MU‘IWI:

l
l

33% W

 

THS

USE OF THE HEALTH BELIEF MCAL‘ EL EN

iLLLZQZNC" RC AAL'AA AS C” LAR

E‘ESK PAC” RS EN TEE-i:
CLZNECAL SET NEG

- BY

K’vai‘i SAV STATE -.. W322“?

.ﬁ

xAS“: 02-? SCEENCL

~CC LL36 252...; CF”! ‘URSJN‘:

 

 

LIBRARY
Michigan State
_ University

 

 

 

 

 

 

 

- . nab—u a...” '

 

PLACE IN RETURN BOX to remove this checkout from your record.
TO AVOID FINES return on or before date due.
MAY BE RECALLED with earlier due date if requested.

 

DATE DUE

DATE DUE

DATE DUE

 

mm“? 5 ’0 R

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6/01 cJClRC/DatoDuopGS—p. 15

USE OF THE HEALTH BELIEF MODEL IN SCREENING FOR
CARDIOVASCULAR RISK FACTORS IN THE CLINICAL SETTING

By

Mollie Vento Hudson

A SCHOLARLY PROJECT
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF SCIENCE

College of Nursing

1998

ABSTRACT
USE OF THE HEALTH BELIEF MODEL IN SCREENING FOR
CARDIOVASCULAR RISK FACTORS IN THE CLINICAL SETTING
By

Mollie Vento Hudson

Heart disease kills nearly two Americans in ﬁve, but it is largely preventable as
many of the risk factors that predispose an individual to heart disease are modiﬁable. An
assessment tool that could be used to screen individuals based on the risk factors of
smoking, hypertension, hyperlipidemia, physical inactivity, and obesity would be of great
value to the advanced practice nurse in educating patients about their role in preventing
heart disease.

This project evaluates three existing tools for how well they address the above
risk factors, for clinical utility, and for degree of ﬁt with the Health Belief ModeL The
tools evaluated are Anderson, Wilson, Odell, & Kannel (1991); Thorsen, Jacobs, Grimm,
Keys, Taylor, & Blackburn (1979); and Kannel & Schatzkin (1983).

None of the instruments evaluated address physical inactivity or obesity, and they
vary with respect to how they treat smoking. None of the tools takes into consideration
the subjective elements that determine an individual’s likelihood of adopting change, so

they are of comparably low merit when used in context of the Health Belief Model.

This project points to the need for a more comprehensive risk assessment and one
that includes elements of the Health Belief Model most predictive of a person engaging in

health-protecting behaviors.

For my parents, who taught me to believe in myself, and whose lives directed me

toward this profession. And for Rick, for his patience and support along the way.

TABLE OF CONTENTS

Introduction ........................................... 1
Review of Literature ..................................... 4
Conceptual Framework .................................. 12
Evaluation of Tools ..................................... 17
Summary and Implications for Practice ...................... 23

LIST OF TABLES

Evaluation of Instruments .............................. 28

LIST OF FIGURES

The Health Belief Model .............................. 27
The Thorsen slide rule ................................ 29

iv

Introduction

Despite advances in treatment, heart disease remains the leading cause of
death in the United States, claiming more than 700,000 victims in 1995, the latest
year for which statistics are available (National Center for Health Statistics, 1995).
The American Heart Association reports that nearly two Americans in ﬁve will
eventually die of heart disease. Coronary heart disease (CHI)) also has signiﬁcant
costs beyond mortality. Selig (1991) reports that CHI) costs the country more
than $60 billion per year in health care, lost wages, and productivity.

Despite the grim statistics, heart disease is largely preventable. It is well
accepted that certain risk factors predispose an individual to cardiovascular
disease, but a signiﬁcant number of these risk factors are modiﬁable. Lifestyle
factors such as diet, physical activity, and smoking all contribute to a person’s
overall risk of developing cardiovascular disease. Hopkins and Williams (1986)
state, “The total disease preventable by reducing risk exposure to more nearly
optimal levels may be calculated to be 90%.” (p.3). Those diseases that can be
most affected by a preventive approach are coronary artery disease (CAD),
hypertension, cerebrovascular disease, and peripheral vascular disease (Selig,
1991). Paﬁenbarger, Hale, Brand, and Hyde (1977) concluded ﬁom a study of
longshoremen in San Francisco that elimination of the adverse inﬂuences of
inactivity, smoking, and hypertension might have reduced the fatal heart attack rate

among participants in the 22-year study by as much as 88%.

The advanced practice nurse (APN) is in a unique position to assist patients
in identifying their risk of cardiovascular disease. Central to the role of the APN is
health promotion and disease prevention. The 1986 Ofﬁce of Technology
Assessment report concluded that nurse practitioners are more adept than
physicians in providing services that depend on communication with patients and
preventive actions. Further, the report stated that nurse practitioners perform
better than most physicians in the provision of supportive care and health
promotion activities.

With the large number of people affected by cardiovascular disease, it is
clear that APNs in family practice will encounter clients who either have symptoms
of clinical disease or are at signiﬁcant risk for disease as a result of modiﬁable or
nonmodiﬁable risk factors. As clinicians, counselors, assessors, and change agents,
nurse practitioners in primary care have the opportunity to impact clients’
cardiovascular health on an ongoing basis. The goal of APNs is to reduce the
incidence of cardiovascular disease by teaching clients about their role in disease
prevention and by developing mutual goals and treatment plans speciﬁc to their
cardiovascular risk factors.

In one study, individuals who received risk factor screening and education
had signiﬁcantly lower risk factor levels, including lower blood cholesterol levels,
lower diastolic blood pressure, lower resting heart rate, and increased selection of
low-ﬁat and low-sodium meals in restaurants (Murray, Luepker, Pirire, Grimm,
Bloom, Davis, & Blackburn, 1986). These data suggest that systematic screening

and education may result in lower population risk for coronary heart disease.

Even with clients who do not demonstrate risk factors, prevention of
cardiovascular disease is a component of the APN’s role. Prevention is viewed as
either primary or secondary. Primary prevention focuses on asymptomatic
individuals and stresses the importance of a healthy weight, regular exercise, and a
low-fat diet. Secondary prevention is aimed at people with modiﬁable risk factors
or those who have developed preclinical disease but have not yet manifested
symptoms. Smoking cessation, weight reduction, and control of hypertension and
hyperlipidemia are all be goals of the APN with this population. This project will
focus on ﬁve of the most common modiﬁable risk factors identiﬁed in the
literature: smoking, hypertension, hyperlipidemia, physical inactivity, and obesity
(Anderson, Wilson, Odell, & Kannel, 1991).

The purpose of this project is to evaluate existing screening tools used to
identify individuals at high risk for developing cardiovascular disease. The risk
indices to be evaluated are as follows: Anderson, Wilson, Odell, & Kannel (1991);
Thorsen, Jacobs, Grimm, Keys, Taylor, & Blackburn (1979); and Kannel &
Schatzkin (1983). The tools will be evaluated for completeness regarding known
risk factors and also with respect to how well they ﬁt philosophically with the
Health Belief Model. Although the targeted population for this project is adults,
the ideal risk index should also be able to be used with adolescents as there is
evidence that atherosclerotic lesions are not uncommon among teenagers
(Kwiterovich, 1995).

The conceptual framework for this project is the Health Belief Model

(HBM). Well known in nursing as a paradigm for health-protecting behavior, the

HBM has been used in a number of studies addressing cardiovascular health
(Mirotznik, Feldman, & Stein, 1995; Terborg, Hibbard, & Glasgow, 1995; and
Sommers, Andres, & Price, 1995). The HBM proposes that there are variables
that directly affect an individual’s predisposition to take action to protect health.

The Health Belief Model is an appropriate framework for interventions by
APNs in the treatment of cardiovascular disease as the roles of the APN directly
relate to key concepts within the model. Educator, assessor, change agent, and
counselor are but a few roles that APNs apply directly to concepts within the
HBM and could be used to help clients develop skills to make behavior changes.
By facilitating mutual goals tint focus on improved cardiovascular health, APNs
can develop successful treatment plans.

Regardless of whether clients present as asymptomatic with no or few risk
factors or whether they have preclinical cardiovascular disease with my risk
factors, APNs who use the Health Belief Model as a conceptual ﬁamework can be

of great use to a client interested in improving his or her health.

Review of Literature

Hopkins and Williams (1986) identiﬁed 246 coronary risk factors, the most
important of which were age, sex, strong family history, cigarette smoking,
hypertension, plasma lipoprotein levels, diabetes, and obesity. One study (Rich-

Edwards, Stampfer, Manson, Rosner, Hankinson, Colditz, Willett, & Hennekens,

1997) even suggested that birth weight of less than ﬁve pounds may be associated
with increasing risk of adult coronary heart disease and stroke.

The body of literature documenting risk factors for cardiovascular disease
is signiﬁcant, and there is consensus among researchers that an individual can
greatly reduce his or her risk of coronary artery disease, cerebrovascular disease,
and peripheral vascular disease by making appropriate lifestyle changes (Vogler,
McClearn, Snieder, Boomsma, Palmer, deKnijﬂ', & Slagboom, 1997; Sutterer,
Carey, Silver, & Nash, 1989). The relationship between hyperlipidemia, physical
inactivity, obesity, and smoking and an increased incidence of cardiovascular
disease is well documented (Hopkins & Williams, 1986; Blair, Kohl, Paﬂenbarger,
Clark, Cooper, & Gibbons, 1995). There is less consensus on hypertension as
absolute risk factor, but it remains a reﬂection of relative risk and one that health
care providers must evaluate in light of a client’s total cardiovascular risk picture
(Alderman, 1995). In addition, nonmodiﬁable risk factors include family history,
age, and male sex, although Hamel and Oberle (1996) have suggested that
cardiovascular disease in women may be more common than current statistics
show. Studies of twins demonstrate that familial risk of premature coronary heart
disease can be attributed substantially to genetics rather than to environmental
factors that are common to family members (Vogler et a1, 1997).

Atherosclerosis, the process which leads to coronary heart disease and
stroke, is intimately linked with hypercholesterolemia, a signiﬁcant problem in the
United States. Data ﬁom the National Health and Nutrition Examimtion Survey

showed that 52 million Americans age 20 and older are candidates for dietary or

drug therapy, based on blood cholesterol levels of 200 mg/dl or higher (Sempos,
Cleeman, Carroll, Johnson, Bachorik, Gordon, Burt, Briefel, Brown, Lippel, &
Rifkind, 1993).

Further, the total cholesterol levels of young men have been found to be
strongly predictive of cardiovascular disease in midlife (Gotto, 1994). In the Johns
Hopkins Precursors Study, members of medical school graduating classes from
1949 to 1964 were followed for 27 to 42 years. Cholesterol levels were measured
from one to 11 times while in medical school, and these levels correlated strongly
with the development of cardiovascular disease later in life. Risk of coronary heart
disease was ﬁve times higher for the men with the highest cholesterol levels than it
was for men with the lowest cholesterol. A difference of 36 mg/dl in initial total
cholesterol was signiﬁcantly associated with an increased risk of death before age
50 (Klag, Ford, Mead, He, Whelton, Liang, & Levine, 1993).

Almost 30 years ago, information ﬁ'om the F ramingharn study suggested
that independent of other variables, cholesterol was a signiﬁcant contributor to
coronary heart disease and atherothrombotic brain infarctions (Gordon, Sorlie, &
Kannel, 1971). Follow-up Studies of the Frarningham Heart Study showed that for
both men and women younger than 50, cardiovascular death rates and deaths due
to all causes were directly related to cholesterol levels (Anderson, Castelli, &
Levy, 1987).

The Multiple Risk Factor Intervention Trial (MRF IT), concluded that the
6-year risk of death ﬁ'om coronary heart disease in normotensive, nonsmoking,

middle-aged men with total cholesterol levels of less than 182 mg/dl was one

fourth that of men with cholesterol levels of 245 mg/dl or higher (Multiple Risk
Factor Intervention Trial Research Group, 1982).

The National Cholesterol Education Program (N CEP) of the National
Heart, Lung, and Blood Institute recommends that adults 20 and older should have
a measurement of total blood cholesterol every ﬁve years and that lipoprotein
analysis should be performed on all clients with coronary heart disease. In the
absence of CHD, lipoprotein analysis should be done if: (1) total cholesterol
exceeds 240 mg/dl; (2) total cholesterol is 200-239 and the client also has two or
more CHI) risk factors; or (3) HDL cholesterol is less than 35 mg/dl (Clinician’s
Handbook of Preventive Services, 1995).

Smoking is an important modiﬁable risk factor for development of
cardiovascular disease and can hardly be overemphasized. Blood pressure and
heart rate tend to be higher in smokers. They also have lower levels of protective
HDL cholesterol. Nicotine scars the lining of blood vessels, and these roughened
blood vessels, coupled with smokers’ increased platelet aggregation, hasten
atherosclerosis (Fried & Becker, 1993).

The Nurses’ Health Study has found that smoking is a dominant
contributor to coronary heart disease and that a strong dose-response relationship
exists between cigarettes and development of disease (Colditz, 1995). Colditz also
reported that for the nurses who quit smoking, the risk of CI-ID dropped 14%
within two years.

Consistent evidence of an association between obesity and cardiovascular

disease has been reported in epidemiological studies, but it is not clear whether

obesity is an independent risk factor or whether it contributes to other risk factors
such as hyperlipidemia and hypertension (Hubert, F einleib, McNamara, & Castelli,
1983). Several anthropometric measurements have been associated with
cardiovascular risk. Excess total body fat is most commonly indexed by body mass
index (BMI), but waist/hip ratio (WI-IR) and waist/thigh ratio (WTR) have also
been used. Oshaug, Bugge, Bjonnes, & Ryg (1995) stated that BMI has a direct
association with overall mortality in both men and women, but that WI-IR is the
best indicator ofvisceral fat mass. It is this visceral fat mass that seems to be
linked to the development of hypertension, hyperlipidemia, and coronary artery
disease. Vogler et a1. (1997) proposed that there is a strong genetic component to
obesity, regardless of the anthropometric measurement used to index it. Although
estimates of the importance of genetic and environmental inﬂuences vary among
the studies Vogler et a1. reviewed, they reported that 40 to 60% of the variability
in BMI can be attributed to genetic effects. Colditz (1995) found a strong
relationship between weight and risk of heart disease, even concluding that women
of average weight are at increased risk.

Regardless of how obesity is measured or whether its cause is behavioral or
genetic, it remains predictive of cardiovascular disease. Weight control and the
beneﬁts of regular aerobic exercise, therefore, are subjects APNs must address
with clients. In one study, mortality rates were compared for men who underwent
two physical examinations. Some of the men who were unﬁt on initial examination
became physically ﬁt by the second examination (Blair et al., 1995). In this study,

physical ﬁtness was operationalized to exercise test tolerance to a standard

treadmill protocol with test duration being the variable of interest. Physical ﬁtness
was analagous to aerobic power. The mortality rates of the men who became
physically ﬁt by the subsequent exam were 45% lower than the men who were still
unﬁt at the second exam. By corrrparison, men who were physically ﬁt at both
exams had one-third the mortality of the men who were physically unﬁt at both
exams.

Paﬂenbarger, Lauglin, Girm, & Black (1970) found that longshoremen
with more sedentary jobs sustained coronary death rates one third higher than their
colleagues with more physically demanding responsibilities. Paﬁ‘enbarger and Hale
(1975) later concluded that repeated bursts of high energy output established a
level of protection against coronary mortality.

The relative risk of CHD associated with inactivity varies among studies,
but Powell, Thompson, Caspersen, & Kendrick (1987) reported the risk generally
ranges ﬁom 1.5 to 2.4 with a median of 1.9. They concluded the better studies
tend to report higher relative risks associated with inactivity. Further, the risks
appear to be similar in magnitude to that of hypertension, hyperlipidemia, and
smoking. It is suggested that physical activity may reduce the incidence of CHI)
by retarding atherosclerosis, altering the structure of coronary arteries, reducing
vasospasm, enhancing myocardial electrical stability, or increasing ﬁbrinolysis
(Powell et al., 1987).

Hypertension is also considered a risk factor for cardiovascular disease.
According to Alderman (1995), there is a linear relationship between blood

pressure level and the likelihood of a cardiovascular event, but blood pressure

alone is a poor predictor of disease. Among the 325,348 participants in the
MRFIT study, those with diastolic pressure of at least 90m Hg were
approximately twice as likely to suffer a heart attack or stroke as those with
diastolic pressures less than 90 mm Hg. However, in a 15-year follow-up to the
Framingharn study, less than one third of the individuals whose only risk factor
was a systolic pressure higher than 195 mm Hg (the highest category) had a heart
attack or stroke (Madhaven & Alderman, 1981). Alderman (1995) stated that
normotensive smokers with an unfavorable family history and high cholesterol are
at much higher risk of a cardiovascular event than individuals with low absolute
risk, even with signiﬁcant systolic blood pressure.

In intervention trials, a 5 to 6mm Hg drop in diastolic pressure was
associated with a 42% decrease in cerebrovascular events. Interestingly, this
beneﬁt did not vary according to whether the subjects had mild, moderate, or
severe hypertension. For coronary events, the protection was less, but the
incidence still decreased for all blood pressure reductions, regardless of initial
pressure readings (Alderman, 1995).

Current studies seem to suggest that blood pressure reduction in high-risk
normotensive clients can decrease incidence of myocardial infarction (The SOLVD
investigators, 1991), but that treatment of hypertension should depend on
individualized assessment of absolute risk and potential beneﬁt of treatment rather
than application of any arbitrary threshold of blood pressure level.

Abnormal glucose metabolism is widely accepted as a risk factor for the

development of cardiovascular disease (Hopkins & Williams, 1986; Sutterer,

10

Carey, Silver, & Nash, 1989). Both the Nurses’ Health Study and the studies of
San Francisco longshoremen concluded that diabetes increases an individual’s risk
of coronary heart disease and stroke (Colditz, 1995; Paffenbarger, Hale, Brand, &
Hyde, 1977). Although a valid risk factor and one that APNs must address in
practice, diabetes is outside the scope of this project.

In addition to the risk factors discussed earlier, elevated plasma
homocysteine is emerging as a possible cardiovascular risk. Researchers suggest
that hyperhomocysteinaemia may be of similar signiﬁcance to hyperlipidemia as a
risk factor for premature atherosclerosis (Still & McDowell, 1998). The
mechanism by which homocysteine may cause vascular disease remains poorly
deﬁned, but homocysteine levels in the range of 15-30 umol/l have been associated
with premature atherosclerosis in several large, case-controlled prospective studies
(Still & McDowell, 1998; Bots, Launer, Lindemans, Hoﬁnan, & Grobbee, 1997).
Folic acid has been implicated as having a role in lowering plasma homocysteine
levels, but the optimal folate status has not been determined (Ward, McNulty,
McPartlin, Strain, Weir, & Scott, 1997). Research into homocysteine and its
relationship to cardiovascular disease continues and warrants attention in the
ﬁrture.

Although the literature abounds with discussion of risk factors and
screening of clients at risk of heart disease, few articles discuss screening tools or
the questionnaires used to quantify these risks. Laboratory data, anthropometric
measurements, even blood pressures all provide quantitative data, but readers are

at a loss as to how to determine how many risk factors equals how much risk of

11

cardiovascular disease. A few articles describe assessment tools such as the
FANTASTIC Lifestyle Assessment (Mitchell, 1996), the 17-item Duke Health
Proﬁle (Parkerson, Broadhead, & Chin-Kit, 1991), and the Carter Center Health
Risk Appraisal used by the HeartSmart Program (Johnson, Powers, Bao, Harsha,
& Berenson, 1994), but the assessment tools are either not included in the article
or are not speciﬁc for cardiovascular disease. Gran (1994) included an
interpretation of risk category (below average, about average, somewhat above
average, or considerably above average) with the recommended intervention.
Gran listed the ﬁve classiﬁcations of physical activity and how family history of
cardiovascular disease was determined (“Did your mother or Ether or a sibling
have a heart attack or stroke before 60 years of age? Yes/No). In summary, the
literature is plentiful with respect to the risk Ectors associated with cardiovascular
disease, speﬁcially hyperlipidemia, obesity, physical inactivity, smoking, and
hypertension. In general, however, the body of literature lacks a simple, speciﬁc,

clinically focused tool for evaluation of these cardiovascular disease risk Ectors.

Conceptual Framework

Theories and ﬁ‘arneworks provide direction for research and focus for
clinical practice. Understanding the contributing Ectors of health-protecting
behavior is crucial to developing interventions designed to alter clients’ behaviors

so as to reduce their risk of cardiovascular disease. The Health Belief Model

12

(HBM) is a psychosocial ﬁ'amework developed to explain individual decision
making in terms of health-related behaviors. The usefulness of the Health Belief
Model is its ability to examine health beliefs and relationships with other variables,
and therefore possibly alter beliefs so that changes in behavior are more likely. See
Figure 1.

The HBM was developed in the 19505 by Hochbaum, Kegeles, Leventhal,
and Rosenstock as a method for examining why some people take actions to avoid
illness while others Eil to take protective action (Pender, 1996). The HBM is
rooted in select aspects of the theories of Kurt Lewin, who proposed that
individuals exist in a life space composed of regions, some of which are positively
valued, some of which are negatively valued, while still others are neutral.
Diseases were postulated to be areas of negative value, and they could thus be
expected to exert a force that would move a person away ﬁ'om that region
(Mikhail, 1981). The aspects of Lewin’s theories that form the basis of the HBM
have to do with goal setting and level of aspiration. Level of aspiration is deﬁned
as the degree of difﬁculty of attaining the goal to which the person is striving
(Rosenstock, 1974). When it is necessary to choose between different levels of
difﬁculty, the choice is made based on the relative valence of these levels and the
individual’s subjective probability of success. According to Lewin, a goal deemed
reasonably probable will be chosen over one deemed highly improbable even
though the improbable success is more highly valued. The theory holds that mo st
people aspire to levels that are close to or only slightly higher than their

performance in the past (Becker, 1974).

13

The HBM proposes that the likelihood of a person taking action relative to
a health problem is determined by the person’s readiness to take action and by the
perceived beneﬁt of the action minus the perceived barriers to taking the action.
That emphasis on subjectivity is the heart of the Health Belief Model. The most
important variables in the model deal with the current subjective state of the client.
Postulates in the theory include: (1) the degree to which a person is
psychologically ready to take action as it relates to a health condition is determined
by the individual’s perceived susceptibility or vulnerability to contracting the
condition and perceptions of the severity of the condition should they contract it;
(2) the person’s evaluation of the proposed health action is evaluated in terms of
its feasibility and effectiveness measured against his or her perception of barriers or
costs to the proposed action; and (3) a stimulus, either internal (such as symptoms)
or external (such as the experience of family member or ﬁiend or media exposure)
must serve as a cue to action to trigger the relevant health behavior (Becker &
Mairnan, 1975). Thus, in order for individuals to take action to avoid heart
disease, they would need to believe that they are personally susceptible to heart
disease and that its development would negatively affect some aspect of their lives.
In addition, they would have to believe that taking a particular action, such as
beginning an exercise program, would reduce either their susceptibility to disease
or its severity. Finally, they would have to believe that taking the health-protecting
action would not entail overcoming important psychological barriers, such as cost,

convenience, pain, or embarrassment (Rosenstock, 1974).

14

In addition to these elements, modifying Ectors are thought to affect the
course of action (Mikhail, 1981). Modifying factors include demographic variables
(age, sex, cultural inﬂuences), sociopsychological variables (personality, social
class, peer or reference group), and structural variables (knowledge about or
previous experience with a disease).

Motivation, only assumed in the original Health Belief Model, was added
to the model in its second generation (Becker & Maiman, 1975). Deﬁned as
“differential emotional arousal in individuals caused by some given class of stimuli,
for example, health matters” (Becker & Maimain, 1975, p. 17), motivation is
conceptualized as concern about general health matters, willingness to seek and
accept medical care, intent to comply, and demonstration of positive health
behaviors. In the second generation of the model, modifying factors were
expanded and classiﬁed as demographic, structural, attitudinal, interactional, and
enabling. Cost, duration, complexity of side effects, and accessibility of treatment
were added as structural Ectors.

The Health Belief Model has been used in innumerable articles relating to
preventive and health-promoting behaviors. It has been used to predict compliance
with antihypertensive medication therapy, exercise behaviors, dietary compliance,
use of condoms to prevent sexually transmitted diseases, and determinants of
immunization behavior, among others (Becker et al., 1979). Its emphasis on the
client’s subjective state and its preventive focus make it an appropriate paradigm
for assessment of risk factors for cardiovascular disease and development of

interventions to reduce clients’ risks.

15

Providing education with the aim of affecting clients’ perceived
susceptibility is the most obvious arena for APNs to make a difference, and one
study suggests that cardiovascular risk-reducing behaviors may be reinforced by
knowledge of a positive coronary artery computed tomography scan (Wong et al.,
1996). Other studies indicate that knowledge alone is not enough to affect
likelihood of undertaking the health-promoting behavior (Sutterer et a1, 1989) and
suggest that social support (a sociopsychological modifying Ector) also plays a
role in behavior changes (Terborg et al., 1995). In a study examining the
usefulness of the HBM for explaining attendance at a supervised heart disease
exercise program, health motivation and perceived severity of CHI) were
signiﬁcantly associated with exercise adherence, as operationalized by attendance
(Mirotznik et al., 1995). Clearly knowledge alone is not a signiﬁcant motivating
factor. Moylan & Joyce (1993) stressed that nursing professionals need to develop I
strategies to overcome perceived barriers, which Janz & Becker (1984) considered
the most powerful of the HBM dimensions.

In summary, this scholarly project will evaluate three existing indices for
the risk of cardiovascular disease with respect to the modiﬁable risk Ectors of
interest: hyperlipidemia, smoking, hypertension, obesity, and physical inactivity.
The project will use the Health Belief Model as the conceptual ﬁarnework and the
risk assessment tools will be evaluated not only for how well they address the
modiﬁable risk Ectors identiﬁed earlier but also for how well they address the
central concepts of the Health Belief Model. Those concepts include perceived

susceptibility and perceived severity of disease (which together form perceived

l6

risk), perceived barriers to risk-reducing behaviors, and perceived beneﬁts of
engaging in risk-reducing behaviors.

The outcome of this project will identify the most appropriate and clinically
useful tool that APNs can use to identify individuals at highest risk for
cardiovascular disease. The most appropriate tool will also take into consideration
the critical elements of the HBM, a ﬁ’amework that the APN can use in working

with clients to develop interventions to reduce their cardiovascular risk.

Evaluation of Tools

The three tools evaluated have a number of similarities and differences.
Each addresses the risk Ectors of blood pressure, lipid levels, and smoking,
although they vary in how the variables are quantiﬁed and weighted. The
instruments all Eil to address two of the risk Ector identiﬁed in the literature as
signiﬁcant to the development of cardiovascular disease: obesity and physical
inactivity. The tools also vary in their case of use and appropriateness to the
clinical setting with the Thorsen device having the most clinical utility and the
remaining tools being more appropriate as research instruments.

Although all of the tools yield objective data that may be used by APNs to
educate clients about susceptibility to cardiovascular disease, none deals with the
client’s perception of susceptibility. None of the tools addresses the perceived

severity component of perceived risk or barriers to adopting change. The

17

instruments share an ability to show clients numerically how reducing risk factors
can reduce probability of heart disease within given time frames, so beneﬁts of
change is handled in a quantiﬁable manner, but none deal with the subjective state
of the client that is so fundamental to the Health Belief Model. The subjective
state of a patient does not affect true risk, but it does affect the likelihood of the
person adopting changes in behavior to reduce risk of cardiovascular disease.

Each instrument was evaluated on a ﬁve-point Likert scale for how well it
addressed each of the risk factors of interest (hyperlipidemia, hypertension,
smoking, obesity, and physical inactivity). The same scale was applied to the
instruments relative to clinical utility and degree of ﬁt with the Health Belief
Model. In all cases, 1=poor, 2=marginally acceptable, 3mderate, 4=good,
5=superior. The points were then added and the instrument with the highest total
was determined the most appropriate. See Table 1.

Thorsen et a1. (1979) developed a slide rule that computes risk of a future
coronary event based on age, smoking, serum cholesterol level and blood pressure.
The instrument consists of two rotating disks and a movable hairline tab all having
a common center. See Figure 2. The APN can show clients quickly and
concretely how adopting risk-reducing behaviors, such as lowering cholesterol and
quitting smoking, translate into reduced susceptibility to coronary heart disease.
Unfortunately, the other risk Ector the Thorsen instrument uses is age, which is
not modiﬁable. This lessens its ﬁt with the HBM slightly as even if the client

perceives a high degree of risk, high beneﬁts to adopting change and low barriers,

18

he or she cannot do anything to change the risk associated with age. An advantage
of this device is that clear instructions for its use appear in the article.

Disadvantages include that the practitioner would have to construct the
device or contact the author(s) to determine if a slide rule is available. Computer
programs likely make such a device outdated now, but for rapid ease of use in a
clinical setting, this instrument could be used much like a pregnancy wheel that
calculates estirmted date of delivery. Another disadvantage of the Thorsen slide
rule is that the data upon which it was constructed was based on a ﬁve-year
prospective study of 2,571 initially healthy men ages 40-59. This potentially limits
its usefulness with men outside that age category, women of all ages, and clients
with comorbid illnesses.

The data needed to use the tool are easily obtainable in an ofﬁce visit
through history, physical exam, and simple laboratory tests. The self-reporting
nature of number of cigarettes smoked leaves open the possibility of errors
introduced through reporting problems. In its favor, the Thorsen tool is the only
one of the three that treats the number of cigarettes smoked as ordinal data instead
of nominal and allows for the dose-response relationship that the literature
supports (Colditz, 1995).

The Thorsen instrument is of marginal philosophical ﬁt with the Health
Belief Model. It shows a bit more easily than the Anderson et a1. instrument or the
Kannel and Schatzkin tool the beneﬁts of adopting risk-reducing behaviors so it
can be used to educate patients about susceptibility and alter their perceptions

about beneﬁts of quitting smoking, exercising, and reducing cholesterol, for

19

example. However, it Eils to address any of the subjective elements so critical to
evaluating the likelihood that the patient will engage in health-promoting or risk-
reducing belmvior.

The instrument developed by Anderson et al. (1991) is composed of a two
sets of tables, one for men and one for women. Each set of tables assigns points
based on the patient’s age, HDL and total cholesterol levels, systolic blood
pressure, smoking status, and presence of diabetes or left ventricular hypertrophy.
Points are then totaled and a probability given of developing coronary heart
disease with ﬁve and 10 years. This instrument also appears in the literature under
the name of authors Kannel and Wolf. This Anderson et a1. version of the
instrument was selected for this project based on the more complete description of
its design.

Beneﬁts of this device include the use of both HDL and total cholesterol
for determining risk due to dyslipidemia and the inclusion of data to increase
applicability to women. However, there is no assessment of obesity and physical
inactivity, and cigarette smoking is treated as a nominal variable. The authors
provide logistic equations for only the blood pressure risk determination. The
computed positive predictive probability is p=. 16 for men and p=.22 for women.
Because this is a screening device, the low positive predictive probability is
acceptable since it is understood that some number of individuals with false
positive readings will be included. If the device were intended to be diagnostic, the
p values would be unacceptable. The predictive ability of the remaining equations

is not included.

20

The Anderson tool is of similar theoretical ﬁt with the HBM as Thorsen
instrument given its exclusion of any method of assessing patient perception of
susceptibility, severity, beneﬁts, and barriers. It is slightly more cumbersome to
use, and therefore of slightly lower clinical utility than the Anderson tool for
showing patients the beneﬁts of adopting health-promoting activities. For example,
showing the beneﬁts of lowering cholesterol requires a recalculation of total risk
points rather than a simple turn of the slide rule.

The ﬁnal instrument evaluated is tint ofKannel and Schatzkin (1983).
Like Anderson et aL, it consists of charts for men and women where points are
given based on systolic blood pressure, presence of left ventricular hypertrophy
and glucose intolerance, age, and serum cholesterol. Points are totaled and the
practitioner then ﬁnds the corresponding probability of the patient developing
coronary heart disease within six years.

There are a number of shortcomings to the Kannel and Schatzkin
instrument, the most ﬁrndamental being that the tool lacks directions for its use.
Although the charts appear straightforward, they are clinically cumbersome. Age
and serum cholesterol are combined mathematically to produce one point value,
but nowhere on the chart are instructions for making the calculation. Further
compounding the confusion is a clmrt of HDL levels with multiplication Ectors. It
is unclear whether the cholesterol level to be used in computing the age/cholesterol
value is total serum cholesterol or HDL level and how the practitioner should use

the HDL multiplication Ectors listed.

21

Like the other tools evaluated, Kannel and Schatzkin do not address
obesity or physical inactivity. Although smoking is addressed, it is treated as a
nominal variable in the chart for men and not included in the point calculation for
women at all. This is a deﬁnite shortcoming in its usefulness.

This instrument performed at the same level as the Thorsen and Anderson
tools relative to ﬁt with the Health Belief Model and for the same reasons. It Eils
to take into consideration the subjective elements that determine likelihood of
adopting change. Kannel and Schatzkin discuss that modiﬁability is not an
absolute requirement for a given characteristic to be a risk Ector. Although the
HBM includes demographic and sociopsychological variables that are permanent,
the entire focus of the framework is on the subjective evaluation of risk and the
beneﬁts of risk-reducing behavior. In addition, its low clinical utility lessens its
impact for use in educating patients about the beneﬁts of adopting risk-reducing
behaviors.

In summary, the above instruments all Eil to address obesity and physical
inactivity as risk Ectors. They are of comparably low merit when used in context
of the Health Belief Model since none of the tools address the patient’s subjective
state. Ease of use and calculation of lipids and smoking were most pivotal in
determining ﬁnal ranking. The Thorsen tool scored highest, with Anderson next
and Kannel and Schatzkin placing third.

This evaluation points to the need for a more comprehensive risk
assessment tool that takes into consideration more of the known risk Ectors for

cardiovascular disease. The poor performance of all the tools relative to the Health

22

Belief Model points out the need for an assessment tool that includes the elements
most predictive of a person engaging in health-protecting behaviors. The literature
supports the importance of perceived susceptibility and barriers relative to whether
an individual engages in health-promoting behavior (Janz & Becker, 1984). To
ignore these subjective states of patient is to treat them in a biological model only

and not the biopsychosocial model upon which APN practice is based.

Surmnary and Implications for Practice

Heart disease kills two in ﬁve Americans and costs the country billions of
dollars in health care and lost productivity. While Emily history and age are risk
Ectors that patients cannot change, many of the Ectors that contribute a person’s
risk of developing cardiovascular disease are products of lifestyle choices.
Smoking, obesity, hypertension, hyperlipidemia, and physical inactivity have all
been shown to play a role in heart disease, and yet each of these can be modiﬁed
by adopting risk-reducing behaviors.

Advanced practice nurses in Emily practice see clients every day who
present with these risk Ectors. They are in a fortuitous position to make an impact
on clients’ cardiovascuhr health By assessing clients for their modiﬁable and
nonmodiﬁable risk Ectors, APNs can determine how susceptible a client is to heart
disease. Nurse practitioners can then educate clients as to their personal risk

proﬁles and work with them to develop interventions aimed at lowering their risk.

23

The Health Belief Model is an appropriate conceptual framework on which
to base patient counseling regarding risk of disease. If clients lack an internal one
to action such as symptoms of cardiovascular disease, APNs serve as an external
cue to action. They provide information to affect the client’s perceived
susceptibility and perceived severity, which combine to form perceived risk. Nurse
practitioners can also demonstrate the positive impact to clients of adopting health-
protecting behaviors such as lowering cholesterol or quitting smoking.

The availability of a simple, clinically useful tool that quantiﬁes threat of
cardiovascular disease based on modiﬁable risk Ectors would be of great beneﬁt to
APNs. Unfortunately, although the literature abounds with discussion of known
risk factors, the tools either Eil to measure up either in speciﬁcity for heart disease,
in comprehensiveness of risk Ectors, or in clinical utility. Further, none of the risk
assessment devices takes into consideration the subjective state of the client that is
so critical to the likelihood of the person actually engaging in risk-reducing
behaviors.

Among the tools evaluated, the Thorsen et a1. slide rule ranks highest
relative to addressing the identiﬁed risk Ectors and ease of use, although it fares
no better than Anderson et a1. or Kannel and Schatzkin in terms of ﬁt with the
HBM. This evaluation supports the need for the development of a comprehensive,
simple assessment tool that can be used systematically to gather data about clients’
risk Ectors and their perceptions of susceptibility, beneﬁts to change, and barriers.
The ideal tool could be used quickly, making use of data available through history,

physical exam, and simple laboratory tests. It should be applicable to men and

24

women of all ages, with or without comorbid illnesses. This device should
produce a score that could be recorded in the client’s chart and be reevaluated to
track progress in reducing risk or heart disease.

This project has several limitations. First, it is limited by its exclusion of
glucose intolerance as a risk Ector. Although Hopkins and Williams (1986)
concede that it is common for investigators to focus on their favorite risk factors,
diabetes remains a major risk that should be included for the most comprehensive
evaluation possible.

Secondly, although the Thorsen et al., Anderson et al., and Kannel and
Schatzkin tools all perform poorly relative to ﬁt with the Health Belief Model,
their developers were not concerned with implementation based upon a theoretical
model. Rather, they were focused on predictive value of the various risk Ectors in
terms of eventual development of disease. Certainly the tools would be more
useful to APNs if they had a theoretical basis, but one must acknowledge the
authors’ intent in developing the instruments.

Lastly, the Health Belief Model itselfhas several shortcomings. Behaviors
in cardiovascular risk reduction contain both habitual and environmental inﬂuences
distinct from personal perceptions. Smoking, for example, contains a strong
habitual component, and the HBM’S emphasis on attitudes and beliefs may make it
less useﬁrl among smokers whose behavior is largely habitual. Further, although
perceived barriers is thought to be the most predictive component of a person
adopting change, it remains the most poorly deﬁned dimension of the HBM and

can include almost any internal or external negative force working against the

25

desired health-related behavior. Rosenstock, among others, has called for
advances in explaining, predicting, and controlling for this catch-all quality.

The major contribution of this project is the support for future research to
develop a more inclusive, clinically useful tool that addresses both objective data
and the subjective state of the client. The necessary research would ﬁrst require
retrospective studies to determine the most signiﬁcant risk factors and then long-
term prospective studies to evaluate whether interventions to reduce risk factors
resulted in less heart disease. Future research could be both quantitative and
address incidence of disease or it could be qualitative and measure severity of
disease. Advanced practice nurses have a role in research and could be
instrumental in such studies.

An assessment instrument that meets the criteria of inclusiveness and
clinical utility would be of great assistance to APNs and could play a role in

helping clients to reduce their risk of developing cardiovascular disease.

26

INDIVIDUAL PERCEPTIONS

 

Perceived Suceptibility to
Dium eexee
Perceived Serioueneae
(Severity) of Diaeaee “X"

 

Figure 1

MODIFYING FACTORS

LIKELI'HOOD OF ACTION

 

 

Demographic variables (age, eex.
race. ethnicity. etc.)

Sociopeychologicol variable: (perenn-
ality. eocial clue. peer and refer-

 

Perceived benefit: of
preventive action

 

 

 

 

 

 

 

 

————>

ence group pressure, etc.) '1 minua
Structural Variables (kno edge Perceived . re to

about the disease. prior contact home
with the disease. etc.) WIN" action

Perceived raw | mum at Taking

0‘ Recommended Preventive
Dim" “X” J Health Action

 

l

 

 

 

Cue: to Action
Male media campaign
Advice from other:
Reminder poetoard from phyeician
or dentiet
nine- of family member or friend
Newapeper or magazine article

 

 

27

The Health Belief Model (Becker & Maiman, 1975)

Lipids

Hypertension

Smoking

Obesity

Physical inactivity

Ease of use

Fit with HBM

Total

Anderson et a1.

5

16

Table 1

28

Kannel & Schatzkin

2

12

Thorsen et al.

3

19

A" T'Wn

 

 

Figure 2

 

 

 

 

 

Slide rule for calculating risk of cardiovascular disease

(Thorsen et al., 1979)

29

BIBLIOGRAPHY

3O

BIBLIOGRAPHY

Alderman, M. (1995). Quantifying cardiovascular risk in hypertension.
Cardiology Clinics, I 3(4), 519-527.

Anderson, K., Castelli, W., & Levy, D. (1987). Cholesterol and mortality:
30 years of follow-up ﬁ'om the Framingharn Study. Journal of the American
Medical Association, 25 7, 2176-2180.

Anderson, K, Wilson, P., Odell, P., & Kannel, W. (1991). An updated
coronary risk proﬁle: A statement for health professionals. Circulation, 83, 356-
362.

Becker, M (1974). The Health Belief Model and sick role behavior. In M.
Becker aid), The Health Belief Model and personal health behavior. ThoroEre,
NJ: Charles Slack.

Becker, M., & Maiman, L. (1975). Sociobehavioral determinants of
compliance with health and medical recommendations. Medical Care, I 3, 10-24.

Becker, M., Mairmm, L., Kirscht, J ., Haefner, D., Drachman, R., & Taylor,
D. (1979). Patient perceptions and compliance: Recent studies of the Health
Belief Model. In W. Hayes, P. Talor, & J. Sackett (Eds), Compliance in health
care (pp. 78-109). Baltimore: Johns Hopkins University Press.

Blair, 8., Kohl, H., Paffenbarger, R., Clark, D., Cooper, K., & Gibbons, L.
(1995). Physical ﬁtness and all-cause mortality: A prospective study of healthy
men and women. Journal of the American Medical Association, 262, 2395-2401.

Bots, M., Launer, L, Lindemans, J., Hoﬁnan, A., & Grobbee, D. (1997).
Hornocysteine, atherosclerosis and prevalent cardiovascular disease in the elderly:
The Rotterdam Study. Journal of Internal Medicine, 242, 339-347.

Colditz, G. (1995). The Nurses' Health Study: A cohort of women followed
since 1976. Journal of the American Medical Women 's Association, 50(2), 40-44.

Fried, L, & Becker, D. (1993). Smoking and cardiovascular disease. In P.

Douglas (Ed.), Cardiovascular health and disease in women (pp. 217-230).
Philadelphia: Saunders.

31

Gordon, T., Sorlie, P., & Kannel, W. (1971). Coronary heart disease,
atherothrombotic brain infarction, intermittent claudication: A multivariate analysis
of some Ectors related to their incidence. Framingham Study: An

epidemiological investigation of cardiovascular disease(Section 27). Washington,
DC: Govemment Printing Ofﬁce.

Gotto, A. (1994). Cholesterol levels in young adults: Screen and intervene?
Hospital Practice, March 15, 109-116.

Gran, B. (1994). Population based CVD health risk appraisal. Scandanavian
Journal of Social Medicine, 4, 256-263.

Hamel, L., & Oberle, K. (1996). Cardiovascular risk screening for women.
Clinical Nurse Specialist, 10(6), 275-279.

Hopkins, P., & Williams, R (1986). Identiﬁcation and relative weight of
cardiovascular risk Ectors. Cardiology Clinics, 4(3), 3-31.

Hubert, H., Feinleib, M., McNamara, P., & Castelli, W. (1983). Obesity as
an independent risk Ector for cardiovascular disease: A 26-year follow-up of
participants in the Frarningharn heart study. Circulation, 67, 978-987.

Janz, N., & Becker, M. (1984). The Health Belief Model: A decade later.
Health Education Quarterly, 11(1), 1-47.

Johnson, C., Powers, C., Bao, W., Harsha, D., & Berenson, G. (1994).
Cardiovascular risk Ectors of elementary school teachers in a low socioeconomic
area of a metropolitan city: The Heart Smart Program. Health Education
Research, 9(2), 183-191.

Kannel, W., & Schatzkin, A. (1983). Risk factor analysis. Progress in
Cardiovascular Disease, 26(4), 309-332.

Kannel, W., & Wolf, P. (1992). Pulling it all together: Changing the
cardiovascular outlook. American Heart Journal, 123, 264-266.

Klag, M., Ford, D., Mead, L., He, J., Whelton, P., Liang, K., & Levine, D.
(1993). Serum cholesterol in young men and subseqent cardiovascular disease.
New England Journal of Medicine, 328(5), 313-318.

Kwiterovich, P. (1995). Detection and treatment of elevated blood lipids and
other risk Ectors for coronary artery disease in youth Annals of the New York
Academy of Sciences, 748, 313-330.

32

Madhaven, S., & Alderman, M. (1981). The potential effect of blood
pressure reduction on cardiovascular disease: A cautionary note. Archives of
Internal Medicine, 141, 1583-1588.

Mikhail, B. (1981). The Health Belief Model: A review and critical
evaluation of the model, research, and practice. Advances in Nursing Science,
October, 65-82.

Mirotznik, J., Feldman, M., & Stein, R. (1995). The Health Belief Model
and adherence with a community center-based, supervised coronary heart disease
exercise program. Journal of Community Health, 20(3), 233-247.

Mitchell, J. (1996). Patient health questionnaire: Using a patient-focused
assessment tool. Canadian Family Physician, 42, 505-511.

Moylan, G., & Joyce, P. (1993). The achievement of dietary goals in patients
with documented CAD: A test of Nola Pender's Health Promotion Model.
Nursing Scan in Research, 6(6), 3-4.Multiple Risk Factor Intervention Trial
Research Group (1982). Multiple Risk Factor Intervention Trial: Risk factor
changes and mortality results. Journal of the American Medical Association,
248(12), 1465-1477.

Multiple Risk Factor Intervention Trial Research Group (1982). Multiple
Risk Factor Intervention Trial: Risk factor changes and mortality results. Journal
of the American Medical Association, 248(12), 1465-1477.

Murray, D., Luepker, R., Pirie, P., Grimm, R., Bloom, E., Davis, M., &
Blackburn, H. (1986). Systematic risk factor screening and education: A

community-wide approach to prevention of coronary heart disease. Preventive
Medicine, 15, 661-672.

National Center for Health Statistics (1995). Leading Causes of Death.

Ofﬁce of Technology Assessment (1986). Nurse practitioners, physician
assistants, and certiﬁed nurse midwifes: A policy analysis. Health Technology
Case Study #3 7, December.

Oshaug, A., Bugge, K., Bjonnes, C., & Ryg, M. (1995). Use of
anthropometric measurements in assessing risk for coronary heart disease: A

useful tool in worksite health screening? International Archives of Occupational
and Environmental Health, 6 7, 359-366.

Paffenbarger, K, & Hale, W. (1975). Work activity and coronary heart
mortality. New England Journal of Medicine, 292(11), 545-550.

33

Paﬁ‘enbarger, R., Hale, W., Brand, R, & Hyde, R. (1977). Work-energy
level, personal characteristics, and fatal heart attack: A birth-cohort effect.
American Journal of Epidemiology, 105(3), 200-213.

Paffenbarger, R., Laughlin, M., Gima, A., & Black, R. (1970). Work activity
of longshoremen as related to death from coronary heart disease and stroke. New
England Journal of Medicine, 282(20), 1 109-1 1 14.

Parkerson, G., Broadhead, W., Chin-Kit, J. (1991)._Development of the 17-
item Duke Health Proﬁle. Family Practice, 8(4), 396-401.

Pender, N. (1996). Motivation for health behavior. Health promotion in
nursing practice (3rd ed., pp. 35-3 7). Stamford, CT: Appleton & Lange.

Powell, K., Thompson, P., Caspersen, C., & Kendrick, J. (1987). Physical
activity and the incidence of coronary heart disease. Annual Review of Public
Health, 8, 253-287.

Public Health Service's Oﬂice of Disease Prevention and Health Promotion

(1996). Clinician's handbook of preventive services: Put prevention into practice.
Washington, DC: Author.

Rich-Edwards, J., Stampfer, M., Manson, J., Rosner, B., Hankinson, S.,
Colditz, G., Willett, W., & Hennekens, C. (1997). Birth weight and risk of
cardiovascular disease in a cohort of women followed up since 1976. British
Medical Journal, 315, 396-400.

Rosenstock, I. (1974). Historical origins of the Health Belief Model. Health
Education Monographs, 2(4), 328-335.

Selig, P. (1991). The prevention and screening of cardiovascular disease:
An update. Nurse Practitioner Forum, 2(1), 14-18.

Sempos, C., Cleeman, J., Carroll, M., Johnson, C., Bachorik, R, Gordon,
D., Burt, V., Briefel, R., Brown, C., Lippel, K., & Riﬂdnd, B. (1993). Prevalence
of high blood cholesterol among US adults. Journal of the American Medical
Association, 269(13), 3009-3014.

The SOLVD Investigators (1991). Effect of enalapril on survival in patients
with reduced left ventricular ejection ﬁactions and congestive heart failure. New
England Journal of Medicine, 325, 293-302.

Sommers, J. Andres, F., & Price, J. (1995). Perceptions of exercise of mall

walkers utilizing the Health Belief Model. Journal of Health Education, 26(3),
1 58- 166.

34

Still, R., & McDowell, I. (1998). Clinical implications of plasma
homocysteine measurement in cardiovascular disease. Journal of Clinical
Pathology, 51(3), 183-188.

Sutterer, J ., Carey, M., Silver, D., & Nash, D. (1989). Risk factor
knowledge, status, and change in a community screening project. Journal of
Community Health, 14(3), 137-147.

Terborg, J., Hibbard, J., & Glasgow, R. (1995). Behavior change at the
worksite: Does social support make a difference? American Journal of Health
Promotion, 10(2), 125- 1 3 1.

Thorsen, R., Jacobs, D., Grimm, R., Keys, A., Taylor, H., & Blackburn, H.
(1979). Preventive cardiology in practice: A device for risk estimation and
counseling in coronary disease. Preventive Medicine, 8, 548-556.

Vogler, G., McClearn, G., Snieder, H., Boomsma, D., Palmer, R., deKnijﬁ‘,
P., & Slagboom, P. (1997). Genetics and behavioral medicine: Risk factors for
cardiovascular disease. Behavioral Medicine, 22, 141-149.

Ward, M., McNulty, H., McPartlin, J., Strain, J., Weir, D., & Scott, J.
(1997). Plasma homocysteine, a risk factor for cardiovascular disease, is lowered
by physiological doses of folic acid. Quarterly Journal of Medicine, 90, 519-524.

Wong, N., Detrano, R., Diamond, G., Rezayat, C., Mahmoudi, R, Chong,
E., Tang, W., Puentes, G., Kang, X., & Abrahamson, D. (1996). Does coronary
artery screening by electron beam computed tomography motivate potentially
beneﬁcial lifestyle behaviors? American Journal of Cardiology, 78, 1220-1223.

35