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. 2013 Feb 19:10:25.
doi: 10.1186/1479-5868-10-25.

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

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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

Rani Sarmugam et al. Int J Behav Nutr Phys Act. .

Abstract

Background: Discretionary salt use varies according to socio-demographic factors. However, it is unknown whether salt knowledge and beliefs mediate this relationship. This study examined the direct and indirect effect of socio-demographic factors on salt knowledge and discretionary salt use in a sample of 530 Australian adults.

Methods: An internet based cross-sectional survey was used to collect data for this study. Participants completed an online questionnaire which assessed their salt knowledge, beliefs and salt use behaviour. Mplus was used to conduct structural equation modelling to estimate direct and indirect effects.

Results: The mean age of the participants was 49.2 years, and about a third had tertiary education. Discretionary salt use was inversely related to age (r=-0.11; p<0.05), and declarative salt knowledge (knowledge of factual information) scores (r = -0.17; p<0.01), but was positively correlated with misconceptions about salt (r = 0.09; p<0.05) and beliefs about the taste of salt (r = 0.51; p<0.001). Structural equation modelling showed age, education and gender were indirectly associated with the use of discretionary salt through three mediating pathways; declarative salt knowledge, misconceptions about salt and salt taste beliefs.

Conclusions: Inequalities observed between socio-demographic groups in their use of discretionary salt use can potentially be reduced through targeted salt knowledge and awareness campaigns.

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Figures

Figure 1
Figure 1
Theoretical model.
Figure 2
Figure 2
Standardised regression co-efficient based on final model. Gender is coded as 1= male, 2 = female; *p<0.05, **p<0.01, ***p<0.001; †path included in the analysis based on suggested modification indices; only statistically significant (p<0.05) paths are shown in this figure. Indirect paths are reported in Table 4 due to space limitations.

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