Abstract
Background:
Since women's readiness for dietary behavioral change can be one of the most effective fundamental measures for reducing dietary salt intake in line with preventing chronic diseases in developing countries, the present study is aimed to determine the readiness for behavioral change in discretionary salt intake among women living in Tehran.
Methods:
The present cross-sectional study was conducted on 561 women referring to the women care units across city of Tehran. The self-administered questionnaire included assessment of nutrition-related knowledge on salt intake and its association with diseases, discretionary salt intake, stages of change, and self-efficacy of women. In addition, the logistic regression test was used to determine the predictors of women's readiness for behavioral change in discretionary salt intake.
Results:
40% women had someone in the family who had such a limitation (salt intake-limited exposure group), while 81.6% always or often added salt to their foods. Moreover, one-third of the participants were in the stage of pre-contemplation and 41.2% were in the stage of preparation for reducing salt intake. Self-efficacy and salt intake-limited exposure were the two most important determinants of the women's readiness for behavioral change in discretionary salt intake, respectively: (OR = 1.1 95% CI: 1.06--1.14 P < 0.001; OR = 1.58, 95% CI: 1.03--2.42 P < 0.03).
Conclusions:
Results of the present study showed that increased self-efficacy is associated with higher levels of behavioral change among women. Since self-efficacy is very important for initiating and maintaining the behavioral change, women's empowerment for reducing salt intake necessitates putting the emphasis on increased self-efficacy as well as community-based nutritional interventions.
Keywords: Behavior, readiness, salt
Introduction
Behavior change is a process which occurs in individuals with different levels of motivation and readiness for change.[1] Changing health-related behaviors can significantly affect some of the most important causes of death and diseases. Behavior plays a pivotal role in health. Evidence suggests that different behavioral patterns are deeply rooted in sociocultural conditions and depend on cultural background.[2,3] Currently, behavior change interventions have a high potential for changing the current pattern of diseases. It is difficult to change the genetic predisposition to diseases and social conditions, at least in the short term.[4]
Studies which assess individuals’ readiness to change health behaviors reveal that over one-third of people in Netherlands, Germany, Austria, United States of America, Hungary, India, China, South Africa, Brazil, and Japan are at the pre-contemplation stage and few (8--14%) at the preparation stage.[5,6,7] Moreover, increase in age, level of education, and self-efficacy, and the existence of chronic diseases such as hypertension which requires the limitation of salt intake have been associated with placing individuals at higher levels of change.[5,7,8]
A study reported mean salt intake to be 10.3 g per day in urban and rural areas of Ilam Province, Iran.[9] Moreover, it is 11.47 g per day for adults in Isfahan, Iran.[10] In a study on adults (2014), daily salt intake was 9 g for men and 6.96 g for women, with 53.6% being discretionary salt intake or added salt in cooking or at the table.[11] The salt intake among Iranian has been estimated about 10--12 g/day,[12] but based on the most recent study (2018) the per capita salt intake in Iran is 9.25 g per day (95% confidence interval: 9.48--9.56)[13] which is still 2.5 times more than the amount of the World Health Organization (WHO) recommendation. In terms of salt intake, Iran is similar to Denmark (7.1 g/day in women and 10.6 g/day in men), China (12 g/day), Spain (9.8 g/day), and Japan (7.8 g/day).[14,15,16,17] Contrary to most developed countries in which the majority of salt intake (75%) results from processed food,[18,19] 50-60% of daily salt intake in Iran is the salt added in cooking or at the table.[11,20] To reduce salt intake, behavioral interventions are much more effective and cost-effective than population-level interventions.[21,22]
Studies show that in most developing countries including Iran and China, approximately 60--80% of the total salt intake is the salt added in cooking and at the table.[11,15] Therefore, the major solution must focus on discretionary salt intake, with a change in dietary habits and promotion of nutrition literacy and attitude for selecting low-salt foods which must be placed on the agenda so that the level recommended by WHO, that is, less than 5 g per day, can be achieved.[2,23]
No such study has yet been conducted in Iran on the reduction of salt intake. Few studies have been conducted in Iran on discretionary salt intake, and no information is available on Iranian women's readiness for changing this behavior. Thus, it seems necessary to conduct studies in order to examine women's readiness for changing the behavior of discretionary salt intake.
In the Iranian society, women have the most important role in nutrition planning and preparation of food for the family. They have the vital role of dietary patterns choice and shaping the taste of the family.[24] Mothers affect the dietary experience of family members, especially young children, and teach them the preference for the taste of salt.[25] They are responsible for cooking and controlling the amount of salt added to the food. Also, they have the important role of managing the food the family consumes. Thus, they were selected as representatives of the whole family.
The present study aimed to determine the readiness for changing the behavior of discretionary salt intake and its determinants in women residing in Tehran capital city in Iran.
Methods
Theoretical basis of research
The transtheoretical model of stages of change shows the time- and motivation-related aspects of change. This model presupposes that everyone passes the stages of pre-contemplation, contemplation, preparation, action, and maintenance.
Pre-contemplation
In this stage, individuals do not think about changing their behavior in the foreseeable future which is usually defined as the following 6 months. In this stage, individuals have no intention to change their behavior, but are aware of the problem or behavior.
Contemplation
Individuals think about a change in the foreseeable future but not immediately, defined as 1--6 months. In this stage, they are aware of and think about and reexamine their behavior, but have not yet made a decision.
Preparation
Individuals plan for changing their behavior in the near future which is often defined as the next month. In this stage, they decide to change their behavior.
Action
Individuals have significantly changed their lifestyle in the past 6 months. Since action is visible, a change in behavior is often considered synonymous with action.
Maintenance
Individuals maintain change for some time, usually 6 months or more. In this stage, the behavior is stable and permanent and individuals try to prevent regression.[1,2,3]
The model of stages of change have been employed in numerous diet-related studies in order to determine the stages of change in diet behavior, including the reduction of fat intake and increasing fiber, fruit, vegetable, and dairy intake, and its efficiency has been confirmed. Therefore, diet-related behavioral interventions are more effective if they are based on theories of health-related behavior change.[23,26,27,28,29,30,31,32,33,34]
Study design and participants
The present cross-sectional (descriptive-analytic) study was conducted on 561 women referring to women's primary health care units in east of Tehran, Iran, selected through convenience sampling.
Inclusion criteria:
Women who were willing to participate in the study;
were minimum 18 years of age;
were literate;
prepared food for the family;
Non-inclusion criteria:
Pregnant or breastfeed
have a low-sodium diet prescribed by doctors of dietitians;
Data collection tool
In this study, a researcher-made questionnaire was used that included four sections in the fields of nutritional knowledge, discretional salt intake, women's readiness to change behavior, and self-efficacy. The questions in this questionnaire were based on the aims of the study, review of the studies, specific cultural and social conditions of Iranian women.
In the pilot study conducted to determine the ease of understanding the self-efficacy questionnaire, a sample of 15 women completes the questionnaire and expressed their opinions regarding their understanding of items. In the final version, changes were applied which included the adaptation of phrases to participants and creating an appropriate framework for them.
To determine the reliability of the instrument and internal consistency of the self-efficacy items, 30 women completed the questionnaire and Cronbach's alpha of 0.914 showed the internal consistency of this scale. To determine the face and content validity of the questionnaire, a panel of experts (10 experts and professors of nutritional sciences and health education) was used and their opinions regarding the clarity and appropriateness of questions for the objectives were applied. This study was approved by the Committee of Ethics in Research, Shahed University (IR. Shahed.REC.1394.280).
Demographic questionnaire included age, occupation, and level of education. Self-administered questionnaire consisted of several sections. In the first section measuring women's nutritional knowledge was regarding the relationship between high salt intake and diseases. Each correct response received the score of 1.[35] The other question was related to the presence of someone in the family who had limitations on salt intake. The answer “yes” showed the salt intake-limited exposure group in the family.
The second section focused on the discretionary salt intake. Over 60% of salt intake in Tehran is the salt added in cooking or at the table.[11] Also, it is difficult to precisely measure salt intake.[6,36] Therefore, in order to prevent the recall bias and since self-reported avoidance of salt intake has a high correlation with the actual behavior,[37] the habit of adding salt in cooking and at the table was questioned. Respondents could select one of the options of “always,” “often,” “sometimes,” “rarely,” or “never” (respectively scored 1 to 4) for each item on salt intake in cooking or at the table. To classify the responses, the answers given to these questions were congregated as “salt users” or “non-salt users.” Non-salt users were those who never, rarely, or sometimes added salt in cooking or at the table. Salt users were those who had selected the answers “always” or “often.”[38,39]
The third section determined the readiness for changing salt intake which was designed at the scale of stages of change based on the trans-theoretical model using the questionnaire used by Newson et al.[6] With this scale, the intention and decision of individuals regarding discretionary salt intake were determined by selecting one of the five stages of pre-contemplation, contemplation, preparation, action, and maintenance.[1] To determine the predictors of readiness for changing the behavior of discretionary salt intake, stages of change were divided into three categories of pre-contemplation (not ready for change), ready for change (including contemplation and preparation), and action (change has occurred, including action and maintenance).
The fourth section was on women's self-efficacy for reducing salt intake, including six questions scored on a five-point Likert scale from “not at all sure” to “completely sure” (scored 1–5, respectively). Minimum and maximum possible scores were 6 and 30, respectively. Questions in this section were designed based on the Persian adaptation of the general self-efficacy scale (GSES)[40] and self-efficacy in nutritional behavior scale (SENBS).[41]
Statistical analysis
Data were analyzed in SPSS version 16 (SPSS Inc., Chicago, IL, USA). Descriptive data are presented in absolute and relative frequency. The Kolmogorov--Smirnov test was used to determine the normality of quantitative data. The relationship between personal--social variables, knowledge, and self-efficacy with dependent variables (discretionary salt intake and stages of change in salt intake behavior) was assessed using Spearman's correlation and Chi-square test. In all tests, the significant level was <0.05. Logistic regression was employed to determine the predictors of readiness for changing the behavior of discretionary salt intake.
Results
Mean age of women was 36.21 ± 10.1 years, ranging from 18 to 60 years. Most women (36.2%) belonged to the age group of 30--39 years. Older women had lower levels of education (r = 0.287, P < 0.001). The level of education of 46.5% of women was university, and 38.8% (218 women) had high school diploma. Also, 66.6% of women were homemakers. Employed women had levels of education three-fold higher than that of homemakers (P < 0.001). Relative frequency of discretionary salt intake in cooking and at the table based on women's characteristics is presented in Table 1.
Table 1.
Relative frequency of discretionary salt intake in cooking and at the table based on women’s characteristics
| Salt intake in cooking | Salt intake at the table | |||||
|---|---|---|---|---|---|---|
| Yes n=458 | No n=103 | Yes n=128 | No n=433 | |||
| Age (years)* | ||||||
| 30> | 86.1 | 13.9 | 29.5 | 70.5 | ||
| 30-39 | 81.1 | 18.2 | 20.7 | 79.3 | ||
| ≥40 | 77.6 | 22.4 | 19.3 | 80.7 | ||
| P | Chi-square test | 0.114 | Chi-square test | 0.047 | ||
| Level of education | ||||||
| Below high school diploma | 74.4 | 25.6 | 34.1 | 65.9 | ||
| High school diploma | 81.2 | 18.8 | 20.2 | 79.8 | ||
| University | 84.3 | 15.7 | 21.5 | 78.5 | ||
| P | Chi-square test | 0.127 | Chi-square test | 0.029 | ||
| Occupation | ||||||
| Homemaker | 82.6 | 17.4 | 23 | 77 | ||
| Employed | 79.7 | 20.3 | 22.5 | 77.5 | ||
| P | Fisher’s exact test | 0.419 | Fisher’s exact test | 0.915 | ||
| Salt intake-limited exposure | ||||||
| Yes | 77.6 | 22.4 | 24.7 | 75.3 | ||
| No | 84.3 | 15.7 | 21.6 | 78.4 | ||
| P | Fisher’s exact test | 0.046 | 0.412 | |||
| Knowledge level | ||||||
| Desirable | 84.2 | 15.8 | 22.3 | 77.7 | ||
| Undesirable | 78.5 | 21.5 | 23.5 | 76.5 | ||
| P | Fisher’s exact test | 0.1 | 0.762 | |||
| Self-efficacy score | ||||||
| Mean±SD | 18.94±6.3 | 22.45±6.3 | 15.05±5.7 | 20.92±5.9 | ||
| P | Mann-Whitney test | 0.001 | Mann-Whitney test | 0.001 | ||
40% women had someone in the family who had such a limitation (salt intake-limited exposure group). Compared with younger women, more women aging 40 years or older belonged to the salt intake-limited exposure group (P < 0.01). The knowledge of women regarding the relationship between salt intake and diseases was undesirable in 44.7% of cases (i.e., they had answered less than half of the questions correctly), and only 28% of women had a desirable knowledge of this matter. Mean score of self-efficacy for reducing discretionary salt intake was 19.58 ± 6.4.
Discretionary salt intake in cooking
In cooking, over half of the women (52.6%), 163 women (29.1%), and 39 women (6.9%) always, often, and never added salt to food, respectively. Adding salt to food while cooking had a significant correlation with the salt intake-limited exposure (Fisher's exact test, P < 0.01).
Discretionary salt intake at the table
Mean score of self-efficacy was higher in women who did not consume salt at the table than those who did (P < 0.001) [Table 1].
Table 2 shows the total discretionary salt intake based on demographic and other characteristics of women. In general, 66% of women were salt users.
Table 2.
Discretionary salt intake based on the personal-demographic characteristics of women
| Characteristics of women | Salt users n=370 | Non-salt users n=191 | Total n=561 | P | ||||
|---|---|---|---|---|---|---|---|---|
| n | % | n | % | n | % | |||
| Age (years)* | <30 | 122 | 73.5 | 44 | 26.5 | 166 | 29.59 | Chi-squared tests P=0.048 |
| 30-39 | 129 | 63.5 | 74 | 36.5 | 203 | 36.18 | ||
| ≥40 | 119 | 62 | 73 | 38.0 | 192 | 34.22 | ||
| Level of education | Below high school diploma | 56 | 68.3 | 26 | 31.7 | 82 | 14.62 | Chi-squared tests P=0.889 |
| High school diploma | 143 | 65.6 | 75 | 34.4 | 218 | 38.86 | ||
| University | 171 | 65.5 | 90 | 34.5 | 261 | 46.52 | ||
| Occupation | Homemaker | 251 | 67.1 | 123 | 32.9 | 374 | 66.67 | Fisher’s exact test P=0.450 |
| Employed | 119 | 63.6 | 68 | 36.4 | 187 | 33.3 | ||
| Salt intake limitation in the family | No | 223 | 66.0 | 115 | 34.0 | 338 | 60.2 | Fisher’s exact test P=1 |
| Yes | 147 | 65.9 | 76 | 34.1 | 223 | 39.8 | ||
| Knowledge level | Undesirable | 165 | 65.7 | 86 | 34.3 | 251 | 44.7 | Fisher’s exact test P=0.929 |
| Desirable | 205 | 66.1 | 105 | 33.9 | 310 | 55.3 | ||
| Scores of self-efficacy** | Mean±SD | 17.78±6 | 23.08±5.6 | Mann-Whitney test P<0.001 |
||||
Stages of behavior change of discretionary salt intake
In terms of the behavior changing of discretionary salt intake stages, about one-third of women (31.9%) were in pre-contemplation, following by 22.3% in contemplation, 18.9% in preparation, 8.2% in action, and 18.7% in maintenance.
Table 3 presents the relationship between stages of changing the behavior of discretionary salt intake in terms of pre-contemplation, readiness (contemplation and preparation), and action (action and maintenance) with women's characteristics. The stage of change moved forward with increasing the score of self-efficacy (r = 0.42, P < 0.001). A positive correlation was observed between self-efficacy score and categorized levels of change (r = 0.42 and P < 0.001).
Table 3.
Frequency distribution of stages of changing the behavior of discretionary salt intake in women Stages of change in the behavior of discretionary salt intake
| Pre-contemplation n=179 | Contemplation and preparation n=231 | Action and Maintenance n=151 | Total n=561 | P | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| n | % | n | % | n | % | n | % | |||
| Adding salt in cooking | Never | 1 | 2.6 | 4 | 10.3 | 34 | 87.2 | 39 | 6.9 | Chi-squared P<0.001 |
| Sometimes | 2 | 3.1 | 13 | 20.3 | 49 | 76.6 | 64 | 11.4 | ||
| Often | 40 | 24.5 | 56 | 34.4 | 67 | 41.1 | 163 | 29 | ||
| Always | 136 | 46.1 | 158 | 53.6 | 1 | 0.3 | 295 | 52.5 | ||
| Adding salt at the table | Never | 73 | 24.5 | 92 | 30.9 | 133 | 44.6 | 298 | 53.1 | Chi-squared P<0.001 |
| Sometimes | 44 | 32.6 | 75 | 55.6 | 16 | 11.9 | 135 | 24.0 | ||
| Often | 36 | 46.2 | 40 | 51.3 | 2 | 2.6 | 78 | 13.9 | ||
| Always | 26 | 52.0 | 24 | 48.0 | 0 | 0 | 50 | 8.9 | ||
| Level of education | Below high school diploma | 24 | 29.3 | 34 | 41.5 | 24 | 29.3 | 82 | 14.6 | Chi-squared P=0.874 |
| High school diploma | 66 | 30.3 | 93 | 42.7 | 59 | 27.1 | 218 | 38.8 | ||
| University | 89 | 34.1 | 104 | 39.8 | 68 | 26.1 | 261 | 46.5 | ||
| Occupation | Homemaker | 116 | 31 | 164 | 43.9 | 94 | 25.1 | 374 | 66.7 | Chi-squared P=0.169 |
| Employed | 63 | 33.7 | 67 | 35.8 | 57 | 30.5 | 187 | 33.3 | ||
| Age (year) | <30 | 69 | 41.6 | 62 | 37.3 | 35 | 21.2 | 166 | 29.6 | Chi-squared P=0.02 |
| 30-39 | 59 | 29.1 | 82 | 40.4 | 62 | 30.5 | 203 | 36.2 | ||
| ≥40 | 51 | 26.6 | 87 | 45.3 | 54 | 28.1 | 192 | 34.2 | ||
| Knowledge of the relationship between salt intake and diseases | Desirable | 98 | 31.6 | 134 | 43.2 | 78 | 25.5 | 310 | 55.3 | Chi-squared P=0.468 |
| Undesirable | 81 | 32.3 | 97 | 38.6 | 73 | 29.1 | 251 | 44.7 | ||
| Salt intake limitation in the family | No | 118 | 34.9 | 127 | 37.6 | 93 | 27.5 | 338 | 60.2 | Chi-squared P=0.074 |
| Yes | 61 | 27.4 | 104 | 46.6 | 58 | 26.0 | 223 | 39.8 | ||
| Self-efficacy score | Mean±SD | 16.28±5.8 | 19.70±5.9 | 23.33±5.6 | Spearman’s P<0.001 | |||||
Table 4 shows that to determine the factors associated with women's readiness for behavior change, predictor variables, including age, level of education, occupation, salt intake-limited exposure in the family, knowledge level regarding the relationship between salt intake and diseases, and self-efficacy score were entered into logistic regressions. In this Table, we compared pre-contemplation and preparation stage of salt intake among women. Results showed that self-efficacy and salt intake-limited exposure are the most important factors determining women's readiness for changing the behavior of discretionary salt intake compared with women in the pre-contemplation stage.
Table 4.
Logistic regression analysis for some extracted variables
| Odds Ratio | 95% Confidence Interval | P | |
|---|---|---|---|
| Age | 1.002 | 0.978-1.026 | 0.887 |
| Occupation | 1.355 | 0.804-2.283 | 0.254 |
| Level of education | 1.127 | 0.663-1.916 | 0.660 |
| Self-efficacy score | 1.102 | 1.061-1.144 | 0.000 |
| Salt intake- limited exposure | 1.578 | 1.027-2.426 | 0.038 |
Variables entered in to the model: age, occupation, level of education, self-efficacy score, salt intake- limited exposure
Discussion
The present study determined readiness for changing the behavior of discretionary salt intake in adult women residing in Tehran. Results revealed that the majority of women were at the pre-contemplation stage. A limited number of participants were in the maintenance stage and had a significantly higher self-efficacy score compared with those in the pre-contemplation stage.
Level of education is an indicator of the socioeconomic status and helps a healthy diet. In the present study, the level of education had a significant correlation with the amount of salt added at the table, and women with university education consumed less salt at the table than women with an education level below high school diploma. Chen Ji[42] showed that those with a lower level of education consume more salt. Similarly, Jeong showed that those with low income and education level consume more salt.[43] It is clear that a low level of education is a serious limitation for achieving dietary knowledge and selecting healthy dietary behaviors.[44] The role of women is more important considering the pivotal role in buying the ingredients, preparing food, regulating the diet, and dietary choices.[45]
In the present study, occupation as another indicator of socioeconomic status showed no significant correlation with levels of changing the behavior of discretionary salt intake. Chen Ji[42] showed that the level of salt intake was higher in those with lower levels of occupation.
These factors include a range, from individual determinants to environmental, social, and cultural characteristics. They direct individuals to attach importance to the type and quality of food based on economic conditions of the family, ensuring health, and meeting the needs.
A significant positive correlation was observed between stages of change and self-efficacy scores. In other words, self-efficacy scores increased as individuals advanced in the stages of change toward action and maintenance. In the action and maintenance stage, 87% of participants never added salt in cooking. Moreover, 45% of women in this stage never added salt to food at the table.
The study by Newson[6] showed that 22% of the sample often or always added salt to food before tasting it and 58% never added salt to food at the table. Over one-third (34%) of participants were in pre-contemplation and 28% in maintenance stages, and only 8% of the sample were at the stage of readiness for changing the behavior of discretionary salt intake, inconsistent with the results of this research.
Vander Veen[5] used the stages of change model in the Netherland among men and women aged 19--70 years to show that 32% of participants were at the pre-contemplation, 14% at the dynamic (contemplation, preparation, and action), and over half of them (54%) at maintenance stages. Discretionary salt intake in cooking or at the table was lower in the maintenance group than dynamic and pre-contemplation groups.
Results of a study by Ni in New York showed that only 38% of patients who were aware of reducing salt intake actually practice it. Knowing that salt intake must be limited but not acting upon it shows that these individuals are at preparation, contemplation, or pre-contemplation stages of change.[46] Another important point is that despite having knowledge regarding the importance and benefits of a healthy dietary pattern, practices differs.[5,6,46] This shows that specific measures compatible with the social context must be taken by policymakers to change this behavior among women, and education alone does not suffice in planning. Measures such as correction of the dietary environment at home may help.
A quasi-experimental study in Malaysia used the stages of change model to evaluate the behaviors of patients with hypertension in relation to doing regular exercises, reducing salt intake, and increasing the consumption of fruits and vegetables. Results revealed that patients in action and maintenance groups had significantly reduced salt intake compared with those in pre-contemplation and preparation groups.
In recent study by Jeong, the most important aspect of readiness for changing a behavior was a high self-efficacy score.[43] Participants considered environmental support and motivation as the most important factors leading to behavioral change, consistent with the results of Chen.[42]
Assessing Iranian women's readiness for change is the first step toward the assessment and promotion of food and nutrition literacy. Readiness for changing behaviors is a novel concept which receives considerable attention today. However, few studies have been conducted on this issue among women.[36,38,39]
According to studies, the assessment of readiness for changing behaviors has the best results when the context is defined well. In the present study, women's readiness to change the behavior was assessed using a valid questionnaire which included different dimensions of behavior change at home.
Conclusions
Results of the present study showed that the evaluation of the stage of readiness for changing dietary behaviors can be a useful tool in interventional studies which aim to change dietary behaviors since it provides the opportunity to identify readiness for change in different social groups. The evaluation of readiness for changing a behavior is a novel concept in society-level dietary interventions. These instruments and questionnaires can be easily implemented by experts and health workers. Therefore, it can be used in large society-level studies after corrections and final approval.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
Acknowledgments
The present project could never be brought into practice without cooperation of the participating women. Besides, the authors would like to appreciate the authorities of the women care units across Tehran who provided the ground for conducting the present research project.
References
- 1.Prochaska JO, Velicer WF. The transtheoretical model of health behavior change. Am J Health Promot. 1997;12:38–48. doi: 10.4278/0890-1171-12.1.38. [DOI] [PubMed] [Google Scholar]
- 2.Glanz K, Rimer BK, Viswanath K. San Francisco, CA: John Wiley & Sons; 2008. Health Behavior and Health Education: Theory, Research, and Practice. [Google Scholar]
- 3.Prochaska JO, DiClemente CC. The transtheoretical approach. Handbook of psychotherapy integration. 2005;2:147–71. [Google Scholar]
- 4.Gol M A. Health Education and Health Promotion. [Last accessed on 2017 Feb 15]. Available from: http://healthpro.blogfa.com/category .
- 5.Van der Veen J, De Graaf C, Van Dis S, Van Staveren W. Determinants of salt use in cooked meals in the Netherlands: Attitudes and practices of food preparers. Eur J Clin Nutr. 1999;53:388–94. doi: 10.1038/sj.ejcn.1600737. [DOI] [PubMed] [Google Scholar]
- 6.Newson R, 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. doi: 10.1016/j.appet.2013.07.003. [DOI] [PubMed] [Google Scholar]
- 7.Tamaki J, Kikuchi Y, Yoshita K, Takebayashi T, Chiba N, Tanaka T, et al. Stages of change for salt intake and urinary salt excretion: Baseline results from the high-risk and population strategy for occupational health promotion (HIPOP-OHP) study. Hypertension Research. 2004;27:157–66. doi: 10.1291/hypres.27.157. [DOI] [PubMed] [Google Scholar]
- 8.Papadakis S, Pipe AL, Moroz IA, Reid RD, Blanchard CM, Cote DF, et al. Knowledge, attitudes and behaviours related to dietary sodium among 35-to 50-year-old Ontario residents. Can J Cardiol. 2010;26:e164–9. doi: 10.1016/s0828-282x(10)70384-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Rahmani M, Koohkan A, Allahverdian S, Hedayati M. Comparison of dietary iodine intake and Urinary excretion in urban and rural Households of Ilam in 2000. Iran J Endocrinol Metabol. 2000;2:31–7. [Google Scholar]
- 10.Khosravi A, Kelishadi R, Sarrafzadegan N, Boshtam M, Nouri F, Zarfeshani S, et al. Impact of a community-based lifestyle intervention program on blood pressure and salt intake of normotensive adult population in a developing country. J Res Med Sci. 2012;17:235–41. [PMC free article] [PubMed] [Google Scholar]
- 11.Hoshiarrad A, Esmaeeli M, Salehi F. Determination of Sodium intake by dietary intake surveys and validation of the methods with 24 hour urine collections in Tehran. Report of World Health Organization. 2014 Sep [Google Scholar]
- 12.Sarrafzadegan N, Mohammadifard N. Development of Five Years Multi-sectorial National Action Plan for Salt Intake Reduction. Final Report. 2015 [Google Scholar]
- 13.Rezaei S, Mahmoudi Z, Sheidaei A, Aryan Z, Mahmoudi N, Gohari K, et al. Salt intake among Iranian population: The first national report on salt intake in Iran. J Hypertens. 2018;36:2380–9. doi: 10.1097/HJH.0000000000001836. [DOI] [PubMed] [Google Scholar]
- 14.Andersen L, Rasmussen LB, Larsen EH, Jakobsen J. Intake of household salt in a Danish population. Eur J Clin Nutr. 2009;63:598–604. doi: 10.1038/ejcn.2008.18. [DOI] [PubMed] [Google Scholar]
- 15.Chen J, Liao Y, Li Z, Tian Y, Yang S, He C, et al. Determinants of salt-restriction-spoon using behavior in China: Application of the health belief model. PLoS One. 2013;8:e83262. doi: 10.1371/journal.pone.0083262. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Ortega RM, López-Sobaler AM, Ballesteros JM, Pérez-Farinós N, Rodríguez-Rodríguez E, Aparicio A, et al. Estimation of salt intake by 24 h urinary sodium excretion in a representative sample of Spanish adults. Br J Nutr. 2011;105:787–94. doi: 10.1017/S000711451000423X. [DOI] [PubMed] [Google Scholar]
- 17.Toda A, Ishizaka Y, Tani M, Yamakado M. Current dietary salt intake of Japanese individuals assessed during health check-up. Hypertens Res. 2015;38:163–8. doi: 10.1038/hr.2014.154. [DOI] [PubMed] [Google Scholar]
- 18.Anderson CA, Appel LJ, Okuda N, Brown IJ, Chan Q, Zhao L, et al. Dietary sources of sodium in China, Japan, the United Kingdom, and the United States, women and men aged 40 to 59 years: The INTERMAP study. J Am Diet Assoc. 2010;110:736–45. doi: 10.1016/j.jada.2010.02.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Brown IJ, Tzoulaki I, Candeias V, Elliott P. Salt intakes around the world: Implications for public health. Int J Epidemiol. 2009;38:791–813. doi: 10.1093/ije/dyp139. [DOI] [PubMed] [Google Scholar]
- 20.Rafieifar Sh, Pouraram H, Djazayery A, Siassi F, Abdollahi Z, Dorosty AR, et al. Strategies and opportunities ahead to reduce salt intake. Arch Iran Med. 2016;19:729–34. [PubMed] [Google Scholar]
- 21.Asaria P, Chisholm D, Mathers C, Ezzati M, Beaglehole R. Chronic disease prevention: Health effects and financial costs of strategies to reduce salt intake and control tobacco use. Lancet. 2007;370:2044–53. doi: 10.1016/S0140-6736(07)61698-5. [DOI] [PubMed] [Google Scholar]
- 22.Cobiac LJ, Vos T, Veerman JL. Cost-effectiveness of interventions to reduce dietary salt intake. Heart. 2010;93:1920–5. doi: 10.1136/hrt.2010.199240. [DOI] [PubMed] [Google Scholar]
- 23.Greene GW, Rossi SR, Rossi JS, Velicer WF, Fava JL, Prochaska JO. Dietary applications of the stages of change model. J Am Diet Assoc. 1999;99:673–8. doi: 10.1016/S0002-8223(99)00164-9. [DOI] [PubMed] [Google Scholar]
- 24.Stratton P, Bromley K. Families’ accounts of the causal processes in food choice. Appetite. 1999;33:89–108. doi: 10.1006/appe.1999.0233. [DOI] [PubMed] [Google Scholar]
- 25.Stein LJ, Cowart BJ, Beauchamp GK. The development of salty taste acceptance is related to dietary experience in human infants: A prospective study. Am J Clin Nutr. 2012;95:123–9. doi: 10.3945/ajcn.111.014282. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.DiClemente CC, Delahanty JC, Havas SW, Van Orden OR. Understanding self-reported staging of dietary behavior in low-income women. J Health Psychol. 2015;20:741–53. doi: 10.1177/1359105315580213. [DOI] [PubMed] [Google Scholar]
- 27.Greene GW, Redding CA, Prochaska JO, Paiva AL, Rossi JS, Velicer WF, et al. Baseline transtheoretical and dietary behavioral predictors of dietary fat moderation over 12 and 24 months. Eat Behav. 2013;14:255–62. doi: 10.1016/j.eatbeh.2013.01.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Hart Jr A, Tinker L, Bowen DJ, Longton G, Beresford SA. Correlates of fat intake behaviors in participants in the eating for a healthy life study. J Am Diet Assoc. 2006;106:1605–13. doi: 10.1016/j.jada.2006.07.006. [DOI] [PubMed] [Google Scholar]
- 29.Glanz K, Patterson RE, Kristal AR, DiClemente CC, Heimendinger J, Linnan L, et al. Stages of change in adopting healthy diets: Fat, fiber, and correlates of nutrient intake. Health Educ Q. 1994;21:499–519. doi: 10.1177/109019819402100412. [DOI] [PubMed] [Google Scholar]
- 30.Chee Yen W, Mohd Shariff Z, Kandiah M, Taib M, Nasir M. Stages of change to increase fruit and vegetable intake and its relationships with fruit and vegetable intake and related psychosocial factors. Nutr Res Pract. 2014;8:297–303. doi: 10.4162/nrp.2014.8.3.297. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Kushida O, Murayama N. Effects of environmental intervention in workplace cafeterias on vegetable consumption by male workers. J Nutr Educ Behav. 2014;46:350–8. doi: 10.1016/j.jneb.2014.05.001. [DOI] [PubMed] [Google Scholar]
- 32.Reis LCd, Correia IC, Mizutani ES. Stages of changes for fruit and vegetable intake and their relation to the nutritional status of undergraduate students. Einstein (São Paulo) 2014;12:48–54. doi: 10.1590/S1679-45082014AO2926. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Kholdi N, Pirasteh A, Zayeri F, Jafari F, Bastani N. Stages of dairy products consumption change by medical students: The trans theoretical model. Alborz Univ Med J. 2013;2:25–32. [Google Scholar]
- 34.Gulliver P, Horwath C. Women's readiness to follow milk product consumption recommendations: Design and evaluation of a 'stage of change’algorithm. J Hum Nutr Diet. 2001;14:277–86. doi: 10.1046/j.1365-277x.2001.00295.x. [DOI] [PubMed] [Google Scholar]
- 35.Clark-Cutaia MN, Ren D, Hoffman LA, Snetselaar L, Sevick MA. Psychometric validation of the self-efficacy for restricting dietary salt in hemodialysis scale. Top Clin Nutr. 2013;28:384–91. doi: 10.1097/01.TIN.0000437407.76867.65. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Lee JY, Cho DS, Kim HJ. The effect of salt usage behavior on sodium intake and excretion among Korean women. Nutr Res Pract. 2012;6:232–7. doi: 10.4162/nrp.2012.6.3.232. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Mittelmark MB, Sternberg B. Assessment of salt use at the table: Comparison of observed and reported behavior. Am J Public Health. 1985;75:1215–6. doi: 10.2105/ajph.75.10.1215. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Grimes CA, Riddell LJ, Nowson CA. The use of table and cooking salt in a sample of Australian adults. Asia Pac J Clin Nutr. 2010;19:256–60. [PubMed] [Google Scholar]
- 39.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. doi: 10.1186/1479-5868-10-25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Nezami E, Schwarzer R, Jerusalem M. Persian Adaptation (Farsi) of the General Self-Efficacy Scale. 1996. [Last retrieved 2007 Jul 29]. Available from: fuberlin. de/~health/persean.htm .
- 41.Hosein-Nejad M, Aziz-zadeh-Forozi M, Mohammad-Alizadhe S, Haghdoost A. Role of self efficacy predictors in nutritional behaviors of kerman high school female students in 2006-2007 academic year. JSSU. 2008;16:320–8. [Google Scholar]
- 42.Ji C, Cappuccio FP. Socioeconomic inequality in salt intake in Britain 10 years after a national salt reduction programme. BMJ Open. 2014;4:e005683. doi: 10.1136/bmjopen-2014-005683. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Bae JH, Shin DS, Lee SC, Hwang IC. Sodium intake and socioeconomic status as risk factors for development of age-related cataracts: The Korea national health and nutrition examination survey. PLoS One. 2015;10:e0136218. doi: 10.1371/journal.pone.0136218. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Doustmohammadian A, Keshavarz Mohammadi N, Omidvar N, Amini M, Abdollahi M, Eini-Zinab H, et al. Food and nutrition literacy (FNLIT) and its predictors in primary schoolchildren in Iran. Health Promot Int. 2018 doi: 10.1093/heapro/day050. [In Press]. doi: 10.1093/heapro/day050. [DOI] [PubMed] [Google Scholar]
- 45.Parmenter K, Waller J, Wardle J. Demographic variation in nutrition knowledge in England. Health Educ Res. 2000;15:163–74. doi: 10.1093/her/15.2.163. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Ni H, Nauman D, Burgess D, Wise K, Crispell K, Hershberger RE. Factors influencing knowledge of and adherence to self-care among patients with heart failure. Arch Intern Med. 1999;159:1613–9. doi: 10.1001/archinte.159.14.1613. [DOI] [PubMed] [Google Scholar]