In this study, we developed and validated a food frequency questionnaire to assess food and nutrient intakes of adults in Ethiopia. We have observed a higher intake of energy and nutrients when the FFQ was used as compared to the average of the two 24-HRs.
Bland-Altman plots show an overestimation of energy and macro-nutrients (carbohydrate, protein and fat) for various data points. We found a low to moderate level of agreement (correlation coefficients) for energy and nutrient intakes between the two methods. The FFQ did not adequately classify subjects with respect to energy, macro-nutrients and most of the micro-nutrients. For the majority of the food groups, median differences in the intake of foods and nutrients between 24-HRs and FFQ were, overall, small and statistically insignificant. For food groups, a moderate correlation was found between the average of the two 24-HRs and the FFQ. The FFQ showed a fair agreement for cereals, legumes, and roots and tubers.
We found that the FFQ overestimated energy and nutrient intakes relative to the average of the two 24 h diet recalls. Overestimation is a common issue reported in various validation studies (10–15). Overestimation in the present study was moderate for intakes of energy (367.9 kcal), protein (10.5 g) and carbohydrate (77.6 g) and slight for intakes total fat (4.4 g) compared to other validation studies conducted using 24-HRs as their reference method (8, 10–13). Overestimation can be attributed to the subject’s tendency to overestimate their actual intake when they are asked to recall the frequency of a large number of foods consumed in an FFQ. Besides, difficulty in conceptualizing the assigned portion sizes and difficulties in reporting the frequencies of usual intake could be an attributing factor(13). It may have also occurred as a result of purposeful over-reporting of food consumption by subjects (7). The use of shorter questionnaires and advances in portion size estimation techniques are suggested to improve overestimation of intakes by FFQs.
This study found moderate correlations (0.2–0.49) between the average of the two 24-HRs and FFQ for energy (r = 0.24), protein (r = 0.22), and carbohydrate (r = 0.32) and low correlation (< 0.2) for fat (r = 0.05). The low to moderate correlation coefficients found between the two methods for energy and macro-nutrient intakes are comparable with other previous validation studies (8, 10, 12, 16). However, our finding was lower than those reported by other studies using 24-HRs as their reference method (8, 10, 12). The observed decrease in correlation coefficients could be interpreted as being the result of using only a two day 24-HRs as a reference method. It was found that nutrient correlations were lower when the reference method of the dietary questionnaire was conducted fewer than eight times (17). We believe that the observed correlation estimates could be improved with additional days of recall as well as with multiple studies over different seasons.
The lower correlation coefficients for iron and vitamin intakes observed in our study is not uncommon in FFQ validation studies (8, 10, 11, 13, 18). A meta-analysis of FFQ validation studies showed that pooled correlation coefficients of nutrient intakes ( total fat, protein, carbohydrate, alcohol, calcium, iron and vitamins) were lower for FFQ validated against 24-HRs rather than food record (17). The possible reason for a low correlation for the vitamin is that vitamin intake tends to vary greatly from day to day (as many vitamins are found in only a small selection of foods)(8).
We observed a moderate to good correlation for almost all food groups. This is in good agreement with previous validation studies assessing food group intakes (8, 10, 19, 20). The good correlation found for vegetable intakes in our study is higher than those reported by other validation studies (8, 10, 12). This may have occurred because of ease of quantifying vegetable intakes as they are often consumed independently in Butajira. The lower correlation of egg intake in our study, in contrast to other studies (10, 11) may have occurred because of not consuming egg on the days where 24-HR was conducted.
The Bland-Altman plot showed a moderate agreement between the two methods for energy and macro-nutrients. Trend was not observed across the intake level in energy and macronutrient intakes. Similarly, another study also showed a moderate level of agreement with no persistent trend across intake levels using a Bland-Altman plot (12, 21). However, ranges for limits of agreement were relatively wide as opposed to another study (13). The observed wide limits of agreements between FFQ and the reference method are common, hence highlighting the limitation of the FFQ in assessing absolute nutrient intake due to wide variability in how the FFQ measured energy and macronutrient intake relative to the average of the two 24-HRs (4).
A tendency towards a poorer agreement in Vitamin A and iron intake between methods was observed with lower levels of intake as shown by the Bland-Altman plot. This poor agreement in iron intake is also reported in another validation study (22). As indicated by a Bland-Altman plots, a systematic mean difference was not observed across the intake levels of Cereals, legumes, vegetables and beverages. Most of the data points are found between the 95% limits of agreement. However, the plot indicated wide limits of agreement which occurs as a result of increased variability.
The present study showed that the FFQ did not adequately classify subjects with respect to energy, macro-nutrients and most of the micro-nutrients as indicted by cross-classification and weighted kappa results (percentage of individuals in same quartile < 50, K values < 0.2) (5). However, the FFQ showed a fair quartile classification agreement for cereals, legumes, and roots and tubers (K values 0.21–0.4). This finding is consistent with previous studies which reported cross-classification and kappa by categorizing intakes into quartiles (10, 11, 22). We found lower values for energy and nutrients with respect to those reported by other studies using similar intake categories (8, 12, 13, 23). The misclassification and low kappa reported in our study may have occurred due to the insensitivity of FFQ to classify individuals into intake categories. The use of a Food diary as a reference method may have also increased classification agreement in the previous studies. FFQ showed a fair quartile classification agreement for cereals, legumes, and roots and tubers (K values 0.21–0.4). Similarly, other studies reported a fair classification agreement for this particular food group(13, 19). For beverage intake, our study indicated a misclassification (30.5%) into opposite quartile supported by low kappa value (k value < 0.21) showing the poor outcome. Other study reported a similar finding for beverage intake (19).
The present study has some limitations that must be acknowledged. First, given that we used a 24-HR dietary assessment method as our reference method, the sources of error between 24-HR and FFQ may tend to be correlated due to conceptualization of portion sizes. However, to lessen this effect we have used a salted replica of actual foods, pictures and calibrated equipment to estimate portion size. Second, we conducted two 24-HR per participant. This might have influenced the result obtained, particularly for estimating usual intakes of foods not consumed on a daily or regular bases such as meat/poultry/fish and the intake of other specific food groups. Third, participants may have purposefully over reported their intake due to social desirability. However, we gave a detailed explanation for the interviewers on how to explain the purpose of the FFQ to the participants using role-playing, small group exercises, and discussions. Fourth, we did not administer FFQ at the onset of the study; therefore, we cannot assess the reproducibility of the instrument.
The strength of the present study are the development of the FFQ based on the latest local dietary survey, focal group discussions, pre-test, and expert reviews, the use of comprehensive statistical analysis, to assess the validity of the FFQ and the use of an interactive, multiple-pass 24-HR adapted and validated for use in developing countries as our reference method..