1. Background
Drug addiction, defined as a chronic, progressive, and destructive illness, is common throughout the world (1). although there is plenty of studies on heart risk factors among smokers (2), there is a shortage of studies on cardiovascular diseases (CVDs) risk factors among drug addicts (3). A new report shows that after controlling all the cardiac risk factors, drug addiction increases the risk of CVDs up to 38 times (3). In addition, drug addiction is often accompanied by a long-term harmful lifestyle (4). Many health models noted that how the incorrect and dysfunctional understanding of people about cardiovascular risk factors affects their healthy behaviors (5). In other words, the perception of heart risk and prevention of CVDs requires an appropriate understanding of the disease risk factors (5). Perceived heart risk factors (PHRFs) include five classes of biological (gender, age, and family history), environmental (smoke and toxic substances, water and air pollution, and war), physiological (diabetes, high blood pressure, high cholesterol, and obesity), behavioral (nutrition, smoking and drug abuse, sedentary lifestyle, and physical work pressure), and psychological risk factors (anger and hostility, stress, anxiety, and depression) (6). In the primary prevention phase, identification of the cognitive factors associated with adopting a healthy lifestyle can be effective in controlling the fatal cardiac problems. Thus it is necessary to study the relationship between all or some of the PHRF components and the health-promoting lifestyle.
2. Objectives
According to these considerations, the present study was conducted to examine the relationship between the perceived behavioral risk factors of CVDs and a health-promoting lifestyle.
3. Methods
This was a cross-sectional study. In September 2017, 68 patients who referred to methadone maintenance treatment (MMT) centers in Kermanshah city were studied. After obtaining written informed consent, the participants were randomly selected and answered to the questionnaires to participate in the study. Subscale of behavioral risk factors from the PHRFs scale with Iranian validation and standardization (6) and health promoting lifestyle profile (HPLP-II) (7) as valid instruments were provided for the patients by an expert clinical psychologist. The PHRFS was a 25-item-self-report scale that includes five subscales. The scale evaluated biological (3 items), environmental (5 items), behavioral (6 items), psychological (7 items), and physiological risk factors (4 items). Each item was rated on a 5-point Likert-type scale (0: never - 4: very great); higher scores indicated higher perceived risk factors. In the Iranian population, Cronbach’s alpha of the total scale and behavioral components are 0.93 and 0.82, respectively. The scale has a good content and constructs validity (6). HPLP-II is a 52-item-questionnaire with 6 subscales, including health responsibility, physical activity, nutrition, spiritual growth, interpersonal relations, and stress management. The scoring is based on the Likert scale (never = 1, sometimes = 2, often = 3, always = 4). In the Iranian population, Cronbach’s alpha of the total scale is 0.78 (7). The participants completed the forms after receiving the necessary explanations. After confirming no violations of statistical assumptions, data were analyzed using the Pearson correlation coefficient. A multivariate linear regression analysis was used to determine the role of the perceived behavioral risk factors in prediction of the health-promoting lifestyle. All statistical analyses were performed using the SPSS software version 21.
4. Results
According to the findings, the participants (100% male) aged between 18-67 years (with the mean and standard deviation of 39.4 ± 13.3 years). Demographic data showed 52.9% of the samples were married, 51.5% under diploma and 48.5% diploma or higher education, and 72.1% employed. History of hyperlipidemia (30.9%), overweight (17.6%), hypertension (13.2%), myocardial infarction (8.8%), and diabetes (4.4%) were the most common risk factors, respectively. In relation to the main analysis, the correlation between the perceived behavioral risk factors and subscales of the health-promoting lifestyle was shown in Table 1. There is a significant relationship between perceived behavioral risk factors and all subscales of health-promoting lifestyle (P < 0.001).
Table 1.The Pearson Correlation Between the Perceived Behavioral Risk Factors and Subscales of Health-Promoting Lifestyle
| Subscales of Health-Promoting Lifestyle | Mean ± SD | Perceived Behavioral Risk Factors | |
|---|---|---|---|
| r | P Value | ||
| Health responsibility | 21.8 ± 5.2 | 0.422 | 0.001 |
| Physical activity | 14.7 ± 5.7 | 0.388 | 0.001 |
| Nutrition | 23.9 ± 5.5 | 0.599 | 0.001 |
| Spiritual growth | 23.1 ± 4.6 | 0.589 | 0.001 |
| Interpersonal relations | 22.5 ± 4.5 | 0.550 | 0.001 |
| Stress management | 17.8 ± 5.3 | 0.386 | 0.001 |
| Perceived behavioral risk factors | 18.0 ± 3.7 | - | - |
Multivariate regression model for prediction of the components related to the health-promoting lifestyle was shown in Table 2. In the regression model, the perceived behavioral risk factors significantly are able to predict all the components of a health-promoting lifestyle (P < 0.001). The model summary showed that the predictive variable can explain the variance of 14.9% to 35.8% related to the lifestyle components.
Table 2.The Multivariate Regression Model for Prediction of the Components Related to the Health-Promoting Lifestyle
| Predictive Factor | Criterion Variables (Lifestyle Subscales) | B | β | t | P Value |
|---|---|---|---|---|---|
| Perceived Behavioral risk factors | Health responsibilitya | 0.581 | 0.422 | 3.786 | 0.001 |
| Physical activityb | 0.587 | 0.388 | 3.415 | 0.001 | |
| Nutritionc | 0.885 | 0.599 | 6.073 | 0.001 | |
| Spiritual growthd | 0.721 | 0.589 | 5.919 | 0.001 | |
| Interpersonal relationse | 0.667 | 0.550 | 5.353 | 0.001 | |
| Stress managementf | 0.547 | 0.386 | 3.403 | 0.001 |
aSummary of the model for health responsibility: R = 0.422, R2 = 0.178, F = 14.330, P < 0.001.
bSummary of the model for physical activity: R = 0.388, R2 = 0.150, F = 11.663, P < 0.001.
cSummary of the model for nutrition: R = 0.599, R2 = 0.358, F = 36.880, P < 0.001.
dSummary of the model for spiritual growth: R = 0.589, R2 = 0.347, F = 35.033, P < 0.001.
eSummary of the model for interpersonal relations: R = 0.550, R2 = 0.303, F = 28.656, P < 0.001.
fSummary of the model for stress management: R = 0.386, R2 = 0.149, F = 11.583, P < 0.001.
5. Discussion
Our results showed that perceived behavioral risk factors are effective in the prediction of a health-promoting lifestyle. In other words, those who have a better understanding of behavioral risk factors equally adopt a healthier lifestyle. According to the current findings, it can be concluded that understanding harmful heart behavior patterns by drug addicts leads to adopting a comprehensive healthy lifestyle. In agreement with our results, previous studies also showed that appropriate recognition and understanding of harmful behaviors are effective in following a healthy lifestyle (8, 9). The results of a new report indicated that awareness of heart disease and its risk factors leads to adopting a healthy lifestyle (9). Obviously, behavioral risk factors of CVDs can be controlled by the person. This issue is generally associated with internal locus of control. Subsequently, internal locus of control can result in a healthy lifestyle in high-risk populations (10).
Generally, it seems that screening and scanning of drug addicts' behavioral perceptions during methadone therapy has a significant impact on adopting a healthy lifestyle. Subsequently, training must be focused on improved knowledge and perception of the drug addicts about behavioral risk factors of CVDs. It is necessary to highlight the importance of the behavioral risk factors of CVDs by health professionals.
The small sample size and single-sex examination (only men) were the main limitations of this study. Larger sample size and a simultaneous study of men and women in future studies can reduce potential bias and provide more accurate results.
