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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]

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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

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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,

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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

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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

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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

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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

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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

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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

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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

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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

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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.

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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.

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心血管危險因子的修正行為

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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

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