A
1
2
3
4
N/A
1
2
3
4
N/A
1
2
3
4
N/A
109
IN THE PAST MONTH…
15
16
17
18
19
20
21
22
When you had milk, how often was
it 1% or nonfat milk?
When you ate cheese, how often
was it low-fat cheese?
When you ate dessert, how often did
you eat only fruit?
When you ate home-baked cookies,
cakes, and pies, how often were they
made with less butter, margarine, or
oil than the recipe called for?
When you ate frozen desserts, how
often did you choose frozen yogurt,
sherbet, or low-fat or nonfat ice
cream?
When you ate snacks between
meals, how often did you eat raw
vegetables or fresh fruit?
When you sautéed or pan fried
foods, how often did you use Pam®
or other non-stick spray instead of
oil, butter, or margarine?
When you used mayonnaise or
mayonnaise type spread, how often
did you choose low-fat or nonfat
types?
Usually
or
Always
Often
Sometimes
Rarely
or
Never
Not
Applicable
1
2
3
4
N/A
1
2
3
4
N/A
1
2
3
4
N/A
1
2
3
4
N/A
1
2
3
4
N/A
1
2
3
4
N/A
1
2
3
4
N/A
1
2
3
4
N/A
110
EATING HABITS CONFIDENCE SURVEY
Below is a list of things people might do while trying to change their eating habits. We
are mainly interested in salt and fat intake, rather than weight reduction. Whether you are
trying to change your eating habits or not, please rate how confident you are that you
could really motivate yourself to do things like these consistently, for at least six months.
Please circle one number for each item.
I
Maybe
I
know
I
know
How sure are you that you can do these things?
I
can
I
cannot
↓
can
1
Stick to your low fat, low salt foods when you
feel depressed, bored, or tense.
1 2 3 4 5 N/A
2
Stick to your low fat, low salt foods when there
is high fat, high salt food readily available at a
1 2 3 4 5 N/A
party.
3
Stick to your low fat, low salt foods when
dining with friends or co-workers.
1 2 3 4 5 N/A
4
Stick to your low fat, low salt foods when the
only snack close by is available from a vending 1 2 3 4 5 N/A
machine.
5
Stick to your low fat, low salt foods when you
are alone, and there is no one to watch you.
1 2 3 4 5 N/A
6
Eat smaller portions at dinner.
1 2 3 4 5 N/A
7
Cook smaller portions so there are no leftovers.
1 2 3 4 5 N/A
8
Eat lunch as your main meal of the day, rather
than dinner.
1 2 3 4 5 N/A
9
Eat smaller portions of food at a party.
1 2 3 4 5 N/A
10 Eat salads for lunch.
1 2 3 4 5 N/A
11 Add less salt than the recipe calls for.
1 2 3 4 5 N/A
12 Eat unsalted peanuts, chips, crackers, and
pretzels.
1 2 3 4 5 N/A
13 Avoid adding salt at the table.
1 2 3 4 5 N/A
14 Eat unsalted, unbuttered popcorn.
1 2 3 4 5 N/A
15 Keep the salt shaker off the kitchen table.
1 2 3 4 5 N/A
16 Eat meatless (vegetarian) entrees for dinner.
1 2 3 4 5 N/A
111
I
know
How sure are you that you can do these things?
I
cannot
17 Substitute low or non-fat milk for whole milk at
dinner.
1
18 Cut down on gravies and cream sauce.
1
19 Eat poultry and fish instead of red meat at
dinner.
1
20 Avoid ordering red meat (beef, pork, ham,
lamb) at restaurants.
1
Maybe
I
I
know
can
I
↓
can
2
3
4
5
N/A
2
3
4
5
N/A
2
3
4
5
N/A
2
3
4
5
N/A
112
HEALTHY EATING BENEFITS AND BARRIERS SCALE
DIRECTIONS: Below are statements that relate to ideas about healthy eating (following
the recommendations in the daily food guide pyramid). Please indicate the degree to
which you agree or disagree with the statements by answering strongly agree, agree,
disagree, or strongly disagree.
1
2
3
4
5
6
7
8
9
Eating according to the food guide
pyramid helps me to stay healthier.
Healthy eating is unappetizing.
Increasing the fiber in my diet reduces
my chances of getting colon (bowel)
cancer.
Healthy eating is inconvenient.
Reducing the fat in my diet reduces
my chances of getting prostate cancer.
Healthy eating is too expensive.
Reducing the fat in my diet reduces
my chances of getting coronary heart
disease.
Healthy eating is difficult due to the
influence of family/friends.
It takes too much time to prepare
healthy meals.
Strongly
Agree
Agree
Disagree
Strongly
Disagree
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
10
Healthy eating makes me feel better.
1
2
3
4
11
It takes too much time to shop for
healthy foods.
1
2
3
4
Healthy eating helps me to lose
weight.
1
2
3
4
Healthy eating means giving up foods
that I like.
1
2
3
4
1
2
3
4
12
13
14
Eating according to the food guide
pyramid makes me look more
attractive.
113
15
16
17
18
Healthy eating limits my choices
when I eat out.
Healthy eating helps me to be more
fit.
Healthy eating is difficult because
experts keep changing their advice.
Eating according to the food guide
pyramid helps me to have more
energy.
Strongly
Agree
Agree
Disagree
Strongly
Disagree
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
114
115
116
117
118
119
120
121
122

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