Download Factors Associated With Behavior Modification for

Journal of Nursing Research h VOL. 17, NO. 3, SEPTEMBER 2009
Factors Associated With Behavior
Modification for Cardiovascular Risk Factors
in Patients With Coronary Artery Disease
in Northern Taiwan
Ai-Fu Chiou & Huey-Ling Wang* & Paul Chan** & Yu-An Ding***
Kwan-Lih Hsu**** & Hsien-Li Kao*****
ABSTRACT
Background: Studies have demonstrated that improvement
in cardiovascular risk factors may contribute to reduced coronary artery disease (CAD) morbidity and mortality, improved
patient outcomes, and lower medical costs associated with
treating heart disease.
Purpose: The purpose of this study was to understand the
coronary risk factor profile, to have the knowledge of risk factors,
to understand the modifying behaviors, and to understand the
factors associated with modifying behaviors of cardiovascular
risk factors among patients with CAD in northern Taiwan.
Methods: A cross-sectional design was used in this study. Using nonprobability sampling, 156 patients diagnosed with CAD
were interviewed and asked to complete a structural questionnaire in cardiovascular clinics at three medical centers in northern
Taiwan. Data were analyzed by descriptive analysis, Pearson’s
correlation, chi-square tests, and stepwise multiple regression.
Results: A total of 38% of variance of modifying behaviors was
explained by self-efficacy, actual risk factors, work status, and
health beliefs. Self-efficacy was the strongest predictor of behavior to modify cardiovascular risk factors. Age and type ‘‘A’’
personality were the two leading cardiovascular risk factors
for the participants. Most participants could perform modifying
behaviors such as taking medications, eating an appropriate
diet, and following specific lifestyle recommendations. However, participants had relatively lower adherence to monitoring
blood pressure, exercising regularly, and controlling weight.
in the death rate between 1994 and 2004, one of every five
deaths is attributed to CAD (Rosamond et al., 2008, p. 391).
In Taiwan, heart disease became the second leading cause of
death in 2007, second only to cancer (Department of Health,
Executive Yuan, ROC [Taiwan], 2008). Studies have demonstrated that cardiovascular risk factors reduction may contribute to reducing the morbidity and the mortality of CAD,
improving patient outcomes (Keller, Fleury, & MujezinovicWomack, 2003) and reducing the medical costs of heart disease (Zerwic, King, & Wlasowicz, 1997). Although the benefits
of cardiovascular risk factors reduction have been established, only 6.3% of patients with CAD maintain an ideal
body weight, eating five or more fruits and vegetables daily,
performing at least 30 minutes of physical activity, and abstaining from smoking (Ashaye & Giles, 2003). The small
proportion of patients with CAD engaged in modifying behaviors indicates lack of adherence to be a fundamental problem
in cardiovascular risk reduction efforts (Fleury & Sedikides,
2007). Thus, there is a need to better understand the factors
that contribute to patient adherence to modifying behaviors.
RN, PhD, Associate Professor, Department and Institute of Nursing,
National Yang-Ming University;
*RN, PhD, Associate Professor, Department of Nursing, Fu Jen
Catholic University;
**MD, PhD, Professor, Division of Cardiovascular Medicine,
Wan-Fang Hospital;
Conclusions/Implications for Practice: Nurses should assess patient cardiovascular risk factors, health beliefs, and
self-efficacy and then provide comprehensive and adequate
instruction to each based on his or her specific risk factors.
***MD, PhD, Professor, Department of Internal Medicine, National
Yang-Ming University, & Taipei Veteran General Hospital;
KEY WORDS:
*****MD, Director, Cardiovascular Center, National Taiwan
University Hospital Yun-Lin Branch, & Assistant Professor,
School of Medicine, National Taiwan University.
coronary artery disease, risk factor, modifying behavior, selfefficacy, health belief.
Introduction
Coronary artery disease (CAD) is the leading cause of morbidity and mortality in the United States. Even with a decline
****MD, PhD, Chairman, Department of Internal Medicine,
E-Da Hospital, & Assistant Professor, Department of Nursing,
I-Shou University;
Received: March 4, 2009 Revised: May 12, 2009 Accepted:
May 20, 2009
Address correspondence to: Ai-Fu Chiou, No. 155, Li-Nong St. Sec. 2,
Taipei 11221, Taiwan, ROC.
Tel: +886 (2) 2826-7000, ext. 5008; Fax: +866 (2) 2820-2487;
E-mail: [email protected]
221
Journal of Nursing Research
The American Heart Association (AHA) has identified
several modifiable and nonmodifiable cardiovascular risk
factors that increase CAD risk. Modifiable risk factors include smoking, hyperlipidemia, hypertension, physical inactivity, obesity, and diabetes mellitus. Nonmodifiable risk
factors include gender, advancing age, and hereditary factors (AHA, n.d.). Other factors such as stress, type A personality, and drinking too much alcohol may also contribute to
heart disease (AHA, n.d.; Grundy, Pasternak, Greenland,
Smith, & Fuster, 1999). Many studies have confirmed that
these factors are related to increased risk of cardiovascular
events (Kamotho, Ogola, Joshi, & Gikonyo, 2004; Kannel,
2000; Sowers, 2003). For patients with CAD, said risk
factors continue to contribute to disease progression (DalyNee, Brunt, & Jairath, 1999). Therefore, managing and
treating these risk factors are important to reduce the mortality rate of patients with CAD. Strategies to control
cardiovascular risk factors include primary preventive strategies such as control of hypertension and diabetes and
reduction in cholesterol levels and smoking cessation, along
with primordial prevention including regular physical activity, healthy eating, and maintaining an ideal body weight
(Chyun, Amend, Newlin, Langerman, & Melkus, 2003;
Keller et al., 2003; Patel & Adams, 2008).
Research on prevention of cardiovascular risk factors has
been extensively pursued in Western countries (Darr, Astin, &
Atkin, 2008; Fleury & Sedikides, 2007; Meland, Mæland,
& Lærum, 1999; Zerwic et al., 1997). Studies have found
that patients with CAD are largely ignorant of their own
personal risks (Zerwic et al., 1997) and lack understanding
about appropriate lifestyle changes (Darr et al., 2008). Researchers have emphasized the important role of patients’
knowledge (Bush, Kallen, Liles, Bates, & Petersen, 2008;
Fleury & Sedikides, 2007), self-efficacy (Meland et al.,
1999), health beliefs (Robertson & Keller, 1992), and social
support (Chyun et al., 2003) in successful modification of
their cardiovascular risk profile. It is essential to understand
the individual risk profiles and the factors influencing
modifying behaviors of patients with CAD so that nurses
can promote lifestyle modification for patients through
education about the disease and its risk factors. However,
studies exploring the risk profiles of patients with CAD and
factors associated with behavior aimed at modifying cardiovascular risk factors have been limited in Taiwan. Therefore,
the aims of this study were to understand the cardiovascular
risk factor profile, the extent of risk factor awareness, and
the modifying behaviors of patients with CAD in northern
Taiwan. In addition, factors associated with modifying
behaviors of patients with CAD were also explored.
Methods
Sample and Setting
This study used a cross-sectional design with a nonprobability sampling. A total of 156 participants were recruited
222
Ai-Fu Chiou et al.
from cardiovascular clinics at three medical centers in
northern Taiwan. Data were collected from the cardiovascular clinics over a 6-month period. Study inclusion criteria included the following: (a) at least 20 years of age; (b)
diagnosed with CAD by a practicing cardiologist; (c) able
to read, write, speak, and understand Mandarin; and (d)
experiencing no delirium and able to communicate with
others. All participants were referred by practicing cardiologists. No subjects refused to participate in this study.
Data of two participants were excluded from statistical
analysis because of incomplete questionnaires.
Data Collection
All participants were interviewed by two research assistants
who were trained by the investigator and standardized the
data collection process. The assistants explained the purpose
of the study and the interview procedures to the participants.
Written informed consent was obtained from participants.
Before the interview, the assistants measured and recorded
participant blood pressure (BP). Participants were then interviewed to complete a structured questionnaire. Finally,
clinical characteristics including CAD severity (number of
coronary arteries involved), New York Heart Association
(NYHA) functional class, and laboratory data (including total
cholesterol level) were collected from medical records. All
information was confidential, and appropriate institutional
review board approval was obtained from hospitals.
Instruments
Data were collected through a structured questionnaire
and included demographic information and clinical characteristics, knowledge of cardiovascular risk factors, risk
factors profile, cardiovascular risk factor modification behaviors, health beliefs, self-efficacy, and social support.
The reliability and the validity of the questionnaire were
tested by internal consistency and content validity. Content
validity was estimated using a rating and quantification
procedure recommended by Lynn (1986). Six healthcare
professionals who specialized in the treatment of cardiovascular disease patients including cardiologists, senior clinical nurses, and researchers were asked to rate the relevance
of items using a 4-point scale (1 = not relevant, 4 = very
relevant and succinct). All items were rated as acceptable by
the expert group.
Demographic information and clinical
characteristics
Demographic information included participant’s age, gender, marital status, education, religious preference, work
status, and living arrangements. Clinical characteristics included number of hospitalization instances, duration of CAD,
comorbidity (number of other chronic diseases), CAD severity
(number of coronary arteries involved), and NYHA functional class. Demographic variables such as marital status,
Behavior Modification for Cardiovascular Risk Factors
education, religious beliefs, work status, and living arrangements were recorded into two categories.
Knowledge of cardiovascular risk factors
Knowledge of cardiovascular risk factors was measured
with use of a risk factor knowledge scale adapted from
Shen (1985). The scale consists of 18 true/false questions
related to 12 cardiovascular risk factors: smoking (Items 1
and 2), hypertension (Items 3Y4 and 14), obesity (Items 5
and 6), heredity (Item 7), type A personality (Item 8), diabetes mellitus (Items 9 and 10), physical inactivity (Item
11), hyperlipidemia (Items 12 and 13), stress (Item 15),
excessive alcohol consumption (Item 16), and gender and
advancing age (Items 17 and 18). A total knowledge score
was obtained by summing each item with a possible score
range from 0 to 18 (0 = incorrect, 1 = correct). The
knowledge score of each risk factor was obtained by
summing each item score divided by the number of items
in that risk factor. Possible scores for each risk factor
ranged from 0 to 1, with a higher score indicating higher
knowledge of cardiovascular risk factors. The reliability of
the cardiovascular risk factors knowledge scale was supported by internal consistency, with a KuderYRichardson
formula 20 of .74. In addition, content validity was also
supported by an expert panel, with a content validity index (CVI) of .8.
Risk factors profile
The risk factors profile included a 12-item perceived
cardiovascular risk factors checklist and an actual cardiovascular risk factors checklist developed by the first author
based on literature review and experts’ suggestions.
1. Perceived cardiovascular risk factors checklist. Perceived cardiovascular risk factors were rated by the
participants on a checklist of 12 risk factors as ‘‘yes’’
or ‘‘no’’ based on perception of personal risk factors.
The 12 cardiovascular risk factors included high BP,
hyperlipidemia, smoking, diabetes, physical inactivity,
obesity, stress, type A personality, excessive alcohol
consumption, advancing age, gender, and heredity.
2. Actual cardiovascular risk factors checklist. Actual
cardiovascular risk factors indicated objective measurement of participants’ risk factors and were assessed based on participant age, BP, serum total
cholesterol level, height, weight, average cigarettes smoked per day, and frequency of regular exercise.
Advancing age was defined as male participants older
than 45 years or female participants older than 55
years. High BP was defined as having an average systolic BP (SBP) of 140 mmHg or greater or having an
average diastolic BP (DBP) of 90 mmHg or greater
(National Heart Lung and Blood Institute, 2003).
Hyperlipidemia was defined as serum total cholesterol level 200 mg/dl or greater (Grundy et al., 1999).
Smoker was defined as smoking one or more cigarettes
per day. Physical inactivity was defined as regular
VOL. 17, NO. 3, SEPTEMBER 2009
exercise less than three times per week for 30 to 40
minutes per time (Fletcher et al., 1996). Obesity was
defined as a body mass index of 28 k/m2 or higher
(Cheng, 2004). Participants were defined as having
type A personality if the interviewer, during interview proceedings, observed impatience, excessive
time consciousness, insecurity about status, strong
competitiveness, hostility or aggressiveness, or incapability to relax (Daly-Nee et al., 1999). Stress was defined if participants reported stressful life events such
as disease, divorce, or death of a loved one (Maes,
Vingerhoets, & Heck, 1987).
Cardiovascular risk factor modification behaviors
Modifying behaviors were measured by a 12-item modifying behaviors checklist developed by the first author. Items
included modifying behaviors such as taking medications,
eating a regular diet, exercising regularly, quitting smoking,
controlling body weight, relaxing, and maintaining a good
mood. Each item was scored on a scale of 0 to 2, where 0
indicated that participants did not perform the item, 1 indicated that participants partially performed the item, and
2 meant that the item was performed fully. Item 2 (quitting
smoking) and Item 3 (quitting alcohol) were not rated by
nonsmokers or participants who did not drink alcohol
excessively. Therefore, the overall modifying behaviors
score was obtained by summing all items and by dividing
the total score by the number of items rated by participants. The total possible score range for overall modifying
behaviors was between 0 and 2, with a higher score indicating better modifying behavior. In this study, the
Cronbach’s alpha for this modifying behaviors scale was
.67. Content validity was also supported by six experts,
with the CVI of .8.
Health beliefs
Health beliefs were measured by a health belief scale developed by the first author based on the Health Belief
Model (HBM). The HBM was used to explain and to
predict health behaviors according to respondent attitudes
and beliefs. The four constructs of the HBM include perceived susceptibility (beliefs about the chances of contracting the condition), perceived severity (beliefs about the
seriousness of the condition and its consequences), perceived benefits (beliefs about the positive consequences of
taking action), and perceived barriers (beliefs about the
material and psychological costs of taking action; Glanz,
Rimer, & Su, 2005; Rosenstock, Strecher, & Becker, 1988).
In this study, health beliefs were measured using a 10-item
scale consisting of three components: subjects’ perceived disease severity (Items 1Y3), perceived benefits (Items 4Y5 and
9Y10), and perceived barriers (Items 6Y8) of modifying behaviors. Construct validity was estimated with exploratory
factor analysis and varimax rotation. The rotated solution
for the health belief scale yielded three factors accounting
for 57.2% of variance. Factor 1 reflected perceived benefit
223
Journal of Nursing Research
items, Factor 2 reflected perceived disease severity items,
and Factor 3 reflected perceived barrier items.
Participants rated each item on a 4-point Likert scale. A
mean score for perceived disease severity, perceived benefit,
and perceived barrier was obtained by summing all items
then dividing the total score by the number of items
included in each component. A higher mean score indicated
higher perceived disease severity, higher perceived benefits,
and lower perceived barriers. Because Item 7 asked smokers about barriers to quitting smoking, this item was not
rated by nonsmokers. Therefore, the health belief score
was obtained by summing all items and by dividing the total score by the number of rated items. The range of possible
scores for health belief was between 0 and 3, with a higher
score indicating more positive health beliefs. In this study,
content validity and health belief scale reliability were supported by a CVI of .92 and a Cronbach’s alpha of .76.
Self-efficacy
Perceived self-efficacy was defined as ‘‘a judgment of one’s
capability to accomplish a certain level of performance’’
(Bandura, 1986, p. 391). The self-efficacy scale included
10 items related to general self-efficacy and 7 items related
to modifying behavior self-efficacy. A general self-efficacy
scale, modified from Sherer and Maddux (1982), was used
to assess participant perceptions about their own capability
to accomplish a given task. Modifying behavior self-efficacy
examined participants’ level of confidence for discrete
behavioral changes such as taking medication, exercising
regularly, and consuming a healthy diet. Participants rated
each item on a 4-point Likert scale. Because Item 3 of the
modifying behavior self-efficacy scale asked smokers about
their perceived confidence in being able to quit smoking, it
was not rated by nonsmokers. Mean scores for the general
and modifying behavior self-efficacy subscales were obtained by summing each item and by dividing the total
score by the number of rated items. The possible score
range for both self-efficacy subscales was between 1 and 4,
with a higher score reflecting higher self-efficacy. In this
study, the reliability coefficient was .72 for the general selfefficacy scale and .78 for the modifying behavior self-efficacy
scale. Content validity was also supported by an expert panel
with a CVI of .92 and .86, respectively.
Social support
Social support was measured using a social support scale
modified from Liu (1995). The modified social support
scale included 12 items of material, physical, and emotional support. Participants rated each item on a 4-point
Likert scale. Score were summed, giving a potential score
range from 12 to 48, with a higher score indicating greater
perceived support. The Cronbach’s alpha for the original
scale was .73. TestYretest reliability was .86 at a 1-week
interval (Liu, 1995, 1999). In this study, reliability was
224
Ai-Fu Chiou et al.
supported with a Cronbach’s alpha of .76. Content validity was established with a CVI of .82.
Data Analysis
All data were analyzed using SPSS software, version 14.0
(SPSS Inc., Chicago, IL). Descriptive statistics including
mean, standard deviation, and percentage were used to
describe demographic information (age, gender, marital
status, education, religious belief, work status, and living
arrangement), clinical characteristics (number of hospitalizations, duration of CAD, comorbidity, CAD severity,
and NYHA functional class), risk factor profiles, knowledge of cardiovascular risk factors, risk factor modification behaviors, and psychosocial variables such as health
beliefs, self-efficacy, and social support. A Pearson correlation coefficient was used to determine correlations between modifying behaviors and participant age, number of
perceived risk factors, number of actual risk factors,
knowledge of risk factors, health beliefs, general selfefficacy, and modifying behavior self-efficacy. A t test was
used to examine modifying behavior variance attributable
to category variables including gender, marital status, education, work status, living arrangements, and religious beliefs. Stepwise multiple regression analysis was used to
examine predictors of overall modifying behaviors. Predictors of modifying behaviors were identified through a
review of the literature (Bush et al., 2008; Chyun et al.,
2003; Fleury & Sedikides, 2007; Meland et al., 1999;
Robertson & Keller, 1992). The 14 predictors considered
included participant age, gender, marital status, work
status, education, living arrangements, CAD severity,
number of other chronic diseases, knowledge, number of
perceived risk factors, number of actual risk factors, selfefficacy, health beliefs, and social support. A p value G.05
was considered statistically significant. Minimum sample
size was calculated using Statistics Calculators 2.0 (Daniel
Soper, Fullerton, CA). With a medium effect size, a
minimum of 135 subjects were required to reach a power
of .8 for an alpha of .05 for a total of 14 predictors.
Results
Participant Characteristics
A total of 156 patients with CAD participated in this study.
Demographic information and clinical characteristics on
participants are presented in Table 1. The mean age of
participants was 70 years (SD = 10.1 years), ranging from
38 to 88 years. The largest participant age group was 65 to
74 (38.2%). Most were men (74.4%), married (82.1%),
living with their spouse and/or children (92.9%), unemployed (80.1%), and held a high school or a higher education (55.8%). Most participants had at least one chronic
condition such as hypertension (60.3%), hyperlipidemia
(39.1%), or diabetes (21.8%). The mean number of
Behavior Modification for Cardiovascular Risk Factors
VOL. 17, NO. 3, SEPTEMBER 2009
TABLE 1.
Participant Demographics (N = 156)
Variable
M T SD
Age
70 T 10.1
n
%
Gender
Male
Female
116
40
74.4
25.6
Marital status
Married
Single/widowed
128
28
82.1
17.9
69
87
44.2
55.8
Religious beliefs
None
Buddhist/Christian/other
48
108
30.6
69.4
Work status
Employed
Unemployed
31
125
19.9
80.1
Living alone
Yes
No
11
145
7.1
92.9
22
80
21
33
14.1
51.3
13.5
21.2
57
68
31
36.5
43.6
19.9
111
41
3
1
71.2
26.3
1.9
0.6
94
61
34
60.3
39.1
21.8
Education level
Below high school
Above high school
Duration of CAD
G1 year
1Y5 years
6Y10 years
910 years
CAD severity
One-vessel CAD
Two-vessel CAD
Three-vessel CAD
1.80 T 0.73
Number of hospitalizations
NYHA classification
Class I
Class II
Class III
Class IV
2.78 T 1.78
Comorbidity
Hypertension
Hyperlipidemia
Diabetics
1.83 T 1.08
Cholesterol (mg/dl)
201.73 T 33.58
SBP (mmHg)
132.74 T 16.80
DBP (mmHg)
77.47 T 10.29
General self-efficacy
2.99 T 0.34
Modify behavior self-efficacy
3.33 T 0.41
Health beliefs
Perceived disease severity
Perceived benefit
Perceived barrier
2.02
1.38
2.14
2.60
Social support
T
T
T
T
0.38
0.64
0.60
0.51
35.54 T 4.70
Note. CAD = coronary artery disease; NYHA = New York Heart Association;
SBP = systolic blood pressure; DBP = diastolic blood pressure.
comorbidities was 1.83 (SD = 1.08). The mean cholesterol
level of all participants was 201.73 mg/dl (SD = 33.58
mg/dl). Mean SBP and DBP were 132.74 mmHg (SD = 16.8
mmHg) and 77.47 mmHg (SD = 10.29 mmHg), respectively. Sixty-eight participants (43.6%) had a two-vessel
CAD. Most participants (71.2%) were classified as NYHA
function class I. Half of the participants (51.3%) had a
CAD duration of 1 to 5 years.
In terms of psychosocial variables, mean scores for general self-efficacy and modifying behavior self-efficacy were
2.99 T 0.34 and 3.33 T 0.41, respectively. This result indicated that participants had a relatively higher self-efficacy
perception. Participants tended to hold positive health beliefs, with a mean score of 2.02 T 0.38. Participants also
showed higher perceived benefits and lower perceived
barriers and perceived their CAD severity as mild to moderate. The mean score of 35.54 (SD = 4.70) in social support indicated that participants perceived a relatively high
level of social support.
Participant Perceived and Actual Risk
Factors
Table 2 shows perceived and actual risk factors of participants. The mean number of perceived and actual risk
factors was 5.04 T 1.71 and 5.41 T 1.57, respectively. All
participants had at least one risk factor. A total of 82.7%
of the participants perceived advancing age as their own
risk factor, whereas 75.6% of the participants perceived
type A personality as their own risk factor. Therefore, advancing age and type A personality were two leading cardiovascular risk factors perceived by participants as their
own risk factors. The third and the fourth leading risk
factors perceived by participants as their own risk factors
were hypertension (59.6%) and hyperlipidemia (57.1%).
There were significant differences between participants’
perceived risk factors and their actual risk factors in terms
of advancing age, gender, hypertension, and smoking (p G
.001). For example, 96.2% of the participants had a risk
factor of advancing age, whereas only 82.7% of the participants perceived advancing age as their own risk factor.
Only 39.7% of the participants perceived gender as their
own risk factor, although 74.4% of the participants actually faced a gender-based risk factor (i.e., being male).
Although 59.6% of the participants perceived hypertension as their own risk factor, only 38.5% of the participants registered an SBP of 140 mmHg or greater or a DBP
of 90 mmHg or greater during the interview. Only 16.7%
of the participants perceived smoking as their own risk
factor, although 42.9% were smokers.
Cardiovascular Risk Factor Knowledge
and Modifying Behaviors
Table 3 shows participant knowledge of cardiovascular risk
factors. The mean knowledge score of risk factors for
225
Journal of Nursing Research
Ai-Fu Chiou et al.
TABLE 2.
Comparison of Participant Perceived and Actual Risk Factors (N = 156)
Perceived Risk Factor
Risk Factor
Advancing age
Type A personality
Hypertension
Hyperlipidemia
Physical inactivity
Gender
Heredity
Obesity
Stress
Diabetes mellitus
Smoking
Excessive alcohol
Number of risk factors
M T SD (range)
Actual Risk Factor
n
%
Rank
n
%
Rank
#2
129
118
93
89
70
62
62
58
48
28
26
15
82.7
75.6
59.6
57.1
44.9
39.7
39.7
37.2
30.8
17.9
16.7
9.6
1
2
3
4
5
6
6
8
9
10
11
12
150
122
60
103
60
116
62
54
45
35
67
96.2
78.2
38.5
66.0
38.5
74.4
39.7
34.6
28.8
22.4
42.9
1
2
7
4
7
3
6
8
9
11
5
Y
76.44***
0.60
29.49***
2.58
2.71
50.27***
0.00
0.45
0.28
0.26
43.98***
Y
5.04 T 1.71 (1Y10)
Y
5.41 T 1.57 (1Y9)
***p G .001.
stress, physical inactivity, and excessive alcohol consumption was above 0.9. This indicates that participants had
good knowledge on these risk factors. The three lowest
knowledge scores for risk factors were gender, diabetes,
and advancing age. Therefore, participants had a poor understanding of the relationship of gender, diabetes, advancing age, and the likelihood of contracting cardiovascular
disease. Table 4 shows participant modifying behaviors of
cardiovascular risk factors. Most participants could perform modifying behaviors, particularly in terms of taking
medication, eating a regular diet, quitting smoking, and
maintaining a regular lifestyle. However, participants had a
relatively lower adherence in terms of monitoring BP, exercising regularly, and controlling weight.
Correlations Between Modifying Behaviors
and Predictive Variables
Pearson’s correlation showed that overall scores of
modifying behaviors were significantly positive related to
participant age (r = .21, p = .007), health beliefs (r = .26,
p = .001), perceived benefits (r = .24, p = .003), perceived
barriers (r = .31, p = .000), general self-efficacy (r = .24,
p = .003), and modifying behavior self-efficacy (r = .47,
p = .000). These results indicated that participants who
were older and had more positive health beliefs, more
perceived benefits, less perceived barriers, and higher selfefficacy levels could perform more modifying behaviors. In
TABLE 4.
TABLE 3.
Participant Knowledge Scores for
Cardiovascular Risk Factors (N = 156)
Behavior to Modify Cardiovascular
Risk Factors (N = 156)
Modifying Behavior
Cardiovascular Risk Factor
Stress
Physical inactivity
Excessive alcohol
Hyperlipidemia
Type A personality
Hypertension
Smoking
Heredity
Obesity
Age
Diabetes mellitus
Gender
Total knowledge score
226
M T SD
Rank
T
T
T
T
T
T
T
T
T
T
T
T
T
1
2
3
4
4
6
7
8
9
10
11
12
0.92
0.91
0.90
0.88
0.88
0.83
0.82
0.79
0.77
0.60
0.37
0.34
13.50
0.27
0.29
0.30
0.27
0.33
0.22
0.32
0.41
0.30
0.49
0.34
0.47
2.96
Taking medications
Eating a regular diet
Quitting smoking
Maintaining a regular lifestyle
Eating low-fat diet
Eating low-cholesterol diet
Quitting alcohol
Eating a low-salt diet
Relaxing and maintaining a good mood
Monitoring BP
Exercising regularly
Controlling body weight
Overall modifying behaviors
Note. BP = blood pressure.
M T SD
Rank
1.94 T 0.27
1.72 T 0.55
1.68 T 0.68
1.65 T 0.60
1.58 T 0.56
1.48 T 0.54
1.47 T 0.67
1.47 T 0.61
1.41 T 0.59
1.32 T 0.80
1.29 T 0.77
1.28 T 0.71
1.52 T 0.29
1
2
3
4
5
6
7
7
9
10
11
12
Behavior Modification for Cardiovascular Risk Factors
VOL. 17, NO. 3, SEPTEMBER 2009
TABLE 5.
Stepwise Multiple Regression of
Modifying Behaviors (N = 156)
Predictor
Self-efficacy
Actual risk
factors
Work status
Health beliefs
R2
Change
"
t
Adjusted
R2
.389
j.221
5.93***
j3.40***
.240
.294
.240
.054
j.246
.239
j3.78***
3.67***
.331
.381
.037
.050
Note. The reference groups of work status were participants who were
unemployed.
***p G .001.
addition, modifying behavior was significantly and negatively related to perceived risk factors (r = j.21, p = .009)
and actual risk factors (r = j.32, p = 000). Therefore,
participants who perceived more risk factors or actually
had more risk factors would perform fewer modifying
behaviors. In this study, comorbidity, CAD severity, knowledge of risk factors, perceived disease severity, and social
support were not found to correlate with modifying behaviors (r = .00 to .11, p 9 .05).
The results of a t test showed no significant differences
in modifying behaviors for category variables including
gender, marital status, education, living arrangements, or
religious beliefs (t = j0.747 to 1.517, p 9 .05). Nevertheless, participants who were not employed had significantly
better modifying behaviors (t = j3.715, p = .000).
Factors Associated With Risk Factor
Modification Behaviors
Stepwise multiple regression analysis was used to examine
factors contributing to modifying behaviors. Table 5 shows
a total of 38% variance of modifying behaviors to be
explained by self-efficacy, actual risk factors, work status,
and health beliefs. Self-efficacy explained 24% of variance
in the modifying behavior score. The addition of actual
risk factors, work status, and health beliefs increased the
variance of modifying behavior score and accounted for
5.4%, 3.7%, and 5%, respectively. These results revealed
that participants with higher perception of self-efficacy,
fewer actual risk factors, no current job, and more positive
health beliefs had better modifying behaviors.
Discussion
This present study found that a total of 38% of modifying
behavior variance was explained by self-efficacy, actual
risk factors, work status, and health beliefs. Results indicated that better modifying behavior was explained by
higher self-efficacy, fewer risk factors, unemployment, and
more positive health beliefs. Self-efficacy was the strongest
predictor of modifying behavior for cardiovascular risk
factors. Although participants showed relatively good
knowledge of cardiovascular risk factors, this study did
not find significant correlations between knowledge and
modifying behaviors. According to Bandura’s Social Cognitive Theory, perceived self-efficacy was the most powerful
determinant of behavioral change (Bandura, 1986). Selfefficacy may influence one’s behavior, motivation, thought
patterns, and emotional reactions. Bandura (1986) also argued that knowledge was necessary but insufficient for
accomplished performances because personal capabilities
judgment mediated the relationship between knowledge
and action. Many studies also proposed the critical role of
self-efficacy on the process of behavioral change in cardiac
patients such as smoking cessation (Meland et al., 1999;
Yates & Thain, 1985), weight control, and exercise
(Gulanick, 1991; Meland et al., 1999; Robertson & Keller,
1992; Vidmar & Rubinson, 1994).
Health beliefs were also found to be a predictor of
modifying behavior in this study. According to the HBM,
health-related action depended on the simultaneous occurrence of three factors: sufficient motivation, perceived
threat, and perceived barriers (Rosenstock et al., 1988). To
accomplish successful behavioral change, people must be
motivated to take action, feel threatened by their behavioral patterns, and believe that change results in a valued
outcome. Similar to the findings of Robertson and Keller
(1992), this study confirmed better modifying behavior to
correlate significantly to higher perceived modifying behavior benefits and lower perceived barriers. However,
no significant relationship was found between modifying
behavior and perceived threat of heart disease. This may
be explained by the relatively lower level of perceived disease severity with a mean score of 1.38 (SD = 0.64) for
participants in this study. This is reasonable, as most
participants (80%) had a one-vessel or a two-vessel CAD
with NYHA class I, and they may have perceived that
their heart disease was not a serious threat to their life.
This study found that advancing age and type A personality were two leading cardiovascular risk factors for
participants. There was a discrepancy between participant
perceived risk factors and actual risk factors. Approximately 60% of the participants perceived hypertension as
their own risk factors, whereas only 38.5% actually had a
BP of 140/90 mmHg or greater. Most participants were
taking antihypertensive agents; thus, their BP were under
control. Therefore, although lifestyle modification is useful
in modifying risk factors, pharmacotherapy is likely to be
the most useful for those at higher risk (Patel & Adams,
2008). On the other hand, 42.9% of the participants were
smokers, whereas only 16.7% of the participants regarded
smoking as their own risk factor. This discrepancy may
raise the importance of patient education on individual
specific risk factors.
In terms of risk factor knowledge, participants had a
lower knowledge score on gender and diabetes. More than
80% of the participants did not know how to control
227
Journal of Nursing Research
diabetes, and nearly half of the participants failed to
recognize diabetes as a cardiovascular risk factor. Additionally, most participants were confused about advancing
age as an increased risk factor. Older participants had a
significantly lower knowledge score with regard to advancing age than younger participants (p G .001). Since the
incidence of CAD and the prevalence of most modifiable
risk factors increase with age, clinical nurses should pay
more attention to health education in elderly patients with
CAD. This finding is in accordance with a previous study
(Blomqvist & Hallberg, 2002).
Compared with studies of CAD patients in the United
States (Ashaye & Giles, 2003) and other countries
(Aghaeishahsavari, Noroozianavval, Veisi, Parizad, &
Samadikhah, 2006; Bhatt et al., 2006), the results of this
study for Taiwanese patients with CAD had some commonalities and differences. One similarity was that most
respondents with CAD had more than one risk factor:
93% of patients in Iran (Aghaeishahsavari et al., 2006) and
100% of Taiwanese participants in this study fell into this
scenario. Obesity (93.5%) and diabetes (58.4%) were the
leading risk factors in Western countries (Bhatt et al.,
2006), whereas advancing age (96.2%) and type A
personality (78.2%) were the most common risk factors
reported by Taiwanese patients with CAD in this study.
On the other hand, only 34.6% and 22.4% of Taiwanese
participants had obesity and diabetes, respectively. In
addition, only 6.3% of US patients with CAD engaged in
all four healthy lifestyle behaviors including maintaining an
ideal body weight, eating a healthy diet, and performing
regular physical activity (Ashaye & Giles, 2003), whereas
12% of Taiwanese participants performed all four modifying behaviors. Our study also confirmed that knowledge
did not lead necessarily to risk-reducing behavior. In
addition, our participants had good knowledge about
stress, physical inactivity, and excessive alcohol consumption, whereas US patients were most knowledgeable about
the relationships of exercise and cholesterol to heart disease
(Avis, McKinlay, & Smith, 1990). These comparisons may
indicate the differing risk factor profiles and the modifying
behaviors between Western and Taiwanese cultures. However, due to the small sample size of this study, comparisons should only be made with caution.
Limitations of this study include the fact that a crosssectional survey cannot examine causal relationships among
variables. In addition, because participants were selected
only from three medical centers in northern Taiwan, the
results may not be representative of other geographic locations. Therefore, a larger, multicenter, systematic sampling study with a longitudinal design is recommended.
A second limitation of this study was a reflection of
the questionnaires used. Although well developed, these
instruments were not tailored for our study purposes. In
addition, some consisted of too many items, which can
exhaust subjects and lead to missing data. To overcome
this limitation, the first author designed several instruments.
228
Ai-Fu Chiou et al.
Although questionnaire reliability and validity were supported in the pilot study, Cronbach’s alpha of the modifying behaviors scale was not sufficient in this study.
Further psychometrical evaluation of reliability and validity for these questionnaires is needed. This study only
aimed to examine factors contributing to the modifying
behaviors of patients with CAD. Investigators of future
studies may wish to focus on developing appropriate interventions and evaluating the effectiveness of interventions on risk factor modifying behaviors for patients with
CAD.
Conclusions
This study attempted to examine the explanatory variables
of risk factor modifying behaviors of patients with CAD.
Although patient education about CAD risk factors may
increase patients’ knowledge of CAD risk factors, patients’
adherence to regimens geared to modify risk factors
remains insufficient. In our study, self-efficacy and health
beliefs greatly contributed to patient modifying behavior.
Nurses play an important role in enhancing patient health
promotion behaviors. Therefore, this study suggests that
nurses should assess patients’ perceived and actual risk
factors, their health beliefs, and self-efficacy to identify
patients at greater risk of not adhering to behavior modification. Nursing interventions may focus on providing comprehensive patient education based on individual learning
needs to enhance self-efficacy.
Acknowledgments
The authors thank all study subjects for their participation.
The study was supported by grants from the Office of
Research and Development, Fu Jen Catholic University
(324-1-2001-1-24). They are also grateful to Cheu-Ye Liau,
MSN, RN, for her comments and to Hsiao-Yun Huang,
PhD, for his suggestions regarding statistical analysis.
References
Aghaeishahsavari, M., Noroozianavval, M., Veisi, P., Parizad,
R., & Samadikhah, J. (2006). Cardiovascular disease risk
factors in patients with confirmed cardiovascular disease.
Saudi Medical Journal, 27(9), 1358Y1361.
American Heart Association. (n.d.). Risk factors and coronary
heart disease. Retrieved April 8, 2009, from http://www.
amercanheart.org/presenter.jhtml?identifier=4726
Ashaye, M. O., & Giles, W. H. (2003). Are heart disease patients
more likely to have healthy lifestyle behaviors? Results from
the 2000 behavioral risk factor surveillance survey. Journal
of Cardiovascular Risk, 10(3), 207Y212.
Avis, N. E., McKinlay, J. B., & Smith, K. W. (1990). Is cardiovascular risk factor knowledge sufficient to influence behavior?
American Journal of Preventive Medicine, 6(3), 137Y144.
Bandura, A. (1986). Self-efficacy. In A. Bandura (Ed.), Social
foundations of though and action: A social cognitive theory
(pp. 390Y453). Englewood, NJ: Prentice-Hall, Inc.
Behavior Modification for Cardiovascular Risk Factors
Bhatt, D. L., Steg, P. G., Ohman, E. M., Hirsch, A. T., Ikeda, Y.,
Mas, J. L., et al. (2006). International prevalence, recognition, and treatment of cardiovascular risk factors in outpatients with atherothrombosis. Journal of the American
Medical Association, 295(2), 180Y189.
Blomqvist, K., & Hallberg, I. R. (2002). Managing pain in older
persons who receive home-help for their daily living. Perceptions by older persons and care providers. Scandinavian
Journal of Caring Science, 16(3), 319Y328.
Bush, R. L., Kallen, M. A., Liles, D. R., Bates, J. T., & Petersen, L. A.
(2008). Knowledge and awareness of peripheral vascular
disease are poor among women at risk for cardiovascular
disease. Journal of Surgical Research, 145(2), 313Y319.
Cheng, T. O. (2004). Chinese body mass index is much lower as
a risk factor for coronary artery disease [Letter to the editor].
Circulation, 109(14), e184.
Chyun, D. A., Amend, A. M., Newlin, K., Langerman, S., &
Melkus, G. D. (2003). Coronary heart disease prevention and
lifestyle interventions: Cultural influences. Journal of Cardiovascular Nursing, 18(4), 302Y318.
Daly-Nee, C., Brunt, H., & Jairath, N. (1999). Risk and coronary
heart disease. In N. Jairath (Ed.), Coronary heart disease and
risk factor management: A nursing perspective (pp. 3Y19).
Philadelphia: W. B. Saunders.
Darr, A., Astin, F., & Atkin, K. (2008). Causal attributions, lifestyle change, and coronary heart disease: Illness beliefs of
patients of South Asian and European origin living in the
United Kingdom. Heart & Lung, 37(2), 91Y104.
VOL. 17, NO. 3, SEPTEMBER 2009
Kannel, W. B. (2000). Elevated systolic blood pressure as a cardiovascular risk factor. American Journal of Cardiology, 85(2), 251Y255.
Keller, C., Fleury, J., & Mujezinovic-Womack, M. (2003). Managing cardiovascular risk reduction in elderly adults. By promoting and monitoring healthy lifestyle changes, health care
providers can help older adults improve their cardiovascular
health. Journal of Gerontological Nursing, 29(6), 18Y23.
Liu, S. J. (1995). The health conception and the health behavior
of the older women. Journal of National Taipei College of
Nursing, 3, 1Y52.
Liu, S. J. (1999). The exploration of life attitudes and life satisfaction for elderly with chronic illness. The Journal of Nursing
Research, 7(4), 294Y306.
Lynn, M. R. (1986). Determination and quantification of content
validity. Nursing Research, 35, 382Y385.
Maes, S., Vingerhoets, A., & Heck, G. V. (1987). The study of
stress and disease: Some developments and requirement.
Social Science and Medicine, 25(6), 567Y578.
Meland, E., Mæland, J. G., & Lærum, E. (1999). The importance
of self-efficacy in cardiovascular risk factor change. Scandinavian Journal of Public Health, 27(1), 11Y17.
National Heart Lung and Blood Institute. (2003). The seventh
report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure:
The JNC 7 Express. Retrieved April 4, 2009, from http://www.
nhlbi.nih.gov/guidelines/hypertension/jncintro.htm
Patel, S., & Adams, M. R. (2008). Prevention of cardiac disease:
Lifestyle modification or pharmacotherapy? Internal Medicine Journal, 38(3), 199Y203.
Department of Health, Executive Yuan. ROC (Taiwan). (2008,
October 20). 2007 statistics of causes of death. Retrieved May
14, 2009, from http://www.doh.gov.tw/EN2006/DM/DM2_
p01.aspx?class_no=390&now_fod_list_no=10422&level_no=
2&doc_no=70151
Robertson, D., & Keller, C. (1992). Relationships among health
beliefs, self-efficacy, and exercise adherence in patients with
coronary artery disease. Heart and Lung, 21(1), 56Y63.
Fletcher, G. F., Balady, G., Blair, S. N., Blumenthal, J., Caspersen,
C., Chaitman, B., et al. (1996). Statement on exercise: Benefits
and recommendations for physical activity programs for all
Americans: A statement for health professionals by the
Committee on Exercise and Cardiac Rehabilitation of the
Council on Clinical Cardiology, American Heart Association.
Circulation, 94(4), 857Y862.
Rosamond, W., Flegal, K., Furie, K., Go, A., Greenlund, K.,
Haase, N., et al. (2008, June 21). Heart disease and stroke
statisticsV2008 update: A report from the American Heart
Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 2008, 117, e25Ye146. (Originally work
retrieved December 17, 2007, from http://circ.ahajournals.
org/cgi/reprint/CIRCULATIONAHA.107.187998)
Fleury, J., & Sedikides, C. (2007). Wellness motivation in
cardiac rehabilitation: The role of self-knowledge in cardiovascular risk modification. Research in Nursing & Health,
30(4), 373Y384.
Rosenstock, I. M., Strecher, V. J., & Becker, M. H. (1988). Social
learning theory and the health belief model. Health Education Quarterly, 15(2), 175Y183.
Glanz, K., Rimer, B. K., & Su, S. M. (2005). Theory at a glance: A
guide for health promotion practice. Retrieved April 4, 2009,
from http://www.cancer.gov/PDF/481f5d53-63df-41bc-bfaf5aa48ee1da4d/TAAG3.pdf
Shen, S. Y. (1985). An evaluation of patient education program
on risk factors of coronary heart disease. The Journal of
Nursing, 32(1), 61Y74.
Sherer,M. D., & Maddux, J. E. (1982). The self-efficacy scale: Construction and validation. Psychological Reports, 51, 663Y671.
Grundy, S. M., Pasternak, R., Greenland, P., Smith, S. Jr., &
Fuster, V. (1999). Assessment of cardiovascular risk by use
of Multiple-Risk-Factor Assessment Equations: A statement
for healthcare professionals from the American Heart Association and the American College of Cardiology. Circulation,
100(13), 1481Y1492.
Vidmar, P. M., & Rubinson, L. (1994). The relationship between
self-efficacy and exercise compliance in a cardiac population.
Journal of Cardiopulmonary Rehabilitation, 14(4), 26Y154.
Gulanick, M. (1991). Is phase 2 cardiac rehabilitation necessary
for early recovery of patients with cardiac disease? A randomized, controlled study. Heart & Lung, 20(1), 9Y15.
Yates, A. J., & Thain, J. (1985). Self-efficacy as a predictor of
relapse following voluntary cessation of smoking. Addictive
Behaviors, 10, 291Y298.
Kamotho, C., Ogola, E. O., Joshi, M., & Gikonyo, D. (2004). Cardiovascular risk factor profile of black Africans undergoing coronary angiography. East African Medical Journal, 81(2), 82Y86.
Zerwic, J. J., King, K. B., & Wlasowicz, G. S. (1997). Perceptions
of patients with cardiovascular disease about the causes of
coronary artery disease. Heart & Lung, 26(2), 92Y98.
Sowers, J. R. (2003). Obesity as a cardiovascular risk factor.
American Journal of Medicine, 115(Suppl. 8A), 37SY41S.
229
心血管危險因子的修正行為
Journal of Nursing Research
VOL. 17, NO. 3, SEPTEMBER 2009
北台灣地區冠狀動脈疾病患者的
心血管危險因子修正行為之相關因素
邱愛富　汪慧鈴*　陳保羅**　丁予安***　許寬立****　高憲立*****
背　景
研究顯示降低心血管危險因子可以降低冠狀動脈疾病的罹病率和死亡率、改善患者預
後及降低醫療成本。
目　的
了解北台灣地區冠狀動脈疾病患者的心血管危險因子、認知、及其修正行為的情形，
並探討影響心血管危險因子修正行為的相關因素。
方　法
採橫斷式設計，以立意取樣，針對北部三家醫學中心的心臟內科門診冠狀動脈疾病患
者為收案對象，共有156位冠狀動脈疾病患者接受訪談完成結構式問卷。資料以SPSS
14.0進行分析，統計方法包括平均數、標準差、百分比等描述性統計，Pearson相關係
數，卡方檢定和複迴歸。
結　果
自我效能、實際危險因子數、工作狀態、以及健康信念等變項可用來解釋38%的危險因
子修正行為的變異量。其中以自我效能最具有預測力。年齡和A型人格是冠狀動脈疾病
患者最常見的心血管危險因子。大多數的患者能夠做到按時服藥、規律飲食及生活作
息，但是對於定期量血壓、規律運動、和控制體重則有較低的遵從性。
結　論
建議護理人員應該評估冠狀動脈疾病患者的心血管危險因子、自我效能、和健康信念
情形，並提供患者個別化的整體性照護。
關鍵詞：冠狀動脈疾病、危險因子、修正行為、自我效能、健康信念。
國立陽明大學護理學系暨研究所副教授　輔仁大學護理學系副教授*　萬芳醫院心臟血管科主治醫師**　國立
陽明大學內科部暨台北榮民總醫院教授***　義大醫院內科部部長、義守大學護理系助理教授****　台大雲林
分院心血管中心主任、國立臺灣大學醫學院醫學系助理教授*****
受文日期：98年3月4日　修改日期：98年5月12日　接受刊載：98年5月20日
通訊作者地址：邱愛富　11221台北市北投區立農街二段155號
230