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Structural role of perceived benefits and
barriers to receiving macronutrients in women
with metabolic syndrome; a path analysis study
Siamak Mohebi- Leila Azadbakht- Mohammad Hozoori- Mahmoud Parham-
Aziz Kamran- Gholamreza Sharifirad
Introduction
Metabolic syndrome is a set of metabolic
abnormalities known through abdominal obesity,
hypertension, insulin resistance and blood lipids
abnormalities which is also considered as an
independent risk factor for cardiovascular
diseases and type 2 diabetes.
• The prevalence of MetS in Asian population is
estimated about 25.8%.
• The incidence of this syndrome increased
from 30.1% to 34.7% in Iran during 2010-
2014.
• Metabolic syndrome is associated with lifestyle
factors such as diet, smoking and physical activity.
• Previous published papers in Iran showed that fruit,
vegetable, dairy, whole grain and red meat are
associated with MetS.
• The nutritional self-care is one of the most
important approach for the control of MetS risk
factors.
• Nowadays, it is recognized that the feeding behavior of
an individual is not only influenced by his nutritional
knowledge but also can be influenced by several other
factors.
• The perceived benefits and barriers are the two well-
known structures in some of the health-related
behaviors as central elements in most of the theories of
behavior change such as Heath Belief Model and
Pender's Health Promotion Model.
Materials and Methods
• A cross-sectional design was used for this study in 2012.
• Sample subjects were selected from among women with
MetS who were referred to Isfahan's petroleum industry
health care centers.
• In this study in order to fit the structural equation model
(SEM) the number of samples obtained were 329
through observing 80% power, significance level of 0.05,
30 degrees of freedom and a maximum RMSE of 5%.
• Two questionnaires were used to collect data:
• A researcher made questionnaire based on health
promotion model which its validity and reliability
was approved in a separate step after the design.
• Also, the feeding behaviors of the samples were
checked through a 24 hour dietary recall for 3
days.
Results
• The mean age of patients was 44.8±8.1 years old
and mean of suffering duration from the syndrome
was 2.1± 0.9 years.
• Also, 1.2% had primary education, 8.2% guidance
school, 56.2% high school and 34.3% had academic
degree.
• The 41.3% were employed and 58.7% were
housewives.
• The mean and standard deviation of the
perceived benefits scores of feeding behavior
in this study were 24.3±3.3 and mean and
standard deviation of perceived barriers scores
were 28.1±4.8.
Items Strongly
agreeAgree Disagree
Strongly Disagree
diet may reduce psychological stress 61.4 22.5 5.2 10.9I figure it is better to fit the diet 29.8 59.3 5.8 5.2the diet does not need to visit the doctor frequently.
0 32.2 43.2 24.6
the recommended diet does not cost much money.
61.4 7.6 11.6 19.5
I follow the recommended diet is easy. 9.7 70.8 5.8 13.7diet is recommended for the control of hyperglycemia.
42.6 57.4 0 0
I will be following a diet recommended for longer life.
81.5 18.5 0 0
diet will improve daily functioning. 35.3 64.7 0 0
Table 1: The frequency distribution of perceived benefits questions
scores regarding the nutritional behavior in the studied group
Table 2: The frequency distribution of perceived barriers questions
scores regarding the nutritional behavior in the studied group
ItemsStrongly
agreeAgree Disagree
Strongly Disagree
Two things I can not cook due to busy life. 61.7 29.5 8.8 0Follow the diet requires a lot of time during the day.
63.2 28 8.8 0
Follow the diet deprives me of the pleasure of food.
61.7 37.7 0.6 0
Follow the diet makes me tired. 59.9 31.3 8.8 0Buying food diet is costly. 91.2 0 8.8 0My diet is not going to a party. 80.2 10.9 0.3 8.5Follow the diet has prompted me to do some of life's responsibilities.
72.6 18.5 0.3 8.5
I too agree that the diet is not followed. 9.7 81.5 0.3 8.5
• The results showed that the mean of energy
intake was 2512.4±344.2 kcal, protein intake
was 70.9±27.2 gr, carbohydrate consumed was
420±89.3 gr and fat intake was 61.6±20.2 gr
per day.
Table 3: Correlation matrix between the perceived benefit
and barriers with the intake of macronutrients in the
studied group
Studied variable energy protein carbohydrate fat
Perceived
benefits
r -0.549 -0.119 -0.504 -0.663
P 0.002 0.003 <0.001 <0.001
Perceived
barriers
r 0.571 0.238 0.578 0.691
P 0.009 0.031 <0.001 <0.001
Table 4: The value of model fitting in the path analysis in order to
predict the intake of macronutrients in the studied group
Model fit indexRecommended
valueTheoretical
modelFinal model
Conclusions
Chi-square statistic 0.05 ≤ 0 0.962 FittedDegrees of freedom of the Chi-square statistic
3.00≥ 0.059 0.012 Fitted
Goodness of fit index (GFI) ≤0.90 0.928 0.997 FittedAdjusted goodness of fit index (AGFI)
≤0.80 0.5 0.951 Fitted
Normed fit index (NFI) ≤0.90 0.808 0.996 FittedComparative fit index (CFI) ≤0.90 0.808 0.953 FittedRoot mean square residual (RMR)
≥0.09 0.262 0.021 Fitted
Root mean square error of approximation (RMSEA)
≥0.10 0.217 0.041 Fitted
Table 5: The value of model fitting in the path analysis in order to
predict the intake of macronutrients in the studied group
Model fit indexRecommended
valueTheoretical
modelFinal model
Conclusions
Chi-square statistic 0.05 ≤ 0 0.973 FittedDegrees of freedom of the Chi-square statistic
3.00≥ 0.060 0.012 Fitted
Goodness of fit index (GFI) ≤0.90 0.922 0.998 FittedAdjusted goodness of fit index (AGFI)
≤0.80 0.5 0.962 Fitted
Normed fit index (NFI) ≤0.90 0.817 0.996 FittedComparative fit index (CFI) ≤0.90 0.817 0.953 FittedRoot mean square residual (RMR)
≥0.09 0.253 0.020 Fitted
Root mean square error of approximation (RMSEA)
≥0.10 0.217 0.031 Fitted
Table 6: Direct, indirect and total impact coefficients of perceived
benefits and barriers on energy, protein, carbohydrate and fat intake
per day in the target group
variables Direct effect Indirect effect Total effect
perceived benefits
energy -0.084 -0.039 -0.123
protein -0.029 -0.020 -0.049
carbohydrate -0.326 -0.089 -0.415
fat -0.401 -0.104 -0505
perceived barriers
energy 0.409 0.123 0.532
protein 0.145 0.021 0.166
carbohydrate 0.293 0.064 0.357
fat 0.471 0.217 0.688
Figure 1: Diagram of path analysis to predict macronutrient intake in the final
model
Energy intake
Protein intake
Carbohydrate intake
Fat intake
Social support
Commitment to plan of action
Immediate competing demands
& preferences
Situational influences
Perceived benefits
Perceived barriers
Perceived self-efficacy
Activity related affect
0.35
-0.30
-0.45
-0.24
0.75
-0.51
-0.55
0.72
0.18
0.71
0.13
-0.11
-0.63
-0.33
-0.82
-0.08
-0.02
-0.33 -0.40 0.41
0.15 0.29
0.47
• The final model showed that perceived barriers
along with other investigated structures, can predict
72% of changes in energy intake, 51% of changes in
protein intake, 42% of changes in carbohydrate
intake and 88% of changes in lipid intake,
• and perceived benefits can predict 22% of changes in
energy intake, 18% of protein, 35% of carbohydrates
and 56% of lipid.
Conclusion
• The results of this study showed that the perceived
benefits in patients with metabolic syndrome were
in relatively good condition,
• but perceived barriers were in poor condition.
• In this study, confirmed the improper conditions of
nutritional status in women, because of the
macronutrients intake in the studied patients was
more than RDA.
• In this study, perceived barriers have a greater
predictive ability to determine macronutrient
intake than perceived benefits.
• So, according to many perceived barriers, we
cannot expect proper nutritional care in these
patients.
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