ORIGINAL RESEARCH
Knowledge and health beliefs related to heart disease risk among adults with type 2 diabetes Elizabeth Tovar, PhD, RN, FNP-C (Assistant Professor)1 & Michele C. Clark, RN, PhD, LMFT (Associate Professor)2 1 2
College of Nursing, University of Kentucky, Lexington, Kentucky Las Vegas School of Nursing, University of Nevada, Las Vegas, Nevada
Keywords Diabetes type 2; cardiovascular risk; patient education; beliefs. Correspondence Elizabeth Tovar, PhD, RN, FNP-C, College of Nursing, University of Kentucky, 404 Masterson Station Drive, Lexington, KY 40511. Tel: 859-323-6611; Fax: 859-323-1057; E-mail: [email protected] Received: 18 July 2013; accepted: 14 October 2013 doi: 10.1002/2327-6924.12172
Abstract Purpose: The purpose of this descriptive correlational study was to describe knowledge of cardiovascular disease (CVD) risk and to explore relationships between this knowledge and health beliefs and adherence among adults with type 2 diabetes. Data sources: A convenience sample of 212 adults with type 2 diabetes completed the Heart Disease Fact Questionnaire and the health beliefs related to CVD Scale. Conclusions: Knowledge was high for the majority of the sample. Deficits included the link between cholesterol and heart disease; CVD risk factors; and exercises for lowering CVD risk. Significant between-group differences occurred across education level (p = .021) and race (p = .045); participants with less education and who were Hispanic had the lowest knowledge scores. Among the health belief model variables, knowledge was only a significant predictor of perceived benefits (p = .033) and barriers (p = .00). The most common sources of information about diabetes and CVD were TV/radio/magazine/newspaper, healthcare providers, and patient education brochures, with substantially less exposure to CVD information. Implications for practice: This study identified content to emphasize in interventions to improve awareness of CVD risk among adults with diabetes. Hispanic patients and those with low education levels are particularly in need of interventions appropriate to their education level and cultural orientation.
Introduction In the United States, heart disease and stroke are the number one cause of death and disability among adults with diabetes, accounting for 65% of deaths in this population (American Heart Association [AHA], 2012). Patients with diabetes have a two- to fourfold increased risk for heart disease or stroke and diabetes is a major modifiable risk factor for cardiovascular disease (CVD) along with hypertension, obesity, and high cholesterol, among others (World Heart Federation, 2013). Unfortunately, despite the well-established evidence for the increased risk for CVD in patients with diabetes, most patients with diabetes are not aware that they are more susceptible to CVD or associated complications because of their diabetes (American Diabetes Association & American College of Cardiology [ADA/ACC], 2002). In a survey of approximately 2000 people with diabetes, 68% of the C 2014 American Association of Nurse Practitioners
respondents did not consider CVD to be a complication of diabetes; more than 50% did not feel at risk for heart conditions or stroke; 60% did not feel at risk for high blood pressure or cholesterol; and awareness was lowest among elderly and minority persons with diabetes (ADA/ACC, 2002). A more recent study evaluating the effects of the National Diabetes Education Program (NDEP, 2009) identified similar levels of awareness, with only 34% of respondents recognizing that CVD is a serious complication of diabetes. These data illustrate that the majority of patients with diabetes, in particular the elderly and minority, are not aware of the relationship between diabetes and heart disease or stroke. Interventions that increase knowledge and awareness of CVD risk factors should be included in strategies that aim to reduce the incidence and burden of CVD in patients with diabetes. However, knowledge is a necessary but not sufficient component of behavior change (Simons-Morton, McLeroy, & 1
Knowledge and health beliefs related to heart disease risk
Wendel, 2012), thus other factors that influence health behaviors need to be targeted as well, such as health beliefs that have been found to be predictive of a wide range of health behaviors (Janz, 1988; Rosenstock, 1974, 2004). According to the health belief model (HBM; Rosenstock, 2004; Rosenstock, Strecher, & Becker, 1988), an individual’s behavior is the result of his or her health beliefs or subjective value that he or she places on a given outcome (e.g., the desire to avoid illness or to get well) and the belief or expectation that a particular action will lead to that outcome. The central constructs of this model include subjective perceptions of susceptibility, severity, benefits, barriers, self-efficacy, and exposure to cues to action, which are any physical or environmental factor that serves as a health motivator, such as patient education materials. In this study, the HBM was applied to diet and exercise behaviors of patients with diabetes and was conceptualized in the following way: For the individual with diabetes to adopt and sustain a healthy diet and regular exercise to reduce their risk of CVD morbidity and mortality, he must perceive that he is susceptible to heart attack or stroke (perceived susceptibility to CVD), that the severity of having a heart attack or stroke is great (perceived severity of CVD), and that adopting a healthy diet and regular exercise will reduce the risks of having a heart attack or stroke (perceived benefits) without excessive difficulty or negative side effects (perceived barriers). Additionally, adoption of healthy diet and regular exercise behaviors depends on self-efficacy beliefs, or the confidence that one has in performing a behavior or a skill (Bandura, 1994). Research supports the significant influence of knowledge on health beliefs (Dickerson et al., 2005) and health beliefs on adherence behaviors (Olsen, Smith, Oei, & Douglas, 2008; Rosenstock, 2004). As a result it is important to understand the relationships between knowledge and health beliefs related to heart disease risk among adults with diabetes to inform interventions targeting CVD risk reduction in this population. Thus, the purpose of this article is to describe knowledge of heart disease risk and health beliefs related to heart disease risk among adults with type 2 diabetes. Specific aims include: • Describe knowledge of heart disease risk among the participants and identify items that less than 90% of the sample answered correctly. • Identify common sources of information about diabetes and heart disease to which participants are exposed. • Describe health beliefs related to heart disease risk among the participants and evaluate relationships between these health beliefs and heart disease knowledge. 2
E. Tovar & M. C. Clark
• Describe adherence to diet and exercise recommendations among the participants and evaluate relationships between adherence and heart disease knowledge.
Design and methods Design The data presented in this article were collected as part of a larger study designed to explore relationships between selected biopsychosocial factors (knowledge related to CVD risk, cues to action, health beliefs, stage of change, social support, depression, comorbidity, diabetes duration, and socioeconomic status) and diet and exercise adherence, and to evaluate the ability of a theoretical model integrating the HBM and stages of change model to explain diet and exercise adherence among patients with diabetes (Gressle Tovar, 2007). The design of the primary study was a cross-sectional descriptive correlational study. The authors conducted post hoc data analyses, reported in the current manuscript, to further describe knowledge of risk for and health beliefs related to CVD to add to the clinical applicability of the study and to help guide interventions to promote awareness and accurate health beliefs related to risk for CVD in patients with diabetes.
Participants and procedures Using convenience sampling techniques, nonhospitalized adults with type 2 diabetes were recruited from outpatient clinics and community settings in Southeast Texas and Central North Carolina from April 24, 2006 to March 16, 2007. A total of 212 participants completed a series of brief, anonymous surveys assessing demographic information and study variables. Permission for recruitment at each site was obtained through expedited review from the institutional review boards associated with each site.
Measures The demographic and health history survey contained standard demographic and personal characteristics, including age (in years) gender, race/ethnicity, marital status (married, divorced, widowed, and never married), and education (five ordered categories, ranging from “less than high school” to “post graduate education”). Health history information included duration of diabetes, number of comorbidities, smoking status, and current medications. Knowledge sources (cues to action). For this study cues to action were identified as sources of information related to diabetes or CVD by which the participant
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could have obtained knowledge. This was measured by asking the participants if they had received information from a variety of sources such as support groups, healthcare professionals, friends or family, Internet, television, radio, newspaper, or informational brochures (see Table 4 for specific sources and item wording). Two separate sets of questions were asked using the same questions tailored to either diabetes or CVD to which participants responded “yes” (= 1) or “no” (= 0); each set received a total score with higher scores indicating more exposure to cues to action. Heart disease knowledge. Knowledge of heart disease was assessed using the Heart Disease Fact Questionnaire (HDFQ; Wagner, Lacey, Chyun, & Abbott, 2005). This scale, composed of 25 items with possible responses of “True,” “False,” and “Don’t know,” was scored so that one point was given for each correctly answered item, with each incorrect or “Don’t know” response receiving a 0. The total score was the number of correct responses with a potential range of 0–25, so that higher scores are indicative of greater knowledge of CVD. A sample item is “People with diabetes rarely have high cholesterol” (false is correct). The original scale was tested in a population of 524 patients with diabetes, 74% of whom had type 2 diabetes (Wagner et al., 2005). Internal consistency was demonstrated with Kuder–Richardson 20 internal consistency coefficient of 0.77. In the current sample, the Kuder–Richardson 20 for the scored version of this scale was 0.72. Health beliefs related to heart disease. Health beliefs related to heart disease were measured by the health beliefs related to CVD scale (HBCVD; Tovar, Rayens, Clark, & Nguyen, 2010). This scale consists of 25 items among four subscales that were developed to measure health beliefs related to CVD in adults with type 2 diabetes. Sample items include: “I feel I will have a heart attack or stroke sometime during my life” (susceptibility); “My whole life would change if I had a heart attack or stroke” (severity); “Increasing my exercise will decrease my chances of having a heart attack or stroke” (benefits); and “I do not have time to exercise for 30 minutes a day on most days of the week” (barriers). Response options ranged from strongly disagree (= 1) to strongly agree (= 4) with higher scores indicating stronger beliefs. The original HBCVD was tested with a population of 95 patients with type 2 diabetes (Gressle-Tovar, 2007). Cronbach’s alphas for susceptibility, severity, benefits, and barriers subscale scores were .87, .64, .91, and .68, respectively. Psychometric testing of this instrument in the current sample demonstrated stable factor structure and good reliability among the susceptibility (α = .93) and benefits (α = .82) subscales. The severity (α = .71) and barriers (α = .62) subscales had weaker internal
consistency; however, they still met criteria for acceptable alpha for a new instrument and thus were included in this analysis. Self-efficacy. Self-efficacy was measured by the Multidimensional Diabetes Questionnaire—Self-Efficacy subscale (MDQ-SE; Talbot, Nouwen, Gingras, Gosselin, & Audet, 1997). The scale is composed of seven items that assess aspects of diabetes self-care, such as “How confident are you in your ability to exercise regularly?” Each of the seven items was rated by the participants on an ordinal scale ranging from 1 = “Not at all” to 4 = “Very confident.” The scale total had a possible range of 7–28; higher scores on the scale total are indicative of a greater degree of self-efficacy related to diabetes self-care. The MDQ-SE was tested in a population of 249 noninsulindependent patients with diabetes and showed good internal consistency, with an alpha of .89 (Talbot et al., 1997). Cronbach’s alpha for this scale was .86 in this sample. Adherence. Adherence to diabetes self-care recommendations was assessed using a 13-item scale (Hernandez, 1997); items pertained to diet, exercise, medication, and other self-care behaviors. For example, “I try to keep my weight within the range suggested by my educator.” Each item was rated by participants based on an ordinal scale ranging from 1 = “Never” to 4 = “Always.” The total scale score is the sum of the scores from the individual items, with a possible range from 13 to 52 and higher scores indicating a greater degree of adherence. Pilot test results in a sample of 153 patients with diabetes demonstrated Cronbach’s alpha of .82 and test– retest reliability of .78. Cronbach’s alpha for this sample was .82. The items pertaining to diet (two items) and exercise (three items) were also evaluated individually as a measure of diet adherence and exercise adherence.
Data analysis Descriptive statistics, including means and standard deviations or frequency distributions, were used to summarize the study data and to look for missing or out-of-range values. Analyses of variance tests and simple regression analyses were performed to evaluate group differences and relationships among the variables.
Results Sample The majority of participants were female (67%), Caucasian (62%), and married (60%; see Table 1). The average age was 58.0 years (SD = 13.5), and those who completed the survey ranged in age from 18 to 85. Most 3
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Table 1 Sample characteristics with analyses of variance tests for knowledge scores CVD knowledge total (min. = 0; max. = 25) Characteristic (percentages in parentheses), majority in bold italics Gender Male (33%) Female (67%) Age ࣘ50 (27%) 51–65 (39%) >65 (34%) Race Black (19%) Hispanic (18%) White (62%) Education level Less than high school (6%) High school graduate (23%) Some college/trade school (37%) College graduate (22%) Postgraduate education (9%)
Mean
SD
F
p
20.6 21.4
3.47 2.42
3.66
NS
21.1 21.0 21.2
3.02 2.96 2.51
0.029
NS
20.6 20.2 20.9
4.20 3.20 2.76
3.93
.021*
19.3 20.7 20.9 21.9 22.1
4.20 3.20 2.76 2.27 1.81
2.49
.045*
*p < .05.
participants had at least some postsecondary education. The majority of the participants had been diagnosed with diabetes for 6 years or more (64%), suggesting ample time for learning about their diagnosis and associated risks. Knowledge scores. Mean scores with reliability and other scale statistics for each of the measures are reported in Table 2. The majority of the participants were knowledgeable about heart disease risk in patients with diabetes, with a mean score of 21.14 of 25. Individual items within the HDFQ (Wagner et al., 2005) were evaluated for the percentage of participants who answered each item correctly. The majority of items were answered correctly by more than 90% of the participants. The items that were answered correctly by less than 90% of the participants are presented in Table 3.
Group differences in knowledge scores To determine group differences in heart disease knowledge scores, one-way analysis of variance tests was performed across selected groups (see Table 1). Group differences were found between race (p = .021) and education (p = .045) groups; there were no significant differences between gender or age groups. Of the race groups represented in this sample (white, black, and Hispanic), the white group had the highest knowledge mean score while the Hispanic group had the lowest mean score. Significant differences were also found between education groups with the highest mean knowledge score in the most educated group (postgraduate education) and the lowest mean score in the least educated group (less than high school).
Table 2 Reliability and scale statistics for scales measuring study variables
Scale MDQ-SE HBCVD-SUS HBCVD-SEV HBCVD-BEN HBCVD-BAR TDAQ total TDAQ diet TDAQ exercise HDFQ *
Cronbach’s alpha .86 .93 .71 .82 .62 .82 .63 .87 KR-20 .72*
Mean
Range
Variance
SD
No. of items
20.57 12.8 11.13 19.88 19.50 41.71 5.82 7.66
7–28 5–20 5–20 6–24 9–36 13–52 2–8 3–12
31.11 10.63 7.41 10.60 13.21 35.87 1.77 6.49
5.58 3.26 2.72 3.26 3.64 5.99 1.33 2.55
7 5 5 6 9 13 2 3
21.14
0–25
8.0
2.83
25
Kuder–Richardson 20 used to determine internal consistency of this ordinal scale.
4
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Table 3 Heart Disease Fact Questionnaire items with less than 90% correct responses
Item
Correct response
Frequency correct
Percent correct
True
191
89
False
185
86
True True
176 140
83 65
False
134
63
True
120
56
False
84
41
True
47
23
15. Walking and gardening are considered exercise that will help lower a person’s chance of developing heart disease 20. People with diabetes rarely have high cholesterol 17. High blood sugar puts a strain on the heart 18. If your blood sugar is high over several months it can cause your cholesterol level to go up and increase your risk of heart disease 10. If your “good” cholesterol (high-density lipoprotein [HDL]) is high you are at risk for heart disease 3. The older a person is, the greater their risk of having heart disease 25. Men with diabetes have a higher risk of heart disease than women with diabetes 22. People with diabetes tend to have low HDL (good) cholesterol
Sources of knowledge (cues to action) Frequency analyses of exposure to each cue to action (see Table 4) revealed television, radio, magazine, or newspaper media as the most common source of diseasespecific information for both diabetes and CVD (93% and 75%, respectively) followed by receiving information from their healthcare team (77% and 42%, respectively) and patient education brochures (75% and 41%, respectively). Friends/relatives or the Internet were sources of information for diabetes for nearly half of the participants (49% and 47%, respectively), though much less for CVD information (21% and 29%, respectively). The least common source of information for both was a support group (14% and 1.5%, respectively).
Knowledge and health beliefs Heart disease knowledge score was a significant predictor of perceived benefits of (p = .033) and barriers
(p = .000) to diet and exercise (see Table 5). Knowledge was not a significant predictor of susceptibility (p = .289) or severity (p = .115). Responses for the benefits subscale revealed high item means (3.21–3.45 on a 1–4 scale), which suggests that most participants agreed that exercise and diet are good for them and can decrease their risk for heart attack or stroke. Item means for the barriers subscale were low to moderate ranging from 1.96 to 2.48 indicating relatively low perceived barriers to diet and exercise. Perceptions of susceptibility to heart attack or stroke were also relatively low with item means ranging from 2.41 to 2.63. Finally, severity beliefs were low to moderate with mean scores ranging from 1.79 to 2.81. The mean self-efficacy score for this sample was 20.8 out of a maximum of 28, which indicates moderate to high confidence in their ability for diabetes self-care. Despite high self-efficacy scores and overall high knowledge scores, knowledge was not a significant predictor of selfefficacy (p = .692).
Table 4 Sources of knowledge (cues to action) Source Media (TV, radio, magazine, newspaper): Have you seen or heard information about diabetes/cardiovascular disease from television, magazines, newspapers, or radio? Healthcare team: Have you received education about diabetes/cardiovascular disease from any member of your health care team (e.g., your doctor, nurse, or dietitian)? Patient education brochures: Have you received information brochures about diabetes/cardiovascular disease from your healthcare provider in the mail or public areas? Friends and relatives: Have you received information about diabetes/cardiovascular disease from a friend or relative? Internet: Have you used the Internet to learn more about diabetes/cardiovascular disease on your own? Support group: Have you regularly attended diabetes/cardiovascular disease related support group meetings?
Diabetes
CVD
93%
75%
77%
42%
75%
41%
49% 47% 14%
21% 29% 1.5%
5
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Table 5 Simple regressions for knowledge as predictor of health beliefs and adherence to diet and exercise recommendations
Susceptibility Severity Benefits Barriers Self-efficacy Diet adherence Exercise adherence
Constant
Unstandardized coefficient
Standardized coefficient
T
Sig
14.77 13.72 16.07 24.42 18.99 1.369 1.361
−0.091 −0.117 0.184 −0.323 0.068 0.045 0.050
−0.079 −0.120 0.157 −0.259 0.029 0.032 0.059
−1.06 −1.58 2.143 −3.55 0.397 1.394 0.916
.289 .115 .033* .000** .692 .166 .547
*p < .05; **p < .01.
Knowledge and adherence Participants reported moderate rates of adherence to diet and exercise (diet mean = 5.82 out of 8 and exercise mean 7.75 out of 12). Heart disease knowledge was not a significant predictor of either diet (p = .166) or exercise (p = .547) adherence.
Discussion While targeting knowledge by itself is not sufficient to significantly improve adherence rates, it does positively influence health behaviors and thus is a necessary component of interventions that aim to improve adherence and decrease risk for disease. The identification of knowledge deficits related to the study participants’ risk for heart disease and evaluation of differences in knowledge scores between groups is an important contribution of this study, as this information can be used to inform educational components of behavior change intervention strategies. A significant number of the HDFQ items were answered correctly by less than 90% of the participants. This content should be emphasized in future education interventions. Of particular importance are items related to the relationship between cholesterol and heart disease, gender differences in CVD risk, diabetes as a risk factor for CVD, age as a risk factor for CVD, and exercises to lower risk of CVD. In addition, differences across race and education groups were found to influence knowledge scores. Participants with less than a high school education had the lowest knowledge scores compared to other education groups. Also, Hispanic participants had the lowest knowledge scores compared to other ethnic groups. This is an important clinical finding because Hispanics are almost twice as likely to have diabetes as their non-Hispanic white counterparts (Centers for Disease Control [CDC], 2012). In particular, healthcare providers need to ensure that Hispanic patients are receiving adequate and culturally appropriate information about their risk for diabetic 6
CVD and that education efforts are appropriate to the education level of the target population. This investigation also identified the most common sources of information for both diabetes and CVD information as TV/radio/magazine/newspaper, healthcare providers, and patient education brochures. For future interventions these sources should be targeted to inform the public about diabetes and CVD risk. The fact that participants reported exposure to far less information about CVD risk than diabetes information highlights an important discrepancy and suggests that we could be doing better at educating patients about their risk for diabetic CVD. In this analysis, two subscales in the HBM were significantly associated with heart disease knowledge. Knowledge of heart disease risk had a positive relationship with benefits of diet and exercise and a negative relationship with barriers to diet and exercise. These findings indicate that as knowledge increases, perceived benefits also increase and perceived barriers decrease. This finding supports the importance of emphasizing the benefits of diet and exercise and ways to overcome barriers in educational interventions targeting CVD knowledge in adult patients with type 2 diabetes. In this sample, perceived barriers were actually lower than the authors anticipated given the only moderate rates of adherence to diet and exercise, with the mean barriers score indicating that participants were more likely to disagree than agree that the selected barriers were in fact barriers. This inverse relationship is consistent with previous findings by Dickerson et al. (2005), who found that diabetes knowledge is inversely associated with perceived barriers to diabetes care. Given the myriad of potential barriers, it is possible that this scale did not capture other important barriers that may have influenced adherence behaviors and this was likely a factor in the stability issues with this subscale. Nevertheless, this finding related to barriers is interesting and worth further exploration. Knowledge related to CVD risk did not predict perceived susceptibility to or severity of heart attack or stroke. This finding was not expected, as one would think
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that knowledge of heart disease risk would include understanding of risk for heart attack or stroke, and needs to be explored further. It is possible that this occurred because the questions in the knowledge questionnaire referred to heart disease and not specifically to heart attack or stroke, which was the wording used in the susceptibility and severity items. Future studies using instruments that are more consistent in their terminology may yield different findings. Finally, knowledge of heart disease risk did not predict self-efficacy for diabetes self-care nor did it predict adherence to diet or exercise. This finding was not surprising and is consistent with the notion that knowledge is a necessary but not sufficient element important for behavior change (Simons-Morton et al., 2012).
Implications In this study, scores on the HDFQ (Wagner et al., 2005) revealed several areas of deficient knowledge related to heart disease risk in patients with diabetes. In particular, age, gender, and lipid-related factors should be emphasized in patient education efforts. Race and education level differences in knowledge scores were also identified. While educational interventions to improve knowledge of CVD risk in patients with diabetes are important for all groups, emphasis should be placed on ensuring that strategies are appropriate for the reading and education level as well as the ethnic group of the individual. Targeting knowledge of CVD risk can play an important role in health behaviors through perceived benefits and barriers to diet and exercise. Effective strategies for improving knowledge of heart disease risk among adults with type 2 diabetes should consider focusing resources on radio, television, newspaper, or magazines as well as healthcare providers and patient education brochures as primary sources of sharing information with this population.
References American Diabetes Association and American College of Cardiology (ADA/ACC). (2002). The Diabetes-Heart Disease Link: Surveying attitudes, knowledge and risk—Executive summary. Retrieved from http://www.diabetes.org/uedocuments/executivesummary.pdf
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American Heart Association (AHA). (2012). Cardiovascular disease & diabetes. Why Diabetes Matters. Retrieved from http://www.heart.org/HEARTORG/ Conditions/Diabetes/WhyDiabetesMatters/Cardiovascular-DiseaseDiabetes UCM 313865 Article.jsp Bandura, A. (1994). Self-efficacy. In V. S. Ramachaudran (Ed.), Encyclopedia of human behavior (Vol. 4, pp. 71–81). New York: Academic Press. Centers for Disease Control (CDC). (2012). Summary Health Statistics for U.S. Adults: 2010, Table 8. Vital and health statistics (p. 6). Hyattsville, MD: U.S. Department of Health and Human Services. Dickerson, F. B., Goldberg, R. W., Brown, C. H., Kreyenbuhl, J. A., Wohlheiter, K., Fang, L., ... Dixon, L. B. (2005). Diabetes knowledge among persons with serious mental illness and type 2 diabetes. Psychosomatics, 46(5), 418–424. Gressle Tovar, E. (2007). Relationships between psychosocial factors and adherence to diet and exercise in adults with type 2 diabetes: A test of a theoretical model. Ph.D. dissertation, University of Texas Medical Branch at Galveston, Galveston, TX. Hernandez, C. A. (1997). The development and pilot testing of the Diabetes Activities Questionnaire (TDAQ): An instrument to measure adherence to the diabetes regimen. Applied Nursing Research, 10(4), 202–211. Janz, N. K. (1988). The health belief model in understanding cardiovascular risk-factor reduction behaviors. Cardiovascular Nursing, 24(6), 39–41. National Diabetes Education Program (NDEP). (2009). National Diabetes Education Program Survey of the public’s knowledge, attitudes, and practices related to diabetes: 2008 executive summary. Silver Spring, MD: National Diabetes Education Program, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases. Olsen, S., Smith, S., Oei, T., & Douglas, J. (2008). Health belief model predicts adherence to CPAP before experience with CPAP. European Respiratory Journal, 32(3), 710–717. Rosenstock, I. (1974). Historical origins of the health belief model. Health Education Monographs, 2(4), 328–335. Rosenstock, I. (2004). Health belief model. Encyclopedia of psychology (pp. 78– 80). American Psychological Association: Washington, DC. Rosenstock, I. M., Strecher, V. J., & Becker, M. H. (1988). Social learning theory and the health belief model. Health Education Quarterly, 15(2), 175–183. Simons-Morton, B., McLeroy, K. R., & Wendel, M. (2012). Learning, teaching and counseling. Behavior theory in health promotion practice and research. Burlington, MA: Jones and Bartlett Learning. Talbot, F., Nouwen, A., Gingras, J., Gosselin, M., & Audet, J. (1997). The assessment of diabetes related cognitive and social factors: The Multidimensional Diabetes Questionnaire. Journal of Behavioral Medicine, 20(3), 291–312. Tovar, E. G., Rayens, M. K., Clark, M., & Nguyen, H. (2010). Development and psychometric testing of the Health Beliefs Related to Cardiovascular Disease Scale: Preliminary findings. Journal of Advanced Nursing, 66(12), 2772– 2784. Wagner, J., Lacey, K., Chyun, D., & Abbott, G. (2005). Development of a patient questionnaire to measure heart disease risk knowledge in people with diabetes: The Heart Disease Fact Questionnaire. Patient Education and Counseling, 58, 82–87. World Heart Federation. (2013). Cardiovascular disease risk factors. Retrieved from http://www.world-heart-federation.org/cardiovascularhealth/cardiovascular-disease-risk-factors/
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Knowledge and health beliefs related to heart disease risk among adults with type 2 diabetes Elizabeth Tovar, PhD, RN, FNP-C (Assistant Professor)1 & Michele C. Clark, RN, PhD, LMFT (Associate Professor)2 1 2
College of Nursing, University of Kentucky, Lexington, Kentucky Las Vegas School of Nursing, University of Nevada, Las Vegas, Nevada
Keywords Diabetes type 2; cardiovascular risk; patient education; beliefs. Correspondence Elizabeth Tovar, PhD, RN, FNP-C, College of Nursing, University of Kentucky, 404 Masterson Station Drive, Lexington, KY 40511. Tel: 859-323-6611; Fax: 859-323-1057; E-mail: [email protected] Received: 18 July 2013; accepted: 14 October 2013 doi: 10.1002/2327-6924.12172
Abstract Purpose: The purpose of this descriptive correlational study was to describe knowledge of cardiovascular disease (CVD) risk and to explore relationships between this knowledge and health beliefs and adherence among adults with type 2 diabetes. Data sources: A convenience sample of 212 adults with type 2 diabetes completed the Heart Disease Fact Questionnaire and the health beliefs related to CVD Scale. Conclusions: Knowledge was high for the majority of the sample. Deficits included the link between cholesterol and heart disease; CVD risk factors; and exercises for lowering CVD risk. Significant between-group differences occurred across education level (p = .021) and race (p = .045); participants with less education and who were Hispanic had the lowest knowledge scores. Among the health belief model variables, knowledge was only a significant predictor of perceived benefits (p = .033) and barriers (p = .00). The most common sources of information about diabetes and CVD were TV/radio/magazine/newspaper, healthcare providers, and patient education brochures, with substantially less exposure to CVD information. Implications for practice: This study identified content to emphasize in interventions to improve awareness of CVD risk among adults with diabetes. Hispanic patients and those with low education levels are particularly in need of interventions appropriate to their education level and cultural orientation.
Introduction In the United States, heart disease and stroke are the number one cause of death and disability among adults with diabetes, accounting for 65% of deaths in this population (American Heart Association [AHA], 2012). Patients with diabetes have a two- to fourfold increased risk for heart disease or stroke and diabetes is a major modifiable risk factor for cardiovascular disease (CVD) along with hypertension, obesity, and high cholesterol, among others (World Heart Federation, 2013). Unfortunately, despite the well-established evidence for the increased risk for CVD in patients with diabetes, most patients with diabetes are not aware that they are more susceptible to CVD or associated complications because of their diabetes (American Diabetes Association & American College of Cardiology [ADA/ACC], 2002). In a survey of approximately 2000 people with diabetes, 68% of the C 2014 American Association of Nurse Practitioners
respondents did not consider CVD to be a complication of diabetes; more than 50% did not feel at risk for heart conditions or stroke; 60% did not feel at risk for high blood pressure or cholesterol; and awareness was lowest among elderly and minority persons with diabetes (ADA/ACC, 2002). A more recent study evaluating the effects of the National Diabetes Education Program (NDEP, 2009) identified similar levels of awareness, with only 34% of respondents recognizing that CVD is a serious complication of diabetes. These data illustrate that the majority of patients with diabetes, in particular the elderly and minority, are not aware of the relationship between diabetes and heart disease or stroke. Interventions that increase knowledge and awareness of CVD risk factors should be included in strategies that aim to reduce the incidence and burden of CVD in patients with diabetes. However, knowledge is a necessary but not sufficient component of behavior change (Simons-Morton, McLeroy, & 1
Knowledge and health beliefs related to heart disease risk
Wendel, 2012), thus other factors that influence health behaviors need to be targeted as well, such as health beliefs that have been found to be predictive of a wide range of health behaviors (Janz, 1988; Rosenstock, 1974, 2004). According to the health belief model (HBM; Rosenstock, 2004; Rosenstock, Strecher, & Becker, 1988), an individual’s behavior is the result of his or her health beliefs or subjective value that he or she places on a given outcome (e.g., the desire to avoid illness or to get well) and the belief or expectation that a particular action will lead to that outcome. The central constructs of this model include subjective perceptions of susceptibility, severity, benefits, barriers, self-efficacy, and exposure to cues to action, which are any physical or environmental factor that serves as a health motivator, such as patient education materials. In this study, the HBM was applied to diet and exercise behaviors of patients with diabetes and was conceptualized in the following way: For the individual with diabetes to adopt and sustain a healthy diet and regular exercise to reduce their risk of CVD morbidity and mortality, he must perceive that he is susceptible to heart attack or stroke (perceived susceptibility to CVD), that the severity of having a heart attack or stroke is great (perceived severity of CVD), and that adopting a healthy diet and regular exercise will reduce the risks of having a heart attack or stroke (perceived benefits) without excessive difficulty or negative side effects (perceived barriers). Additionally, adoption of healthy diet and regular exercise behaviors depends on self-efficacy beliefs, or the confidence that one has in performing a behavior or a skill (Bandura, 1994). Research supports the significant influence of knowledge on health beliefs (Dickerson et al., 2005) and health beliefs on adherence behaviors (Olsen, Smith, Oei, & Douglas, 2008; Rosenstock, 2004). As a result it is important to understand the relationships between knowledge and health beliefs related to heart disease risk among adults with diabetes to inform interventions targeting CVD risk reduction in this population. Thus, the purpose of this article is to describe knowledge of heart disease risk and health beliefs related to heart disease risk among adults with type 2 diabetes. Specific aims include: • Describe knowledge of heart disease risk among the participants and identify items that less than 90% of the sample answered correctly. • Identify common sources of information about diabetes and heart disease to which participants are exposed. • Describe health beliefs related to heart disease risk among the participants and evaluate relationships between these health beliefs and heart disease knowledge. 2
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• Describe adherence to diet and exercise recommendations among the participants and evaluate relationships between adherence and heart disease knowledge.
Design and methods Design The data presented in this article were collected as part of a larger study designed to explore relationships between selected biopsychosocial factors (knowledge related to CVD risk, cues to action, health beliefs, stage of change, social support, depression, comorbidity, diabetes duration, and socioeconomic status) and diet and exercise adherence, and to evaluate the ability of a theoretical model integrating the HBM and stages of change model to explain diet and exercise adherence among patients with diabetes (Gressle Tovar, 2007). The design of the primary study was a cross-sectional descriptive correlational study. The authors conducted post hoc data analyses, reported in the current manuscript, to further describe knowledge of risk for and health beliefs related to CVD to add to the clinical applicability of the study and to help guide interventions to promote awareness and accurate health beliefs related to risk for CVD in patients with diabetes.
Participants and procedures Using convenience sampling techniques, nonhospitalized adults with type 2 diabetes were recruited from outpatient clinics and community settings in Southeast Texas and Central North Carolina from April 24, 2006 to March 16, 2007. A total of 212 participants completed a series of brief, anonymous surveys assessing demographic information and study variables. Permission for recruitment at each site was obtained through expedited review from the institutional review boards associated with each site.
Measures The demographic and health history survey contained standard demographic and personal characteristics, including age (in years) gender, race/ethnicity, marital status (married, divorced, widowed, and never married), and education (five ordered categories, ranging from “less than high school” to “post graduate education”). Health history information included duration of diabetes, number of comorbidities, smoking status, and current medications. Knowledge sources (cues to action). For this study cues to action were identified as sources of information related to diabetes or CVD by which the participant
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could have obtained knowledge. This was measured by asking the participants if they had received information from a variety of sources such as support groups, healthcare professionals, friends or family, Internet, television, radio, newspaper, or informational brochures (see Table 4 for specific sources and item wording). Two separate sets of questions were asked using the same questions tailored to either diabetes or CVD to which participants responded “yes” (= 1) or “no” (= 0); each set received a total score with higher scores indicating more exposure to cues to action. Heart disease knowledge. Knowledge of heart disease was assessed using the Heart Disease Fact Questionnaire (HDFQ; Wagner, Lacey, Chyun, & Abbott, 2005). This scale, composed of 25 items with possible responses of “True,” “False,” and “Don’t know,” was scored so that one point was given for each correctly answered item, with each incorrect or “Don’t know” response receiving a 0. The total score was the number of correct responses with a potential range of 0–25, so that higher scores are indicative of greater knowledge of CVD. A sample item is “People with diabetes rarely have high cholesterol” (false is correct). The original scale was tested in a population of 524 patients with diabetes, 74% of whom had type 2 diabetes (Wagner et al., 2005). Internal consistency was demonstrated with Kuder–Richardson 20 internal consistency coefficient of 0.77. In the current sample, the Kuder–Richardson 20 for the scored version of this scale was 0.72. Health beliefs related to heart disease. Health beliefs related to heart disease were measured by the health beliefs related to CVD scale (HBCVD; Tovar, Rayens, Clark, & Nguyen, 2010). This scale consists of 25 items among four subscales that were developed to measure health beliefs related to CVD in adults with type 2 diabetes. Sample items include: “I feel I will have a heart attack or stroke sometime during my life” (susceptibility); “My whole life would change if I had a heart attack or stroke” (severity); “Increasing my exercise will decrease my chances of having a heart attack or stroke” (benefits); and “I do not have time to exercise for 30 minutes a day on most days of the week” (barriers). Response options ranged from strongly disagree (= 1) to strongly agree (= 4) with higher scores indicating stronger beliefs. The original HBCVD was tested with a population of 95 patients with type 2 diabetes (Gressle-Tovar, 2007). Cronbach’s alphas for susceptibility, severity, benefits, and barriers subscale scores were .87, .64, .91, and .68, respectively. Psychometric testing of this instrument in the current sample demonstrated stable factor structure and good reliability among the susceptibility (α = .93) and benefits (α = .82) subscales. The severity (α = .71) and barriers (α = .62) subscales had weaker internal
consistency; however, they still met criteria for acceptable alpha for a new instrument and thus were included in this analysis. Self-efficacy. Self-efficacy was measured by the Multidimensional Diabetes Questionnaire—Self-Efficacy subscale (MDQ-SE; Talbot, Nouwen, Gingras, Gosselin, & Audet, 1997). The scale is composed of seven items that assess aspects of diabetes self-care, such as “How confident are you in your ability to exercise regularly?” Each of the seven items was rated by the participants on an ordinal scale ranging from 1 = “Not at all” to 4 = “Very confident.” The scale total had a possible range of 7–28; higher scores on the scale total are indicative of a greater degree of self-efficacy related to diabetes self-care. The MDQ-SE was tested in a population of 249 noninsulindependent patients with diabetes and showed good internal consistency, with an alpha of .89 (Talbot et al., 1997). Cronbach’s alpha for this scale was .86 in this sample. Adherence. Adherence to diabetes self-care recommendations was assessed using a 13-item scale (Hernandez, 1997); items pertained to diet, exercise, medication, and other self-care behaviors. For example, “I try to keep my weight within the range suggested by my educator.” Each item was rated by participants based on an ordinal scale ranging from 1 = “Never” to 4 = “Always.” The total scale score is the sum of the scores from the individual items, with a possible range from 13 to 52 and higher scores indicating a greater degree of adherence. Pilot test results in a sample of 153 patients with diabetes demonstrated Cronbach’s alpha of .82 and test– retest reliability of .78. Cronbach’s alpha for this sample was .82. The items pertaining to diet (two items) and exercise (three items) were also evaluated individually as a measure of diet adherence and exercise adherence.
Data analysis Descriptive statistics, including means and standard deviations or frequency distributions, were used to summarize the study data and to look for missing or out-of-range values. Analyses of variance tests and simple regression analyses were performed to evaluate group differences and relationships among the variables.
Results Sample The majority of participants were female (67%), Caucasian (62%), and married (60%; see Table 1). The average age was 58.0 years (SD = 13.5), and those who completed the survey ranged in age from 18 to 85. Most 3
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Table 1 Sample characteristics with analyses of variance tests for knowledge scores CVD knowledge total (min. = 0; max. = 25) Characteristic (percentages in parentheses), majority in bold italics Gender Male (33%) Female (67%) Age ࣘ50 (27%) 51–65 (39%) >65 (34%) Race Black (19%) Hispanic (18%) White (62%) Education level Less than high school (6%) High school graduate (23%) Some college/trade school (37%) College graduate (22%) Postgraduate education (9%)
Mean
SD
F
p
20.6 21.4
3.47 2.42
3.66
NS
21.1 21.0 21.2
3.02 2.96 2.51
0.029
NS
20.6 20.2 20.9
4.20 3.20 2.76
3.93
.021*
19.3 20.7 20.9 21.9 22.1
4.20 3.20 2.76 2.27 1.81
2.49
.045*
*p < .05.
participants had at least some postsecondary education. The majority of the participants had been diagnosed with diabetes for 6 years or more (64%), suggesting ample time for learning about their diagnosis and associated risks. Knowledge scores. Mean scores with reliability and other scale statistics for each of the measures are reported in Table 2. The majority of the participants were knowledgeable about heart disease risk in patients with diabetes, with a mean score of 21.14 of 25. Individual items within the HDFQ (Wagner et al., 2005) were evaluated for the percentage of participants who answered each item correctly. The majority of items were answered correctly by more than 90% of the participants. The items that were answered correctly by less than 90% of the participants are presented in Table 3.
Group differences in knowledge scores To determine group differences in heart disease knowledge scores, one-way analysis of variance tests was performed across selected groups (see Table 1). Group differences were found between race (p = .021) and education (p = .045) groups; there were no significant differences between gender or age groups. Of the race groups represented in this sample (white, black, and Hispanic), the white group had the highest knowledge mean score while the Hispanic group had the lowest mean score. Significant differences were also found between education groups with the highest mean knowledge score in the most educated group (postgraduate education) and the lowest mean score in the least educated group (less than high school).
Table 2 Reliability and scale statistics for scales measuring study variables
Scale MDQ-SE HBCVD-SUS HBCVD-SEV HBCVD-BEN HBCVD-BAR TDAQ total TDAQ diet TDAQ exercise HDFQ *
Cronbach’s alpha .86 .93 .71 .82 .62 .82 .63 .87 KR-20 .72*
Mean
Range
Variance
SD
No. of items
20.57 12.8 11.13 19.88 19.50 41.71 5.82 7.66
7–28 5–20 5–20 6–24 9–36 13–52 2–8 3–12
31.11 10.63 7.41 10.60 13.21 35.87 1.77 6.49
5.58 3.26 2.72 3.26 3.64 5.99 1.33 2.55
7 5 5 6 9 13 2 3
21.14
0–25
8.0
2.83
25
Kuder–Richardson 20 used to determine internal consistency of this ordinal scale.
4
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Table 3 Heart Disease Fact Questionnaire items with less than 90% correct responses
Item
Correct response
Frequency correct
Percent correct
True
191
89
False
185
86
True True
176 140
83 65
False
134
63
True
120
56
False
84
41
True
47
23
15. Walking and gardening are considered exercise that will help lower a person’s chance of developing heart disease 20. People with diabetes rarely have high cholesterol 17. High blood sugar puts a strain on the heart 18. If your blood sugar is high over several months it can cause your cholesterol level to go up and increase your risk of heart disease 10. If your “good” cholesterol (high-density lipoprotein [HDL]) is high you are at risk for heart disease 3. The older a person is, the greater their risk of having heart disease 25. Men with diabetes have a higher risk of heart disease than women with diabetes 22. People with diabetes tend to have low HDL (good) cholesterol
Sources of knowledge (cues to action) Frequency analyses of exposure to each cue to action (see Table 4) revealed television, radio, magazine, or newspaper media as the most common source of diseasespecific information for both diabetes and CVD (93% and 75%, respectively) followed by receiving information from their healthcare team (77% and 42%, respectively) and patient education brochures (75% and 41%, respectively). Friends/relatives or the Internet were sources of information for diabetes for nearly half of the participants (49% and 47%, respectively), though much less for CVD information (21% and 29%, respectively). The least common source of information for both was a support group (14% and 1.5%, respectively).
Knowledge and health beliefs Heart disease knowledge score was a significant predictor of perceived benefits of (p = .033) and barriers
(p = .000) to diet and exercise (see Table 5). Knowledge was not a significant predictor of susceptibility (p = .289) or severity (p = .115). Responses for the benefits subscale revealed high item means (3.21–3.45 on a 1–4 scale), which suggests that most participants agreed that exercise and diet are good for them and can decrease their risk for heart attack or stroke. Item means for the barriers subscale were low to moderate ranging from 1.96 to 2.48 indicating relatively low perceived barriers to diet and exercise. Perceptions of susceptibility to heart attack or stroke were also relatively low with item means ranging from 2.41 to 2.63. Finally, severity beliefs were low to moderate with mean scores ranging from 1.79 to 2.81. The mean self-efficacy score for this sample was 20.8 out of a maximum of 28, which indicates moderate to high confidence in their ability for diabetes self-care. Despite high self-efficacy scores and overall high knowledge scores, knowledge was not a significant predictor of selfefficacy (p = .692).
Table 4 Sources of knowledge (cues to action) Source Media (TV, radio, magazine, newspaper): Have you seen or heard information about diabetes/cardiovascular disease from television, magazines, newspapers, or radio? Healthcare team: Have you received education about diabetes/cardiovascular disease from any member of your health care team (e.g., your doctor, nurse, or dietitian)? Patient education brochures: Have you received information brochures about diabetes/cardiovascular disease from your healthcare provider in the mail or public areas? Friends and relatives: Have you received information about diabetes/cardiovascular disease from a friend or relative? Internet: Have you used the Internet to learn more about diabetes/cardiovascular disease on your own? Support group: Have you regularly attended diabetes/cardiovascular disease related support group meetings?
Diabetes
CVD
93%
75%
77%
42%
75%
41%
49% 47% 14%
21% 29% 1.5%
5
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Table 5 Simple regressions for knowledge as predictor of health beliefs and adherence to diet and exercise recommendations
Susceptibility Severity Benefits Barriers Self-efficacy Diet adherence Exercise adherence
Constant
Unstandardized coefficient
Standardized coefficient
T
Sig
14.77 13.72 16.07 24.42 18.99 1.369 1.361
−0.091 −0.117 0.184 −0.323 0.068 0.045 0.050
−0.079 −0.120 0.157 −0.259 0.029 0.032 0.059
−1.06 −1.58 2.143 −3.55 0.397 1.394 0.916
.289 .115 .033* .000** .692 .166 .547
*p < .05; **p < .01.
Knowledge and adherence Participants reported moderate rates of adherence to diet and exercise (diet mean = 5.82 out of 8 and exercise mean 7.75 out of 12). Heart disease knowledge was not a significant predictor of either diet (p = .166) or exercise (p = .547) adherence.
Discussion While targeting knowledge by itself is not sufficient to significantly improve adherence rates, it does positively influence health behaviors and thus is a necessary component of interventions that aim to improve adherence and decrease risk for disease. The identification of knowledge deficits related to the study participants’ risk for heart disease and evaluation of differences in knowledge scores between groups is an important contribution of this study, as this information can be used to inform educational components of behavior change intervention strategies. A significant number of the HDFQ items were answered correctly by less than 90% of the participants. This content should be emphasized in future education interventions. Of particular importance are items related to the relationship between cholesterol and heart disease, gender differences in CVD risk, diabetes as a risk factor for CVD, age as a risk factor for CVD, and exercises to lower risk of CVD. In addition, differences across race and education groups were found to influence knowledge scores. Participants with less than a high school education had the lowest knowledge scores compared to other education groups. Also, Hispanic participants had the lowest knowledge scores compared to other ethnic groups. This is an important clinical finding because Hispanics are almost twice as likely to have diabetes as their non-Hispanic white counterparts (Centers for Disease Control [CDC], 2012). In particular, healthcare providers need to ensure that Hispanic patients are receiving adequate and culturally appropriate information about their risk for diabetic 6
CVD and that education efforts are appropriate to the education level of the target population. This investigation also identified the most common sources of information for both diabetes and CVD information as TV/radio/magazine/newspaper, healthcare providers, and patient education brochures. For future interventions these sources should be targeted to inform the public about diabetes and CVD risk. The fact that participants reported exposure to far less information about CVD risk than diabetes information highlights an important discrepancy and suggests that we could be doing better at educating patients about their risk for diabetic CVD. In this analysis, two subscales in the HBM were significantly associated with heart disease knowledge. Knowledge of heart disease risk had a positive relationship with benefits of diet and exercise and a negative relationship with barriers to diet and exercise. These findings indicate that as knowledge increases, perceived benefits also increase and perceived barriers decrease. This finding supports the importance of emphasizing the benefits of diet and exercise and ways to overcome barriers in educational interventions targeting CVD knowledge in adult patients with type 2 diabetes. In this sample, perceived barriers were actually lower than the authors anticipated given the only moderate rates of adherence to diet and exercise, with the mean barriers score indicating that participants were more likely to disagree than agree that the selected barriers were in fact barriers. This inverse relationship is consistent with previous findings by Dickerson et al. (2005), who found that diabetes knowledge is inversely associated with perceived barriers to diabetes care. Given the myriad of potential barriers, it is possible that this scale did not capture other important barriers that may have influenced adherence behaviors and this was likely a factor in the stability issues with this subscale. Nevertheless, this finding related to barriers is interesting and worth further exploration. Knowledge related to CVD risk did not predict perceived susceptibility to or severity of heart attack or stroke. This finding was not expected, as one would think
E. Tovar & M. C. Clark
that knowledge of heart disease risk would include understanding of risk for heart attack or stroke, and needs to be explored further. It is possible that this occurred because the questions in the knowledge questionnaire referred to heart disease and not specifically to heart attack or stroke, which was the wording used in the susceptibility and severity items. Future studies using instruments that are more consistent in their terminology may yield different findings. Finally, knowledge of heart disease risk did not predict self-efficacy for diabetes self-care nor did it predict adherence to diet or exercise. This finding was not surprising and is consistent with the notion that knowledge is a necessary but not sufficient element important for behavior change (Simons-Morton et al., 2012).
Implications In this study, scores on the HDFQ (Wagner et al., 2005) revealed several areas of deficient knowledge related to heart disease risk in patients with diabetes. In particular, age, gender, and lipid-related factors should be emphasized in patient education efforts. Race and education level differences in knowledge scores were also identified. While educational interventions to improve knowledge of CVD risk in patients with diabetes are important for all groups, emphasis should be placed on ensuring that strategies are appropriate for the reading and education level as well as the ethnic group of the individual. Targeting knowledge of CVD risk can play an important role in health behaviors through perceived benefits and barriers to diet and exercise. Effective strategies for improving knowledge of heart disease risk among adults with type 2 diabetes should consider focusing resources on radio, television, newspaper, or magazines as well as healthcare providers and patient education brochures as primary sources of sharing information with this population.
References American Diabetes Association and American College of Cardiology (ADA/ACC). (2002). The Diabetes-Heart Disease Link: Surveying attitudes, knowledge and risk—Executive summary. Retrieved from http://www.diabetes.org/uedocuments/executivesummary.pdf
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American Heart Association (AHA). (2012). Cardiovascular disease & diabetes. Why Diabetes Matters. Retrieved from http://www.heart.org/HEARTORG/ Conditions/Diabetes/WhyDiabetesMatters/Cardiovascular-DiseaseDiabetes UCM 313865 Article.jsp Bandura, A. (1994). Self-efficacy. In V. S. Ramachaudran (Ed.), Encyclopedia of human behavior (Vol. 4, pp. 71–81). New York: Academic Press. Centers for Disease Control (CDC). (2012). Summary Health Statistics for U.S. Adults: 2010, Table 8. Vital and health statistics (p. 6). Hyattsville, MD: U.S. Department of Health and Human Services. Dickerson, F. B., Goldberg, R. W., Brown, C. H., Kreyenbuhl, J. A., Wohlheiter, K., Fang, L., ... Dixon, L. B. (2005). Diabetes knowledge among persons with serious mental illness and type 2 diabetes. Psychosomatics, 46(5), 418–424. Gressle Tovar, E. (2007). Relationships between psychosocial factors and adherence to diet and exercise in adults with type 2 diabetes: A test of a theoretical model. Ph.D. dissertation, University of Texas Medical Branch at Galveston, Galveston, TX. Hernandez, C. A. (1997). The development and pilot testing of the Diabetes Activities Questionnaire (TDAQ): An instrument to measure adherence to the diabetes regimen. Applied Nursing Research, 10(4), 202–211. Janz, N. K. (1988). The health belief model in understanding cardiovascular risk-factor reduction behaviors. Cardiovascular Nursing, 24(6), 39–41. National Diabetes Education Program (NDEP). (2009). National Diabetes Education Program Survey of the public’s knowledge, attitudes, and practices related to diabetes: 2008 executive summary. Silver Spring, MD: National Diabetes Education Program, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases. Olsen, S., Smith, S., Oei, T., & Douglas, J. (2008). Health belief model predicts adherence to CPAP before experience with CPAP. European Respiratory Journal, 32(3), 710–717. Rosenstock, I. (1974). Historical origins of the health belief model. Health Education Monographs, 2(4), 328–335. Rosenstock, I. (2004). Health belief model. Encyclopedia of psychology (pp. 78– 80). American Psychological Association: Washington, DC. Rosenstock, I. M., Strecher, V. J., & Becker, M. H. (1988). Social learning theory and the health belief model. Health Education Quarterly, 15(2), 175–183. Simons-Morton, B., McLeroy, K. R., & Wendel, M. (2012). Learning, teaching and counseling. Behavior theory in health promotion practice and research. Burlington, MA: Jones and Bartlett Learning. Talbot, F., Nouwen, A., Gingras, J., Gosselin, M., & Audet, J. (1997). The assessment of diabetes related cognitive and social factors: The Multidimensional Diabetes Questionnaire. Journal of Behavioral Medicine, 20(3), 291–312. Tovar, E. G., Rayens, M. K., Clark, M., & Nguyen, H. (2010). Development and psychometric testing of the Health Beliefs Related to Cardiovascular Disease Scale: Preliminary findings. Journal of Advanced Nursing, 66(12), 2772– 2784. Wagner, J., Lacey, K., Chyun, D., & Abbott, G. (2005). Development of a patient questionnaire to measure heart disease risk knowledge in people with diabetes: The Heart Disease Fact Questionnaire. Patient Education and Counseling, 58, 82–87. World Heart Federation. (2013). Cardiovascular disease risk factors. Retrieved from http://www.world-heart-federation.org/cardiovascularhealth/cardiovascular-disease-risk-factors/
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