Dietary diversity and nutrient intake adequacy among women in Iwo local government area, Osun State Nigeria

Abstract

Dietary diversity has been considered a potential 'proxy' indicator to reflect nutrient intake adequacy. The study assessed the Dietary Diversity and Nutrient Intake Adequacy among adult women in Iwo Local Government (LGA) area of Osun State. Apparently healthy 250 adult women were the respondents in the study. The anthropometric measurements of the respondents were taken using standard equipment. A 24-hour dietary recall questionnaire was used to assess the food intake of the respondents, which was converted into nutrient intake using adapted Total Dietary Assessment (TDA) software. Dietary Diversity Score (DDS) was created using a 15-food group model. Nutrient intake was evaluated using Nutrient Adequacy Ratio (NAR). Mean adequacy ratio (MAR) was calculated as an indicator of nutrient intake adequacy. Statistical Package for Social Sciences (SPSS) software was used for analysis of the data collected. More than half (56.4%) of the respondents had normal body weight, 15.2% were underweight, 21.2% were overweight and 7.2% were obese. The diet of the respondents consisted of food items mainly from food groups such as cereals, white roots and tubers, vitamin A-rich vegetables, spices, condiments and beverages. Fruits and foods from milk and milk products were the least consumed. The mean DDS was 8.29±1.3, 4.4% of the respondents had high DDS, 94.4% had minimum DDS and 1.2% had low DDS. The MAR was 2.56±0.69, the intake of some nutrients such as carbohydrate, fat, iron, zinc and protein was above the recommended dietary intake and there was inadequate intake for some nutrients such as calcium, vitamin C and potassium among the respondents. Consumption of foods from groups such as meat and meat products, eggs and poultry and fish were found to be significantly related with nutrient intake adequacy as respondents with adequate intake of nutrients consumed more from these food groups. These results indicate that dietary diversity is an indicator of nutrient intake adequacy. Most of the respondents consumed foods from cereals, white roots and tubers, vegetables, legumes, oil and fats, spices and condiments than from other food groups.

Dietary Diversity and Micronutrient Intake of Adult Women in Ogun State, Nigeria (Case Study)

“…Darboux et al, 2022 on Assessment of the nutritional status and dietary intake of women of childbearing age in the city of Comè, Benin.The minimum dietary diversity for women score showed that majority (71.3%) of the respondent had a low dietary diversity score and 28.2% of the respondents had a minimum dietary diversity score. It has been demonstrated that dietary variety scores are reliable and affordable markers for evaluating nutritional quality at the population level(Oladoyinbo et al, 2017). This study's mean DDS (3.92) was lower than the mean DDS reported by earlier research.…”

Dietary Diversity and Nutritional Adequacy among an Older Spanish Population with Metabolic Syndrome in the PREDIMED-Plus Study: A Cross-Sectional Analysis

“…Dietary guidelines worldwide have promulgated the benefits of a variety of dietary intake, mainly because it is easier to provide the necessary amount of nutrients with a highly diverse diet. This could be especially important for obesity and chronic disease management [47,48]. In this regard, monotonous diets usually imply unhealthy eating habits, as well as the worsening in the progression of certain diseases, for example CVD [49].…”

Dietary Intake and Associated Factors Among Elderly Persons Receiving Social Assistance Grant for Empowerment (SAGE) in Northern Uganda

“…The mean adequacy ratio (MAR) was computed as a proxy for su cient nutrient intake of the population. The mean ARs were calculated for eleven micronutrients: zinc, thiamine, niacin, ribo avin, calcium, iron, vitamin A, vitamin B6, vitamin C, vitamin B12, and folate (Adebukola et al, 2017). The real NAR values were truncated at 1 in the MAR computation, meaning that a nutrient with a high NAR could not compensate for a nutrient with a low NAR (Adebukola et al, 2017).…”

“…The mean ARs were calculated for eleven micronutrients: zinc, thiamine, niacin, ribo avin, calcium, iron, vitamin A, vitamin B6, vitamin C, vitamin B12, and folate (Adebukola et al, 2017). The real NAR values were truncated at 1 in the MAR computation, meaning that a nutrient with a high NAR could not compensate for a nutrient with a low NAR (Adebukola et al, 2017). NAR = (Nutrient intake ÷ Recommended intake) NAR = (Nutrient intake ÷ Recommended intake) × 100%…”