|Year : 2015 | Volume
| Issue : 2 | Page : 102-108
Sodium intake prediction with health promotion model constructs in rural hypertensive patients
Aziz Kamran1, Gholamreza Sharifirad2, Yousef Shafaeei3, Leila Azadbakht4
1 Assistant Professor, Department of Public Health, School of Health, Ardabil University of Medical Sciences, Ardabil, Iran
2 Professor, Department of Public Health and Health Education and Promotion, School of Health, Qom University of Medical Sciences, Qom, Iran
3 Assistant professor, Department of Surgery, Ardabil University of Medical Sciences, Ardabil, Iran
4 Professor, Food Security Researches Center, Isfahan University of Medical Sciences, Isfahan, Iran
|Date of Web Publication||25-May-2015|
Department of Public Health, School of Health, Qom University of Medical Sciences, Qom
Source of Support: Ardabil University of Medical Science., Conflict of Interest: None
| Abstract|| |
Background: Hypertension is the most common cause of cardiovascular disease, and the growing epidemic is a serious warning to pay more attention to this disease. The aims of this study were to examine the relationships between the health promotion model (HPM) constructs and sodium intake, and to determine the predictive power of the HPM constructs as the possible mediators of sodium intake in rural Iranian hypertensive patients. Materials and Methods: This cross-sectional study was conducted on 671 hypertensive patients in Ardabil, Iran in 2013. The data were obtained during a 25-40 min face-to-face conversation by validated and reliable instruments. The nutritional data were assessed with Nutritionist version 4 (N4) software. Descriptive statistics, Spearman's correlations were calculated using SPSS Statistics version 18.0. Structural equation modeling was conducted using AMOS version 18. Results: Sodium intake was negatively correlated with perceived benefits (r = -0.707; P < 0.01), perceived self-efficacy (r = -0.719; P < 0.01), situational influences (r = -0.590; P < 0.01), interpersonal influences (r = -0.637; P < 0.01), commitment to action (r = -0.605; P < 0.01), affects related behavior (r = -0.499; P < 0.01), and positively associated with the perceived barriers score (r = 0.563; P < 0.01). The structural equation modeling showed that the model explained 63.0% of the variation in sodium intake. Conclusions: HPM constructs were significantly associated with sodium intake and dietary perceptions based on HPM constructs can predict acceptable rate of the variation of sodium intake. Therefore, we suggest using this model constructs to improve the effectiveness of nutritional interventions.
Keywords: Health promotion model (HPM), hypertension, rural, self-efficacy
|How to cite this article:|
Kamran A, Sharifirad G, Shafaeei Y, Azadbakht L. Sodium intake prediction with health promotion model constructs in rural hypertensive patients. Indian J Public Health 2015;59:102-8
|How to cite this URL:|
Kamran A, Sharifirad G, Shafaeei Y, Azadbakht L. Sodium intake prediction with health promotion model constructs in rural hypertensive patients. Indian J Public Health [serial online] 2015 [cited 2021 May 13];59:102-8. Available from: https://www.ijph.in/text.asp?2015/59/2/102/157517
| Introduction|| |
The worldwide high prevalence of hypertension and its serious complications on organs has become a health problem in all countries.  Hypertension is the most common cause of cardiovascular disease,  and its growing epidemic is a serious warning to pay more attention to this disease. Several studies have been conducted in Iran, which have had different results but have shown in general that 25-35% of adults are hypertensive.  Some of the studies and literature have announced that the prevalence of hypertension in rural populations are similar to that in urban populations, but some have shown that hypertension prevalence is higher in rural populations. 
Many factors of high blood pressure and obesity are directly influenced by nutritional factors, and the role of nutrition in these diseases is undeniable. Diet is an effective non-pharmacologic strategies, but behavior changing and maintenance is not easy  because the greatest responsibility of dietary adherence is related to the patient  and self-care is important for blood (BP) control in these patients. Evidences showed that interventions to change dietary behaviors and to reduce sodium consumption to control BP are considered as a cost effective investment in public health.  Nevertheless, in Iran, no information is accessible on the amount of sodium intake in the rural population group and on their behavior. Although the American Heart Association recommends that the 1,500 mg daily upper limit be applied to everyone,  studies have shown that sodium intake is twice the recommended amount in American hypertensive patients, and the mean value is estimated at 3340 mg.  In another study, salt intake in patients was 13-17 g per day.  In a study conducted in Iran, salt intake in adult women was estimated to average 10 g per day  and higher than 12 g in hypertensive patients.  Unfortunately, most of the hypertensive patients do not actually adhere to recommendations ,, and only less than half of the patients generally accept healthy diet as a part of their treatment.  Several studies have indicated that eating habits are wrong , In Barikani's research, only 9.1% of the patients in the study ate low salty foods, and 27.1% consumed high salty foods. 
Dietary adherence is a lifelong action in this group, and internal desires and temptations play a role as a barrier around this issue. It can be inferred that the health promotion model (HPM) can be useful in predicting the dietary behavior. This model was first proposed by Pender.  She described the model (1996) as a framework to explore the complex biological psychological processes that lead to changes in behavior and the motivation to improve health. 
To our knowledge, no study has so far examined the role of HPM constructs as the possible mediators of sodium intake. Considering the importance of perception as a likely influence on salt consumption and its importance for salt reduction,  there is a need to clarify the role of beliefs according to HPM constructs in relation to sodium intake behavior within the population. Therefore, the aims of this study were to examine the relationships between HPM constructs and sodium intake, and to determine the possible mediating roles of HPM constructs as the possible mediators of sodium intake in rural Iranian hypertensive patients.
| Materials and Methods|| |
The study was conducted in Ardabil, Iran in 2013. The study population was composed of all hypertensive patients over 30 years of age who had health center records. Considering the mean of salt intake 12.5 g in Iranian hypertensive and pre-hypertensive patients as reported by Khaledifar (2013)  and Khosravi (2012),  alpha error of 5%, 1 g allowable error, the sample size calculated was 600. However, we assumed that some individuals might be lost. Therefore the sample size was increased by 15% to a total of 680 individuals, 9 patients did not completely respond to the questionnaire; therefore, they were excluded from the data analysis so, rate of response was 98.7%. The participants were selected using the multistage random sampling method from 6 rural health centers (out of 13) that covered 30 villages totally. The criteria for identifying hypertension was derived from the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation and Treatment of High Blood Pressure (JNC 7). 
Rural health care providers, who were trained for the questionnaire, visited the selected subjects in their homes to complete the face-to-face interviews. Institutional ethical clearance was obtained before starting the study and the written informed consent was taken from the subjects and this study was approved and supported by the Institutional Review Board of Ardabil University of Medical Sciences No: 9206. The individuals with cognitive dysfunctions preventing them from understanding the questions or giving clear answers, those affected by chronic diseases, such as diabetes, who required specific nutritional behavior, and those who did not agree to complete the study were excluded.
The data gathering instruments consisted of 9 sections including the questions of demographic information, perceived benefits (9 items), perceived barriers (10 items), perceived self-efficacy (10 items), affects related to behavior (8 items), interpersonal influences (9 items), situational influences (7 items), commitment to behavior (9 items), and a 3-day food record at home. The questions of perceived benefits and perceived barriers were designed based on a 4-point Likert scale (strongly agree, agree, disagree, and strongly disagree) so that the appropriate response scored from 1 to 4. The questions of perceived self-efficacy were designed based on a 10-point scale (1 to 10) so that the appropriate response scored from 1 to 10. The questions of affects related to behavior, interpersonal influences, situational influences, and commitment to behavior were designed based on a 5-point Likert scale (never, rarely, sometimes, often, and always) so that the appropriate response scored from 1 to 5. The face validity of the instrument was obtained based on the comments from 11 nutritionists, health promotion professionals, and general physicians in an expert panel. All the items were evaluated in terms of clarity and expression by considering the expert opinions, and the relevant changes were made. The content validity was calculated and approved with the content validity index (CVI) and content validity ratio (CVR). A convenience sample of 20 rural individuals who did not have any medical or research backgrounds were also asked to provide feedback on the questionnaire in terms of its language and clarity. Cronbach's coefficient alpha was calculated for the reliability analysis of each sub-dimension. The items that had an item-total correlation of <0.40 were discarded from the measure. For each factor, a Cronbach's coefficient alpha value was considered acceptable and 3-day food records have been commonly used in practical settings that include recording food intake for 2 weekdays and 1 weekend day and its validity reported be acceptable as dietary assessment tool. 
The HPM scale was applied to all the participants, and the demographic characteristics (age, gender, education level, job, and personal and/or familial history of hypertension) were examined. The information was obtained during a 35-50 min face-to-face conversation.
The nutritional data (sodium intake) were gathered by a 3-day food record is designed to get an accurate description of typical daily diet (all of the foods and beverage) and extracted with Nutritionist version 4 (N4) software. Descriptive statistics and Spearman's correlations were calculated using SPSS Statistics version 18.0 (SPSS Inc., Chicago, IL, USA). Structural equation modeling (SEM) was conducted using AMOS version 18. A path analysis was conducted based on the proposed theoretical model shown in [Figure 1].
Model fit was tested using the comparative fit indices (CFI), standardized root mean square residual (SRMR), and root mean square error of approximation (RMSEA). The models were deemed to be acceptable when the fit indices met the following criteria: GFI ≥0.9, CFI ≥0.9, AGFI ≥0.8, and RMSEA ≤0.1.  The data were analyzed using maximum likelihood parameter (MLR) estimates with standard errors and a Chi-square test statistic because of their non-normal distributions that are robust to non-normality. We report all the effects that have a P-value of 0.05 or lower as "significant."
| Results|| |
[Table 1] shows the socio-demographic characteristics of the study participants. More than half of the respondents (74.8%) were female and about two-thirds of the respondents (75.9%) had primary education. The mean age and disease duration were 50.2 and 5.9 years, respectively [Table 1].
[Table 2] shows the scores of HPM constructs and sodium intake in the study participants and the correlations between HPM constructs and sodium intake. Sodium intake was negatively correlated with perceived benefits (r = −0.727; P < 0.01), perceived self-efficacy (r = −0.755; P < 0.01), situational influences (r = −0.588; P < 0.01), interpersonal influences (r = −0.622; P < 0.01), commitment to action (r = -0.610; P < 0.01), affects related behavior (r = -0.482; P < 0.01), and positively associated with the perceived barriers score (r = 0.563; P < 0.01) [Table 2].
The structural equation modeling based on the proposed theoretical model [Figure 1] showed the final model [Table 3], and showed that the data had a good fit (df/χ 2 = 2.6, CFI = 0.999, AGFI = 0.953, NFI = 0.999, RMSEA = 0.06). The model [Figure 2] explained 63.0% of the variation in sodium intake. Perceived benefits (β = −0.32, P < 0.01), perceived barriers (β = 0.14, P < 0.01), perceived self-efficacy (β = −0.27, P < 0.01), situational influences (β = −0.06, P < 0.01), interpersonal influences (β = −0.18, P < 0.01), commitment to action (β = 0.11, P < 0.01), and affects related behavior (β = −0.13, P < 0.01) were directly associated with sodium intake.
|Table 3: Structural equation modeling fi tness based on the HPM constructs on sodium intake prediction|
Click here to view
| Discussion|| |
To our knowledge, this is the first study that has investigated the relationship between sodium intake and perceptions of rural patients based on HPM constructs. The study provides important information that can help to design effective interventions to reduce sodium intake in the target group. The amount of sodium intake in this study was nearly twice the recommended dose (1,500 mg),  which was expected given the desirability of the foods with a salty taste among the Iranian people.  However, considering that these patients are at high risk of complications and risk of cardiovascular diseases, this result is very disturbing. Since they receive care in rural health centers, recommendations should be provided to them. Therefore, extensive studies are needed to find the real causes of high levels of salt intake. Similar studies conducted in Iran were not found to compare their results with our findings. However, salt consumption was 2.2 times the recommended dose in American hypertensive patients. 
Nutritional perceptions can be effective in dietary behavior.  In this study, we examined the patients' perceptions based on HPM, and the results showed that sodium intake was significantly associated with perceived barriers. This means that sodium intake increased with an increase in perceived barriers. According to the participants' expressions, the major perceived barriers included difficulty of preparing low-salt diets separately, exclusion of certain food tastes, boringness of continuous dietary adherences, exclusion of participation in parties, and the high cost of regimen foods. This is consistent with Hardin's study (2013) that reported the affecting factors of dietary adherence to be difficulty of changing personal habits, restriction of access to healthy foods, costs, difficulty of preparation, restriction of knowledge on the health benefits of healthy diets, family attitudes, and way of incorporating healthy foods in meals. 
Misperceptions of health information are barriers to health promotion. Some persons do not have any information on the salt needed per day. , In a qualitative study, patients reported "my doctor said that I do not add salt to food, and I have no other information".  Therefore, people who have more knowledge are more likely to have healthy behaviors to reduce their sodium than those with a negative attitude. 
Unfortunately, some people have a negative attitude towards reducing sodium intake and assume that their physical power reduces with lowering salt intake. , In Smith's study (2006), most of the participants, especially men, said "it is difficult to reduce salt intake" and they believed that salt is the taste of food and there is no alternative. They expressed that whenever they had attempted to reduce its consumption, they had used it more than before. However, those affected by heart disease believed that food without salt is meaningless. 
The concept behind these statements based on our impressions is the concept of self-efficacy, which means one's own perceived ability to adopt a low-salt diet. The self-efficacy score of the patients in our study was 45 out of 100. It is clear that the patients had poor self-efficacy, and path analyses showed that this concept had a negative significant relationship with sodium intake, meaning that sodium intake decreased with increasing efficacy. These findings are consistent with the results of the previous study.  Self-efficacy can predict 27% of the sodium intake variations. This finding indicates that self-efficacy increasing techniques should be widely used by health care centers for patient education. In general, low self-efficacy and high perceived barriers are perhaps related to poor patient care system. Unfortunately, the accuracy of knowledge was not examined in this study.
In this study, self-efficacy significantly correlated with interpersonal influences (r = 0.660, P < 0.001), Also, the concept of interpersonal influences had a negative significant association with sodium intake and was able to predict 18% of the variations in sodium intake. This concept of situational influences had a significant positive correlation with the commitment to reduce sodium. Consequently, we can say that subjective norms of environment can be effective in reducing salt intake. In other studies, attitude and subjective norms have been reported as the influencing factors of salt intake reduction. In Motlagh's study, 43% of intention variations to reduce salt in the food during cooking and 18% of intention variations to refrain from consuming high salty foods were predicted by attitudes and subjective norms. 
It seems that, in this model, perceived self-efficacy is affected by activity-related affects. The more positive the affect is, the higher the perception of self-efficacy will be. Therefore, it is mutual, meaning that, with an increase in the perception of self-efficacy, the positive affects will increase. Self-efficacy has an impact on functioning perceived barriers. Higher self-efficacy leads to lower perception of the target behavior fulfilling barriers. Self-efficacy stimulates health promotion behavior directly through efficient expectation and influences on perceived barriers, and through determining commitment level or insisting on planning behavior indirectly. 
Guardia's study (2006) showed that between a person's attitude towards sodium reduction in meat products and acceptability of low-sodium sausages, attitude was the most important predictor of behavioral intentions, and the subjective norm had a significant effect on behavioral intention.  These results in total reveal the importance of attention to perceptions and subjective norms in reducing sodium intake in hypertensive patients. In conclusion, it should be noted that food behavior is influenced by demographic, social, and psychological contexts, and understanding these contexts is important to make the necessary changes in behavior modification.
This study has several limitations. First, as a cross-sectional study, the findings can only be used to examine associations, and not to draw inferences regarding causality. Second, the model only explained the variation in sodium intake. Therefore, future studies should be extended to study other factors that may mediate the relationship between sociodemographic factors and sodium intake, such as knowledge and salt taste. Also, female preponderance of the study population is another of our study limitations. In contrast, the path analysis with the optimal sample size and the valid and reliable tools are the strengths of this study. Also, to our knowledge, this is the first study that used the HPM model in assessing rural hypertensive patients' nutritional perceptions and predicting sodium intake with these perceptions in Iran and other countries. In this study, dietary intake was assessed with 3 days food recording instead of food recall; because food recall depended on memory and studies showed underreporting errors with food recall tool,  therefore, the results were more likely to be closer to reality.
| Conclusions|| |
Sodium intake in the patients was nearly twice the recommended amount for a special effort to reduce, and this intervention will not be easy because patients do not have a good attitude to adopt a low-sodium diet. Their low self-efficacy should be considered in interventions and mere informing will not be helpful. In this study, HPM predicted 63% of the changes in sodium intake, and there was a significant relationship between perceived benefits and barriers with dietary self-care behavior. Also, perceived barriers significantly correlated with sodium intake. Perceived barriers may influence health behaviors by creating a barrier to act directly as well as indirectly through reducing commitment and devotion. Thus, despite low self-efficacy and high perceived barriers, a favorable reduction in sodium intake cannot be expected in these patients.
| Acknowledgements|| |
This study was funded by Ardabil University of Medical Sciences and approved with No: 9206. Therefore, we appreciate the Health and Research Deputy and all the professors supporting this research.
| References|| |
Baghianimoghadam MH, Mirzaei M, Rahimdel T. Role of health beliefs in preventive behaviors of individuals at risk of cardiovascular diseases. J Health Syst Res 2013;8:1151-8.
Abdollahy AA, Bazrafshan HR, Salehi A, Behnampour N, Hosayni SA, Rahmany H, et al
. Epidemiology of hypertension among urban population in Golestan province in north of Iran. J Gorgan Univ Med Sci 2007;8:37-41.
Haghdoost AA, Sadeghirad B, Rezazadehkermani M. Epidemiology and heterogeneity of hypertension in Iran: A systematic review. Arch Iran Med 2008;11:444-52.
Chaman R, Yunesian M, Hajimohamadi A, Taramsari MG. Investigating hypertension prevalence and some of its influential factors in an ethnically variant rural sample. JKH, Asia Pac J Clin Nutr 2008;3:39-42.
Cornell S, Briggs A. Newer treatment strategies for the management of type 2 diabetes mellitus. J Pharm Pract 2004;17:49-54.
Truswell AS. What nutrition knowledge and skills do primary care physicians need to have, and how should this be communicated? Eur J Clin Nutr 1999;53(Suppl 2): S67-71.
Wang G, Labarthe D. The cost-effectiveness of interventions designed to reduce sodium intake. J Hypertens 2011; 29:1693-9.
Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, et al
.; American Heart Association StrategicPlanning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: The American Heart Association's strategic Impact Goal through 2020 and beyond. Circulation 2010;121:586-613.
Ayala C, Gillespie C, Cogswell M, Keenan NL, Merritt R. Sodium consumption among hypertensive adults advised to reduce their intake: National health and nutrition examination survey, 1999-2004. J Clin Hypertens (Greenwich) 2012;14:447-54.
Cornélio M, Gallani MC, Godin G, Rodrigues RC, Mendes RD, Nadruz Junior W. Development and reliability of an instrument to measure psychosocial determinants of salt consumption among hypertensive patients. Rev Lat Am Enfermagem 2009;17:701-7.
Motlagh Z, Mazloomy S, Khosravi HM, Morowatisharifabad M, Askarshahi M. Salt intake among women refer to medical health centers, Yazd, Iran, 2011. J Shahid Sadoughi Univ Med Sci 2011;19:550-60.
Khosravi A, Toghianifar N, Sarrafzadegan N, Gharipour M, Azadbakht L. Salt intake, obesity, and pre-hypertension among Iranian adults: A cross-sectional study. Pak J Med Sci 2012;28:297-302.
He FJ, MacGregor GA. A comprehensive review on salt and health and current experience of worldwide salt reduction programmes. J Hum Hypertens 2009;23:363-84.
Brown IJ, Tzoulaki I, Candeias V, Elliott P. Salt intakes around the world: Implications for public health. Int J Epidemiol 2009;38:791-813.
Ghassemi H, Harrison G, Mohammad K. An accelerated nutrition transition in Iran. Public Health Nutr 2002;5:149-55.
Chan YM, Molassiotis A. The relationship between diabetes knowledge and compliance among Chinese with non-insulin dependent diabetes mellitus in Hong Kong. J Adv Nurs 1999;30:431-8.
Bashour HN. Survey of dietary habits of in-school adolescents in Damascus, Syrian Arab Republic. East Mediterr Health J 2004;10:853-62.
Sakamaki R, Toyama K, Amamoto R, Liu CJ, Shinfuku N. Nutritional knowledge, food habits and health attitude of Chinese university students--a cross sectional study. Nutr J 2005;4:4.
Barikani A, Saeedi F. Prevalence of hypertension among women aged 30+ in Minoodar region of Qazvin in 2009. J Qazvin Univ Med Sci 2010;14:41-8.
Marriner TA, Raile AM. Nursing Theorists and Their Work. 5 th
ed. St Louis: Mosby; 2002. p. 14-31.
Pender NJ, Murdaugh CL, Parsons MA. Health Promotion in Nursing Practice. 6 th
ed: Boston, MA: Prentice Hall; 2010. p. 57.
Sarmugam R, Worsley A, Wang W. An examination of the mediating role of salt knowledge and beliefs on the relationship between socio-demographic factors and discretionary salt use: A cross-sectional study. Int J Behav Nutr Phys Act 2013;10:25.
Khaledifar A, Gharipour M, Bahonar A, Sarrafzadegan N, Khosravi A. Association between salt intake and albuminuria in normotensive and hypertensive individuals. Int J Hypertens 2013;2013:523682.
Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, et al
.; Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. National Heart, Lung, and Blood Institute; National High Blood Pressure Education Program Coordinating Committee. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension 2003;42:1206-52.
Yang YJ, Kim Mk, Hwang SH, Ahn Y, Shim JE, Kim DH. Relative validities of 3-day food records and the food frequency questionnaire. Nutr Res Pract 2010;4:142-8.
Zendehdel M, Paim LH. Predicting consumer attitude to use on-line shopping: Context of Malaysia. Life Sci J 2013; 10:497-501.
Healthy People 2010 With Understanding and Improving Health and Objectives for Improving Health. Vol. 2. Washington, DC: US Government Printing Office; 2000.
Van der Veen JE, De Graaf C, Van Dis SJ, Van Staveren WA. Determinants of salt use in cooked meals in The Netherlands: Attitudes and practices of food preparers. Eur J Clin Nutr 1999;53:388-94.
Hardin-Fanning F. Adherence to a Mediterranean diet in a rural Appalachian food desert. Rural Remote Health 2013;13:2293.
Grimes CA, Riddell LJ, Nowson CA. Consumer knowledge and attitudes to salt intake and labelled salt information. Appetite 2009;53:189-94.
Sheahan SL, Fields B. Sodium dietary restriction, knowledge, beliefs, and decision-making behavior of older females. J Am Acad Nurse Pract 2008;20:217-24.
Zhing J, Xu AQ, Ma JX, Shi XM, Guo XL, Engelgau M, et al
. Dietary Sodium Intake: Knowledge, Attitudes and Practices in Shandong Province, China, 2011. PLoS One 2013;8:e58973.
Newson RS, Elmadfa I, Biro G, Cheng Y, Prakash V, Rust P, et al.
Barriers for progress in salt reduction in the general population. An international study. Appetite 2013;71:22-31.
Smith SL, Quandt SA, Arcury TA, Wetmore LK, Bell RA, Vitolins MZ. Aging and eating in the rural, southern United States: Beliefs about salt and its effect on health. Soc Sci Med 2006;62:189-98.
Woodward DR, Ball PJ, Beard TC. Sodium intake and self-efficacy. Asia Pac J Clin Nutr 2001;25:S57.
Mohebi S, Sharifirad G, Feizi A, Botlani S, Hozori M, Azadbakht L. Can health promotion model constructs predict nutritional behavior among diabetic patients? J Res Med Sci 2013;18:346-59.
Guàrdia MD, Guerrero L, Gelabert J, Gou P, Arnau J. Consumer attitude towards sodium reduction in meat products and acceptability of fermented sausages with reduced sodium content. Meat Sci 2006;73:484-90.
Zhang J, Temme EH, Sasaki S, Kesteloot H. Under- and overreporting of energy intake using urinary cations as biomarkers: Relation to body mass index. Am J Epidemiol 2000;152:453-62.
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3]