|Year : 2022 | Volume
| Issue : 1 | Page : 36-42
Socioeconomic and clinical factors associated with disease-related knowledge of cardiac rehabilitation patients in Brazil
Jessica B Loures1, Gabriela S S. Chaves2, Renata C Ribas3, Raquel R Britto2, Marian P Marchiori1, Gabriela L M. Ghisi4
1 Clinics Hospital, Federal University of Minas Gerais, Belo Horizonte, Brazil
2 Physiotherapy Department, Federal University of Minas Gerais, Belo Horizonte, Brazil
3 RC Physiotherapy Clinic, Belo Horizonte, Brazil
4 Cardiovascular Prevention and Rehabilitation Program, Toronto Rehabilitation Institute, University Health Network, Toronto, Canada
|Date of Submission||08-Oct-2021|
|Date of Acceptance||29-Dec-2021|
|Date of Web Publication||31-Jan-2022|
Dr. Gabriela L M. Ghisi
Toronto Rehabilitation Institute, University Health Network, 347 Rumsey Road, Toronto, Ontario - M4G 1R7,
Source of Support: None, Conflict of Interest: None
Objective: The objective of this study was to identify socioeconomic and clinical factors associated with disease-related knowledge of cardiac rehabilitation (CR) patients. Methods: Adults with coronary artery disease (CAD) were recruited during CR Phase 1 and completed questionnaires on the 1st day of Phase 2. Disease-related knowledge was assessed by the short version of the CAD Education Questionnaire. Socioeconomic status was defined by educational level, family income, and employment status. MannWhitney U and Spearman correlation were calculated to determine the association of knowledge with socioeconomic factors, number of risk factors, and wait time between hospital discharge and start of outpatient CR. Results: A convenience sample of 39 patients were recruited. Overall, the mean knowledge was 12.00 ± 3.3, which corresponds to 60% of possible scores. Monthly family income and number of risk factors influenced medical condition knowledge (P < 0.05), and employment status influenced total knowledge (P = 0.005) and risk factor knowledge (P = 0.002). Participants with three or more risk factors presented significantly higher knowledge (P = 0.02). Those that waited more than 17 weeks to start the CR presented significantly lower knowledge (P = 0.04). Conclusion: Participants with low income and unemployed were more likely to have inadequate disease-related knowledge; however, the entire sample presented low understanding of their condition. Public health strategies and educational interventions must continue to focus on these vulnerable groups.
Keywords: Attitudes, cardiac rehabilitation, patient education as topic, socioeconomic factors, health knowledge, practice
|How to cite this article:|
Loures JB, S. Chaves GS, Ribas RC, Britto RR, Marchiori MP, M. Ghisi GL. Socioeconomic and clinical factors associated with disease-related knowledge of cardiac rehabilitation patients in Brazil. Heart Mind 2022;6:36-42
|How to cite this URL:|
Loures JB, S. Chaves GS, Ribas RC, Britto RR, Marchiori MP, M. Ghisi GL. Socioeconomic and clinical factors associated with disease-related knowledge of cardiac rehabilitation patients in Brazil. Heart Mind [serial online] 2022 [cited 2022 Sep 28];6:36-42. Available from: http://www.heartmindjournal.org/text.asp?2022/6/1/36/336891
| Introduction|| |
Cardiovascular disease (CVD) is the leading cause of morbidity and mortality globally, with low- and middle-income countries (LMICs) carrying approximately 80% of this burden. Patients with cardiac conditions benefit significantly from participation in comprehensive cardiac rehabilitation (CR),, which is considered a Class 1 recommended component of the continuum of care for these patients., Comprehensive CR is an outpatient approach to primary or secondary prevention of CVD, composed of structured exercise, management of risk factors, psychosocial counseling, and comprehensive patient education.,
Despite well-documented benefits, CR is insufficiently available and underutilized, especially in LMICs. In particular, the rate of CR utilization among patients with low socioeconomic status (SES) in these countries is worrying., Indeed, robust evidence demonstrates an association between SES and health of cardiac patients,,, including high incidence of comorbidities and risk factors, and greater morbidity and mortality, among patients with low SES. However, studies evaluating the association between SES and cardiovascular health in LMICs are limited and often do not address the influence of these factors to important core components of CR programs, including patient education.
Disease-related knowledge is one of the main outcomes used to indicate the effectiveness of patient education strategies in cardiac care, and often targeted by prevention programs.,, While knowledge does not directly translate into behavior change,, it is a prerequisite for lifestyle modifications and ultimately improvements in cardiac health. Indeed, many behavior change theories rely on knowledge, such as the health belief model, theory of planned behavior, and social cognitive theory., Clearly, monitoring patients' disease-related knowledge is essential in CR settings in LMICs and can help guide programming. The literature also suggests that the ability to affect clinical behavior through educational interventions is unlikely to increase until the factors that affect this relationship are better understood, which include SES.
There is a paucity of research on the effects of socioeconomic variables in LMICs, including in CR participants.,, The present study focused on the influence of socioeconomic factors – represented by level of education, monthly family income, and employment status – in disease-related knowledge of CR patients with one or multiple risk factors in the LMIC of Brazil. Brazil has one of the greatest burdens of CVD in the world and the lowest CR capacity, with most programs not including patient education as a core component,,, although its efficacy has been empirically proven., Therefore, the aim of this study was to identify socioeconomic and clinical factors associated with disease-related knowledge of CR patients in the LMIC of Brazil.
| Methods|| |
The study design was prospective and observational. Ethics approval was obtained from the Research Board of the Federal University of Minas Gerais (UFMG; Belo Horizonte, Brazil; 74/2018). Data were collected between May and October/2018. All participants provided written informed consent.
Setting and participants
The study was conducted in the Brazilian city of Belo Horizonte, located in the geographically expansive state of Minas Gerais (21.7 million people). Minas Gerais is in the middle of Brazil – in the most developed region economically, but sharing boarder with the poorest one – and is often introduced as a good example of the socioeconomic variation that exists across the country. Ischemic heart disease is the cause of most death and disability in Minas Gerais.
A convenience sample of adults with diagnosis of coronary artery disease (CAD) were recruited during Phase 1 of CR (inhospital). All of these patients were referred to Phase 2 (outpatient CR) and completed a set of questionnaires at the 1st day of the program. The exclusion criteria were the following: being illiterate, and any visual or cognitive condition that would preclude the participant from completing the surveys. Participants have received informal education during their hospital stay.
Disease-related knowledge was assessed using the short version of the CAD Education Questionnaire (CADE-Q SV), which was validated in Brazilian Portuguese. CADE-Q SV has 20 items covering information about medical condition, risk factors, physical activity, nutrition, and psychosocial risk. Respondents should identify true statements, with each correct answer corresponding to one point. Higher scores indicate better knowledge.
Sociodemographic characteristics were self-reported by participants and included level of education, monthly family income, and employment status. Clinical characteristics were extracted from medical records and included CR referral indication, cardiac risk factors (i.e., hypertension, diabetes, dyslipidemia, smoking, sedentary lifestyle, stress, and depression), and time waiting for outpatient CR (i.e., weeks between hospital discharge and CR Phase 2 admission). In addition, participants reported if informal education was given when they received their cardiac diagnosis.
Statistical analysis was performed using SPSS version 25 (IBM Corp, Armonk, NY). Descriptive statistics were used to describe participants' sociodemographic, socioeconomic, and clinical characteristics. Chi-square analysis for categorical variables (i.e., sex, level of education, family income, and employment and clinical status) and t-tests for continuous variables (i.e., age) were used to compare proportions of respondents across different characteristics. The Shapiro–Wilk test was used to check for normality of CADE-Q SV scores and nonparametric tests were planned to be used if normality could not be assumed.
MannWhitney U-test was used to compare knowledge scores between socioeconomic groups (named level of education, monthly family income, and employment status). Spearman correlation coefficients were calculated to determine the association between disease-related knowledge and number of risk factors. For this analysis, participants were divided into two groups in accordance with the number of risk factors, based on recommendations from previous studies: Group 1 (2 or less risk factors) and Group 2 (3 or more risk factors)., The correlation between knowledge and informal education received at the time of cardiac diagnosis was also calculated. Finally, MannWhitney U-test was also used to compare mean knowledge scores across weeks waiting for CR and Spearman correlation coefficient was also calculated.
| Results|| |
Overall, 39 participants signed the consent form, completed the knowledge questionnaire, and self-reported their SES. [Table 1] presents the socioeconomic and clinical characteristics of participants. As shown, our sample was significantly younger, with lower educational level, family income between 2 and 3 minimum wages per month, and retired.
|Table 1: Socioeconomic and clinical characteristics of participants (n=39)|
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As for the disease-related knowledge, the mean total score of CADE-Q SV for the entire sample was 12.00 ± 3.3, which corresponds to 60% of total knowledge. In regard to knowledge areas, questions about physical activity presented the lowest scores (1.7 ± 0.9) and questions about risk factors the highest scores (2.8 ± 1.0).
Disease-related knowledge: Association with socioeconomic characteristics
[Figure 1], [Figure 2], [Figure 3 present the median and interquartile range of total knowledge scores overall and per knowledge area compared to level of education, monthly family income, and employment status, respectively. Significant differences were found on total and risk factor knowledge by employment status and medical knowledge by monthly family income. In summary, participants employed or retired had significantly higher knowledge (total, P = 0.005; and about risk factors, P = 0.002) than those unemployed, and participants with a monthly income of two or more Brazilian minimum wages had significantly higher knowledge about their medical condition than those that receive one or less Brazilian minimum wage per month (P = 0.02).
|Figure 1: Box-and-whisker plot showing median and interquartile range of total knowledge scores overall and per knowledge area by level of education (n = 39). Note: Lower whisker shows minimum values, lower box line shows 25th percentile, mid-line shows median values, upper box line shows 75th percentile, and upper whisker shows maximum values|
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|Figure 2: Box-and-whisker plot showing median and interquartile range of total knowledge scores overall and per knowledge area by family income (n = 39). Note: Lower whisker shows minimum values, lower box line shows 25th percentile, mid-line shows median values, upper box line shows 75th percentile, and upper whisker shows maximum values. *P < 0.05. Family income in Brazil is characterized by minimum wages per month. One minimum wage is 1100 BRL or 193.60 USD (April/2021)|
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|Figure 3: Box-and-whisker plot showing median and interquartile range of total knowledge scores overall and per knowledge area by employment status (n = 39). Note: Lower whisker shows minimum values, lower box line shows 25th percentile, mid-line shows median values, upper box line shows 75th percentile, and upper whisker shows maximum values. *P < 0.05|
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Disease-related knowledge: Association with clinical characteristics
Overall, the sample had a mean of 2.9 ± 1.4 risk factors (minimum = 0, maximum = 6).
[Table 2] presents the characteristics and knowledge of participants when divided into two groups in accordance with the number of risk factors. As shown, the only difference in knowledge scores was identified in the questions related to medical condition, where participants with three or more risk factors presented significantly higher knowledge than the ones with less risk factors (P = 0.02). In addition, results showed a significant correlation between number of risk factors and knowledge about psychosocial risk (r = 0.32; P = 0.045).
|Table 2: Characteristics and knowledge level of participants when divided into two groups in accordance with the number of risk factors (n=39)|
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In regard to informal education, 5 (12.8%) participants reported they have been educated by their health-care providers when the cardiac diagnosis was given to them. Results showed a significant positive correlation between informal education and total disease-related knowledge scores (r = 0.33; P = 0.04), risk factor knowledge (r = 0.38; P = 0.01), and physical activity knowledge (r = 0.39; P = 0.02).
Disease-related knowledge: Association with time between hospital discharge and start of cardiac rehabilitation
There was a mean of 8.7 ± 4.7 weeks (minimum = 2 weeks, maximum = 24 weeks) between hospital discharge and start of CR Phase 2. As illustrated in [Figure 4], total disease-related knowledge was significantly low for those that waited more than 17 weeks between hospital discharge and start of the CR program (P < 0.05; n = 8) compared to the other groups (i.e., less of equal to 4 weeks, n = 8; between 5 and 8 weeks, n = 12; between 9 and 16 weeks, n = 11); however, there were no significant correlations between knowledge and number of weeks between discharge and start of CR.
|Figure 4: Disease-related knowledge scores by weeks between hospital discharge and start of CR Phase 2. *P < 0.05 compared to the other groups. Total of participants in each group as follows: Less of equal to 4 weeks (n = 8), between 5 and 8 weeks (n = 12), between 9 and 16 weeks (n = 11), and above 17 weeks (n = 8)|
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| Discussion|| |
The primary objective of this study was to test competing hypothesis about the ways in which socioeconomic and clinical factors interact to influence disease-related knowledge of CR patients. Results from this study identified that monthly family income and number of risk factors influenced knowledge about medical condition, and employment status influenced total knowledge and knowledge about risk factors. Level of education was not significantly associated with patients' knowledge, although it has been a strong and constant predictor of disease awareness.,, This pattern of findings – combined to the fact that the mean total knowledge score of the overall sample was low compared to other studies using this knowledge tool,, – suggests that patients are in need for knowledge. This conclusion is also supported by a significant correlation between receiving informal education prior to CR and total, risk factor, and physical activity knowledge.
To our knowledge, this is the first study in LMICs that has attempted to assess the factors associated with disease-related knowledge of CR patients. Other studies in high-income countries, however, have examined associations between SES and patients' knowledge of cardiovascular risk factors.,,, In general, these studies have found that participants with low SES are more likely to have less knowledge of risk factors, but there is no information related to the knowledge of other important components of CR programs, such as physical activity. It is known that self-management – a pillar to cardiac care – is unachievable if patients are unaware of their condition., For CR patients, education should cover exercise, nutrition, and psychosocial well-being, among other topics, as they are core components of these programs and associated with certain skills and behaviors that patients should follow to improve their health. As SES has been identified as a barrier to effective self-management in cardiac patients, assessing differences in knowledge of specific areas by social groups can guide health educators on best teaching and support of self-management.
Unique to this study is the portrayal of waiting time to start CR after hospital discharge in the context of disease-related knowledge. Although there were no significant associations, total disease-related knowledge was significantly lower for those that waited more than 17 weeks after hospital discharge to start CR. Current guidelines state that cardiac patients should start CR programs within 4 weeks of hospital discharge; however, actual practice is variable, and delays are common in high-income countries, with no known information about LMIC. More research is needed to understand the consequences of longer wait times to patients' knowledge about their condition. In addition, inhospital CR should be structured to provide patients with educational tools to guide them to these waiting weeks.
The relationship between low SES and insufficient disease-related knowledge is unclear. A possible mechanism highlighted by the literature is that there are limited opportunities to learn about their conditions among patients with low SES. Health literacy – the skills and competencies of persons and organizations to meet the complex demands of health in modern society – plays a key role in this mechanism, as limited health literacy is associated with both low SES and less understanding of health information. In addition, individuals unemployed or retired and with low family income may not be able to afford ways to increase their knowledge. Practical direction on how to address these barriers is needed.
These results should be interpreted with caution. The first limitation is related to the generalizability of our findings and selection bias, as this study was conducted at one site. Our sample size was also small, which limits the conclusions of our findings. Second, the factors that constitute an individual's SES vary between location and culture but are generally demonstrated by four markers named income level, educational attainment, employment status, and environmental factors (e.g., neighborhood socioeconomic characteristics)., Our study has not collected information about the latter, which could also be investigated in future studies. Third, due to the study design, causal conclusions cannot be drawn. A fourth limitation is the reporting bias on SES because these measures were self-reported.
| Conclusion|| |
Participants with low income, unemployed, with <3 risk factors and that waited more than 17 weeks to start outpatient CR after hospital discharge were more likely to have inadequate disease-related knowledge; however, the entire sample presented low understanding of their condition. Public health strategies and educational interventions must continue to focus on these vulnerable groups.
Despite the results found in our study, we understand that all individuals – independent of their SES and clinical status – had insufficient disease-related knowledge and are in need to be educated. Although in the context of CR it should be expected that these patients increase their knowledge and awareness over time, programs should consider the context within which disadvantaged patient is managing their care, and understand the barriers they face, allocating resources to support and educate these patients properly. More research is needed to understand CR patients' unique information needs and if they change between subjects with different socioeconomic indicators since education programs should always consider individual differences and include distinct messages for subgroups of the population.
Since a lack of knowledge may lead to the adoption of unhealthy behaviors and subsequent increases in cardiovascular morbidity and mortality, health education targeting all cardiac patients should be included as part of the continuum of care. Cardiac College™, a comprehensive, evidence-based virtual patient education, is available open access in Brazilian Portuguese for programs to use and can be a useful resource in this context.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Roth GA, Johnson C, Abajobir A, Abd-Allah F, Abera SF, Abyu G, et al.
Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll Cardiol 2017;70:1-25.
Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, et al.
Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380:2197-223.
Grace SL, Turk-Adawi KI, Contractor A, Atrey A, Campbell N, Derman W, et al.
Cardiac rehabilitation delivery model for low-resource settings. Heart 2016;102:1449-55.
Oldridge N, Taylor RS. Cost-effectiveness of exercise therapy in patients with coronary heart disease, chronic heart failure and associated risk factors: A systematic review of economic evaluations of randomized clinical trials. Eur J Prev Cardiol 2020;27:1045-55.
Simon M, Korn K, Cho L, Blackburn GG, Raymond C. Cardiac rehabilitation: A class 1 recommendation. Cleve Clin J Med 2018;85:551-8.
Herdy AH, López-Jiménez F, Terzic CP, Milani M, Stein S, Carvalho T, et al.
South American guidelines for cardiovascular disease prevention and rehabilitation. Arq Bras Cardiol 2014;103:1-31.
Chaves G, Turk-Adawi K, Supervia M, Pio CS, Abu-Jeish A, Mamataz T, et al
. Cardiac rehabilitation dose around the world: Variation and correlates. Circ Cardiovasc Qual Outcomes 2020;13:e005453.
Pesah E, Turk-Adawi K, Supervia M, Lopez-Jimenez F, Britto R, Ding R, et al.
Cardiac rehabilitation delivery in low/middle-income countries. Heart 2019;105:1806-12.
Graversen CB, Johansen MB, Eichhorst R, Johnsen SP, Riahi S, Holmberg T, et al.
Influence of socioeconomic status on the referral process to cardiac rehabilitation following acute coronary syndrome: A cross-sectional study. BMJ Open 2020;10:e036088.
Ritchey MD, Maresh S, McNeely J, Shaffer T, Jackson SL, Keteyian SJ, et al.
Tracking cardiac rehabilitation participation and completion among medicare beneficiaries to inform the efforts of a national initiative. Circ Cardiovasc Qual Outcomes 2020;13:e005902.
Clark AM, DesMeules M, Luo W, Duncan AS, Wielgosz A. Socioeconomic status and cardiovascular disease: Risks and implications for care. Nat Rev Cardiol 2009;6:712-22.
Schultz WM, Kelli HM, Lisko JC, Varghese T, Shen J, Sandesara P, et al.
Socioeconomic status and cardiovascular outcomes: Challenges and interventions. Circulation 2018;137:2166-78.
Winkleby MA, Jatulis DE, Frank E, Fortmann SP. Socioeconomic status and health: How education, income, and occupation contribute to risk factors for cardiovascular disease. Am J Public Health 1992;82:816-20.
Alter DA, Iron K, Austin PC, Naylor CD; SESAMI Study Group. Socioeconomic status, service patterns, and perceptions of care among survivors of acute myocardial infarction in Canada. JAMA 2004;291:1100-7.
Horne BD, Muhlestein JB, Lappé DL, Renlund DG, Bair TL, Bunch TJ, et al.
Less affluent area of residence and lesser-insured status predict an increased risk of death or myocardial infarction after angiographic diagnosis of coronary disease. Ann Epidemiol 2004;14:143-50.
Alboni P, Amadei A, Scarfò S, Bettiol K, Ippolito F, Baggioni G. In industrialized nations, a low socioeconomic status represents an independent predictor of mortality in patients with acute myocardial infarction. Ital Heart J 2003;4:551-8.
Ghisi GL, Abdallah F, Grace SL, Thomas S, Oh P. A systematic review of patient education in cardiac patients: Do they increase knowledge and promote health behavior change? Patient Educ Couns 2014;95:160-74.
Anderson L, Brown JP, Clark AM, Dalal H, Rossau HK, Bridges C, et al.
Patient education in the management of coronary heart disease. Cochrane Database Syst Rev 2017;6:CD008895.
Barnason S, White-Williams C, Rossi LP, Centeno M, Crabbe DL, Lee KS, et al.
Evidence for therapeutic patient education interventions to promote cardiovascular patient self-management: A scientific statement for healthcare professionals from the American Heart Association. Circ Cardiovasc Qual Outcomes 2017;10:e000025.
Ghisi GL, Grace SL, Thomas S, Oh P. Behavior determinants among cardiac rehabilitation patients receiving educational interventions: An application of the health action process approach. Patient Educ Couns 2015;98:612-21.
Kennedy T, Regehr G, Rosenfield J, Roberts SW, Lingard L. Exploring the gap between knowledge and behavior: A qualitative study of clinician action following an educational intervention. Acad Med 2004;79:386-93.
Oldridge NB, Streiner DL. The health belief model: Predicting compliance and dropout in cardiac rehabilitation. Med Sci Sports Exerc 1990;22:678-83.
Armitage CJ, Conner M. Efficacy of the theory of planned behaviour: A meta-analytic review. Br J Soc Psychol 2001;40:471-99.
Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory. Hoboken, New Jersey, USA: Practice-Hall, Inc; 1986.
Rimal RN. Closing the knowledge-behavior gap in health promotion: The mediating role of self-efficacy. Health Commun 2000;12:219-37.
Howe LD, Galobardes B, Matijasevich A, Gordon D, Johnston D, Onwujekwe O, et al.
Measuring socio-economic position for epidemiological studies in low- and middle-income countries: A methods of measurement in epidemiology paper. Int J Epidemiol 2012;41:871-86.
Shanmugasegaram S, Perez-Terzic C, Jiang X, Grace SL. Cardiac rehabilitation services in low- and middle-income countries: A scoping review. J Cardiovasc Nurs 2014;29:454-63.
Britto RR, Supervia M, Turk-Adawi K, Chaves GS, Pesah E, Lopez-Jimenez F, et al.
Cardiac rehabilitation availability and delivery in Brazil: A comparison to other upper middle-income countries. Braz J Phys Ther 2020;24:167-76.
Pesah E, Supervia M, Turk-Adawi K, Grace SL. A review of cardiac rehabilitation delivery around the world. Prog Cardiovasc Dis 2017;60:267-80.
Chaves GS, Ghisi GL, Grace SL, Oh P, Ribeiro AL, Britto RR. Effects of comprehensive cardiac rehabilitation on functional capacity in a middle-income country: A randomised controlled trial. Heart 2019;105:406-13.
Chaves GS, Lima de Melo Ghisi G, Britto RR, Grace SL. Maintenance of gains, morbidity, and mortality at 1 year following cardiac rehabilitation in a middle-income country: A wait-list control crossover trial. J Am Heart Assoc 2019;8:e011228.
GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020;396:1204-22.
Ghisi GL, Sandison N, Oh P. Development, pilot testing and psychometric validation of a short version of the coronary artery disease education questionnaire: The CADE-Q SV. Patient Educ Couns 2016;99:443-7.
Ghisi GL, Chaves GS, Loures JB, Bonfim GM, Britto R. Validation of the Brazilian-Portuguese version of a short questionnaire to assess knowledge in cardiovascular disease patients (CADE-Q SV). Arq Bras Cardiol 2018;111:841-9.
Lee DS, Chiu M, Manuel DG, Tu K, Wang X, Austin PC, et al.
Trends in risk factors for cardiovascular disease in Canada: Temporal, socio-demographic and geographic factors. CMAJ 2009;181:E55-66.
Zeitouni M, Clare RM, Chiswell K, Abdulrahim J, Shah N, Pagidipati NP, et al.
Risk factor burden and long-term prognosis of patients with premature coronary artery disease. J Am Heart Assoc 2020;9:e017712.
Potvin L, Richard L, Edwards AC. Knowledge of cardiovascular disease risk factors among the Canadian population: Relationships with indicators of socioeconomic status. CMAJ 2000;162:S5-11.
Kayaniyil S, Ardern CI, Winstanley J, Parsons C, Brister S, Oh P, et al.
Degree and correlates of cardiac knowledge and awareness among cardiac inpatients. Patient Educ Couns 2009;75:99-107.
Wartak SA, Friderici J, Lotfi A, Verma A, Kleppel R, Naglieri-Prescod D, et al.
Patients' knowledge of risk and protective factors for cardiovascular disease. Am J Cardiol 2011;107:1480-8.
Ghisi GL, Chaves GS, Ribeiro AL, Oh P, Britto RR, Grace SL. Comprehensive cardiac rehabilitation effectiveness in a middle-income setting: A randomized controlled trial. J Cardiopulm Rehabil Prev 2020;40:399-406.
Ghisi GL, Grace SL, Anchique CV, Gordillo X, Fernandez R, Quesada D, et al.
Translation and evaluation of a comprehensive educational program for cardiac rehabilitation patients in Latin America: A multi-national, longitudinal study. Patient Educ Couns 2021;104:1140-8.
Marofi Z, Bandari R, Heravi-Karimooi M, Rejeh N, Montazeri A. Cultural adoption, and validation of the Persian version of the coronary artery disease education questionnaire (CADE-Q): A second-order confirmatory factor analysis. BMC Cardiovasc Disord 2020;20:345.
Tsuji M, Arima H, Ohkubo T, Nakamura K, Takezaki T, Sakata K, et al.
Socioeconomic status and knowledge of cardiovascular risk factors: NIPPON DATA2010. J Epidemiol 2018;28 Suppl 3:S46-52.
Davis SK, Winkleby MA, Farquhar JW. Increasing disparity in knowledge of cardiovascular disease risk factors and risk-reduction strategies by socioeconomic status: Implications for policymakers. Am J Prev Med 1995;11:318-23.
Tchicaya A, Lorentz N, Demarest S, Beissel J. Persistence of socioeconomic inequalities in the knowledge of cardiovascular risk factors five years after coronary angiography. Eur J Cardiovasc Nurs 2018;17:136-47.
Riegel B, Moser DK, Buck HG, Dickson VV, Dunbar SB, Lee CS, et al.
Self-care for the prevention and management of cardiovascular disease and stroke: A scientific statement for healthcare professionals from the American Heart Association. J Am Heart Assoc 2017;6:e006997.
Barlow J, Wright C, Sheasby J, Turner A, Hainsworth J. Self-management approaches for people with chronic conditions: A review. Patient Educ Couns 2002;48:177-87.
Kabboul NN, Tomlinson G, Francis TA, Grace SL, Chaves G, Rac V, et al.
Comparative effectiveness of the core components of cardiac rehabilitation on mortality and morbidity: A systematic review and network meta-analysis. J Clin Med 2018;7:514.
Mead H, Andres E, Ramos C, Siegel B, Regenstein M. Barriers to effective self-management in cardiac patients: The patient's experience. Patient Educ Couns 2010;79:69-76.
Piepoli MF, Corrà U, Adamopoulos S, Benzer W, Bjarnason-Wehrens B, Cupples M, et al.
Secondary prevention in the clinical management of patients with cardiovascular diseases. Core components, standards and outcome measures for referral and delivery: A policy statement from the cardiac rehabilitation section of the European Association for Cardiovascular Prevention & Rehabilitation. Endorsed by the committee for practice guidelines of the European Society of Cardiology. Eur J Prev Cardiol 2014;21:664-81.
Marzolini S, Blanchard C, Alter DA, Grace SL, Oh PI. Delays in referral and enrolment are associated with mitigated benefits of cardiac rehabilitation after coronary artery bypass surgery. Circ Cardiovasc Qual Outcomes 2015;8:608-20.
Arpey NC, Gaglioti AH, Rosenbaum ME. How socioeconomic status affects patient perceptions of health care: A qualitative study. J Prim Care Community Health 2017;8:169-75.
Sørensen K, Van den Broucke S, Fullam J, Doyle G, Pelikan J, Slonska Z, et al.
Health literacy and public health: A systematic review and integration of definitions and models. BMC Public Health 2012;12:80.
[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2]