|Year : 2021 | Volume
| Issue : 3 | Page : 190-197
A causal model for the control of risk factors for cardiovascular diseases using a new temperamental personality theory in the general population of Western Iran: The mediating role of self-regulation
Ali Zakiei1, Habibolah Khazaie1, Mohammadreza Alimoradi2, Amirmehdi Kadivarian3, Nader Rajabi-Gilan4, Saeid Komasi5
1 Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
2 Department of Psychology, Razi University, Kermanshah, Iran
3 Department of Psychology, AZAD University, Kermanshah, Iran
4 Social Development and Health Promotion Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
5 Department of Psychiatry, Student Research Committee, Kurdistan University of Medical Sciences, Sanandaj; Department of Neuroscience and Psychopathology Research, Mind GPS Institute, Kermanshah, Iran
|Date of Submission||05-Oct-2021|
|Date of Decision||06-Nov-2021|
|Date of Acceptance||11-Nov-2021|
|Date of Web Publication||14-Dec-2021|
Mind GPS Institute, Nasr Boulevard, 404 Mokhaberat, Kermanshah
Source of Support: None, Conflict of Interest: None
Objective: Given the need for further studies on health-promoting behaviors, the present study aimed to investigate the antecedents of controlling risk factors for cardiovascular diseases (CVDs) according to the affective and emotional composite temperament (AFECT) model and the mediating role of self-regulation. Materials and Methods: The samples of this cross-sectional study included 776 people resident in Kermanshah in Western Iran in 2019. The participants were selected using a cluster sampling method. Data were collected using the controlling the risk factors for cardiovascular disease questionnaire (CRC), the short form self-regulation questionnaire, and the AFECT scale. The structural equation modeling was used to analyze the data. Results: The analysis results indicated that there was a significant correlation between dimensions of AFECT and self-regulation with the control of risk factors for CVDs. The results also indicated that the dimensions of AFECT did not directly affect the control of risk factors for CVDs, but the mediating role of self-regulation was confirmed in this regard. Conclusion: According to the results, the dimensions of AFECT could affect the control of risk factors for CVDs through self-regulation; hence, the role of self-regulation in controlling CVDs should be taken into account.
Keywords: Cardiovascular diseases, control, risk factors, self-regulation, temperament
|How to cite this article:|
Zakiei A, Khazaie H, Alimoradi M, Kadivarian A, Rajabi-Gilan N, Komasi S. A causal model for the control of risk factors for cardiovascular diseases using a new temperamental personality theory in the general population of Western Iran: The mediating role of self-regulation. J Pract Cardiovasc Sci 2021;7:190-7
|How to cite this URL:|
Zakiei A, Khazaie H, Alimoradi M, Kadivarian A, Rajabi-Gilan N, Komasi S. A causal model for the control of risk factors for cardiovascular diseases using a new temperamental personality theory in the general population of Western Iran: The mediating role of self-regulation. J Pract Cardiovasc Sci [serial online] 2021 [cited 2022 May 29];7:190-7. Available from: https://www.j-pcs.org/text.asp?2021/7/3/190/332496
| Introduction|| |
The new health paradigm indicates that being healthy depends on several factors, including personality traits. Given this new paradigm, researchers have sought to present models for predicting health-promoting behavior based on various factors.,,, Examination of previous models indicates that those models are all general, and are not presented for a specific disease, but each disease may have different causes of prevalence. Therefore, it is necessary to provide a model for each disease according. Furthermore, most previous models ignored the roles of heredity and behavioral genetics. In our new model, we examined the role of temperament that is an inherited aspect of personality. Furthermore, human beings can act according to their own will and logic so that the mechanism of this behavior should be specified in any model. We investigate the role of self-regulation in the present section.
Cardiovascular diseases (CVDs) are important causes of death and disability worldwide and have significantly grown in recent years. Studies have mentioned changes in diet, inactivity and low activity, smoking, poor diet, obesity, and stress as the causes of increasing these diseases.,, The causes of CVDs can be divided into two factors, unmodifiable and modifiable; the earlier include factors such as age, sex, and genetics that are not controlled by individuals, but the latter includes factors such as smoking, high blood pressure, nonnormal blood glucose, high cholesterol, stress, obesity, and sedentary lifestyle that can be controlled by individuals. Studies indicate that risk factors such as smoking, high blood pressure, and high cholesterol alone cannot be strong antecedents of coronary heart diseases. Several researchers have claimed that psychological characteristics such as negative emotional states, depression, anger, hostility, anxiety, psychological stressors, social relationships, and social support are significant antecedents of CVDs. Recent advances in behavioral medicine have drawn the attention of health psychologists to key roles of nonbiological factors in the development of these diseases. Familiarity with the main roles of risk factors for CVDs provides a new paradigm in epidemiological methods. Hence that understanding these risk factors create an important perspective on the prevention, etiology, course, and treatment of this important problem. If risk factor control begins at the end of the youth, it will delay the growth and development of the early stages of the diseases and their progression, and ultimately reduce or delay their onset. The high prevalence of CVDs highlights the importance of tackling them. Therefore, it is necessary to identify and investigate biological and psychological risk factors in the prevention and treatment of these diseases. Personality is important as a risk factor and a predictor of heart health. According to some psychologists, personality consists of two parts, temperament and character. Characters are related to upbringing and acquired abilities and are influenced by personal growth and development, but temperament is only influenced by genetics. Lara et al.(2012) proposed a new model based on the temperament aspect of personality. This model is called the affective and emotional composite temperament (AFECT) that covers many concepts of personality theories.
Self-regulation is a multidimensional structure that includes cognitive, motivational-affective, and physiological processes affecting the active control of purposeful actions. Self-regulation plays an active role in systems such as active control, initiation, moderation, continuity, and coordination actions. Self-regulation is a mediator between personality and health. The self-regulation scale was also developed to measure health-related coping behaviors and their relationship with the main dimensions of personality. The studies have confirmed a health-disease factor, according to which some personality traits are prone to a disease such as cancer or CVDs. The results of a 15-year longitudinal study indicated that self-regulation is associated with mortality from cancer, CVDs, and other diseases.
It seems that identifying the relevant factors and antecedents of heart health-related behaviors can prevent CVDs and reduce the impact of behaviors relating to CVDs such as smoking, inactivity, and consumption of fatty and salty foods, and the need to identify the causes of these behaviors. Since there was not enough study on the nature of these factors in Iran, the present study sought to provide a model with the use of personality traits as antecedents of controlling heart health-related behaviors to investigate such issues in which dimensions are not well known and also to gain more experience about these behaviors. Therefore, the present study aimed to investigate the antecedents of controlling risk factors for CVDs based on the AFECT and self-regulation models.
| Materials and Methods|| |
Design and participants
The statistical population of the cross-sectional study consisted of all residents of Kermanshah in the age range of 30–65 years who had been living in the city in 2019 for at least 5 years. In the present study, we needed to select a sample in which the proposed research model could be tested. According to statisticians, 15 individuals were needed for each variable in structural equation modeling (SEM) because 26 variables were examined in the present study for which the minimum sample was equal to 390, but the number was doubled (n = 780) for more generalizability and reduction of the first type error, but it increased to 800 considering factors such as the drop of participants or noncooperation. After data collection, 776 questionnaires were found to be acceptable. The sampling method had a multistage cluster method; hence, the city was first divided into six districts based on the municipality divisions, and then three districts were selected from the districts. Sampling was then performed according to statistical blocks (based on the division of statistical blocks of the Statistical Centre of Iran), and 3 blocks were randomly selected from each district so that a total of 9 statistical blocks were selected and 87 individuals were randomly selected from each block. The questioners then went to different neighborhoods of the city and asked the selected people to cooperate with the questioners if they wanted.
Ethical considerations were taken into account in the study in a way that the research objectives were clearly explained to the participants. Participation in the study was completely voluntary and without any obligation, and they were assured that the results of studies and tests were completely confidential. In this regard, the principle of confidentiality was observed, the participant's identity information was not recorded, and only the codes provided by the participants were used to identify the data. At all stages of the research, including data collection, the participants had the right to cancel the continuation of the research, and the informed consent was obtained from all participants, and they signed the consent forms. It is worth mentioning that the study was registered in the Research Deputy of Kermanshah University of Medical Sciences in Iran and received an ethical license from the ethics committee of the university.
Inclusion and exclusion criteria
Inclusion criteria of the study were as follows: At least 30 years of age and at most 65 years of age; having at least 5 years of residence in Kermanshah; not having mental diseases; having full consent to participate in the study, and having at least a third-grade secondary school degree. Incomplete answer sheets were also excluded from the study before the analysis.
The predictive variables include the temperaments including subscales, namely volition, anger, inhibition, sensitivity, coping, and control in the emotional aspect, and depression, anxiety, apathetic, cyclothymic, dysphoric, volatile, obsessive, euthymic, hyperthymic, euphoric, disinhibited, and euphoric in the affective aspect. The mediating variable is self-regulation. In the present study, the control of risk factors for CVDs (diet, smoking, exercise, stress management, medical procedures, planing, and control) is criterion or dependent variable.
Demographic information questionnaire
We used a self-reported demographic information questionnaire to measure personal, social, and economic indices. The questionnaire contained information about the age, sex, marital status, education level, and employment status of the samples.
Controlling the risk factors for cardiovascular disease questionnaire (CRC)
The CRC developed by Reshadat et al.(2017) is consisting of 23 items that use to assess the control of CVDs risk factors. Six subscales of the questionnaire are include diet, exercise, stress, medical procedures, smoking, and planning. The questionnaire was scored from 0 to 5 based on the Likert scale: Zero (never), one (very low), two (low), three (somewhat), four (high), and five (very high). The total score is between 0 and 115, and a higher score indicated more control over high-risk behaviors for heart health. Reshadat et al.(2017) were approved the questionnaire reliability and validity using Cronbach's alpha (α = 0.80), test-retest (0.76), and exploratory and confirmatory factor analyses methods. Furthermore, the questionnaire reliability was examined in the present study using Cronbach's alpha (α = 0.77).
The short form self-regulation questionnaire
The self-regulation questionnaire (SRQ) that was designed by Carey (2004) is a 31-item questionnaire based on the SRQ designed by Brown et al.(1998). The questionnaire assesses the capacity and intensity of self-regulation. Research indicates that the questionnaire is single-factor and is scored as strongly disagree = 1 to strongly agree = 5, and eventually each person is scored between 31 and 155. The total score of the questionnaire, which is the total score obtained, indicates the degree of self-regulation. The results of a study by Neal and Carey confirmed the validity and reliability of the questionnaire using Cronbach's alpha (α = 0.86). In the present study, we used exploratory factor analysis to evaluate the face validity of the self-regulatory questionnaire. After investigating the factor loadings of the questions, we deleted 4 items of the questionnaire due to the lack of necessary factor loading (0.40). They were items 6, 7, 23, and 27. Furthermore, the questionnaire reliability was examined in the present study using Cronbach's alpha (α = 0.93).
The affective and emotional composite temperament scale
Lara et al., designed this scale to identify dimensions of the affective-emotional temperament of the personality., This scale evaluates the affective-emotional aspects separately and has a total of 62 items. The emotional aspect of the scale is a 7-point bipolar scale that is divided into six subscales, including volition, anger, inhibition, sensitivity, coping, and control. In the affective aspect, the dimensions are divided into 12 dimensions, including depression, anxiety, apathetic, cyclothymic, dysphoric, volatile, obsessive, euthymia, hyperthymia, euphoric, disinhibited, and euphoric. Cronbach's alpha coefficient of the scale was reported to be 0.86, and it was between 0.75 and 0.91 for subscales. Furthermore, the questionnaire reliability was examined in the present study using Cronbach's alpha for dimensions of emotional temperament (α = 0.89) and affective temperament (α = 0.67).
The present study was correlational and had a SEM type. The descriptive and inferential statistical methods in the present study were as follows: Cronbach's alpha method for evaluating the reliability of tools, and the confirmatory factor analysis for evaluating their validity; descriptive statistical methods, including mean and standard deviation for describing the status of variables, and simple correlation coefficient between variables for examining the relationship between variables, and testing the relevant hypotheses; statistical analyses for examining the assumptions of SEM; SEM method for testing the model in the research; and the macro bootstrapping method suggested by Preacher and Hayes (2008) for investigating indirect relationships and investigating the indirect effect of temperament on heart health-related behaviors. We performed data analysis using the SPSS-19 (IBM SPSS, Armonk, NY, USA) and AMOS-19.
| Results|| |
The results of the analysis were performed on 776 people, of whom 481 (62%) were female. [Table 1] presents the comprehensive demographic characteristics of the sample.
To examine the relationship between AFECT and the control of risk factors for CVDs, we used the Pearson correlation coefficient test and presented its results in [Table 2]. The results of [Table 2] indicate that there was a significant relationship between dimensions of emotional temperaments and control of risk factors for CVDs. The results were as follows: There was a correlation coefficient of 0.37 between volition and control of risk factors for CVDs, a correlation coefficient of − 0.27 between anger and control of risk factors for CVDs, −0.23 between inhibition and control of risk factors for CVDs, 0.38 between sensitivity and control of risk factors for CVDs, 0.41 between control and risk factors for CVDs, and 0.41 between control component of emotional temperament dimensions and total score of control of risk factors for CVDs, all of which had a significant correlation coefficient at a level of P < 0.001. The results of the analysis also indicated that there was a significant relationship between dimensions of emotional temperament (rather than obsessive and irritability components) with the control of risk factors for CVDs.
|Table 2: Correlation coefficients between Affective and Emotional Composite Temperament Scale and self-regulation with the control of risk factors for cardiovascular diseases|
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Furthermore, we used the Pearson correlation coefficient test to investigate the relationship between self-regulation and control of risk factors for CVDs, and the results are presented in [Table 2]. The results of [Table 2] indicate that there is a positive and significant correlation between components of self-regulation and control of risk factors for CVDs. The results indicated that the correlation coefficient between self-regulation and total score of controlling risk factors for CVDs was 0.52 that was significant at a level of P < 0.001.
We utilized the path analysis test with AMOS as shown in [Figure 1] to investigate the mediating role of self-regulation in the relationship between AFECT and the control of risk factors for CVDs.
Based on the analysis results, dimensions of AFECT and self-regulation could predict 45% of changes in the control of risk factors for CVDs. [Table 3] presents standardized and nonstandardized coefficients of paths of this model. [Table 3] presents the analysis results, indicating that dimensions of AFECT do not directly affect the control of risk factors for CVDs, but self-regulation with a standard coefficient of 0.86 has a direct effect on the control of risk factors for CVDs.
[Table 4] shows the standardized and nonstandardized coefficients of indirect effects for investigating the mediating role of self-regulation in the relationship of emotional temperament and controlling risk factors for CVDs. The results of [Table 4] indicate the standardized coefficient of 0.47 for the path of positive emotion dimensions toward the control risk factors for CVDs through self-regulation, and it is significant at a level of P < 0.001, but there was not any significant standardized coefficient of the path for negative emotions toward controlling risk factors for CVDs through self-regulation; in other words, the mediating role of self-regulation is rejected in the relationship between dimensions of negative emotions to control the risk factors for CVDs. The results also indicate that the mediating effect of self-regulation is confirmed in the relationship between emotional dimensions and control of risk factors for CVDs. Indirect standard coefficients were 0.17 for positive emotions and − 0.19 for negative emotions.
|Table 4: Standardized and nonstandardized coefficients of indirect effects|
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| Discussion|| |
Given the need for further studies on health-promoting behaviors, the present study aimed to investigate the antecedents of controlling risk factors for CVDs based on the AFECT and self-regulation models. The results indicated that there were significant relationships between dimensions of emotional temperament and control of risk factors for CVDs. In more detail, the present results showed that there is a positive relationship between the volition component and the control of risk factors for CVDs. In other words, the more “volition” is higher in individuals, the more they can control risky behaviors for CVDs, and the less it is, the less control they have over these behaviors. The “volition” dimension refers to inner energy that is stable and can motivate a person to do things. This dimension is associated with motivation and pleasure, and the fact is that performing any behavior requires motivation, and controlling risky behaviors of CVDs is not an exception to this rule. Therefore, if individuals have less volition, they have less desire and motivation to control these behaviors.
There is a negative relationship between anger and control of risk factors for CVDs. Therefore, the more “anger” people have, the less they can control the risky behaviors for CVDs, and the less anger they have, the more they can control these behaviors. The present results were consistent with previous studies. A review study examined the role of anger and hostility as risk factors for CVDs, and the findings confirmed the role of these personality factors in CVDs. The results of a another study indicated that anger increased the risk for CVDs. Behavior control required a cognitive process based on the analysis of the advantages and disadvantages of behavior. People with a high level of anger were weak due to high physiological stimulation in cognitive fields. Thus, the control of risk factors for CVDs decreased in them.
Our findings showed there is a negative relationship between inhibition and control of risk factors for CVDs, indicating that the more “inhibition” people have, the less they can control the risky behaviors of CVDs, and the less inhibition, the greater the control of these behaviors. Although we expected the opposite results, the results of a meta-analysis showed that inhibitory control training has a small effect on health behaviors. The inhibition variable as a temperamental personality concept is mainly rooted in psychobiological approaches. Therefore, this variable may not have a strong relationship with health concepts such as control of risk factors.
There is a positive relationship between sensitivity and control of risk factors for CVDs, indicating that the more “sensitive” people are, the more they can control risky behaviors for CVDs, and the lower the sensitivity, the less control they have over these behaviors. The sensitivity dimension deals with the individual's vulnerability to interpersonal problems and life events. It seems that people with high levels of this dimension of emotional temperament, refrain from doing these behaviors due to fear of harm caused by risky behaviors of CVDs.
In accordance with Park and Iacocca, the present results show that there is a positive relationship between coping and controlling risk factors for CVDs, indicating that the more “coping” people have, the more they can control risky behaviors for CVDs, and the lower the coping component, the less control they can have over these behaviors. The coping dimension refers to the individuals' ability to process the current life problems and indicates the solution of life problems through acquired experiences. It seems that people, who have more of this dimension, can manage stress, use problem-solving strategies well, and better control unhealthy behaviors. Therefore, coping can increase the sense of ability to control risky behaviors of CVDs. According to Park and Iacocca, the concepts of coping and stress management along with self-regulation are important in health behavior.
There is a positive relationship between the control of risk factors for CVDs, indicating that the more “control” people have, the more they can control risky behaviors for CVDs, and the less control there is, the less control they have over these behaviors. The emotional temperament dimension of control includes cases such as monitoring, awareness and concentration growth, planning strategies, sense of responsibility, and organization; hence, people with high levels of control can better plan for their health, avoid high-fat and high-salt foods, and generally control risky behaviors for CVDs.
The analysis results also indicated that there were significant relationships between dimensions of emotional temperament (rather than obsessive and irritability components) with the control of risk factors for CVDs. The results were consistent with previous studies., In a study titled “personality traits as risk factors for mortality due to myocardial infarction and coronary heart diseases,” the results that study showed, there was a significant relationship between personality traits and mortality due to myocardial infarction and CVDs. Furthermore, the results of a study indicated that emotions such as anxiety, depression, and anger were risk factors for CVDs. Consistent with our findings, the results of two studies on large samples showed that stress and the presence of stressful events in life along with depression are among the most important risk factors for CVDs., However, these studies have not examined the mediating variable and have only dealt with the correlations between these variables. Negative emotions, because of their unpleasant effects, seem to put a person in a position to think about reducing these effects. Therefore, people who do not have strategies to regulate these emotions use measures such as eating high-fat foods, smoking, and the like as strategies for regulating emotions.
Consistent with the findings of previous studies,, the results of the present study also indicated that there was a positive relationship between self-regulation and control of risk factors for CVDs. Therefore, the higher “self-regulation” people have, the more they can control the risky behaviors of CVDs, and the lower self-regulation they have, the less they can control these behaviors. The results of a previous study confirm the mediating role of self-regulation in the relationship between negative emotions such as anxiety, hostility, and depression with risk factors for CVDs. The results of this study showed that the relationship between self-regulation and CVDs is still stable, excluding the effects of positive and negative emotions. The model proposed by Kubzansky et al.(2011) also showed that self-regulation plays a key role in controlling high-risk behaviors such as smoking, overweight and obesity, and high cholesterol levels and can eliminate the harmful effects of negative emotion. The results of another study showed that self-regulation is associated with a reduced risk of CVDs by modifying people's lifestyles. Self-regulation includes cognitive, motivational-emotional, and physiological processes affecting the active control of purposeful actions. Self-regulation plays an active role in systems such as active control, initiation, moderation, continuity, and coordination actions. Self-regulation includes an individual's ability to organize and self-manage behaviors to achieve goals. Therefore, people who have more self-regulation can control risky behaviors of CVDs due to better planning and self-management.
The present results confirm the main hypothesis of the research and generally indicated that self-regulation played a mediating role in the relationship between AFECT and control of risk factors for CVDs. Therefore, negative dimensions of AFECT lead to risky behaviors of CVDs when people have less self-regulation, and positive dimensions lead to such behaviors when their self-regulation is low. The results also confirmed the role of self-regulation in the relationship between personality and health. The results of a study aimed at investigating the role of self-regulation in the relationship between personality traits and health-related behaviors such as smoking and overeating showed that positive personality traits can lead to improved health when self-regulation is enhanced. Previously, the role of cognitive variables similar to self-regulation has also been proposed. For example, Reshadat et al.(2017) examined the relationship between heart disease health literacy, personality traits, and heart disease risk perception by controlling heart health-related behaviors through the role of self-efficacy mediator. The results indicated that there was a significant correlation between all personality traits and control of risk factors for CVDs. There was also a significant correlation between self-efficacy, heart-related health literacy, and perception of heart disease risk with control of risk factors for CVDs.
The research results indicated that people with positive affective-emotional dimensions had the necessary facilities to regulate emotions and cognitions because of the hereditary ability. They were better able to have cognitive abilities such as self-regulation, and the abilities played essential roles in controlling risk factors for CVDs. Self-regulation also leads to a higher perception of stress, which is a factor in the development of heart diseases; hence, these people can manage it.
| Conclusion|| |
According to the research results, self-regulation played a mediating role in the relationship between personality and control of heart health-relating behaviors. Our results also indicated that AFECT dimensions led to CVDs when self-regulation was low in the individuals; hence, this is an important point that should be taken into consideration in the prevention of CVDs. The important point is that self-regulation is a skill that can be taught and learned by people. Therefore, we suggest considering the role of self-regulation in reducing the prevalence of CVDs.
This study was approved by the ethics committee of Kermanshah University of Medical Sciences, Kermanshah, Iran (IR.KUMS.REC.1398.440).
The authors appreciate the Kermanshah University of Medical Sciences and the study participants. We also appreciate the research team of the Mind GPS Institute (Kermanshah, Iran) for research consulting on the project.
Financial support and sponsorship
The project was funded by the Kermanshah University of Medical Sciences, Kermanshah, Iran (ID: 980331).
Conflicts of interest
There are no conflicts of interest.
| References|| |
Wittayapun Y, Tanasirirug V, Butsripoom B, Ekpanyaskul C. Factors affecting health-promoting behaviors in nursing students of the faculty of nursing, Srinakharinwirot University, Thailand. Thai Journal of Public Health 2010;40:215-25.
Sharma M. Theoretical Foundations of Health Education And Health Promotion. Burlington, Massachusetts: Jones and Bartlett Publishers; 2016.
Gaines A, Turner LW. Improving fruit and vegetable intake among children. Calif J Health Promot 2009;7:52-66.
Hamilton G, Goddard P, Baldwin J, Pollux P, Bourke P. Generating Ogives to (i) Analyse Perceptual Changes Associated with Ambiguous Figures, and (ii) Reaction Times in an Inhibition-of-return (IOR) Task; 2002.
Saghafipour A, Nejati J, Mozaffari E, Rezaei F, Gharlipour Z, Mirheydari M. The effectiveness of education based on BASNEF model on promoting preventive behavior of cutaneous leishmaniasis in students. Int J Pediatr 2017;5:5125-36.
Thayer JF, Yamamoto SS, Brosschot JF. The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. Int J Cardiol 2010;141:122-31.
Grau M, Bongard V, Fito M, Ruidavets JB, Sala J, Taraszkiewicz D, et al
. Prevalence of cardiovascular risk factors in men with stable coronary heart disease in France and Spain. Arch Cardiovasc Dis 2010;103:80-9.
Gohlke H. Prävention durch Lebensstiländerung: Was ist gesichert? Herz 2004;29:139-44.
Oliver-McNeil S, Artinian NT. Women's perceptions of personal cardiovascular risk and their risk-reducing behaviors. Am J Crit Care 2002;11:221-7.
Varo JJ, Martínez-González MA, de Irala-Estévez J, Kearney J, Gibney M, Martínez JA. Distribution and determinants of sedentary lifestyles in the European Union. Int J Epidemiol 2003;32:138-46.
Porche DJ. Cardiovascular risk in Men. J Nurse Pract 2007;3:82-3.
Barrett-Connor E. Gender differences and disparities in all-cause and coronary heart disease mortality: Epidemiological aspects. Best Pract Res Clin Endocrinol Metab 2013;27:481-500.
Denollet J, Schiffer AA, Spek V. A general propensity to psychological distress affects cardiovascular outcomes: Evidence from research on the type D (distressed) personality profile. Circ Cardiovasc Qual Outcomes 2010;3:546-57.
Toulideh F, Mashali H, Madmoli M, Rahmani R. Some risk factors and effective agents on cardiovascular diseases incidence: The cross-sectional study in three cities in Southwest of Iran. Int J Ayurvedic Med 2019;10:175-8.
McMahan CA, Gidding SS, McGill HC Jr. Coronary heart disease risk factors and atherosclerosis in young people. J Clin Lipidol 2008;2:118-26.
Reddy KS, Satija A. The Framingham heart study: Impact on the prevention and control of cardiovascular diseases in India. Prog Cardiovasc Dis 2010;53:21-7.
Smedslund G, Rundmo T. Is Grossarth-Maticek's coronary-prone Type II an independent predictor of myocardial infarction? Pers Individ Dif 1999;27:1231-42.
Gois C, Akiskal H, Akiskal K, Figueira ML. The relationship between temperament, diabetes and depression. J Affect Disord 2012;142:S67-71.
Lara DR, Bisol LW, Brunstein MG, Reppold CT, de Carvalho HW, Ottoni GL. The affective and emotional composite temperament (AFECT) model and scale: A system-based integrative approach. J Affect Disord 2012;140:14-37.
Calkins S, Fox N. Self-regulation Process in Early Personality Development: A Multi-level Approach to the Study of Childhood Social With-Drawal and Aggression; 2002.
Eisenberg N, Fabes RA, Guthrie IK, Reiser M. Dispositional emotionality and regulation: Their role in predicting quality of social functioning. J Pers Soc Psychol 2000;78:136-57.
Bermúdez J. Personality and health-protective behaviour. Eur J Pers 1999;13:83-103.
Grossarth-Maticek R, Eysenck H, Boyle GJ, Heeb J, Costa S, Diel I. Interaction of psychosocial and physical risk factors in the causation of mammary cancer, and its prevention through psychological methods of treatment. J Clin Psychol 2000;56:33-50.
Holbert RL, Lee J, Esralew S, Walther WO, Hmielowski JD, Landreville KD. Affinity for political humor: An assessment of internal factor structure, reliability, and validity. Humor 2013;26:551-72.
Reshadat S, Komasi S, Zakiei A, Ghasemi SR, Saeidi M. A Study of the Psychological Predictors Controlling the Risk Factors of Cardiovascular Diseases; 2016.
Neal DJ, Carey KB. A follow-up psychometric analysis of the self-regulation questionnaire. Psychol Addict Behav 2005;19:414-22.
Chalabianloo GR, Abdi R, Rasoulzadeh M, Sheikh S. Psychometric Properties of Affective and Emotional Composite Temperament Scale (AFECT) in Students; 2016.
Borelli WV, Lara DR. Dysfunctional traits in obese women and underweight men. J Affect Disord 2014;169:30-5.
Masoudnia E. Hostility, anger and risk of coronary artery atherosclerosis. SSU J 2011;18:540-51.
Grunbaum JA, Vernon SW, Clasen CM. The association between anger and hostility and risk factors for coronary heart disease in children and adolescents: A review. Ann Behav Med 1997;19:179-89.
Leite L, Machado LN, Lara DR. Emotional traits and affective temperaments in alcohol users, abusers and dependents in a national sample. J Affect Disord 2014;163:65-9.
Allom V, Mullan B, Hagger M. Does inhibitory control training improve health behaviour? A meta-analysis. Health Psychol Rev 2016;10:168-86.
Day RC, Freedland KE, Carney RM. Effects of anxiety and depression on heart disease attributions. Int J Behav Med 2005;12:24-9.
Park CL, Iacocca MO. A stress and coping perspective on health behaviors: Theoretical and methodological considerations. Anxiety Stress Coping 2014;27:123-37.
Bahremand M, Alikhani M, Zakiei A, Janjani P, Aghaei A. Emotion risk-factor in patients with cardiac diseases: The role of cognitive emotion regulation strategies, positive affect and negative affect (a case-control study). Glob J Health Sci 2016;8:173-9.
Jokela M, Pulkki-Råback L, Elovainio M, Kivimäki M. Personality traits as risk factors for stroke and coronary heart disease mortality: Pooled analysis of three cohort studies. J Behav Med 2014;37:881-9.
Kubzansky LD, Thurston RC. Emotional vitality and incident coronary heart disease: Benefits of healthy psychological functioning. Arch Gen Psychiatry 2007;64:1393-401.
Rosengren A, Hawken S, Ounpuu S, Sliwa K, Zubaid M, Almahmeed WA, et al
. Association of psychosocial risk factors with risk of acute myocardial infarction in 11119 cases and 13648 controls from 52 countries (the INTERHEART study): Case-control study. Lancet 2004;364:953-62.
Xu T, Li W, Teo K, Wang XY, Liu LS, Yusuf S. Association of psychological risk factors and acute myocardial infarction in China: The INTER-HEART China study. Chin Med J 2011;124:2083-8.
Kubzansky LD, Park N, Peterson C, Vokonas P, Sparrow D. Healthy psychological functioning and incident coronary heart disease: The importance of self-regulation. Arch Gen Psychiatry 2011;68:400-8.
Jiwani RB, Cleveland LM, Patel DI, Virani SS, Gill SL. Understanding self-regulation behaviors in South Asians with coronary artery disease: A mixed-methods study. J Cardiovasc Nurs 2017;32:281-7.
Lemos MS. Students' goals and self-regulation in the classroom. Int J Educ Res 1999;31:471-85.
Hampson SE, Edmonds GW, Barckley M, Goldberg LR, Dubanoski JP, Hillier TA. A Big Five approach to self-regulation: Personality traits and health trajectories in the Hawaii longitudinal study of personality and health. Psychol Health Med 2016;21:152-62.
[Table 1], [Table 2], [Table 3], [Table 4]