Predictors of Adopting Heart-Health-Preventive Behaviors among High School Female Students
Abstract
Objective(s): This study aimed mainly to determine the predictors of adopting heart-health-preventive behaviors among high school female students.Methodology: Part of the study was an experimental randomized controlled trial used to guide this study. The study was carried out at Fatima Al-Zahraa High School for females in Al-Diwaniyah City. The study included a simple random sample of 142 high school female students (72 students in each of the study and control groups). The study instrument consists of subjects’ sociodemographic characteristics. It includes the Expanded Health Belief Model Scales for Heart-Health Preventive Behaviors (Perceived Susceptibility to Develop Heart Disease Scale, Perceived Severity of Heart Disease Scale, Perceived Barriers to Adopt Heart-Health Preventive Behaviors Scale, Perceived Benefits of Adopting Heart-Health Preventive Behaviors Scale, Self-Efficacy of Adopting Heart-Health Preventive Behaviors Scale, Health Motivation for Adopting Heart-Health Preventive Behaviors Scale, Subjective Norms for Adopting Heart-Health Preventive Behaviors Scale, Cues-to-Action Related to Adopting Heart-Health Preventive Behaviors Scale, and Behavioral Intention to Adopt Heart-Health Preventive Behaviors Scale. Data were analyzed using the statistical package for social science (SPSS), version 26. The statistical measures of frequency, percent, mean, standard deviation, Repeated Measures ANCOVA, linear regression, One-way analysis of variance (ANOVA), and independent-sample t-test will be used.
Results of the study: There are significant differences in the values of the Perceived Susceptibility of developing cardiovascular disease over time for participants both in the study and control groups (F = 109.284, df = 2, p < .01 vs. F = 25.484, df = 2, p < .01).
Conclusion: The greater the Subjective Norms for adopting heart-health-preventive behaviors, the better the Behavioral Intention of adopting heart-health-preventive behaviors.
The greater the Ques-To-Action for adopting heart-health-preventive behaviors, the better the Behavioral Intention of adopting heart-health-preventive behaviors.
Predictors of Adopting Heart-Health-Preventive Behaviors among High School Female Students
Afnan Hadi Khudair 1*, Mohammed Baqer Habeeb2
- Academic Nurse, MSc, Department of Community Health Nursing, College of Nursing, University of Baghdad, Baghdad, Iraq. E-mail (Corresponding Author) Afnan.Hadi1206b@conursing.uobaghdad.edu.iq
- Instructor, PhD, Department of Community Health Nursing, College of Nursing, University of Baghdad, Baghdad, Iraq. E-mail: mhabdali1@my.okcu.edu
Corresponding author:Afnan Hadi Khudair
Email: Afnan.Hadi1206b@conursing.uobaghdad.edu.iq
ABSTRACT
Objective(s): This study aimed mainly to determine the predictors of adopting heart-health-preventive behaviors among high school female students.
Methodology: Part of the study was an experimental randomized controlled trial used to guide this study. The study was carried out at Fatima Al-Zahraa High School for females in Al-Diwaniyah City. The study included a simple random sample of 142 high school female students (72 students in each of the study and control groups). The study instrument consists of subjects’ sociodemographic characteristics. It includes the Expanded Health Belief Model Scales for Heart-Health Preventive Behaviors (Perceived Susceptibility to Develop Heart Disease Scale, Perceived Severity of Heart Disease Scale, Perceived Barriers to Adopt Heart-Health Preventive Behaviors Scale, Perceived Benefits of Adopting Heart-Health Preventive Behaviors Scale, Self-Efficacy of Adopting Heart-Health Preventive Behaviors Scale, Health Motivation for Adopting Heart-Health Preventive Behaviors Scale, Subjective Norms for Adopting Heart-Health Preventive Behaviors Scale, Cues-to-Action Related to Adopting Heart-Health Preventive Behaviors Scale, and Behavioral Intention to Adopt Heart-Health Preventive Behaviors Scale. Data were analyzed using the statistical package for social science (SPSS), version 26. The statistical measures of frequency, percent, mean, standard deviation, Repeated Measures ANCOVA, linear regression, One-way analysis of variance (ANOVA), and independent-sample t-test will be used.
Results of the study: There are significant differences in the values of the Perceived Susceptibility of developing cardiovascular disease over time for participants both in the study and control groups (F = 109.284, df = 2, p < .01 vs. F = 25.484, df = 2, p < .01).
Conclusion: The greater the Subjective Norms for adopting heart-health-preventive behaviors, the better the Behavioral Intention of adopting heart-health-preventive behaviors.
The greater the Ques-To-Action for adopting heart-health-preventive behaviors, the better the Behavioral Intention of adopting heart-health-preventive behaviors.
Key Words: Health Beliefs, High School Students, Behavioral Intention, Heart-Health-Prevention Behavior.
Received: 30 March 2022, Accepted: 11 June 2022, Available online: 28 August 2022
INTRODUCTION
Cardiovascular illnesses are the leading cause of death among noncommunicable diseases, killing 17.9 million people each year. Cardiovascular diseases are long-term conditions caused by a mix of genetic, physiological, environmental, and behavioral factors (WHO, 2021). CVD was expected to be the major cause of death in both developing and developed countries in 2010. (WHO, 2016). Cardiovascular disease claimed the lives of 17.9 million individuals worldwide in 2019, accounting for 32% of all deaths. Heart attacks and strokes were responsible for 85 percent of these deaths (WHO, 2021).
The rate of ischemic heart disease in hospitalized female patients in Iraq in 2016 was 16.11 percent, according to the Iraqi Ministry of Health. This rate was 20.15 percent in the Governorate of AL-diwaniyah in 2016. Cerebrovascular disease was the leading cause of death in 2016, accounting for 60.69 percent of all deaths, followed by ischemic heart disease at 8.38 percent and hypertension at 6.13 percent. In the same context, cerebrovascular illness was the leading cause of death for women in 2016, accounting for 11.98 percent, followed by heart failure at 9.70 percent and ischemic heart disease at 8.91 percent.
The rate of ischemic heart disease in hospitalized female patients in Iraq in 2017 was 15.64 percent. This rate was 19.2 percent in the Governorate of AL-diwaniyah. Cerebrovascular disease was the leading cause of death in 2017, accounting for 10.38 percent of all deaths, followed by ischemic heart disease (9.34 percent) and hypertension (8.48 percent). In the same context for females in 2017, cerebrovascular disease was the leading cause of death with 10.74 percent, followed by heart failure with 8.90 percent and ischemic heart disease with 8.67 percent.
Ischemic heart disease affects 13.5 percent of female hospitalized patients in Iraq in 2019. This rate was 18.7% in the Governorate of AL-diwaniyah. Ischemic heart disease is the leading cause of mortality in 2019, accounting for 12.03 percent of all deaths, followed by cerebrovascular illness at 8.4 percent and hypertension at 6.8 percent. Ischemic heart disease was the leading cause of mortality for women in 2019, accounting for 13.7 percent of all deaths, followed by cerebrovascular illness at 10.6 percent and hypertension at 9.08 percent.
METHOD
This study was guided by an experimental randomized controlled trial. The most conclusive technique to prove causation is to use experimental designs. Researchers use these designs because they ensure a high level of internal validity because random assignment creates very similar experimental and control groups. The study was carried out at Fatima Al-Zahraa High School for females in Al-Diwaniyah City.
The study included a partial simple random sample of high school female students who agreed to participate in this study. The study subjects were recruited from three grades in this school which Fourth Grade, Fifth Grade, Sixth Grade. Subjects were randomly assigned into both study and control groups; 72 students for the study group and 72 students for the control group. The simple random sampling involved having the lists of students’ names in Fatima Al-Zahraa High School for females generated on Microsoft Office Word software. The names of students in each grade were cut in identical pieces of paper, in the same color of paper, and folded in the same way. The names of students of each grade were put in a separate container. One of the student researcher’s colleagues started stirring well the pieces of paper and drawing one piece alternatively. The first piece drawn would be in the study group and the second one would in the control group till obtaining the required sample size for each grade for each of the study and control groups. The final sample size is 144.
After receiving the approval of the College of Nursing, University of Baghdad for the study, the student researcher discussed study details with officials at the selected high school. The general purpose of the study was explained to the participants, as well as how to complete the questionnaire, to ensure that they understand that participation is optional and that they can withdraw at any time they wish to. The student researcher assured participants that the confidentiality of their data will be safeguarded and securely maintained during and following study participation. The student researcher further assured study participants that their identities will remain anonymous in the presentation, reporting, and/or any eventual publication of the study.
RESULTS
Table 1
Multivariate Tests of the Within-subjects for the Perceived Susceptibility of developing cardiovascular disease
|
Multivariate Testsa |
|||||||
|
Effect |
Value |
F |
Hypothesis df |
Error df |
Sig. |
Partial Eta Squared |
|
|
Per Susc (Study) |
Pillai's Trace |
.757 |
109.248b |
2.000 |
70.000 |
.000 |
.757 |
|
Wilks' Lambda |
.243 |
109.248b |
2.000 |
70.000 |
.000 |
.757 |
|
|
Hotelling's Trace |
3.121 |
109.248b |
2.000 |
70.000 |
.000 |
.757 |
|
|
Roy's Largest Root |
3.121 |
109.248b |
2.000 |
70.000 |
.000 |
.757 |
|
|
Effect |
Value |
F |
Hypothesis df |
Error df |
Sig. |
Partial Eta Squared |
|
|
Per Susc (Control) |
Pillai's Trace |
.421 |
25.448b |
2.000 |
70.000 |
.000 |
.421 |
|
Wilks' Lambda |
.579 |
25.448b |
2.000 |
70.000 |
.000 |
.421 |
|
|
Hotelling's Trace |
.727 |
25.448b |
2.000 |
70.000 |
.000 |
.421 |
|
|
Roy's Largest Root |
.727 |
25.448b |
2.000 |
70.000 |
.000 |
.421 |
|
Table 2
Mauchly's Test of Sphericity for the Perceived Susceptibility of developing cardiovascular disease
|
Mauchly's Test of Sphericitya |
|||||||
|
Within Subjects Effect |
Mauchly's W |
Approx. Chi-Square |
df |
Sig. |
Epsilonb |
||
|
Greenhouse-Geisser |
Huynh-Feldt |
Lower-bound |
|||||
|
Susceptibility |
.917 |
6.035 |
2 |
.049 |
.924 |
.947 |
.500 |
Table 3
Tests of Within-Subjects Effects for the Perceived Susceptibility of developing cardiovascular disease
|
Source |
Type III Sum of Squares |
df |
Mean Square |
F |
Sig. |
Partial Eta Squared |
|
|
Susceptibility (Study) |
Sphericity Assumed |
2941.815 |
2 |
1470.907 |
134.622 |
.000 |
.655 |
|
Greenhouse-Geisser |
2941.815 |
1.847 |
1592.414 |
134.622 |
.000 |
.655 |
|
|
Huynh-Feldt |
2941.815 |
1.895 |
1552.787 |
134.622 |
.000 |
.655 |
|
|
Lower-bound |
2941.815 |
1.000 |
2941.815 |
134.622 |
.000 |
.655 |
|
|
Error (Susceptibility Study) |
Sphericity Assumed |
1551.519 |
142 |
10.926 |
|
|
|
|
Greenhouse-Geisser |
1551.519 |
131.165 |
11.829 |
|
|
|
|
|
Huynh-Feldt |
1551.519 |
134.512 |
11.534 |
|
|
|
|
|
Lower-bound |
1551.519 |
71.000 |
21.852 |
|
|
|
|
|
Source |
Type III Sum of Squares |
df |
Mean Square |
F |
Sig. |
Partial Eta Squared |
|
|
Susceptibility (Control) |
Sphericity Assumed |
595.583 |
2 |
297.792 |
20.300 |
.000 |
.222 |
|
Greenhouse-Geisser |
595.583 |
1.865 |
319.411 |
20.300 |
.000 |
.222 |
|
|
Huynh-Feldt |
595.583 |
1.913 |
311.341 |
20.300 |
.000 |
.222 |
|
|
Lower-bound |
595.583 |
1.000 |
595.583 |
20.300 |
.000 |
.222 |
|
|
Error (Susceptibility Control) |
Sphericity Assumed |
2083.083 |
142 |
14.670 |
|
|
|
|
Greenhouse-Geisser |
2083.083 |
132.389 |
15.735 |
|
|
|
|
|
Huynh-Feldt |
2083.083 |
135.820 |
15.337 |
|
|
|
|
|
Lower-bound |
2083.083 |
71.000 |
29.339 |
|
|
|
|
DISCUSSION
The values of the Perceived Benefits of adopting heart-health preventive behaviors for the study group noticeably increase by time with significant difference in these values. The omnibus effect (measure of association) for this analysis was .699, which indicates that approximately 69% of the total variance in the Perceived Benefits of adopting heart-health preventive behaviors values is accounted for by the variance in the administered intervention. The pairwise comparison demonstrated that there were statistically significant differences in these value over time. These findings reflect the positive effect of the administered Expanded Health Belief Model-based intervention in making invariant, consistent change in the Perceived Benefits of adopting heart-health preventive behaviors. This finding is consistent with that obtained by Hosseini et al., (2017) and Malverdy and Kazemi (2016) who stated that there was a positive and weak link between physical activity duration and the Perceived Benefits. Also, Healthy nutrition behaviors have a positive significant association with the Perceived Benefits, according to Morowa tisharif abad et al., (2018). On the other hand, this finding is inconsistent with that obtained by Dumitrescu and Iacob (2021) who stated that there were no significant links between the Perceived Benefits and healthy eating.
The values of the Perceived Barriers to adopt heart-health preventive behaviors for the study group noticeably increase by time with significant difference in these values. The omnibus effect (measure of association) for this analysis was .699, which indicates that approximately 69% of the total variance in the Perceived Barriers to adopt heart-health preventive behaviors values is accounted for by the variance in the administered intervention. The pairwise comparison demonstrated that there were statistically significant differences in these value over time. These findings reflect the positive effect of the administered Expanded Health Belief Model-based intervention in making invariant, consistent change in the Perceived Barriers to adopt heart-health preventive behaviors. This finding is congruent with those obtained by Mo et al., (2016) who concluded that the Perceived Barriers was displayed to be strongly linked with physical activity. Also, Alexandra (2018), Roth et al., (2018), and Ross and Melzer (2016) stated that students with obesity face more Perceived Barriers to Exercise than students of normal weight. According to Malverdy and Kazemi (2016), the Perceived Barriers had a strong and substantial relationship with the Stage of Change of nutrition practices. Kaufman et al., (2018) stated that participants who reported higher Perceived Barriers of smoking-related diseases were less likely to stop smoking. On the other hand, this finding was incongruent with those obtained by Rahmati-Najarkolaei et al., (2015); Almutari and Orji (2021); Ar-yuwat et al., (2013), and Hosseini et al., (2017) who stated that there was a weak and negative relationship between Perceived Barriers and physical activity behavior. Rahmati-Najarkolaei et al., (2015) and Wang and Li (2013) demonstrated a negative relationship between the Perceived Barriers and nutrition behavior.
CONCLUSIONS
The greater the Subjective Norms for adopting heart-health-preventive behaviors, the better the Behavioral Intention of adopting heart-health-preventive behaviors. The greater the Ques-To-Action for adopting heart-health-preventive behaviors, the better the Behavioral Intention of adopting heart-health-preventive behaviors.
RECOMMENDATIONS
There is a need for the community health nurses to initiate collaboration with the Ministry of Health, Ministry of Higher Education and Scientific Research, Mass Media, and non-governmental organizations to establish health campaigns that seek to raise public’s health awareness about the severity of cardiovascular disease and the vital role of heart-health preventive behaviors.
ETHICALCONSIDERATIONSCOMPLIANCEWITHETHICALGUIDELINES
This study was completed following obtaining consent from the University of Baghdad.
FUNDING
This research did not receive any grant from funding agencies in the public, commercial, or non-profit sectors.
AUTHOR’SCONTRIBUTIONS
Study concept, Writing, Reviewing the final edition by all authors.
DISCLOSURESTATEMENT:
The authors report no conflict of interest.
REFERENCES
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Hosseini, H., Moradi, R., Kazemi, A., & Shahshahani, M. S. (2017). Determinants of physical activity in middle-aged woman in Isfahan using the health belief model. Journal of Education and Health Promotion, 6.
Malverdy, Z., & Kazemi, A. (2016). Health beliefs and stages of changes to improve behaviors among obese and overweight women undergoing preconception care. Iranian Journal of Nursing and Midwifery Research, 21(6), 595.
Morowatisharifabad, M. A., Amani, F., Kaseb, F., & Namayandeh, S. M. (2018). Predictors of healthy nutrition behaviors among elderlies of Kalat county based on Health Belief Model. Elderly Health Journal, 4(1), 11-17.
Dumitrescu, C., & Iacob, C. I. (2021). Predicting Healthy Eating: Conscientiousness versus the Health Belief Model. Romanian Journal of Applied Psychology, 23(1), 18-24.
Mo, P. K., Chong, E. S., Mak, W. W., Wong, S. Y., & Lau, J. T. (2016). Physical activity in people with mental illness in Hong Kong: Application of the health belief model. Journal of Sport and Exercise Psychology, 38(2), 203-208.
Roth, S. E., Gill, M., Chan-Golston, A. M., Rice, L. N., Crespi, C. M., Koniak-Griffin, D., ... & Prelip, M. L. (2019). Physical activity correlates in middle school adolescents: Perceived benefits and barriers and their determinants. The Journal of School Nursing, 35(5), 348-358.
Kaufman, A. R., Dwyer, L. A., Land, S. R., Klein, W. M., & Park, E. R. (2018). Smoking-related health beliefs and smoking behavior in the National Lung Screening Trial. Addictive Behaviors, 84, 27-32.
Rahmati-Najarkolaei, F., Tavafian, S. S., Fesharaki, M. G., & Jafari, M. R. (2015). Factors predicting nutrition and physical activity behaviors due to cardiovascular disease in tehran university students: application of health belief model. Iranian Red Crescent Medical Journal, 17(3).
Almutari, N., & Orji, R. (2021, June). Culture and Health Belief Model: Exploring the determinants of physical activity among Saudi adults and the moderating effects of age and gender. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (pp. 138-146).
Ar-Yuwat, S., Clark, M. J., Hunter, A., & James, K. S. (2013). Determinants of physical activity in primary school students using the health belief model. Journal of Multidisciplinary Healthcare, 6, 119.
Wang, E. S. T., & Li, Y. L. (2015). The effect of stress and visible health problems on the intent to continue health food consumption. British Food Journal, 117(1)
World Health Organization. (2012). WHO global report: Mortality attributable to tobacco. Georgia: World Health Organization. Retrieved April 17, 2022, from http://apps.who.int/iris/bitstream/handle/10665/44815/9789241564434_eng.pdf;jsessionid=BCC31131C3067D214045410E16FB6B58?sequence=1
World Health Organization. (2021). Cardiovascular diseases. Retrieved from https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1
Hosseini, H., Moradi, R., Kazemi, A., & Shahshahani, M. S. (2017). Determinants of physical activity in middle-aged woman in Isfahan using the health belief model. Journal of Education and Health Promotion, 6.
Malverdy, Z., & Kazemi, A. (2016). Health beliefs and stages of changes to improve behaviors among obese and overweight women undergoing preconception care. Iranian Journal of Nursing and Midwifery Research, 21(6), 595.
Morowatisharifabad, M. A., Amani, F., Kaseb, F., & Namayandeh, S. M. (2018). Predictors of healthy nutrition behaviors among elderlies of Kalat county based on Health Belief Model. Elderly Health Journal, 4(1), 11-17.
Dumitrescu, C., & Iacob, C. I. (2021). Predicting Healthy Eating: Conscientiousness versus the Health Belief Model. Romanian Journal of Applied Psychology, 23(1), 18-24.
Mo, P. K., Chong, E. S., Mak, W. W., Wong, S. Y., & Lau, J. T. (2016). Physical activity in people with mental illness in Hong Kong: Application of the health belief model. Journal of Sport and Exercise Psychology, 38(2), 203-208.
Roth, S. E., Gill, M., Chan-Golston, A. M., Rice, L. N., Crespi, C. M., Koniak-Griffin, D., ... & Prelip, M. L. (2019). Physical activity correlates in middle school adolescents: Perceived benefits and barriers and their determinants. The Journal of School Nursing, 35(5), 348-358.
Kaufman, A. R., Dwyer, L. A., Land, S. R., Klein, W. M., & Park, E. R. (2018). Smoking-related health beliefs and smoking behavior in the National Lung Screening Trial. Addictive Behaviors, 84, 27-32.
Rahmati-Najarkolaei, F., Tavafian, S. S., Fesharaki, M. G., & Jafari, M. R. (2015). Factors predicting nutrition and physical activity behaviors due to cardiovascular disease in tehran university students: application of health belief model. Iranian Red Crescent Medical Journal, 17(3).
Almutari, N., & Orji, R. (2021, June). Culture and Health Belief Model: Exploring the determinants of physical activity among Saudi adults and the moderating effects of age and gender. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (pp. 138-146).
Ar-Yuwat, S., Clark, M. J., Hunter, A., & James, K. S. (2013). Determinants of physical activity in primary school students using the health belief model. Journal of Multidisciplinary Healthcare, 6, 119.
Wang, E. S. T., & Li, Y. L. (2015). The effect of stress and visible health problems on the intent to continue health food consumption. British Food Journal, 117(1)
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