Original Article

Associations Between Health Literacy and Dietary Intake: A Cross-sectional Study of Adults With Metabolic Syndrome in Thailand

Please cite this article as follows: Mungvongsa A, Yangyuen S, Jareanpon C, Somdee T. Associations between health literacy and dietary intake: a cross-sectional study of adults with metabolic syndrome in Thailand. J Educ Community Health. 2023; 10(3):136-144. doi:10.34172/jech.2447

Introduction

Based on the global data, 20%–25% of adults suffer from metabolic syndrome (MetS), which is a cluster of common abnormalities, including hypertension, elevated fasting plasma glucose (FPG), high blood cholesterol or triglycerides, and abdominal obesity (1). Adults with MetS are twice as likely to have type 2 diabetes mellitus (T2DM) and five times more likely to have cardiovascular disease (CVD) than people without MetS (2). The major causes of MetS are abdominal obesity and the resulting insulin resistance, most probably due to overeating and a sedentary lifestyle, which can cause an imbalance between dietary intake (DI) and calorie use (3). Healthy nutrition and physical activity are protective factors against MetS (1). Previous studies revealed that DI is associated with MetS (4-6). Approximately 80% of Thai people have levels of DI that exceed the recommended intake, which increases the risk of MetS (7). One study reported that Korean women with high carbohydrate intake had an increased risk of MetS (8). By contrast, another study reported that those with MetS had higher fat intake than those without it among older adults in the Balearic Islands (9). Therefore, additional research is needed to fully understand the role of dietary and nutrient intake in the development of MetS.

Health literacy (HL) is a relatively new concept in health promotion. A low HL score is a risk factor that is potentially modifiable and is associated with behaviors that increase the risk of MetS (1), such as poor diet (3), inadequate physical activity (10), and unhealthy lifestyle (11). HL is related to the individual’s ability to access and understand health information on making decisions about health promotion and disease prevention (12). Individuals with high levels of HL are much more capable of understanding available nutritional information and making healthier choices. In Thailand, there are studies demonstrating limited HL among many adults. Several studies have shown that HL was significantly associated with MetS. Tajdar et al (1) reported that low HL was associated with a high incidence of MetS among older people in Germany, and Cheng et al reported that, among patients aged 23–88 years receiving health check-ups in Taipei, low HL was associated with MetS in women but not in men (13). A study conducted in Iran found that HL was lower in people who consumed junk food than in those who did not consume fast food, suggesting that HL is associated with fast-food intake (14). In addition, a study of school-age children in Iran found that a high HL was linked with a healthy dietary pattern, suggesting that HL may play an important role in shaping DI (3). However, no similar study has been conducted on adults, and there is no evidence available on the potential relationship between HL and diet in Thailand (15,16). Therefore, we investigated differences in HL score and DI between participants with and without MetS and tested for an association between HL score and DI. We hypothesized that higher HL would be correlated with reduced DI, contributing to a decreased risk of MetS.

Materials and Methods

Participants

This cross-sectional study was conducted from May 2022 to May 2023 in Phetchaburi province, central Thailand. The target population of our study was adults aged 18–59 years, who lived in the Phetchaburi province and received services from 119 primary care units, including two urban health centers, one large-scale health-promoting hospital, and 116 health-promoting hospitals. The population included 248 767 people.

Data Collection and Sampling

The target sample size was computed using G*Power version 3.1.9.7. Considering an effect size (large) of 0.35, a power of 0.80, 12 independent variables, and a significance level of 0.05, the minimum sample size was calculated to be 2106. However, after considering a drop-out rate of 20%, it was estimated to be 2527 (17). A total of 2527 people who agreed to participate in this study formed the study group. Inclusion criteria were providing informed consent, being 18–59 years old, being able to read, write, and speak Thai, and having no disabilities. By contrast, a lack of interest in participating, insufficient time, and incomplete survey submissions were among the exclusion criteria. A total of 2527 people who met the inclusion criteria were selected using a multistage sampling technique. In the first stage, Phetchaburi province in Thailand was selected using purposive sampling method. In the second stage, two sub-districts were selected using lottery method from a list of sub-districts. In the third stage, the participants were selected by systematic random sampling from each primary care service. We determined the sampling interval, which was number four, and selected number two as the random start. Then, we added the sampling interval periodically to select additional samples by selecting every fourth participant from the list and excluding if they were not present or refused to participate in the study.

Questionnaires

A self-reported questionnaire was developed based on a literature review that consisted of three parts as follows:

General Demographic Characteristics Questionnaire

General demographic characteristics included gender, age, marital status, education level, socioeconomic status, and health-related behaviors, such as smoking, alcohol consumption, exercise, duration of sleep, and history of chronic disease. The socioeconomic status of the participants was classified into lower (contractors or general employees), middle (entrepreneurs, independent tradesmen, business owners, and farmers), or upper classes (professionals with specific skills, managers, and administrators).

Our questionnaire asked about the intensity of exercise, which was classified as low (less than 150 min/wk of moderate activity or 75 min/wk of strenuous exercise), moderate (150-300 min/wk of moderate activity or 75 min/wk of vigorous exercise), and high (more than 300 min/wk of exercise) (18). The question about the duration of sleep was closed-ended: “In the last week, on average, how many hours of sleep did you get per day (including daytime naps)?”. The responses were < 8 hours or ≥ 8 hours (19).

Health Literacy Questionnaire-18

The Health Literacy Questionnaire-18 (HLQ-18), a self-made scale modelled after the HLQ^TM was used in the study (20). This summated rating scale consists of 18 items measuring 6 dimensions including access, cognition, communication, self-management, media, and decision-making (Cronbach’s α = 0.93). A participant’s total score was the sum of the scores for all items (range 0–90).

Dietary and Nutrient Intake

In this study, DI refers to the amount of food, including specific types of food and calories consumed daily by an individual. Nutrient intake refers to the amount of macronutrients, such as carbohydrates, proteins, and fats consumed daily by an individual (21). The Semi-quantitative Food Frequency Questionnaire (Semi-FFQ) was developed and validated based on a cross-sectional study conducted by Overvad (22). The Semi-FFQ consisted of 54 food items, including their sources and portion sizes, from the Thai Dietary Reference Intake (Thai DRIs) database (23). Food records were collected on three days, which included two working days (Monday and Friday) and one rest day (Saturday or Sunday), with one week between them. Our questionnaire is considered adequate based on the evaluated standards of Thai DRIs. Participants completed the questionnaires under the supervision of the researchers. Participants were given approximately 30 minutes to complete the forms.

Anthropometric and Biochemical Markers

Anthropometric measurements were performed to assess nutritional status, including weight (measured using a digital scale) and height (measured using a Detecto scale). Body mass index (BMI) was calculated as weight (kg)/(height (m)², using self-reported values. We used the Asian BMI classification system determined by the World Health Organization (WHO) (8), which includes underweight ( < 18.5 kg/m²), normal weight (18.5–22.9 kg/m²), overweight (23–24.9 kg/m²), obese class 1 (25–29.9 kg/m²), and obese class 2 ( > 30 kg/m²). We diagnosed MetS according to the new International Diabetes Federation (IDF) criteria for South Asians, which were based on Chinese, Malay, and Asian Indian populations. These guidelines specify that individuals diagnosed with MetS must have a waist circumference (WC) cut-off point of ≥ 90 cm for men and ≥ 80 cm for women, in addition to two of the following four factors: systolic blood pressure (BP) of ≥ 130 mm Hg and/or diastolic BP of ≥ 85 mmHg, FPG of ≥ 100 mg/dL, triglyceride (TG) level of ≥ 150 mg/dL, and high-density lipoprotein cholesterol (HDL-C) cut-off point of < 40 mg/dL for men and < 50 mg/dL for women (24). WC was measured in the horizontal plane at the mid-point between the iliac crest and the lowest rib. Doctors measured BP with a sphygmomanometer while the patients were seated. Blood samples were collected in the morning after at least 8 hours of overnight fasting. The blood volume collected was approximately 15–20 mL for the analysis of serum biochemistry, lipid profile (including HDL-C and TG), and FPG. Laboratory analysis of fasting serum lipids and FPG was performed by the medical laboratory technologist in each hospital.

Statistical Analysis

Data were analyzed using a Statistical Package for the Social Sciences (SPSS) version 25.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were expressed as mean and standard deviation (SDs) for continuous variables and frequencies and percentages for categorical variables. We tested whether continuous variables were normally distributed using the Kolmogorov-Smirnov test. Independent t tests were used to test for differences in means between the groups with MetS and without MetS. The univariate linear regression analysis was performed to study the association between HL score and DI. The univariate linear regression analysis was applied for each of the independent variables with a P value of less than 0.25 which should be included in the first multiple linear regression controlled for the effect of other covariates. Associations between DI and various factors were presented as mean differences with 95% confidence intervals (CIs). The level of statistical significance was set at P value < 0.05 (two-tailed).

Results

Study Populations

This cross-sectional study included 1195 adults with MetS and 1332 (52.7%) adults without MetS. Overall, the prevalence of MetS was 47.28%, according to IDF criteria. The mean age of the participants with MetS was higher than the mean age of those without it (51.0 years vs 47.6 years; P< 0.05). MetS was observed in 36.2% and 63.8% of men and women, respectively (P < 0.05). The majority of participants with MetS were married (79.3%), had an education level of primary school or lower (53.2%), were in the middle socioeconomic class (56.4%), and had a history of chronic disease (74.3%). Participants with MetS had a higher BMI than those without it (28.02 ± 4.89 km/m² vs. 22.64 ± 3.64 km/m²) and reported almost no exercise. The comparison of characteristics of participants in the two groups is shown in Table 1.

Description of HL Score by Participant Characteristics

The HLQ-18 consisted of 18 questions. The mean HL score was 48.71 ± 14.46. The mean HL scores of participants with MetS were 43.71 ± 13.19 and 47.26 ± 14.19 for men and women, respectively. The mean HL scores of participants without MetS were 52.80 ± 14.54 and 48.87 ± 14.06 for men and women, respectively (data not shown). HL scores were significantly lower in participants with MetS than in those without it (45.98 ± 13.94 vs. 51.18 ± 14.47; P< 0.05). Among participants with MetS, the HL score of women was higher than that of men (47.26 ± 14.19 vs. 43.71 ± 13.19; P< 0.05) (Table 2).

Description of Dietary and Nutrient Intake by Participant Characteristics

There were significant differences in DI, as measured by 54 items in the Semi-FFQ, between participants with and without MetS (Table 3). Mean DI of participants with MetS was 2259.10 ± 465.03 kcal/d, with a fat intake of 36.5%, carbohydrate intake of 47.2%, and protein intake of 14.8%. Interestingly, both calorie and fat intake were higher than the recommended limits of the Thai DRIs and WHO. DI of participants with MetS was significantly higher than that of those without it (2259.10 ± 465.03 /kcal/d vs. 1805.23 ± 333.19 /kcal/d; P< 0.05). Participants with MetS had higher fat intake, lower carbohydrate intake, and lower protein intake than participants without MetS (P< 0.05). Men with MetS had higher DI than women with MetS (2567.83 ± 415.11 /kcal/d vs. 2084.30 ± 395.92 /kcal/d; P < 0.05).

Association Between HL and DI

Tables 4 and 5 show the results of univariate linear regression and multiple linear regression analyses, indicating an association between HL score and other independent variables related to DI. Based on the results of the univariate linear regression analysis, the HL score was significantly associated with DI (ꞵ = −0.132, P< 0.05; Table 4). Based on the results of the multiple linear regression, this association remained significant after adjustment for potential confounding variables, including gender, education level, smoking habits, alcohol use, exercise, and BMI. HL score was significantly negatively associated with DI (ꞵ = −0.053, P< 0.05; Table 5).

Discussion

This study investigated HL score, DI, and nutrient intake of participants with and without MetS and the association between HL score and DI among adults with MetS in Thailand.

In the first part of our study, we found that HL score was lower in participants with MetS than in those without it. Another study conducted in Germany reported similar results. In participants aged 18–60 years old, the risk of MetS increased as the HL score decreased. Therefore, adults with lower HL were more likely to have MetS than those with higher HL (1). Individuals with lower HL have more difficulty understanding the written and oral instructions given by health care professionals, resulting in poor self-care decisions and more health problems (25). Contrarily, high levels of HL are associated with the ability to understand available nutrition information and make healthier choices (26). Indeed, a study of working adults in Thailand found that protective factors against MetS in women were nutrition literacy and behavior (27). Most studies suggest that poor HL is a risk factor for developing MetS (1). However, two studies conducted in Japan and Taipei reported contradictory results concerning the association between HL and the prevalence of MetS. Yokokawa et al (28) studied the association between HL and the prevalence of MetS among Japanese people and concluded that the proportions of participants with MetS, defined as a WC of ≥ 90 cm and FPG of ≥ 110 mg/dL, did not differ significantly between higher and lower HL groups. They also found a negative association between HL and MetS among men; however, they found no significant relationship among women. On the other hand, Cheng et al (13) reported that, among participants aged 23–88 years receiving health check-ups in Taipei, the proportion of people with MetS was higher in the lower HL score group. The P for trend between MetS and HL was marginally significant. In female participants, lower HL was significantly related to a higher prevalence of MetS; the trend between HL and MetS was not significant in men. This might be due to the fact that HL is a key factor in adopting preventive lifestyle behaviors among adults with MetS. Women are more likely to have healthier lifestyles than men, particularly in terms of smoking and alcohol consumption (28). A study of 1817 Japanese individuals in the city of Fukushima found that HL was associated with access to health information and health behaviors. They explained that a high HL may be associated with the characteristics of a healthier lifestyle, which can contribute to a lower prevalence of MetS among men (29). This may be because people with higher levels of HL are more likely to adopt appropriate behaviors and be able to perceive and act on health-related information (30). In the present study, we found that adults with MetS had low HL scores. Therefore, the high HL score may play a role in promoting self-care behaviors and skills and adopting and maintaining DI patterns among adults with MetS.

Our second aim was to investigate dietary and nutrient intake in participants with and without MetS. We found that participants with MetS had a diet with higher fat intake (36.5%), but lower carbohydrate intake (47.2%) and protein intake (14.8%) than those without MetS. The WHO recommends that fat intake should not exceed 30% of total caloric intake (31). Our results showed that the fat intake of participants with MetS was above the recommended limit (20%–35% of total caloric intake), but the carbohydrate and protein intake of the same participants was within the recommended range (45%–65% and 10%–15% of total caloric intake, respectively). Two studies conducted in the Balearic Islands and Japan have found results similar to ours. Julibert et al (9) compared older adults with MetS and those without MetS in terms of fat intake and reported that the participants with MetS had higher fat intake than those without it. Freire et al (32) found that after adjusting for confounding variables, MetS was positively associated with fat intake among Japanese Brazilians. Considering specific food groups, only fried food intake was shown to be associated with an increased risk of MetS. This result is in agreement with another study that examined the role of specific dietary fats, which emphasized dietary guidelines and also showed a significant relationship between fatty acid consumption and the risk of MetS (33). This might be because an energy-dense high-saturated fat diet promotes MetS, insulin resistance, and obesity (33). On the contrary, some studies have found that linoleic acid consumption was inversely related to MetS, which corroborates the benefits of reducing the risk of diseases involving insulin resistance (32). Linoleic acid reduced the incidence of CVD and T2DM, possibly by decreasing risk factors for those diseases, for instance, by reducing blood cholesterol levels and the resulting impact on insulin and glucose metabolisms (34). Unlike the PREDIMED study, they did not find a significant association between DI and MetS. The PREDIMED randomized trial determined the long-term effects of the Mediterranean diet on the prevalence of MetS in older adults. The participants were randomly assigned to 1 of the 3 dietary interventions: a Mediterranean diet supplemented with nuts, a Mediterranean diet supplemented with extra virgin olive oil, and a low-fat diet. The risk of progressing MetS did not differ between those on the Mediterranean diet and the control group. In addition, a diet supplemented with nuts or olive oil was not associated with a decreased prevalence of MetS compared to a low-fat diet (35). Olive oil supplementation is consistent with the efficacy of dietary monounsaturated fatty acids for reduction of risk factors associated with MetS and CVD (36). On the contrary, a study of older adults in the Balearic Islands found that participants with MetS had higher total fat intake than those without MetS (9). The high caloric density of high-fat diets plays an important role in weight gain (37). Therefore, high dietary fat intake causes excessive caloric intake and increases the risk of MetS. Our study showed that fat intake in participants with MetS exceeded the recommended intake. Therefore, providing knowledge of adequate fat intake, with the goal of reducing fat and caloric intake, is a key strategy for the prevention of MetS.

In addition, we evaluated the association between the HL score and DI among adults with MetS in Thailand. Our results revealed a significant negative association between the HL score and DI, indicating that high HL scores result in reduced, and therefore better, DI. These findings correspond with those of Buja et al (15), who conducted a systematic literature review on the relationship between HL and DL. They reported that three studies revealed an association between high HL and a low sugar intake, indicating that healthier dietary practices are associated with a greater perception of food labels and suggesting that HL might play a role in improving adult’s diets. Interestingly, a study conducted in Iran, which revealed the association between HL and junk food intake in adults, found that HL was lower in those who consumed junk food than in those who did not consume junk food, indicating that those with lower HL levels were more likely to consume junk food. The findings suggest that HL is associated with the level of fast-food intake (14). Surprisingly, some studies revealed that DI was not significantly associated with HL. A study performed in Switzerland found no significant association between HL and salt intake, but the awareness variable “salt content impacts food/menu choice” was associated with salt intake (38).

One of the strengths of this study was the large sample size and precise effect estimates adjusted for potential confounders. Additionally, our research has investigated and analyzed several important variables related to DI in adults with MetS. Besides, estimates of DI were derived from multiple days (2-3 days/week), which are considered more representative of usual intake. Our study has weaknesses due to the large sample size taking a long time to obtain data collection and analysis. Moreover, there are some limitations to this study. The main limitation of our study is its cross-sectional design; therefore, causality cannot be inferred. Another limitation is that food records collected have recall bias that can occur whenever an attempt is made to collect data retrospectively. Further longitudinal research is required to demonstrate the causal association between HL and DL.

Conclusion

The findings of this study showed differences between HL score and DI in participants with MetS and those without MetS, indicating lower HL scores and higher calorie and fat intake in patients with MetS compared with those without Mets. In addition, this study revealed a negative association between HL score and DI (energy) in adults with MetS and identified HL score as a protective factor for MetS. Therefore, future research should be done to develop intervention programs related to HL effectiveness in improving DI appropriately and adequately to decrease the risk of MetS.

Acknowledgements

The authors would like to thank all the participants. The present study would not have been possible without their contributions.

Authors’ Contribution

Conceptualization: Aravan Mungvongsa.

Data curation: Aravan Mungvongsa, Chatklaw Jareanpon.

Formal analysis: Aravan Mungvongsa.

Methodology: Suneerat Yangyuen, Thidarat Somdee.

Project administration: Aravan Mungvongsa.

Supervision: Thidarat Somdee.

Validation: Suneerat Yangyuen, Chatklaw Jareanpon.

Writing–original draft: Aravan Mungvongsa.

Writing–review & editing: Aravan Mungvongsa, Thidarat Somdee.

Competing Interests

The authors have no conflict of interests associated with the material presented in this paper.

Ethical Approval

The study was approved by the Ethics Committee of Mahasarakham University (Date and Number: 28032022/ECMSU109398). The Ethics Committee of the Phetchaburi Provincial Public Health Office granted the approval to conduct research in primary healthcare facilities. All participants took part in the study voluntarily and gave informed consent, as required by our ethics committee.

Funding

The authors received financial support for research from the Faculty of Public Health, Mahasarakham University, Thailand (ID reference: PHD005/2023).

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