Background
Accurate assessment of dietary intakes is a major pillar of nutrition research, counseling and intervention [
1]. The use of valid dietary assessment tools is essential to compare dietary intakes to current nutritional recommendations and to measure associations between diet and health outcomes [
2,
3]. Dietary assessment is especially important during pregnancy, as inadequate or excess nutrition during this period can adversely affect both the mother and the fetus [
4‐
6]. Considering that pregnant women’s dietary needs and intakes are influenced by numerous endo- and exogenous factors, evaluating the adequacy of diet throughout pregnancy can be complex. Physiological changes occurring in pregnancy, such as rises in blood volume, extracellular liquids, adipose tissue and placental weight, lead to an increase in dietary requirements and can heighten or suppress appetite [
7,
8]. Moreover, nausea, attitudes and behaviors towards food, body image perception as well as socioeconomic status have all been known to impact pregnant women’s food intakes [
9‐
11].
In pregnant women, as in the general population, food records (FR) are known as the gold standard for dietary assessment [
12,
13]. Despite their validity, FR and other pen-paper dietary assessment methods are time consuming, both for participants and research personnel. FR are also prone to a high social desirability bias [
2,
14]. For these reasons, web-based FR and web-based recall methods, such as food frequency questionnaires (FFQ) and 24-h dietary recalls, are gaining popularity. Compared to traditional/pen-paper methods, web-based methods are more cost and time effective as they can generate nutritional data automatically and are less prone to data entry errors [
15‐
17]. It has also been reported that web-based tools tend to enhance completion rates by reducing the burden associated with pen-paper dietary assessment methods [
18]. For example, contrary to FR, FFQ and dietary recalls are less time consuming because they do not require the participant to weigh or measure every food item consumed. [
19]. On the other hand, both FFQ and dietary recalls are subject to memory bias, as participants are asked to report their dietary intakes retrospectively [
20]. Moreover, FFQ are more often used to evaluate diets over longer periods of time (e.g. 1 to 12 months), as opposed to dietary recalls, which generally assess short-term dietary intakes [
2,
19]. Therefore, in a context like pregnancy, where various factors can impact appetite on a daily basis, using multiple 24-h dietary recalls might provide the most accurate estimate of pregnant women’s dietary intakes [
4,
21].
Although web-based 24-h dietary recalls are now more frequent in epidemiological studies, very few have been validated in a pregnant population [
22]. Since the validation of a dietary assessment tool in the target population is a necessary first step prior to interpreting and generalizing data [
23,
24], this study aims to assess the relative validity of a self-administered web-based 24-h dietary recall (R24W) against a 3-day FR among a population of French Canadian pregnant women. We hypothesize that the R24W is a valid tool to assess dietary intakes in pregnant women.
Results
Characteristics of the participants are presented in Table
1. Our sample included 60 pregnant women with a mean age of 32.5 ± 3.5 years old and an average pre-pregnancy BMI of 25.3 ± 5.8 kg/m
2. Ninety-eight percent of our participants were Caucasian and 81.7% had completed a university degree. In the 1st trimester of pregnancy, almost all participants (86.7%) reported nauseous symptoms, as opposed to the 2nd and 3rd trimesters, where much smaller proportions of women reported such symptoms.
Table 1
Participants’ characteristics (N = 60)
Age (years) | 32.5 ± 3.5 |
BMI (kg/m2) | 25.3 ± 5.8 |
Ethnicity – Caucasian | 59 (98.3) |
Education |
High school | 2 (3.3) |
College | 9 (15.0) |
University | 49 (81.7) |
Household incomea |
< 40,000 $ | 3 (5.0) |
40,000 – 59,999 $ | 8 (13.3) |
60,000 – 79,999 $ | 10 (16.7) |
80,000 – 99,999 $ | 13 (21.7) |
> 100,000 $ | 25 (41.7) |
Gestational age at baseline (weeks) | 9.3 ± 0.7 |
Number of children |
Noneb | 22 (36.7) |
1 | 33 (55.0) |
2 | 4 (6.7) |
3 | 1 (1.7) |
Experienced nausea |
1st trimester | 52 (86.7) |
2nd trimester | 20 (33.3) |
3rd trimester | 14 (23.3) |
Five women completed 8 of the 9 required R24W and 2 additional women partially completed one of their 3 FR (2 out of 3 days). We did not exclude these women from our statistical analyses because including them did not affect our results. Completion statistics for the R24W are presented in Table
2. Average R24W completion time significantly decreased at each trimester (
p < 0.001) and the number of items reported was significantly lower in the 3rd compared to the 2nd trimester (
p = 0.02).
Table 2
Completion statistics of the R24W
Completion time (minutes) | 21.8 (7.5) | 19.2 (6.8) | 16.3 (5.1) | < 0.0001 |
Reported meals (N) | 5.1 (0.9) | 4.8 (1.1) | 5.0 (1.2) | 0.13 |
Reported items (N) | 20.5 (4.0) | 20.3 (4.1) | 19.4 (4.0) | 0.025 |
In order to lighten the figures and tables shown in the results section of this article, only 2nd trimester validity analyses are presented. Results from the 1st and 3rd trimesters are available in the supplementary material, and some of these results are briefly presented throughout the section below. Overall, 2nd trimester results were representative of the association and agreement between the R24W and the FR throughout pregnancy, as they were statistically stronger than the 1st but not the 3rd trimester results.
Table
3 presents differences between the data from R24W and the FR for the 2nd trimester, as well as Pearson correlation coefficients. Mean intakes of energy, fat, % fat, saturated fatty acids (SFA), cholesterol, vitamin C, vitamin D, magnesium, phosphorus, zinc and calcium were significantly different between both tools (average difference: 12.2%;
p < 0.05). Results were similar in other trimesters (Additional file
1: Table S1), as differences between mean R24W and FR intakes were significant for 8 variables in both the 1st (energy, fat, SFA, riboflavin, vitamin D, phosphorus, calcium and sodium; average difference: 13.0%) and 3rd trimesters (% fat, SFA, cholesterol, vitamin B
6, vitamin D, magnesium, phosphorus and calcium; average difference: 11.4%). In the 2nd trimester, the highest percent differences were observed for vitamin D (20.6%;
p = 0.02) and calcium (21.6%;
p = 0.0003). Pearson correlation coefficients in the 2nd trimester ranged from 0.27 to 0.76 and were all significant with the exception of vitamin B
12 (
r = 0.03;
p = 0.83). Similarly, all 3rd trimester correlations were significant, but non-significant correlations were observed for % fat (
r = 0.09;
p = 0.50), folic acid (
r = 0.20;
p = 0.13) and vitamin B
12 (
r = 0.19;
p = 0.14) in the 1st trimester.
Table 3
Differences between mean dietary intakes reported by the R24W and the FR in the 2nd trimester
Energy (kcal) | 2357 (489) | 2239 (506) | 5.3* | 0.68* |
Carbohydrates (g) | 286.7 (67.7) | 279.1 (76.2) | 2.7 | 0.76* |
Fat (g) | 94.3 (26.1) | 84.9 (22.6) | 11.1* | 0.60* |
Proteins (g) | 99.6 (20.1) | 98.5 (22.6) | 1.1 | 0.37* |
% Carbohydrates | 48.7 (5.4) | 49.7 (6.0) | −2.0 | 0.53* |
% Fat | 35.8 (4.9) | 34.1 (4.8) | 5.0* | 0.48* |
% Proteins | 17.2 (3.0) | 17.8 (3.1) | −3.4 | 0.58* |
SFA (g) | 34.7 (11.7) | 30.3 (10.4) | 14.5* | 0.48* |
Cholesterol (mg) | 289.4 (95.2) | 322.6 (134.4) | −10.3 | 0.29* |
Vitamin A (μg) | 943.6 (412.4) | 932.0 (630.3) | 1.2 | 0.31* |
Thiamin (mg) | 2.0 (0.6) | 1.9 (0.6) | 5.3 | 0.68* |
Riboflavin (mg) | 2.4 (0.6) | 2.3 (0.7) | 4.3 | 0.46* |
Niacin (mg) | 26.7 (5.7) | 27.2 (9.3) | −1.8 | 0.48* |
Vitamin B6 (mg) | 1.9 (0.5) | 1.9 (0.6) | 0 | 0.44* |
Folic Acid (μg) | 400.5 (100.8) | 405.9 (144.1) | −1.3 | 0.54* |
Vitamin B12 (μg) | 5.5 (2.6) | 5.1 (2.6) | 7.8 | 0.03 |
Vitamin C (mg) | 139.9 (85.6) | 162.0 (83.9) | −13.6* | 0.69* |
Vitamin D (IU) | 272.0 (132.6) | 225.6 (131.4) | 20.6* | 0.27* |
Magnesium (mg) | 398.9 (100.4) | 359.3 (113.9) | 11.0* | 0.57* |
Phosphorus (mg) | 1678.1 (370.8) | 1557.1 (384.4) | 7.8* | 0.43* |
Zinc (mg) | 13.3 (3.1) | 11.9 (3.0) | 11.8* | 0.28* |
Iron (mg) | 15.9 (4.7) | 15.9 (4.9) | 0 | 0.58* |
Calcium (mg) | 1372.3 (500.0) | 1128.3 (447.0) | 21.6* | 0.51* |
Potassium (mg) | 3302.9 (826.4) | 3326.1 (848.5) | −0.7 | 0.62* |
Sodium (mg) | 3322.0 (891.3) | 3088.9 (1038.3) | 7.5 | 0.51* |
Dietary fibers (g) | 24.8 (8.2) | 24.2 (6.8) | 2.5 | 0.71* |
Average | | | 6.7 | 0.50 |
The R24W classified 79.1% (range 68.3-90.0%) of the participants in the same or adjacent quartile compared with the FR in the 2
nd trimester (Table
4). Furthermore, misclassification, i.e. when a participant is classified in the 1
st quartile by one tool and in the 4
th by the other, occurred in 3.9% (range 0-10.0%) of all cases in the 2
nd trimester. Cross-classification analyses revealed similar results in the 1
st and 3
rd trimesters (Additional file
2: Table S2). Weighted kappa scores ranged from 0.09 to 0.49 (average 0.32) in the 2
nd trimester, with the lowest value being for cholesterol. Significant Spearman correlations between the mean of both tools and the difference between both tools (proportional bias) were observed for cholesterol and niacin in the 2nd trimester (Table
5). A proportional bias was also observed for thiamin and vitamin C as well as for carbohydrates, % carbohydrates, % fat, thiamin, niacin, vitamin B6, folic acid and fibers in the 1st and 3rd trimesters, respectively (Additional file
3: Table S3).
Table 4
Cross-classification of intakes by quartiles and weighted kappa coefficient in the 2nd trimester
Energy | 43.3 | 38.3 | 81.6 | 1.7 | 0.39 |
Carbohydrates | 53.3 | 33.3 | 86.6 | 3.3 | 0.49 |
Fat | 40.0 | 41.7 | 81.7 | 1.7 | 0.36 |
Proteins | 40.0 | 38.3 | 78.3 | 5.0 | 0.31 |
% Carbohydrates | 41.7 | 38.3 | 80.0 | 5.0 | 0.33 |
% Fat | 33.3 | 45.0 | 78.3 | 1.7 | 0.28 |
% Proteins | 51.7 | 35.0 | 86.7 | 1.7 | 0.49 |
Saturated fatty acids | 38.3 | 43.3 | 81.6 | 6.7 | 0.31 |
Cholesterol | 21.7 | 46.7 | 68.4 | 3.3 | 0.09 |
Vitamin A | 30.0 | 41.7 | 71.7 | 1.7 | 0.20 |
Thiamin | 48.3 | 38.3 | 86.7 | 1.7 | 0.47 |
Riboflavin | 33.3 | 51.7 | 85.0 | 1.7 | 0.33 |
Niacin | 26.7 | 41.7 | 68.4 | 1.7 | 0.15 |
Vitamin B6 | 38.3 | 35.0 | 73.3 | 8.3 | 0.23 |
Folic Acid | 40.0 | 33.3 | 73.3 | 3.3 | 0.28 |
Vitamin B12 | 30.0 | 38.3 | 68.3 | 8.3 | 0.12 |
Vitamin C | 40.0 | 50.0 | 90.0 | 0 | 0.44 |
Vitamin D | 33.3 | 38.3 | 71.6 | 8.3 | 0.17 |
Magnesium | 50.0 | 38.3 | 88.3 | 5.0 | 0.47 |
Phosphorus | 40.0 | 38.3 | 78.3 | 1.7 | 0.33 |
Zinc | 31.7 | 40.0 | 71.7 | 10.0 | 0.15 |
Iron | 43.3 | 36.7 | 80.0 | 3.3 | 0.36 |
Calcium | 40.0 | 48.3 | 88.3 | 1.7 | 0.41 |
Potassium | 41.7 | 33.3 | 75.0 | 3.3 | 0.31 |
Sodium | 48.3 | 31.7 | 80.0 | 8.3 | 0.36 |
Dietary fibres | 46.7 | 36.7 | 83.4 | 3.3 | 0.41 |
Average | 39.4 | 39.7 | 79.1 | 3.9 | 0.32 |
Table 5
Seven criteria validity analysis of the R24W in the 2nd trimester
Criteria for good (G) outcome | ≥0.50 | ≥ 50% in same quartile; < 10% in opposite quartile | ≥0.61 | 0-10.9% | P > 0.05 | P > 0.05 | |
Criteria for acceptable (A) outcome | 0.20-0.49 | | 0.20-0.60 | 11.0-20% | | | |
Criteria for poor (P) outcome | < 0.20 | < 50% in same quartile; ≥10% in opposite quartile | < 0.20 | > 20% | P ≤ 0.05 | P ≤ 0.05 | |
Energy | G | P-G | A | G | P | G | 2 |
Carbohydrates | G | G-G | A | G | G | G | 0 |
Fat | G | P-G | A | A | P | G | 2 |
Proteins | A | P-G | A | G | G | G | 1 |
% Carbohydrates | G | P-G | A | G | G | G | 1 |
% Fat | A | P-G | A | G | P | G | 2 |
% Proteins | G | G-G | A | G | G | G | 0 |
SFA | A | P-G | A | A | P | G | 2 |
Cholesterol | A | P-G | P | G | G | P | 3 |
Vitamin A | A | P-G | A | G | G | G | 1 |
Thiamin | G | P-G | A | G | G | G | 1 |
Riboflavin | A | P-G | A | G | G | G | 1 |
Niacin | A | P-G | P | G | G | P | 3 |
Vitamin B6 | A | P-G | A | G | G | G | 1 |
Folic Acid | G | P-G | A | G | G | G | 1 |
Vitamin B12 | P | P-G | P | G | G | G | 3 |
Vitamin C | G | P-G | A | A | P | G | 2 |
Vitamin D | A | P-G | P | P | P | G | 4 |
Magnesium | G | G-G | A | A | P | G | 1 |
Phosphorus | A | P-G | A | G | P | G | 2 |
Zinc | A | P-P | P | A | P | G | 4 |
Iron | G | P-G | A | G | G | G | 1 |
Calcium | G | P-G | A | P | P | G | 3 |
Potassium | G | P-G | A | G | G | G | 1 |
Sodium | G | P-G | A | G | G | G | 1 |
Dietary fibres | G | P-G | A | G | G | G | 1 |
Total of poor outcomes | 1 | 23-1 | 5 | 2 | 10 | 2 | 44 |
Average | | | | | | | 1.7 |
A summary of all agreement and association analyses conducted in the 2nd trimester is presented in Table
5, based on the classification suggested by Lombard et al. [
29]. In the 2nd trimester, the number of poor outcomes by variable ranged from 0 (carbohydrates and % protein) to 4 (vitamin D and zinc). In the 1st trimester, % fat, vitamin D and folic acid were the variables with the highest number of poor outcomes (
n = 4), and all 3rd trimester variables accumulated less than 4 poor outcomes each (Additional file
3: Table S3).
Discussion
This is the first study to assess the relative validity of a web-based 24-h dietary recall in comparison with a 3-day FR among a population of pregnant women in all trimesters. All agreement and association analyses showed that, for most nutrients, the R24W can provide an estimation of pregnant women’s dietary intakes that is similar to the one obtained with a 3-day FR. Our results demonstrate that the R24W can be used to evaluate dietary intakes throughout pregnancy. To our knowledge and as mentioned by Vézina-Im et al. (2014) [
30], there is a lack of current literature relevant to the use of this type of tool. Therefore, our analyses must be compared with those of studies that validated web-based 24-h dietary recalls against a FR in non-pregnant adult populations.
Overall, Pearson correlations between both tools assessed in the 2
nd trimester of pregnancy have shown good associations. Indeed, our results revealed that 14 nutrients had a correlation coefficient above or equal to 0.50 which Masson et al. (2003) [
31] identified as the minimal recommended value to qualify a correlation as good. Correlations of this magnitude are, however, expected when two distinct measurement methods are used to assess the same variables (e.g. vitamin D measured by the R24W vs measured by a 3-day FR) [
23,
32,
33]. Our results are similar to those of Frankenfeld et al. (2012) [
34], who compared 2 web-based 24-h dietary recalls (ASA24) with a 4-day dietary record in 93 non pregnant Americans and found correlation coefficients that ranged from 0.06 to 0.76. Similarly, Timon et al. (2017) [
35] compared 2 web-based 24-h dietary recalls (Foodbook24) with a 4-day FR among 40 non pregnant adults and observed correlation coefficients ranging from 0.32 to 0.75, which is also comparable to our results.
In our study, we observed a correlation coefficient of 0.03 (
p = 0.83) for vitamin B
12 intakes in the 2
nd trimester, indicative of a weak association between the FR and the R24W for this nutrient. Nevertheless, this lack of association does not necessarily mean that the R24W cannot assess vitamin B
12 intakes accurately. In fact, mean intakes of vitamin B
12 reported by the R24W and the FR did not differ significantly in the 2
nd trimester (5.5 ± 2.6μg with the R24W vs 5.1 ± 2.6μg with the FR;
p = 0.46), as well as in the 1
st and 3
rd trimesters. Moreover, in all trimesters, no proportional bias was observed for vitamin B
12 intakes. Similarly, even though Frankenfeld et al. (2012) [
34] observed a lower correlation coefficient (
r = 0.06) for vitamin E intakes reported by the ASA24 and the FR, they found no significant difference between vitamin E intakes reported by both tools. Likewise, Comrie et al. (2009) [
36], who compared a web-based food recall checklist (FoRC) with a 4-day FR among 53 University students, observed a weaker correlation (
r = 0.30) between intakes of % fat reported by both tools, but the mean difference between both tools was not significant. Thus, in the present study, we interpret the weak association between both tools for one particular nutrient as a justification to conduct additional tests, such as paired student-t test, cross-classification and Bland-Altman analysis, but not as a justification to invalidate the data [
29].
For the majority of nutrients, no significant differences were detected between mean intakes reported by the R24W and the FR, in the 2nd trimester. However, some significant differences were observed for total energy, fat, % fat, SFA, vitamin C, vitamin D, magnesium, phosphorus, zinc and calcium. For these nutrients, average difference between intakes reported by the FR and the R24W was 12.2%, which is considered as an acceptable gap according to Lombard et al. (2015) [
29]. Overall, intakes reported by the R24W were higher than those reported by the FR for 17 out of 26 variables. Similar results were obtained by Timon et al. (2017) [
35], where significant differences between the Web-based 24HR and the FR were observed in intakes of % fat, protein, dietary fibers, riboflavin, iron, potassium and sodium. In comparison, De Keyzer et al. (2011) [
37] compared two computer assisted 24-h recalls (24HR) with a 5-day FR and found significant differences between intakes of energy and 8 nutrients (fat, fatty acids, cholesterol, alcohol, vitamin C, thiamine, riboflavin and iron) reported by both tools. Furthermore, De Keyzer et al. (2011) [
37] also observed higher intakes with the 24HR than with the FR and suggested it might be due to portion size estimation by food photographs. In fact, although photographs are generally useful to accurately recall portion size, some studies have mentioned considerable over and/or underestimations of the real amount of food eaten when participants were asked to use photographs to recall their food intakes [
38‐
40]. On the other hand, a previous validation study comparing the R24W with known dietary intakes found that the self-reported portion sizes were, on average, only 3.2 g higher than the real portion sizes offered to participants [
28]. However, the same study observed that portion sizes smaller than 100 g were significantly overestimated by 17.1%. This could partially explain why, in our study, energy, fat, % fat and SFA intakes were higher when reported by the R24W, as smaller portioned food items include fats, sauces, toppings and cheese [
28]. Significantly higher intakes observed with the R24W could also be explained by the presence of social desirability and reactivity bias, both frequently observed with the FR [
2,
14]. Therefore, it is possible that our participants had an increased tendency to underestimate and/or underreport portion sizes when completing the FR in comparison to when they completed the R24W.
Cross-classification analyses in the 2nd trimester revealed an acceptable agreement on an individual-level between the R24W and the FR. Classification of participants in the same and in the same or adjacent quartiles averaged 39.4% (range: 21.7-53.3%) and 79.1% (range: 68.3-90.0%), respectively. These results are similar to those observed by Frankelfeld et al. (2012) [
34] and Timon et al. (2017) [
35] in which ranking of participants in the same or adjacent quartiles ranged from 62.6% to 79.8% and from 69.2% to 92.3%, respectively. Moreover, in our study, only zinc was characterized by a gross misclassification (opposing quartiles) of more than 10% of participants. This is especially important since the ability of a method to accurately rank participants according to their dietary intakes is essential in the evaluation of diet-disease associations [
23]. Furthermore, weighted kappa scores averaged 0.32, thus representing an acceptable agreement according to the terminology of Lombard et al. [
29] and indicating that the ranking of participants in the same or adjacent quartiles was not attributable to chance [
29]. It is important to mention that a perfect agreement between the R24W and the FR was very unlikely to be observed, considering that both tools did not evaluate dietary intakes on the same 3 days. Moreover, since within-person day-to-day variability is high for both 24-h dietary recalls and FR and since both tools were completed on 3 different days, the ranking of participants is complex [
41]. Day-to-day variability may be further enhanced during pregnancy, as pregnant women’s dietary intakes may be influenced by nausea and vomiting. [
42]. It is possible that some women experienced more nausea and vomiting during the 3 days they completed the FR compared to the 3 R24W days, or vice-versa. Thus, a woman could have been ranked in the 1st quartile according to her intakes reported by one tool and in the 3rd or 4th quartile according to her intakes reported by the other tool. For these reasons, weighted kappa coefficients and the results of cross-classification analyses should be interpreted with caution and in combination with the other validity analyses conducted in this study.
As previously mentioned, analyses conducted in the 3rd trimester were statistically stronger when compared to those of the 1st and 2nd trimesters, thus suggesting a greater relative validity in the 3rd trimester. This might be explained, in part, by participant’s increased experience and comfort with completing both tools towards the end of their pregnancy. By the third trimester, participants had completed six R24W and two 3-day FR. This could also partially explain the significant decrease in completion time across trimesters of pregnancy. Moreover, fewer participants reported experiencing nausea in the 3rd trimester, which may also explain why there were less variations between dietary intakes recorded by the last R24W and FR, compared with the 1st and 2nd trimesters. Since our study is the first to assess the validity of a web-based 24-h recall in all trimesters of pregnancy, we were not able to compare our results to previous literature and these suppositions should be interpreted with caution.
In the 2nd trimester, only 2 (vitamin D and zinc) out of 26 variables accumulated a total of 4 poor association and agreement outcomes. Similar results were observed in the 1st (% fat, folic acid and vitamin D) and 3rd trimesters. Lombard et al. (2015) [
29] suggested that nutrients with the highest number of poor outcomes, %fat, folic acid, vitamin D and zinc in our results, might not be consumed on a daily basis by the studied population and would be better assessed by a FFQ rather than a R24W or a FR. This is of particular interest considering that folic acid is essential during pregnancy and plays an important role in fetal growth and development [
43,
44]. Moreover, deficiencies in folic acid are associated with higher risk of birth defects, particularly neural tube defects [
44]. Thus, an inaccurate intake estimation of this nutrient could be harmful, especially among pregnant women that are less compliant with their prenatal dietary supplements [
45]. The combined use of a FFQ with the R24W and even biomarker analyses should, therefore, be considered to accurately assess dietary intakes of folic acid, vitamin D and zinc [
29]. Globally, better results were observed with the group-level analyses (paired Student t-test and Bland-Altman analysis) in comparison with individual validity outcomes (Pearson correlation, cross-classification analysis and weighted kappa coefficient). This greater validity on a group-level in comparison to individual-level was also observed by Comrie et al. (2009) [
36], although this study did not use all 7 statistical analyses conducted in the present study. However, the accuracy of dietary assessment at an individual level (i.e. when the assessed intakes of an individual accurately reflects real intakes) is not essential to provide valid and useful data on nutrition and health outcomes [
41]. Therefore, the results of our group-level analyses suggest that the R24W is a valid tool to assess average dietary intakes of pregnant women but should be used with caution when counselling dietary changes to pregnant women in a clinical setting.
To our knowledge, this is the first dietary assessment validation study among pregnant women to compare a web-based 24-h recall with a FR by using seven association and agreement analyses for each pregnancy trimester. This summary analysis allowed a more in depth understanding of the accuracy and precision of the R24W, as well as an identification of the R24W’s strengths and limitations. It can be argued that, for a better estimate of usual dietary intakes, additional R24W and FR days would have been needed. Yet, asking our participants to recall and report their dietary intakes for more than 6 days per trimester could have worsened compliance, participation rate, and potentially altered our results. Our validation study also has limited generalizability because our study population was highly educated and almost 100% Caucasian. In addition, our small study sample (n = 60) might have attenuated the strength of some of our statistical analyses, e.g. correlation coefficients and cross-classification. Despite our small study sample, our results showed that the R24W was a relatively valid tool.