Next Article in Journal
Extracts Rich in Nutrients as Novel Food Ingredients to Be Used in Food Supplements: A Proposal Classification
Next Article in Special Issue
The Discovery of Data-Driven Temporal Dietary Patterns and a Validation of Their Description Using Energy and Time Cut-Offs
Previous Article in Journal
Sex–Gender Differences in Adult Coeliac Disease at Diagnosis and Gluten-Free-Diet Follow-Up
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 14
Issue 15
10.3390/nu14153193
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Relative Validity of Food Intake in Each Meal Type and Overall Food Intake Derived Using the Meal-Based Diet History Questionnaire against the 4-Day Weighed Dietary Record in Japanese Adults

by
Kentaro Murakami
1,*,
Nana Shinozaki
1,
Nana Kimoto
1,
Shizuko Masayasu
2 and
Satoshi Sasaki
1
1
Department of Social and Preventive Epidemiology, School of Public Health, University of Tokyo, Tokyo 113-0033, Japan
2
Ikurien-Naka, Ibaraki 311-0105, Japan
*
Author to whom correspondence should be addressed.
Nutrients 2022, 14(15), 3193; https://doi.org/10.3390/nu14153193
Submission received: 8 July 2022 / Revised: 31 July 2022 / Accepted: 2 August 2022 / Published: 4 August 2022
(This article belongs to the Special Issue Dietary Surveys and Nutritional Epidemiology)
Browse Figures
Versions Notes

Abstract

:
We examined the relative validity of food intake for each meal type (breakfast, lunch, dinner, and snacks) and overall food intake obtained through the Meal-based Diet History Questionnaire (MDHQ). In total, 222 Japanese adults (111 for each sex) aged 30–76 years completed the web version of the MDHQ and then the 4-non-consecutive-day weighed dietary record (DR). The number of major food groups (n = 24 in total) for which no statistically significant difference was observed between median intakes estimated using the DR and MDHQ ranged from 8 (snacks) to 12 (dinner) among women, and from 8 (breakfast) to 13 (lunch) among men. The median values of the Spearman’s correlation coefficients between the MDHQ and DR estimates ranged from 0.28 (dinner) to 0.54 (breakfast) among women, and from 0.24 (dinner) and 0.60 (breakfast) among men. Bland–Altman analyses generally showed wide limits of agreement and proportional bias. Similar results were obtained using the paper version of the MDHQ, which was completed after conducting the DR. In conclusion, the MDHQ has a satisfactory ability to estimate median intake and rank individuals according to consumption for many food groups, despite a limited ability to estimate food group intakes on an individual level.

1. Introduction

Suboptimal dietary intake is widely acknowledged as a major risk factor for morbidity and premature death, and improving the quality of diet is now a global priority [1]. An accurate assessment of habitual dietary intake is a prerequisite for uncovering the diet–disease relationships and developing appropriate advice to support favorable changes in dietary behaviors [2]. Traditionally, nutritional epidemiologic research has examined the associations between health outcomes and the intake of individual nutrients or foods, with a gradual shift in the past several decades to examine the associations between health outcomes and overall dietary patterns [3]. More recently, an increasing number of studies have evaluated dietary intake in specific eating occasions or meal patterns [4,5,6]. Examining dietary patterns and intake at each meal level to assess the overall diet may be more pertinent when considering the synergies and interactions during digestion and metabolism [7]. A limited number of studies have suggested that the amount and type of food intake, as well as the circadian timing of food intake, are associated with health status [8], such as obesity [9], metabolic syndrome [10], hyperglycemia [11], non-alcoholic fatty liver disease [9], and muscle strength [12]. Information on diet–health relationships at each meal level would be more relevant for formulating meaningful dietary guidelines and public health messages, as well as for developing effective intervention strategies for promoting healthy eating habits.
Unfortunately, research in this area is constrained by the fact that the primary method of dietary assessment currently employed in most cross-sectional and prospective cohort studies is the food frequency questionnaire, which precludes an informed evaluation of the timing of dietary intake and meal-specific dietary intake [13]. This type of information can be derived using more detailed dietary assessment methods, such as dietary record (DR) and 24-h dietary recall [4]. However, when using these methods, the collection of dietary data for multiple days is essential for the assessment of habitual intake at the individual level, but it is not always feasible because of its expensive and burdensome nature [14], despite the advancement of technology [15]. Taken together, the development and validation of a dedicated fit-for-purpose methodology for collecting data on meal patterns and time of day of dietary intake, which is also inexpensive to administer and has a low participant burden, are required to efficiently improve this research field [4]. In this context, we recently developed the Meal-based Diet History Questionnaire (MDHQ), a self-administered questionnaire designed to estimate the dietary intake for each meal type (breakfast, lunch, dinner, and snacks) separately [16,17].
This study aimed to examine the relative validity of food intake for each meal type and overall food intake obtained through the MDHQ against the 4-day DR.

2. Materials and Methods

2.1. Study Procedure and Participants

This cross-sectional study was based on the data collected from 14 (of the 47) prefectures between August and October 2021. Recruitment of participants and data collection were conducted by our research dietitians (n = 66) with expertise in collecting DR data [18,19]. First, healthy women aged 30–69 years who were willing to participate and were living with their husbands were recruited for this study. For each prefecture, two women from each 10-year age category (30–39, 40–49, 50–59, and 60–69 years) were selected. Their husbands were then recruited (irrespective of age), resulting in 112 individuals invited for each sex. The sample size was determined primarily based on the recommendation made by Cade et al. [14]. To minimize the dropout rate, the potential participants were restricted to individuals who had full understanding of the procedure, were willing to endure the heavy burden of the survey, and showed willingness to complete the entire survey. Meanwhile, dietitians, individuals living with a dietitian, those who had received dietary counseling from a doctor or dietitian, those taking insulin treatment for diabetes, those undergoing dialysis treatment, those without sufficient Internet access, those who had difficulty answering the web-based questionnaires, and pregnant or lactating women were excluded. Those who participated in the study as a couple (one woman and one men) were permitted. Due to the use of the snowball sampling procedure, the number of individuals approached for this study and the number of individuals excluded from this study were not formally recorded.
The study schedule is shown in Figure 1. Each participant was asked to answer the web version of the MDHQ (web MDHQ). After an interval of 7 to 10 days (to ensure the completion of the web MDHQ), a 4-non-consecutive-day weighed DR was conducted for 2 weeks. Finally, after an interval of at least 1 day, the paper version of the MDHQ (paper MDHQ) was answered. We designed this schedule because the main purpose of this study was to evaluate the validity of the web MDHQ; thus, a web MDHQ survey was performed prior to the conduct of DR. A total of 111 women aged 30–69 years and 111 men aged 30–76 years completed the study.
The study was conducted in accordance with the guidelines of the Declaration of Helsinki, and all procedures involving humans were approved by the Ethics Committee of the University of Tokyo Faculty of Medicine (protocol code: 2020326NI; date of approval: 29 January 2021). Written informed consent was obtained from all participants.

2.2. Meal-Based Diet History Questionnaire

Details of the MDHQ have been published elsewhere [16,17]. Briefly, the MDHQ is a self-administered questionnaire designed to estimate dietary intake in the previous month for each meal type (breakfast, morning snack, lunch, afternoon snack, dinner, and night snack).
The MDHQ comprises three parts. Part 1 of the MDHQ includes quantitative questions on the consumption frequency of generic food groups (Tier 1 food groups) for each meal type, with potential answers of 0–7 days/week. An a priori decision was made not to collect information on portion sizes using the MDHQ (except for alcoholic beverages, for which the overall consumption frequency and portion size were assessed in Part 2). This decision was based on our previous observation that the Brief-type Diet History Questionnaire (BDHQ), which assesses the consumption frequency of 58 food items but does not collect information on portion sizes and applies fixed portion sizes for dietary intake calculation, had a similar efficacy in estimating the food and nutrient intake as the Diet History Questionnaire (DHQ), which assesses not only the consumption frequency, but also the portion size of 150 food items [20,21,22]. The limited usefulness of portion size information has also been supported by several previous studies [23,24]. For each of the Tier 1 food groups, sex-specific and meal-type specific fixed portion size was defined as the mean of the intake value of the respective Tier 1 food group in independent 16-day weighed DR data previously collected from 121 Japanese women and 121 Japanese men, comprising 206,837 food item entries [16].
Part 2 of the MDHQ includes questions on the relative consumption frequency of sub-food groups (Tier 2 food group) within one of the generic food groups (Tier 1 food group), with possible answers of “always, often, sometimes, rarely, and never.” By combining the information derived from Parts 1 and 2, the number of foods that can be estimated efficiently can be increased, but within a limited number of questions.
Part 3 of the MDHQ asks about the general eating behaviors, including the relative consumption frequency of brown rice and wholegrain bread, followed by assessment of the baseline characteristics (sex, age, body height, body weight, education level, and current smoking status). All the food groups included in the MDHQ (see supplementary material Table S1) were determined based on the 16-day weighed DR data [16].
In the present study, two modes of MDHQ, which are identical in terms of content, were used: web MDHQ and paper MDHQ. The web MDHQ was prepared using Google Forms. Each question was answered by each participant, and no blanks were permitted. All responses to the web MDHQ automatically allocated into a spreadsheet format were downloaded from Google Drive. The paper MDHQ used in this study was an A4 21-page questionnaire. Responses to all questions were checked by the research dietitians and staff at the study center. If any responses were missing, the participants were asked to answer the questions again in person or by phone. All answers in the paper MDHQ were manually entered into a spreadsheet in duplicate, and any disagreement was checked and corrected.
Data obtained using the web MDHQ and paper MDHQ were converted to a dataset suitable for dietary intake calculation. Estimates of food group intake were calculated using ad hoc calculation algorithms, the details of which are available elsewhere [16]. Briefly, the intake of each Tier 1 food group for each meal type was calculated as the consumption frequency (from Part 1) multiplied by sex-specific and meal-type-specific portion size, as well as by a weighting factor determined by age using the estimated energy requirement [25]. The intake of each Tier 2 food group within the respective Tier 1 food group for each meal type was calculated based on the intake of the respective Tier 1 food group, meal-type-specific standard proportion of Tier 2 food groups (determined during the development process using the 16-day weighed DR data), and weighting factors created based on the data of relative consumption frequency (from Part 2). The overall intake was calculated as the sum of the intake of each meal type. For each alcoholic beverage, the overall intake was calculated as the product of consumption frequency and portion size, and then disaggregated into each meal type using the responses to questions related to alcoholic beverages in Part 1.

2.3. Weighed Dietary Record

The 4-non-consecutive-day weighed DR was selected as the reference method in this validation study. Each recording period consisted of three weekdays (Monday–Friday, except for national holidays) and one weekend day (Saturday, Sunday, or national holidays). For each couple, a recording day was allocated within 2 weeks by research dietitians. Each couple was provided with recording sheets and a digital scale (KS-274, Tanita, Japan; ±2 g precision for 0–500 g and ±3 g precision for 500–2000 g). After receiving written and verbal instructions from the assigned research dietitian, as well as an example of a completed diary sheet, each participant was requested to document and weigh all consumed foods and drinks, both inside and outside of their homes, on each recording day. On certain occasions when weighing was difficult to carry out (e.g., dining out), they were instructed to document as much information as possible, including the brand name of the food and the consumed portion size (based on typical household measures), as well as the details of the leftovers.
The recording sheets used in each survey day were submitted directly to the research dietitian after the survey was completed, who then reviewed the forms and, whenever necessary, sought additional information or modified the record via phone or in-person interview. All collected records were then reviewed by the research dietitians and trained staff at the study center. In accordance with a standardized procedure, the portion sizes estimated using household measures were converted into weights, and the individual food items were coded based on the 2015 version of the Standard Tables of Food Composition in Japan [26]. A total of 1297 food codes were used in the DR. All food codes were classified into one of the Tier 2 or 1 food groups in the MDHQ.
The structure of the food diary sheet used was based on a typical Japanese eating pattern, which comprised breakfast, lunch, dinner, and snacks; these meal types were prescribed in the diary. Thus, the name of the meal type used in the present analysis was based on this classification. For each participant, the estimated intake of individual food groups was calculated for each meal type. Foods expressed in the dry-weight state but consumed after cooking were corrected to represent the amount as consumed. The overall intake was calculated as the sum of the intake from each meal type. For all dietary variables, the mean daily values within the 4-day period were used for each individual.

2.4. Statistical Analysis

Statistical analyses were performed using the SAS statistical software (version 9.4; SAS Institute Inc., Cary, NC, USA). A two-tailed p value of <0.05 was considered significant. The dietary variables examined in this study included intake of Tier 1 and 2 food groups; the investigation of other variables (e.g., energy and nutrients) is under preparation and will be presented elsewhere. The amounts of snacks consumed were combined for analysis due to their relatively low intake in both methods. Analyses were stratified by sex and conducted to examine the overall intake and intake for each meal type (breakfast, lunch, dinner, and snacks). For the Tier 2 food groups, only the overall intake was considered. For each dietary assessment method, total daily energy intake was calculated using the 2015 version of the Standard Tables of Food Composition in Japan [26].
All dietary data were expressed as medians (25th and 75th percentiles) and means (standard deviations). To assess the estimation ability at the group level, the median values of intake derived from the MDHQ were compared with those derived from the DR using the Wilcoxon signed-rank test. The mean values were also compared using a paired t-test. The Spearman’s correlation coefficients between MDHQ and DR estimates were used to assess the ability of the MDHQ to rank individuals in a population. In addition, agreement of the dietary variables between the MDHQ and DR was assessed using the Bland–Altman analysis [27]. Linear regression analysis was also used to examine the proportional bias between the MDHQ and DR [28]. Identical analyses were conducted to assess the web MDHQ and paper MDHQ. The findings on the former are provided in the Results section, whereas those on the latter are provided as supplemental materials.

3. Results

This study included 111 women and 111 men aged 30–69 years and 30–76 years, respectively (Table 1). The mean body mass index values (kg/m2) were 22.7 (standard deviation: 3.3) for women and 23.8 (standard deviation: 3.6) for men. For both women and men, mean total energy intake derived from the DR was significantly (p < 0.001) higher than that derived from either the web MDHQ or the paper MDHQ.

3.1. Median Estimation of Tier 1 Food Groups

The median estimates of daily intake of 24 Tier 1 food groups derived from the DR and web MDHQ are shown in Table 2 for women and Table 3 for men, according to meal type. Among women, the number of food groups (and % of the total number of food groups) for which no statistically significant difference was observed between median intakes estimated using the DR and web MDHQ was 11 (46%) for breakfast, 11 (46%) for lunch, 12 (50%) for dinner, 8 (33%) for snacks, and 10 (42%) for the overall diet. The corresponding number among men was 8 (33%) for breakfast, 13 (54%) for lunch, 12 (50%) for dinner, 12 (50%) for snacks, and 9 (38%) for the overall diet. When comparisons were made using the mean values, the results were similar (Table S2 for women and Table S3 for men).

3.2. Spearman’s Correlation of Tier 1 Food Groups

The Spearman’s correlation coefficients between the estimates of daily intake of the 24 Tier 1 food groups derived from the DR and those derived from the web MDHQ are shown in Table 4, according to meal type. For women, the median values of the Spearman’s correlation coefficients (25th and 75th percentiles) were 0.54 (0.38–0.62) for breakfast, 0.30 (0.21–0.42) for lunch, 0.28 (0.20–0.49) for dinner, 0.47 (0.35–0.54) for snacks, and 0.47 (0.42–0.59) for the overall diet. The corresponding values for men were 0.60 (0.46–0.67) for breakfast, 0.34 (0.26–0.41) for lunch, 0.24 (0.15–0.38) for dinner, 0.39 (0.33–0.45) for snacks, and 0.49 (0.35–0.59) for the overall diet.

3.3. Bland–Altman Analysis of Tier 1 Food Groups

Based on the results of Bland–Altman analysis, the mean differences between estimates of daily intake of the 24 Tier 1 food groups derived from the DR and those derived from the web MDHQ (i.e., web MDHQ minus DR) were −8 to 54 g for breakfast, −19 to 57 g for lunch, −25 to 53 g for dinner, −16 to 45 g for snacks, and −44 to 209 g for the overall diet in women (Table S4). The corresponding values in men (Table S5) were −12 to 49 g for breakfast, −39 to 66 g for lunch, −46 to 77 g for dinner, −19 to 25 g for snacks, and −73 to 212 g for the overall diet. Figure 2 shows the Bland–Altman plots for overall daily intake of rice (mean differences: 2 g for women and −12 g for men), vegetables (−44 and −73 g, respectively), and green tea (81 and 54 g, respectively) as examples of acceptable estimation, underestimation, and overestimation by the web MDHQ, respectively. Regardless of the food group, sex, and meal type, the limits of agreement (mean difference ± 1.96 standard deviation of the difference) were generally wide, indicating poor agreement at the individual level (Figure 2 and Tables S4 and S5). In many cases, the slope of bias was significant, but solid foods tended to be underestimated by the web MDHQ as the average intake increased, while beverages tended to be overestimated by the web MDHQ as the average intake increased (Figure 2 and Tables S4 and S5).

3.4. Estimation of the Tier 2 Food Groups

The same analyses were conducted for the overall intake of Tier 2 food groups. Table 5 shows the median estimates of daily intake of 87 Tier 2 food groups derived using the DR and web MDHQ. The numbers of Tier 2 food groups showing no significant differences between the web MDHQ and DR were 43 (49%) for women and 41 (47%) for men. When comparisons were made using the mean values, the results were similar (Table S6).
The Spearman’s correlation coefficients between estimates of daily intake of 87 Tier 2 food groups derived from the DR and those derived from the web MDHQ are also shown in Table 5. The median values of the Spearman’s correlation coefficients (25th and 75th percentiles) were 0.25 (0.16–0.36) for women and 0.27 (0.15–0.35) for men.
Based on the Bland–Altman analysis (Table S6), the mean differences between the estimates of daily intake of 87 Tier 2 food groups derived from the DR and web MDHQ (i.e., web MDHQ minus DR) were −29 g (yogurt) to 18 g (full-fat milk) for women, and −27 g (white rice) to 33 g (beer) for men. Regardless of food group and sex, the limits of agreement (mean difference ± 1.96 standard deviation of the difference) were generally wide, indicating poor agreement at the individual level (Table S6). In many cases, the slope of bias was significant, showing a general tendency of underestimation by the web MDHQ as the average intake increased (Table S6).

3.5. Results on the Paper Version of the Meal-Based Diet History Questionnaire

Identical analyses of the paper MDHQ were conducted in terms of both Tier 1 food groups (Table S7 for median estimation, Table S8 for mean estimation, Table S9 for Spearman’s correlation, Table S10 for Bland–Altman analysis among women, and Table S11 for Bland–Altman analysis among men) and Tier 2 food groups (Table S12 for all analyses). The results for the paper MDHQ were generally similar to those for the web MDHQ, except for somewhat high Spearman’s correlation coefficients between the paper MDHQ and the DR.

4. Discussion

To our knowledge, this study was the first to examine the usefulness of a self-administered dietary assessment questionnaire specifically designed to estimate the dietary intake for each meal type separately (i.e., MDHQ). Overall, the present analysis suggests that the web and paper versions of the MDHQ showed reasonable relative validity in terms of food intake against the 4-day weighed DR. The MDHQ had a satisfactory ability to estimate median intake and rank individuals according to consumption for many food groups, despite a limited ability to estimate food group intakes at the individual level.
To estimate the overall intake of major food groups (Tier 1 food groups in this study), the present findings are reasonable in comparison with the results of previous relative validation analyses of the DHQ and BDHQ [20]. No significant median differences were noted between the DHQ and 16-day DR in 44% and 41% of food groups among 92 women and 92 men, respectively; moreover, no significant median differences were observed between the BDHQ and 16-day DR in 52% and 55% of food groups among 92 women and 92 men, respectively [20]. In terms of ranking individuals according to their overall intake, the median Spearman’s correlation coefficient in this study was comparable with that for the DHQ (0.43 for women and 0.44 for men) and BDHQ (0.44 for women and 0.48 for men) [20]. These findings are not uncommon in other dietary assessment questionnaires in Japan [29,30,31]. Taken together, at least for the overall intake of major food groups, the present study suggests that the MDHQ’s ability is not inferior to that of the DHQ and BDHQ, the most widely used dietary assessment questionnaires in Japan.
In general, we found that the level of concordance between intake of Tier 1 food groups derived from the MDHQ and 4-day DR was similar across all meal types based on the median intake estimation and impressions from Bland–Altman analysis, but the ability to rank individuals according to intake level was higher for breakfast and snacks than for lunch and dinner. This finding may be due to the large between-person variability of food intake patterns at breakfast and snacks compared with that at lunch and dinner [6,19,32]. Alternatively, this may reflect the complex nature of lunch and dinner in terms of food consumption patterns compared with breakfast and snacks [33,34]. To support this finding, the median value of Pearson’s correlation coefficients between energy intake from 12 food groups estimated using a food frequency questionnaire and a 7-day DR in a small study of Japanese adolescent girls (n = 63) was higher at breakfast (0.71) than that at lunch (0.38) and dinner (0.44); this questionnaire was not designed to assess the snack intake [35]. Similar results were also observed in a small sample of Japanese adults (29 men and 60 women) [36]. Overall, the present findings support the appropriateness of the MDHQ for assessing meal-specific dietary intake.
Nevertheless, the efficacy of MDHQ varied substantially depending on the Tier 1 food groups. For example, its ranking ability (assessed by Spearman’s correlation coefficient) was somewhat high in certain food groups such as rice, miso soup, fruit, dairy products, alcoholic beverages, green tea, barley tea, and coffee. This may be because variations in the intake of these foods are largely determined based on the consumption frequency, which was assessed in Part 1 of the MDHQ. Considering that the MDHQ does not collect any information on portion sizes, this finding may also suggest that the portion size of these foods is occasion specific (or meal type specific), rather than person specific. This speculation may be reasonable because these foods are all central components of the Japanese dietary patterns [32,37,38]. On the contrary, the ranking ability was somewhat low in other food groups such as noodles, potatoes, fish and shellfish, and meat, suggesting that variations in intake of these foods are largely determined by portion size (in addition to consumption frequency), and that the accurate estimation of consumption frequency is challenging for these foods. This may again be reasonable because, in Japan, these foods are usually consumed in combination with a variety of foods (e.g., composite dishes, soup, and salad), in a very small amount (e.g., toppings for noodles), or both, resulting in the variations of portion sizes (as well as difficulty in the accurate estimation). Nevertheless, simply collecting information on a single portion size may not substantially improve the estimation ability of these foods, considering that the relative validity of the DHQ (which allows collection of the portion size information) was not superior to that of the BDHQ (which does not include collection of portion size information) [20,21,22].
The present study also showed that the efficacy of MDHQ varied substantially depending on the Tier 2 food groups. For example, its ranking ability (as assessed by the Spearman correlation coefficient) was somewhat high in certain food groups such as white rice, white bread, natto (fermented soybeans), bananas, low-fat milk, yogurt, high-fat milk, beer, and shochu (Japanese distilled beverages). As we are unaware of validation studies conducted at the food item level, the present study provides valuable insights into the selection and design of dietary surveys in Japan. As the primary measure in dietary assessment is food intake, it is important to understand which foods and meal types are difficult to measure to improve the efficacy of MDHQ, as well as other dietary assessment methods.
Furthermore, irrespective of sex, meal type, and food group, Bland-Altman plots showed poor agreement between the MDHQ and DR at the individual level, although the mean difference was generally small. This is consistent with previous studies on relative validity of the DHQ and BDHQ [20]. In the MDHQ, the use of the fixed portion sizes during dietary intake calculation might at least partly explain poor agreement at the individual level. In any case, the absolute values of food intakes derived from the MDHQ should be interpreted with considerable caution at the individual level.
In this study, the findings for the web MDHQ were generally similar to those for the paper MDHQ, although the Spearman’s correlation coefficients with DR were somewhat high for the paper MDHQ compared with that for the web MDHQ. This is not surprising given that the paper MDHQ was completed after conducting the DR, while the web MDHQ was completed before conducting the DR. Online questionnaires are preferred for administration and processing because they are inexpensive. In real-world settings, not all study participants may be willing to complete the online questionnaires. Thus, a direct comparison between the web and paper versions of the MDHQ is warranted to assess the comparability or compatibility of these two modes.
The present study has several limitations. First, although the survey was conducted in diverse regions (14 of 47 prefectures), the present population was not a nationally representative sample of the Japanese population. As volunteers, the participants may have been biased toward greater health consciousness, higher socioeconomic status, or both. For example, the education level in the present population was higher than that in a national representative sample of women (55.9% completed junior high school or high school, 27.6% completed college or technical school, and 15.6% completed a university degree or higher) and men (52.9%, 12.9%, and 33.7%, respectively) [39]. Meanwhile, the prevalence of current smokers and mean (standard deviation) values of body height, body weight, and body mass index in the present participants were similar to those in a nationally representative sample (women: 7.6%, 154.3 (6.7) cm, 53.6 (9.2) kg, and 22.5 (3.7) kg/m2, respectively; men: 27.1%, 167.7 (6.9) cm, 67.4 (12.0) kg, and 23.9 (3.6) kg/m2, respectively) [40]. Ideally, further validation should be conducted using a more representative sample.
Second, weighed DR was used as a reference method; however, weighed DR is also susceptible to measurement errors due to the erroneous recording and potential changes in eating behavior [14]. However, weighed DR is the first method of choice for validating the dietary assessment questionnaires because the errors in weighed DR are thought to be less correlated with those in dietary assessment questionnaires compared with the errors in 24-h dietary recall or other instruments that rely on memory [14]. Additionally, although the dietary recording period was set to four days, this duration might not be sufficient for capturing estimates of habitual intake (particularly for certain Tier 2 food groups). Considering that increasing the number of recording days in the reference method improves the apparent validity of a dietary assessment questionnaire [14,41], efforts to increase the duration of recording in the reference method would be important in future validation studies.
Finally, the data collection was conducted over a certain period (between August and October 2021; late summer and early autumn in Japan). Considering the seasonal differences in the intake of at least some food groups in Japanese adults [42,43,44], and that the MDHQ only assessed the dietary habits during the previous month, the present data collection would have been conducted throughout the year. However, results of our previous validation study of the DHQ and BDHQ suggested that a single administration of a questionnaire assessing the dietary habits during the previous month may reasonably capture the habitual dietary intake over a longer period (i.e., one year) [20,21,22,45]. There is no strong reason to consider that the MDHQ is an exception in this regard.
In conclusion, the present analysis suggests that both the web and paper versions of the MDHQ showed reasonable relative validity in terms of food intake against the 4-day weighed DR. The MDHQ had a satisfactory ability to estimate median intake and rank individuals according to consumption for many food groups, despite a limited ability to estimate food group intakes on an individual level. The MDHQ, which provides estimates of the dietary intake for each meal type (breakfast, lunch, dinner, and snacks), may be invaluable as a dietary assessment tool for future nutritional epidemiologic research on diet–disease relationships, with a particular focus on meal patterns and time of day of dietary intake, or chrono-nutrition research. The relative validity of MDHQ in other aspects of diet (e.g., nutrient level and diet quality) is currently examined to establish a more solid scientific basis.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu14153193/s1, Table S1: List of food groups estimated from the Meal-based Diet History Questionnaire; Table S2: Mean estimates of daily intakes of Tier 1 food groups (in grams per day) derived from the 4-day weighed dietary record (DR) and those derived from the web version of the Meal-based Diet History Questionnaire (MDHQ) in 111 Japanese women, according to meal type; Table S3: Mean estimates of daily intakes of Tier 1 food groups (in grams per day) derived from the 4-day weighed dietary record (DR) and those derived from the web version of the Meal-based Diet History Questionnaire (MDHQ) in 111 Japanese men, according to meal type; Table S4: Bland–Altman analysis for estimates of daily intakes of Tier 1 food groups (in grams per day) derived from the 4-day weighed dietary record and those derived from the web version of the Meal-based Diet History Questionnaire (MDHQ) in 111 Japanese women, according to meal type; Table S5: Bland–Altman analysis for estimates of daily intakes of Tier 1 food groups (in grams per day) derived from the 4-day weighed dietary record (DR) and those derived from the web version of the Meal-based Diet History Questionnaire (MDHQ) in 111 Japanese men, according to meal type; Table S6: Mean estimates of overall intakes of Tier 2 food groups (in grams per day) derived from the 4-day weighed dietary record (DR) and those derived from the web version of the Meal-based Diet History Questionnaire (MDHQ) and Bland–Altman analysis for these estimates in 111 Japanese women and 111 Japanese men; Table S7: Median estimates of daily intakes of Tier 1 food groups (in grams per day) derived from the paper version of the Meal-based Diet History Questionnaire (MDHQ) in 111 Japanese women and 111 Japanese men, according to meal type; Table S8: Mean estimates of daily intakes of Tier 1 food groups (in grams per day) derived from the paper version of the Meal-based Diet History Questionnaire (MDHQ) in 111 Japanese women and 111 Japanese men, according to meal type; Table S9: Spearman correlation coefficients between estimates of daily intakes of Tier 1 food groups (in grams per day) derived from the 4-day weighed dietary record and those derived from the paper version of the Meal-based Diet History Questionnaire (MDHQ) in 111 Japanese women and 111 Japanese men, according to meal type; Table S10: Bland–Altman analysis for estimates of daily intakes of Tier 1 food groups (in grams per day) derived from the 4-day weighed dietary record (DR) and those derived from the paper version of the Meal-based Diet History Questionnaire (MDHQ) in 111 Japanese women, according to meal type; Table S11: Bland–Altman analysis for estimates of daily intakes of Tier 1 food groups (in grams per day) derived from the 4-day weighed dietary record (DR) and those derived from the paper version of the Meal-based Diet History Questionnaire (MDHQ) in 111 Japanese men, according to meal type; Table S12: Estimates of overall intakes of Tier 2 food groups (in grams per day) derived from the 4-day weighed dietary record (DR) and those derived from the paper version of the Meal-based Diet History Questionnaire (MDHQ), Spearman correlation coefficients, and Bland–Altman analysis in 111 Japanese women and 111 Japanese men.

Author Contributions

Conceptualization, K.M. and N.S.; methodology, K.M., N.S., N.K., S.M. and S.S.; software, K.M.; formal analysis, K.M.; investigation, K.M. and N.S.; resources, K.M. and S.M.; data curation, K.M., N.S., N.K., S.M. and S.S.; writing—original draft preparation, K.M.; writing—review and editing, N.S.; visualization, K.M. and N.S.; supervision, K.M.; project administration, K.M., N.S., N.K., S.M. and S.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported in part by the Institute for Food and Health Science, Yazuya Co., Ltd. The Institute for Food and Health Science, Yazuya Co., Ltd. had no role in the design, analysis, or writing of this manuscript.

Institutional Review Board Statement

The study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the University of Tokyo, Faculty of Medicine (protocol code: 2020326NI; date of approval: 29 January 2021).

Informed Consent Statement

Written informed consent was obtained from each participant.

Data Availability Statement

The datasets generated and analyzed during the present study are not publicly available because of privacy and ethical restrictions imposed by the Ethics Committee of the University of Tokyo, Faculty of Medicine, but are available from the corresponding author upon reasonable request. The web and paper versions of the MDHQ used in this study are available from the corresponding author upon request.

Acknowledgments

The authors would like to thank the research dietitians who conducted the data collection: Tamotsu Noshiro *, Fuki Kudo, Erika Iwasaki, Kazue Fukushi, and Masako Shimooka (Hokkaido); Akiko Sato, Hiromi Kawaguchi, Yuka Takeda, and Akiko Matsunaga (Yamagata); Yoko Isaka, Yuka Ota, Hanayo Kadoi, Akiko Seki, Yuko Takano, Toshie Nakayama, Ayami Murakami, and Masako Yamaguchi (Ibaraki); Kaoru Goto, Yuka Inaba, Akiko Kato, and Miki Hori (Saitama); Hitomi Okahashi, Shinobu Matsui, and Yurina Arai (Tokyo); Suzuyo Takeda, Kumiko Ono, Yayoi Hayashi, Kazumi Takagi, Kaori Otomo, and Mayuko Sakitsu (Kanagawa); Masako Koike, Reiko Kunimatsu, Keiko Kuribayashi, Keiko Hirayama, and Eriko Kiryuu (Niigata); Kazumi Horiguchi, Kotomi Kishikawa, Yasuko Ishii, and Miyuki Yokokoji (Yamanashi); Masako Tanaka, Hideko Uchibayashi, Tomoko Suzuki, Ryoko Mizuno, and Emi Kajiura (Aichi); Yumiko Noutomi, Atsuko Toyokawa, Yasuka Tabuchi, and Junko Ikukawa (Osaka); Sachiko Terao, Mari Matsuda, Mieko Imanaka, and Noriyuki Kubota (Okayama); Yoko Fujii, Izumi Hase, Aki Funada, and Tomomi Saya (Hiroshima); Kyoko Kaku, Kiyoko Katayama, Miki Hamachi, Yoshiko Yahagi, and Chizuru Shibata (Fukuoka); Yuko Soga, Kayoko Iwamoto, Mika Moribe, Miki Hamada, and Kaori Nakamura (Oita). The authors also thank the research team staff at the survey center (University of Tokyo): Akiko Hara, Keika Mine, and Hiroko Onodera. * Deceased.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. GBD 2017 Diet Collaborators. Health effects of dietary risks in 195 countries, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 2019, 393, 1958–1972. [Google Scholar] [CrossRef] [Green Version]
  2. O’Hara, C.; Gibneym, E.R. Meal pattern analysis in nutritional science: Recent methods and findings. Adv. Nutr. 2021, 12, 1365–1378. [Google Scholar] [CrossRef] [PubMed]
  3. Hu, F.B. Dietary pattern analysis: A new direction in nutritional epidemiology. Curr. Opin. Lipidol. 2002, 13, 3–9. [Google Scholar] [CrossRef] [PubMed]
  4. Leech, R.M.; Worsley, A.; Timperio, A.; McNaughton, S.A. Understanding meal patterns: Definitions, methodology and impact on nutrient intake and diet quality. Nutr. Res. Rev. 2015, 28, 1–21. [Google Scholar]
  5. Myhre, J.B.; Loken, E.B.; Wandel, M.; Andersen, L.F. Meal types as sources for intakes of fruits, vegetables, fish and whole grains among Norwegian adults. Public Health Nutr. 2015, 18, 2011–2021. [Google Scholar]
  6. Murakami, K.; Shinozaki, N.; Livingstone, M.B.E.; Fujiwara, A.; Asakura, K.; Masayasu, S.; Sasaki, S. Characterisation of breakfast, lunch, dinner and snacks in the Japanese context: An exploratory cross-sectional analysis. Public Health Nutr. 2022, 25, 689–701. [Google Scholar] [CrossRef] [PubMed]
  7. Jacobs, D.R., Jr.; Steffen, L.M. Nutrients, foods, and dietary patterns as exposures in research: A framework for food synergy. Am. J. Clin. Nutr. 2003, 78, 508S–513S. [Google Scholar]
  8. Almoosawi, S.; Vingeliene, S.; Karagounis, L.G.; Pot, G.K. Chrono-nutrition: A review of current evidence from observational studies on global trends in time-of-day of energy intake and its association with obesity. Proc. Nutr. Soc. 2016, 75, 487–500. [Google Scholar] [CrossRef] [Green Version]
  9. Bo, S.; Musso, G.; Beccuti, G.; Fadda, M.; Fedele, D.; Gambino, R.; Gentile, L.; Durazzo, M.; Ghigo, E.; Cassader, M. Consuming more of daily caloric intake at dinner predisposes to obesity. A 6-year population-based prospective cohort study. PLoS ONE 2014, 9, e108467. [Google Scholar]
  10. Almoosawi, S.; Prynne, C.J.; Hardy, R.; Stephen, A.M. Time-of-day and nutrient composition of eating occasions: Prospective association with the metabolic syndrome in the 1946 British birth cohort. Int. J. Obes. 2013, 37, 725–731. [Google Scholar]
  11. Shi, Z.; Riley, M.; Taylor, A.; Noakes, M. Meal-specific food patterns and the incidence of hyperglycemia in a Chinese adult population. Br. J. Nutr. 2017, 118, 53–59. [Google Scholar] [CrossRef] [Green Version]
  12. Farsijani, S.; Payette, H.; Morais, J.A.; Shatenstein, B.; Gaudreau, P.; Chevalier, S. Even mealtime distribution of protein intake is associated with greater muscle strength, but not with 3-y physical function decline, in free-living older adults: The Quebec longitudinal study on Nutrition as a Determinant of Successful Aging (NuAge study). Am. J. Clin. Nutr. 2017, 106, 113–124. [Google Scholar]
  13. Almoosawi, S.; Vingeliene, S.; Gachon, F.; Voortman, T.; Palla, L.; Johnston, J.D.; Van Dam, R.M.; Darimont, C.; Karagounis, L.G. Chronotype: Implications for epidemiologic studies on chrono-nutrition and cardiometabolic health. Adv. Nutr. 2019, 10, 30–42. [Google Scholar]
  14. Cade, J.; Thompson, R.; Burley, V.; Warm, D. Development, validation and utilisation of food-frequency questionnaires—A review. Public Health Nutr. 2002, 5, 567–587. [Google Scholar]
  15. Cade, J.E. Measuring diet in the 21st century: Use of new technologies. Proc. Nutr. Soc. 2017, 76, 276–282. [Google Scholar]
  16. Murakami, K.; Shinozaki, N.; McCaffrey, T.A.; Livingstone, M.B.E.; Sasaki, S. Data-driven development of the Meal-based Diet History Questionnaire for Japanese adults. Br. J. Nutr. 2021, 126, 1056–1064. [Google Scholar]
  17. Murakami, K.; Shinozaki, N.; Masayasu, S.; Livingstone, M.B.E. Web-based personalized nutrition system for delivering dietary feedback based on behavior change techniques: Development and pilot study among dietitians. Nutrients 2021, 13, 3391. [Google Scholar] [CrossRef] [PubMed]
  18. Asakura, K.; Uechi, K.; Masayasu, S.; Sasaki, S. Sodium sources in the Japanese diet: Difference between generations and sexes. Public Health Nutr. 2016, 19, 2011–2023. [Google Scholar] [CrossRef] [Green Version]
  19. Murakami, K.; Livingstone, M.B.E.; Masayasu, S.; Sasaki, S. Eating patterns in a nationwide sample of Japanese aged 1–79 y from MINNADE study: Eating frequency, clock time for eating, time spent on eating and variability of eating patterns. Public Health Nutr. 2022, 25, 1515–1527. [Google Scholar] [PubMed]
  20. Kobayashi, S.; Murakami, K.; Sasaki, S.; Okubo, H.; Hirota, N.; Notsu, A.; Fukui, M.; Date, C. Comparison of relative validity for food group intake estimated by comprehensive and brief-type self-administered diet history questionnaires against 16 d dietary records in Japanese adults. Public Health Nutr. 2011, 14, 1200–1211. [Google Scholar] [CrossRef] [PubMed]
  21. Kobayashi, S.; Honda, S.; Murakami, K.; Sasaki, S.; Okubo, H.; Hirota, N.; Notsu, A.; Fukui, M.; Date, C. Both comprehensive and brief self-administered diet history questionnaires satisfactorily rank nutrient intakes in Japanese adults. J. Epidemiol. 2012, 22, 151–159. [Google Scholar] [CrossRef] [Green Version]
  22. Murakami, K.; Livingstone, M.B.E.; Fujiwara, A.; Sasaki, S. Reproducibility and relative validity of the Healthy Eating Index-2015 and Nutrient-Rich Food Index 9.3 estimated by comprehensive and brief diet history questionnaires in Japanese adults. Nutrients 2019, 11, 2540. [Google Scholar] [CrossRef] [Green Version]
  23. Noethlings, U.; Hoffmann, K.; Bergmann, M.M.; Boeing, H. Portion size adds limited information on variance in food intake of participants in the EPIC-Potsdam study. J. Nutr. 2003, 133, 510–515. [Google Scholar] [CrossRef]
  24. Schlundt, D.G.; Buchowski, M.S.; Hargreaves, M.K.; Hankin, J.H.; Signorello, L.B.; Blot, W.J. Separate estimates of portion size were not essential for energy and nutrient estimation: Results from the Southern Community Cohort food-frequency questionnaire pilot study. Public Health Nutr. 2007, 10, 245–251. [Google Scholar] [CrossRef] [Green Version]
  25. Ministry of Health, Labour and Welfare, Japan. Dietary Reference Intakes for Japanese. 2020. Available online: https://www.mhlw.go.jp/stf/newpage_08517.html (accessed on 28 June 2022). (In Japanese)
  26. Council for Science and Technology; Ministry of Education, Culture, Sports, Science and Technology, Japan. Standard Tables of Food Composition in Japan 2015, 7th revised ed.; Official Gazette Co-Operation of Japan: Tokyo, Japan, 2015. (In Japanese)
  27. Bland, J.M.; Altman, D.G. Measuring agreement in method comparison studies. Stat. Methods Med. Res. 1999, 8, 135–160. [Google Scholar] [CrossRef]
  28. Montenij, L.J.; Buhre, W.F.; Jansen, J.R.; Kruitwagen, C.L.; de Waal, E.E. Methodology of method comparison studies evaluating the validity of cardiac output monitors: A stepwise approach and checklist. Br. J. Anaesth. 2016, 116, 750–758. [Google Scholar] [CrossRef] [Green Version]
  29. Wakai, K. A review of food frequency questionnaires developed and validated in Japan. J. Epidemiol. 2009, 19, 1–11. [Google Scholar] [CrossRef] [Green Version]
  30. Imaeda, N.; Goto, C.; Sasakabe, T.; Mikami, H.; Oze, I.; Hosono, A.; Naito, M.; Miyagawa, N.; Ozaki, E.; Ikezaki, H.; et al. Reproducibility and validity of food group intake in a short food frequency questionnaire for the middle-aged Japanese population. Environ. Health Prev. Med. 2021, 26, 28. [Google Scholar] [CrossRef] [PubMed]
  31. Yokoyama, Y.; Takachi, R.; Ishihara, J.; Ishii, Y.; Sasazuki, S.; Sawada, N.; Shinozawa, Y.; Tanaka, J.; Kato, E.; Kitamura, K.; et al. Validity of short and long self-administered food frequency questionnaires in ranking dietary intake in middle-aged and elderly Japanese in the Japan Public Health Center-Based Prospective Study for the Next Generation (JPHC-NEXT) protocol area. J. Epidemiol. 2016, 26, 420–432. [Google Scholar] [CrossRef] [Green Version]
  32. Murakami, K.; Livingstone, M.B.E.; Sasaki, S. Meal-specific dietary patterns and their contribution to overall dietary patterns in the Japanese context: Findings from the 2012 National Health and Nutrition Survey, Japan. Nutrition 2019, 59, 108–115. [Google Scholar] [CrossRef]
  33. Murakami, K.; Livingstone, M.B.E.; Sasaki, S. Establishment of a meal coding system for the characterization of meal-based dietary patterns in Japan. J. Nutr. 2017, 147, 2093–2101. [Google Scholar] [CrossRef] [Green Version]
  34. Murakami, K.; Livingstone, M.B.E.; Shinozaki, N.; Sugimoto, M.; Fujiwara, A.; Masayasu, S.; Sasaki, S. Food combinations in relation to the quality of overall diet and individual meals in Japanese adults: A nationwide study. Nutrients 2020, 12, 327. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Watanabe, M.; Yamaoka, K.; Yokotsuka, M.; Adachi, M.; Tango, T. Validity and reproducibility of the FFQ (FFQW82) for dietary assessment in female adolescents. Public Health Nutr. 2011, 14, 297–305. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Adachi, M.; Watanabe, M.; Yamaoka, K.; Tango, T. Validity and reproducibility of a food frequency questionnaire with 82-food items (FFQW82) for nutrition education. Jpn. J. Public Health 2010, 57, 475–485, (In Japanese with English Abstract). [Google Scholar]
  37. Murakami, K.; Livingstone, M.B.E.; Sasaki, S.; Hirota, N.; Notsu, A.; Miura, A.; Todoriki, H.; Fukui, M.; Date, C. Applying a meal coding system to 16-day weighed dietary record data in the Japanese context: Toward the development of simple meal-based dietary assessment tools. J. Nutr. Sci. 2018, 7, e29. [Google Scholar] [CrossRef] [Green Version]
  38. Okubo, H.; Murakami, K.; Sasaki, S.; Kim, M.K.; Hirota, N.; Notsu, A.; Fukui, M.; Date, C. Relative validity of dietary patterns derived from a self-administered diet history questionnaire using factor analysis among Japanese adults. Public Health Nutr. 2010, 13, 1080–1089. [Google Scholar] [CrossRef]
  39. Statistics Bureau, Ministry of Internal Affairs and Communications, Japan. Employment Status Survey. 2017. Available online: https://www.stat.go.jp/data/shugyou/2017/index2.html (accessed on 28 June 2022). (In Japanese)
  40. Ministry of Health, Labour and Welfare. National Health and Nutrition Survey 2019. Available online: https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/kenkou_iryou/kenkou/eiyou/r1-houkoku_00002.html (accessed on 28 June 2022). (In Japanese)
  41. Stram, D.O.; Longnecker, M.P.; Shames, L.; Kolonel, L.N.; Wilkens, L.R.; Pike, M.C.; Henderson, B.E. Cost-efficient design of a diet validation study. Am. J. Epidemiol. 1995, 142, 353–362. [Google Scholar] [CrossRef]
  42. Owaki, A.; Takatsuka, N.; Kawakami, N.; Shimizu, H. Seasonal variations of nutrient intake assessed by 24 hour recall method. Jpn. J. Nutr. 1996, 54, 11–18, (In Japanese with English Abstract). [Google Scholar] [CrossRef] [Green Version]
  43. Mori, S.; Saito, K.; Wakasa, Y. Studies on annual fluctuation of food intake in female college students. Jpn. J. Nutr. 1981, 39, 243–257, (In Japanese with English Abstract). [Google Scholar] [CrossRef]
  44. Tokudome, Y.; Imaeda, N.; Nagaya, T.; Ikeda, M.; Fujiwara, N.; Sato, J.; Kuriki, K.; Kikuchi, S.; Maki, S.; Tokudome, S. Daily, weekly, seasonal, within- and between-individual variation in nutrient intake according to four season consecutive 7 day weighed diet records in Japanese female dietitians. J. Epidemiol. 2002, 12, 85–92. [Google Scholar] [CrossRef] [Green Version]
  45. Murakami, K.; Sasaki, S.; Takahashi, Y.; Okubo, H.; Hirota, N.; Notsu, A.; Fukui, M.; Date, C. Reproducibility and relative validity of dietary glycaemic index and load assessed with a self-administered diet-history questionnaire in Japanese adults. Br. J. Nutr. 2008, 99, 639–648. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Nutrients 14 03193 g001 550
Figure 1. Study schedule. MDHQ, Meal-based Diet History Questionnaire; DR, dietary record.
Figure 1. Study schedule. MDHQ, Meal-based Diet History Questionnaire; DR, dietary record.
Nutrients 14 03193 g001
Nutrients 14 03193 g002 550
Figure 2. Bland–Altman plots assessing the agreement between estimates of overall intakes of rice (a,b), vegetables (c,d), and green tea (e,f) derived from the 4-day weighed dietary record (DR) and those derived from the web version of the Meal-based Diet History Questionnaire (MDHQ) in 111 Japanese women and 111 Japanese men. SD, standard deviation.
Figure 2. Bland–Altman plots assessing the agreement between estimates of overall intakes of rice (a,b), vegetables (c,d), and green tea (e,f) derived from the 4-day weighed dietary record (DR) and those derived from the web version of the Meal-based Diet History Questionnaire (MDHQ) in 111 Japanese women and 111 Japanese men. SD, standard deviation.
Nutrients 14 03193 g002
Table
Table 1. Basic characteristics of the study population 1.
Table 1. Basic characteristics of the study population 1.
VariableWomen (n = 111)Men (n = 111)
Age (years)49.9 ± 10.751.7 ± 11.9
Body height (cm) 2158.4 ± 5.4170.2 ± 6.3
Body weight (kg) 256.9 ± 8.568.9 ± 11.9
Body mass index (kg/m2) 322.7 ± 3.323.8 ± 3.6
Education level (n (%))
 Junior high school or high school28 (25.2)41 (36.9)
 College or technical school55 (49.5)22 (19.8)
 University or higher28 (25.2)48 (43.2)
Current smoking status (n (%))
 Smoker12 (10.8)35 (31.5)
 Nonsmoker99 (89.2)76 (68.4)
Total energy intake (kcal/day)
 4-day DR1724 ± 3352286 ± 493
 Web version of MDHQ1470 ± 3491926 ± 517
 Paper version of MDHQ1509 ± 3201895 ± 420
DR, dietary record; MDHQ, Meal-based Diet History Questionnaire. 1 Values are expressed as mean ± standard deviation, unless otherwise indicated. 2 Based on self-report. 3 Calculated using the self-reported body height and weight.
Table
Table 2. Median estimates of daily intakes of Tier 1 food groups (in grams per day) derived from the 4-day weighed dietary record (DR) and those derived from the web version of the Meal-based Diet History Questionnaire (MDHQ) in 111 Japanese women, according to meal type 1.
Table 2. Median estimates of daily intakes of Tier 1 food groups (in grams per day) derived from the 4-day weighed dietary record (DR) and those derived from the web version of the Meal-based Diet History Questionnaire (MDHQ) in 111 Japanese women, according to meal type 1.
Tier 1 Food GroupBreakfastLunchDinnerSnacksOverall Diet
Web Web Web Web Web
DRMDHQDRMDHQDRMDHQDRMDHQDRMDHQ
Rice 225 (0, 78)36 (0, 90)76 (30, 115)82 (57, 117)88 (37, 131)97 (39, 124)0 (0, 0)0 (0, 0) **204 (133, 285)218 (146, 274)
Bread 215 (0, 40)18 (5, 44)0 (0, 14)5 (0, 10)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0) **30 (14, 51)30 (15, 54)
Noodles 20 (0, 0)0 (0, 0)50 (0, 89)25 (12, 50) ***0 (0, 15)20 (10, 21) ***0 (0, 0)0 (0, 0)61 (23, 100)46 (32, 78)
Miso soup 20 (0, 58)12 (0, 77) **0 (0, 27)0 (0, 51) *39 (0, 92)52 (13, 103) **0 (0, 0)0 (0, 0)98 (32, 155)140 (52, 197) ***
Potatoes 20 (0, 2)0 (0, 6) **5 (0, 16)5 (0, 11)14 (3, 25)15 (7, 22)0 (0, 0)0 (0, 0) ***27 (15, 43)24 (14, 34)
Pulses and nuts 24 (0, 25)6 (0, 17)3 (0, 11)5 (0, 12)20 (9, 35)23 (15, 30)0 (0, 1)0 (0, 0) ***44 (16, 87)37 (24, 57) *
Vegetables 213 (0, 44)12 (0, 60)52 (26, 76)38 (7, 66) ***119 (85, 160)121 (62, 140) ***0 (0, 0)0 (0, 0) ***190 (143, 266)167 (124, 224) ***
Pickled vegetables 20 (0, 0)0 (0, 3) ***0 (0, 2)0 (0, 4) *0 (0, 2)3 (0, 10) ***0 (0, 0)0 (0, 0)2 (0, 6)7 (2, 19) ***
Fruit6 (0, 53)6 (0, 23) *2 (0, 24)0 (0, 20) **1 (0, 18)11 (0, 23) **0 (0, 15)6 (0, 24)50 (15, 104)47 (16, 95)
Fish and shellfish 20 (0, 6)0 (0, 4) *7 (1, 16)3 (0, 12) ***27 (13, 44)19 (18, 28) ***0 (0, 0)0 (0, 0) **45 (26, 63)29 (19, 42) ***
Meat 23 (0, 10)6 (0, 10)21 (9, 33)15 (5, 22) ***51 (30, 69)41 (25, 49) ***0 (0, 0)0 (0, 0) **81 (58, 105)58 (42, 75) ***
Eggs 27 (0, 23)12 (3, 17)10 (1, 18)8 (2, 13) ***7 (1, 14)7 (3, 10) *0 (0, 0)0 (0, 0) ***32 (19, 48)26 (14, 37) ***
Dairy products31 (0, 103)54 (9, 123)3 (0, 25)6 (0, 24)2 (0, 13)6 (0, 12)16 (0, 50)5 (0, 34) ***104 (38, 193)106 (48, 155)
Confectioneries0 (0, 2)0 (0, 8)0 (0, 13)4 (0, 8)0 (0, 0)0 (0, 4)11 (1, 28)40 (14, 69) ***25 (9, 50)59 (29, 90) ***
Fruit and vegetable juice0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0) **0 (0, 0)0 (0, 0) *0 (0, 0)0 (0, 22) **
Alcoholic beverages 30 (0, 0)0 (0, 0) ***0 (0, 1)0 (0, 0) ***2 (0, 87)0 (0, 55)0 (0, 0)0 (0, 0)3 (0, 94)4 (0, 63)
Soft drinks0 (0, 0)0 (0, 0) ***0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 52)8 (0, 43)0 (0, 65)8 (0, 64)
Green tea0 (0, 35)0 (0, 80) ***0 (0, 83)28 (0, 88) *0 (0, 50)0 (0, 89)0 (0, 131)36 (0, 178) *95 (0, 285)172 (0, 433) **
Barley tea0 (0, 10)0 (0, 77) **0 (0, 74)29 (0, 144) ***0 (0, 88)30 (0, 179) **0 (0, 157)75 (0, 224) *54 (0, 390)235 (0, 562) ***
Oolong tea0 (0, 0)0 (0, 0) *0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0) **
Black tea0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 28) **0 (0, 47)0 (0, 53)
Coffee45 (0, 155)149 (15, 205) ***0 (0, 40)27 (0, 102) ***0 (0, 0)0 (0, 0) ***91 (0, 181)103 (38, 213) **206 (86, 362)312 (173, 460) ***
Water0 (0, 25)26 (0, 175) ***0 (0, 0)0 (0, 146) ***0 (0, 0)0 (0, 148) ***50 (0, 159)146 (0, 357) **88 (0, 272)255 (51, 729) ***
Breakfast cereals 40 (0, 0)0 (0, 0) *0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)
1 Values are expressed medians (25th and 75th percentiles). The values derived from the MDHQ were compared with those derived from the DR using the Wilcoxon signed-rank test: * p < 0.05, ** p < 0.01, and *** p < 0.001. 2 Not assessed for snacks in the MDHQ; the intake for snacks was 0 g/day for all participants. 3 Not assessed for breakfast in the MDHQ; the breakfast intake was 0 g/day in all participants. 4 Only assessed for breakfast in the MDHQ; the intake for all other eating occasions was 0 g/day in all participants.
Table
Table 3. Median estimates of daily intakes of Tier 1 food groups (in grams per day) derived from the 4-day weighed dietary record (DR) and those derived from the web version of the Meal-based Diet History Questionnaire (MDHQ) in 111 Japanese men, according to meal type 1.
Table 3. Median estimates of daily intakes of Tier 1 food groups (in grams per day) derived from the 4-day weighed dietary record (DR) and those derived from the web version of the Meal-based Diet History Questionnaire (MDHQ) in 111 Japanese men, according to meal type 1.
Tier 1 Food GroupBreakfastLunchDinnerSnacksOverall Diet
Web Web Web Web Web
DRMDHQDRMDHQDRMDHQDRMDHQDRMDHQ
Rice 238 (0, 119)44 (0, 118)125 (63, 174)139 (82, 183)137 (78, 198)139 (83, 178)0 (0, 0)0 (0, 0) *313 (232, 389)315 (250, 384)
Bread 215 (0, 39)21 (0, 47) *0 (0, 11)0 (0, 13)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0) ***31 (14, 55)34 (10, 69)
Noodles 20 (0, 0)0 (0, 0)63 (2, 135)27 (13, 53) ***0 (0, 30)23 (12, 48) **0 (0, 0)0 (0, 0)91 (46, 170)71 (36, 101) ***
Miso soup 20 (0, 69)0 (0, 77)0 (0, 0)0 (0, 46) *36 (0, 107)51 (0, 103)0 (0, 0)0 (0, 0)101 (39, 174)129 (51, 187)
Potatoes 20 (0, 4)0 (0, 3)4 (0, 14)3 (0, 9) ***15 (3, 29)17 (8, 24)0 (0, 0)0 (0, 0) ***28 (15, 50)22 (10, 37) ***
Pulses and nuts 25 (0, 20)3 (0, 12) **0 (0, 6)3 (0, 11)23 (9, 50)18 (9, 27) ***0 (0, 0)0 (0, 0) ***39 (20, 75)30 (17, 50) ***
Vegetables 213 (0, 46)6 (0, 49)52 (25, 78)37 (0, 66) ***145 (110, 185)127 (69, 157) ***0 (0, 0)0 (0, 0) ***216 (165, 295)173 (106, 241) ***
Pickled vegetables 20 (0, 0)0 (0, 2) **0 (0, 4)0 (0, 7)0 (0, 3)2 (0, 9) ***0 (0, 0)0 (0, 0)3 (0, 12)9 (2, 19) ***
Fruit0 (0, 30)0 (0, 22) ***0 (0, 3)0 (0, 9)0 (0, 15)6 (0, 22)0 (0, 0)0 (0, 14) *34 (1, 74)24 (0, 79)
Fish and shellfish 20 (0, 6)0 (0, 5) **8 (1, 20)7 (0, 20)33 (20, 64)26 (23, 37) ***0 (0, 0)0 (0, 0) **55 (33, 83)39 (27, 53) ***
Meat 27 (0, 15)4 (0, 12)27 (14, 49)21 (13, 32) ***72 (45, 94)41 (32, 53) ***0 (0, 0)0 (0, 0) **112 (82, 145)72 (54, 87) ***
Eggs 213 (0, 26)6 (0, 19) **12 (2, 24)9 (2, 16) ***8 (1, 16)4 (2, 9) **0 (0, 0)0 (0, 0) ***44 (27, 56)28 (16, 42) ***
Dairy products8 (0, 77)9 (0, 114)1 (0, 6)0 (0, 12)2 (0, 15)0 (0, 10)2 (0, 26)0 (0, 14) **57 (13, 169)51 (5, 131) ***
Confectioneries0 (0, 13)0 (0, 4) *0 (0, 6)0 (0, 9)0 (0, 0)0 (0, 0)6 (0, 24)28 (7, 59) ***24 (7, 48)49 (16, 80) ***
Fruit and vegetable juice0 (0, 0)0 (0, 0) **0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0) *0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 18) ***
Alcoholic beverages 30 (0, 0)0 (0, 0) ***0 (0, 1)0 (0, 0) *5 (0, 253)60 (0, 284) *0 (0, 0)0 (0, 54)69 (2, 398)140 (0, 410) **
Soft drinks0 (0, 0)0 (0, 0) *0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 85)0 (0, 73)0 (0, 125)0 (0, 107)
Green tea0 (0, 0)0 (0, 86) ***0 (0, 70)0 (0, 151) *0 (0, 0)0 (0, 31)0 (0, 125)0 (0, 167)85 (0, 277)142 (0, 412) *
Barley tea0 (0, 0)0 (0, 49) *0 (0, 94)0 (0, 135)0 (0, 70)0 (0, 127) *0 (0, 110)15 (0, 213)45 (0, 431)86 (0, 481)
Oolong tea0 (0, 0)0 (0, 0) **0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)
Black tea0 (0, 0)0 (0, 0) *0 (0, 0)0 (0, 0) *0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)
Coffee42 (0, 155)122 (0, 210) ***0 (0, 41)0 (0, 91) ***0 (0, 0)0 (0, 0) *76 (0, 250)137 (0, 325)215 (67, 395)277 (153, 581) ***
Water0 (0, 5)0 (0, 181) ***0 (0, 0)33 (0, 214) ***0 (0, 0)30 (0, 209) ***15 (0, 162)123 (0, 372) **61 (0, 251)319 (14, 791) ***
Breakfast cereals 40 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)0 (0, 0)
1 Values are expressed as medians (25th and 75th percentiles). The values derived from the MDHQ were compared with those derived from the DR using the Wilcoxon signed-rank test: * p < 0.05, ** p < 0.01, and *** p < 0.001. 2 Not assessed for snacks in the MDHQ; the intake of snacks was 0 g/day in all participants. 3 Not assessed for breakfast in the MDHQ; the breakfast intake was 0 g/day in all participants. 4 Only assessed for breakfast in the MDHQ; the intake in all other eating occasions was 0 g/day in all participants.
Table
Table 4. Spearman’s correlation coefficients between estimates of daily intakes of Tier 1 food groups (in grams per day) derived from the 4-day weighed dietary record and those derived from the web version of the Meal-based Diet History Questionnaire (MDHQ) in 111 Japanese women and 111 Japanese men, according to meal type 1.
Table 4. Spearman’s correlation coefficients between estimates of daily intakes of Tier 1 food groups (in grams per day) derived from the 4-day weighed dietary record and those derived from the web version of the Meal-based Diet History Questionnaire (MDHQ) in 111 Japanese women and 111 Japanese men, according to meal type 1.
Tier 1 Food GroupWomenMen
BreakfastLunchDinnerSnacksOverall DietBreakfastLunchDinnerSnacksOverall Diet
Rice 20.77 ***0.43 ***0.51 ***−−−0.61 ***0.75 ***0.56 ***0.59 ***−−−0.54 ***
Bread 20.49 ***0.180.26 **−−−0.41 ***0.68 ***0.39 ***0.15−−−0.56 ***
Noodles 20.19 *0.42 ***0.17−−−0.34 ***0.45 ***0.46 ***0.07−−−0.39 ***
Miso soup 20.77 ***0.48 ***0.65 ***−−−0.65 ***0.74 ***0.34 ***0.68 ***−−−0.67 ***
Potatoes 20.51 ***0.100.23 *−−−0.21 *0.47 ***0.23 *0.18−−−0.25 **
Pulses and nuts 20.64 ***0.35 ***0.27 **−−−0.46 ***0.70 ***0.070.14−−−0.39 ***
Vegetables 20.63 ***0.34 ***0.26 **−−−0.37 ***0.60 ***0.35 ***0.31 **−−−0.46 ***
Pickled vegetables 20.45 ***0.25 **0.07−−−0.43 ***0.42 ***0.26 **0.11−−−0.29 **
Fruit0.57 ***0.44 ***0.38 ***0.27 **0.55 ***0.73 ***0.41 ***0.21 *0.36 ***0.64 ***
Fish and shellfish 20.61 ***0.25 **0.28 **−−−0.41 ***0.53 ***0.190.25 **−−−0.30 **
Meat 20.54 ***0.29 **0.16−−−0.31 ***0.61 ***0.41 ***0.15−−−0.21 *
Eggs 20.47 ***0.29 **0.37 ***−−−0.49 ***0.65 ***0.29 **0.24 *−−−0.66 ***
Dairy products0.62 ***0.19 *0.24 *0.39 ***0.61 ***0.61 ***0.27 **0.130.22 *0.65 ***
Confectioneries0.31 **0.22 *0.170.50 ***0.43 ***0.33 ***0.50 ***0.040.34 ***0.27 **
Fruit and vegetable juice0.180.21 *0.39 ***0.26 **0.45 ***0.60 ***0.26 **0.25 **0.190.55 ***
Alcoholic beverages 3−−−−0.130.63 ***0.45 ***0.73 ***−−−0.050.72 ***0.50 ***0.82 ***
Soft drinks0.29 **0.39 ***0.050.48 ***0.48 ***0.030.28 **0.28 **0.40 ***0.36 ***
Green tea0.55 ***0.51 ***0.51 ***0.37 ***0.53 ***0.67 ***0.33 ***0.33 ***0.41 ***0.55 ***
Barley tea0.55 ***0.59 ***0.58 ***0.59 ***0.64 ***0.50 ***0.46 ***0.56 ***0.43 ***0.57 ***
Oolong tea−0.030.38 ***0.50 ***0.29 **0.43 ***0.42 ***0.36 ***0.43 ***0.29 **0.33 ***
Black tea0.45 ***0.160.39 ***0.53 ***0.47 ***0.59 ***0.39 ***0.180.38 ***0.45 ***
Coffee0.70 ***0.46 ***0.47 ***0.75 ***0.78 ***0.72 ***0.59 ***0.43 ***0.71 ***0.77 ***
Water0.29 **0.30 **0.130.54 ***0.59 ***0.130.160.21 *0.52 ***0.53 ***
Breakfast cereals 40.54 ***−−−−−−−−−0.51 ***0.54 ***−−−−−−−−−0.46 ***
1 Values are expressed as Spearman’s correlation coefficients. * p < 0.05, ** p < 0.01, and *** p < 0.001. 2 Not assessed for snacks in the MDHQ; the intake of snacks was 0 g/day in all participants. 3 Not assessed for breakfast in the MDHQ; the breakfast intake was 0 g/day in all participants. 4 Only assessed for breakfast in the MDHQ; the intake in all other eating occasions was 0 g/day in all participants.
Table
Table 5. Median estimates of overall intakes of Tier 2 food groups (in grams per day) derived from the 4-day weighed dietary record (DR) and those derived from the web version of the Meal-based Diet History Questionnaire (MDHQ) and Spearman’s correlation coefficients between estimates derived from the DR and the web version of MDHQ in 111 Japanese women and 111 Japanese men 1.
Table 5. Median estimates of overall intakes of Tier 2 food groups (in grams per day) derived from the 4-day weighed dietary record (DR) and those derived from the web version of the Meal-based Diet History Questionnaire (MDHQ) and Spearman’s correlation coefficients between estimates derived from the DR and the web version of MDHQ in 111 Japanese women and 111 Japanese men 1.
Tier 2 Food GroupWomenMen
DRWeb MDHQCorrelation 2DRWeb MDHQCorrelation 2
Rice
 White rice203 (131, 285)208 (135, 267)0.60 ***311 (231, 389)301 (220, 355) *0.53 ***
 Brown rice0 (0, 0)0 (0, 9) ***0.24 *0 (0, 0)0 (0, 13) ***0.27 **
Bread
 White bread30 (12, 49)24 (9, 42) *0.39 ***30 (13, 55)27 (7, 60)0.54 ***
  Wholegrain bread0 (0, 0)2 (0, 9) ***0.19 *0 (0, 0)0 (0, 7) ***0.07
Noodles
 Wheat noodles0 (0, 23)30 (15, 46) ***0.160 (0, 45)32 (12, 55) **0.17
 Chinese noodles0 (0, 36)5 (0, 15)0.23 *0 (0, 52)12 (2, 29)0.24 *
 Instant noodles0 (0, 0)0 (0, 3)0.24 **0 (0, 54)1 (0, 5) *0.33 ***
 Spaghetti0 (0, 25)4 (1, 10)0.24 *0 (0, 22)2 (0, 9)0.21 *
 Buckwheat noodles0 (0, 0)1 (0, 5) ***0.24 *0 (0, 0)1 (0, 6) ***0.25 **
Pulses and nuts
 Soy milk0 (0, 0)0 (0, 0)0.53 ***0 (0, 0)0 (0, 0)0.35 ***
 Tofu (i.e., soybean curd)18 (3, 41)29 (17, 45) **0.25 **21 (3, 46)23 (11, 36)0.27 **
 Natto (i.e., fermented soybeans)0 (0, 13)3 (1, 8) *0.54 ***0 (0, 10)2 (0, 6)0.63 ***
 Tofu products1 (0, 4)3 (1, 6)0.36 ***2 (0, 6)1 (0, 4) *0.26 **
 Peanuts and nuts1 (0, 6)0 (0, 0) ***0.161 (0, 6)0 (0, 0) ***0.32 ***
 All other pulses and nuts0 (0, 2)0 (0, 1)0.21 *0 (0, 1)0 (0, 1)0.09
Vegetables
 Edamame (i.e., immature soybeans) and peas0 (0, 2)1 (0, 2)0.130 (0, 3)0 (0, 2)0.30 **
 Seaweeds4 (1, 11)5 (2, 9)0.164 (1, 9)4 (1, 8)0.27 **
 Pumpkins0 (0, 10)3 (0, 5)0.36 ***0 (0, 13)2 (0, 5) *0.46 ***
 Mushrooms10 (2, 18)7 (4, 14) **0.29 **10 (2, 21)6 (1, 11) ***0.28 **
 Cabbage19 (9, 40)14 (8, 25) **0.22 *29 (11, 45)16 (8, 32) ***0.36 ***
 Cucumbers10 (0, 22)11 (6, 17)0.1810 (0, 20)9 (4, 17)0.32 ***
 Bitter melon0 (0, 0)0 (0, 1)0.36 ***0 (0, 0)0 (0, 1)0.47 ***
 Burdock0 (0, 4)0 (0, 1) **0.32 ***0 (0, 5)0 (0, 1) ***0.06
 Radishes6 (0, 18)7 (1, 11)0.37 ***8 (0, 15)7 (1, 15)0.21 *
 Onions22 (13, 35)28 (12, 39)0.25 **28 (13, 41)24 (12, 40)0.29 **
 Chinese cabbage0 (0, 0)0 (0, 2)0.24 **0 (0, 0)0 (0, 2)0.13
 Tomatoes8 (0, 24)24 (11, 37) ***0.37 ***9 (0, 20)18 (6, 40) ***0.33 ***
 Eggplants0 (0, 11)8 (4, 13)0.40 ***0 (0, 13)6 (2, 12)0.33 ***
 Carrots10 (4, 16)13 (6, 21) *0.38 ***10 (4, 18)12 (5, 24)0.33 ***
 Green peppers2 (0, 8)3 (2, 6)0.26 **3 (0, 10)3 (1, 4) **0.30 **
 Broccoli0 (0, 4)2 (0, 3)0.36 ***0 (0, 6)1 (0, 4)0.35 ***
 Green leafy vegetables10 (3, 19)14 (7, 24) *0.32 ***10 (3, 20)10 (2, 18)0.28 **
 Bean sprouts6 (0, 14)3 (1, 6) ***0.35 ***5 (0, 21)2 (1, 5) ***0.36 ***
 Lettuce3 (0, 10)4 (1, 8)0.30 **4 (0, 10)4 (1, 7)0.38 ***
 All other vegetables12 (4, 30)1 (1, 1) ***0.1617 (6, 28)1 (1, 2) ***−0.11
Fruit
 Strawberries0 (0, 0)0 (0, 0) ***0.050 (0, 0)0 (0, 0) **−0.15
 Persimmons0 (0, 0)0 (0, 0)−0.050 (0, 0)0 (0, 0)0.15
 Citrus0 (0, 3)2 (0, 9) **−0.160 (0, 1)0 (0, 6) **0.16
 Kiwi fruit0 (0, 0)0 (0, 1)0.40 ***0 (0, 0)0 (0, 0)0.40 ***
 Watermelon0 (0, 0)0 (0, 1) ***0.120 (0, 0)0 (0, 0) ***0.16
 Pears0 (0, 15)1 (0, 10)0.25 **0 (0, 10)0 (0, 5) *0.35 ***
 Bananas0 (0, 19)5 (0, 27) *0.58 ***0 (0, 18)1 (0, 16)0.64 ***
 Grapes0 (0, 20)2 (0, 8) *0.160 (0, 13)0 (0, 4) *0.28 **
 Melon0 (0, 0)0 (0, 1) ***0.20 *0 (0, 0)0 (0, 0) ***0.15
 Peaches0 (0, 0)0 (0, 3) ***0.110 (0, 0)0 (0, 1) **0.09
 Apples0 (0, 1)0 (0, 5)0.42 ***0 (0, 0)0 (0, 4) **0.32 ***
 All other fruit0 (0, 6)1 (0, 1)−0.010 (0, 4)1 (0, 1)0.08
Fish and shellfish
 Oily fish0 (0, 19)6 (1, 9)0.095 (0, 24)8 (2, 14)0.20 *
 Red meat fish0 (0, 7)4 (1, 7) *0.170 (0, 5)6 (1, 10) **0.17
 Squid and octopus0 (0, 4)0 (0, 2)0.22 *0 (0, 5)1 (0, 4)0.41 ***
 Eel0 (0, 0)0 (0, 0) ***0.010 (0, 0)0 (0, 0) ***−0.09
 Shrimp and crab0 (0, 3)0 (0, 2)0.030 (0, 5)1 (0, 3)0.15
 Shellfish0 (0, 0)0 (0, 1) *0.160 (0, 0)0 (0, 1)0.25 **
 Small fish with bones0 (0, 0)0 (0, 0) *0.32 ***0 (0, 0)0 (0, 0) *0.23 *
 Fish eggs0 (0, 0)0 (0, 0)0.28 **0 (0, 0)0 (0, 0)0.05
 Dried fish0 (0, 1)1 (0, 3)0.150 (0, 0)1 (0, 4)0.21 *
 Salmon1 (0, 14)3 (1, 5) *0.25 **3 (0, 16)4 (1, 8)0.04
 White meat fish0 (0, 5)1 (1, 6)0.110 (0, 12)2 (1, 9)0.19 *
 Ground fish meat products0 (0, 7)4 (1, 6)0.23 *3 (0, 10)4 (1, 7)0.12
 Canned tuna0 (0, 3)0 (0, 1)0.35 ***0 (0, 3)0 (0, 1)0.31 **
 All other fish and shellfish0 (0, 0)0 (0, 0) ***0.000 (0, 0)0 (0, 0) ***−0.18
Meat
 Beef6 (0, 18)7 (1, 11) *0.31 ***10 (1, 24)9 (2, 15) *0.24 *
 Chicken25 (11, 42)13 (9, 23) ***0.24 *36 (18, 71)17 (12, 26) ***0.13
 Processed meat8 (2, 15)8 (4, 15)0.42 ***11 (3, 19)9 (5, 20)0.38 ***
 Pork28 (12, 42)26 (15, 33)0.21 *34 (20, 54)25 (18, 38) **0.04
 Liver0 (0, 0)0 (0, 0) ***0.170 (0, 0)0 (0, 0) ***0.10
 All other meat0 (0, 0)0 (0, 0) ***0.120 (0, 0)0 (0, 0) ***−0.10
Dairy products
 Ice cream0 (0, 8)1 (0, 5)0.38 ***0 (0, 0)0 (0, 4)0.26 **
 Cheese3 (0, 8)2 (0, 4)0.20 *2 (0, 8)0 (0, 3) **0.11
 Low-fat milk0 (0, 0)0 (0, 0)0.49 ***0 (0, 0)0 (0, 0)0.64 ***
 Yogurt29 (0, 103)16 (1, 51) ***0.59 ***8 (0, 64)1 (0, 25) ***0.60 ***
 Full-fat milk17 (0, 62)38 (3, 88) ***0.58 ***0 (0, 27)1 (0, 32) **0.58 ***
 All other dairy products0 (0, 3)0 (0, 1)0.060 (0, 3)0 (0, 1) *0.01
Confectioneries
 Candies, caramels, and chewing gum0 (0, 0)0 (0, 0) ***0.29 **0 (0, 0)0 (0, 1) ***0.19 *
 Japanese bread with a sweet filling0 (0, 0)3 (0, 8) ***0.170 (0, 10)2 (0, 8)0.34 ***
 Snacks made from wheat flour0 (0, 0)2 (0, 5) ***0.37 ***0 (0, 0)1 (0, 6) ***0.29 **
 Jellies0 (0, 0)0 (0, 1) **0.110 (0, 0)0 (0, 0) ***0.18
 Rice crackers0 (0, 2)5 (1, 10) ***0.47 ***0 (0, 0)4 (0, 10) ***0.37 ***
 Chocolates1 (0, 5)7 (1, 17) ***0.33 ***0 (0, 3)3 (0, 13) ***0.22 *
 Biscuits and cookies0 (0, 2)2 (0, 6) ***0.24 *0 (0, 0)0 (0, 3) ***0.20 *
 Cakes0 (0, 13)11 (3, 25) ***0.26 **0 (0, 6)7 (1, 18) ***0.29 **
 Japanese sweets0 (0, 13)4 (1, 16) *0.170 (0, 10)2 (0, 12) *0.28 **
Alcoholic beverages
 Beer0 (0, 0)0 (0, 26)0.77 ***0 (0, 177)48 (0, 304) **0.78 ***
 Sake0 (0, 0)0 (0, 0)0.64 ***0 (0, 0)0 (0, 0)0.30 **
 Shochu (i.e., Japanese distilled beverages)0 (0, 0)0 (0, 2)0.77 ***0 (0, 80)0 (0, 100)0.63 ***
 Wine0 (0, 0)0 (0, 0)0.25 **0 (0, 0)0 (0, 0)0.60 ***
 Whiskey and other spirits0 (0, 0)0 (0, 0)--- 30 (0, 0)0 (0, 0)0.70 ***
1 Values are expressed as medians (25th and 75th percentiles) unless otherwise indicated. The values derived from the MDHQ were compared with those derived from the DR using the Wilcoxon signed-rank test: * p < 0.05, ** p < 0.01, *** p < 0.001. 2 Spearman’s correlation coefficients: * p < 0.05, ** p < 0.01, *** p < 0.001. 3 Not available because the intake derived from the DR was 0 g/day in all participants.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Murakami, K.; Shinozaki, N.; Kimoto, N.; Masayasu, S.; Sasaki, S. Relative Validity of Food Intake in Each Meal Type and Overall Food Intake Derived Using the Meal-Based Diet History Questionnaire against the 4-Day Weighed Dietary Record in Japanese Adults. Nutrients 2022, 14, 3193. https://doi.org/10.3390/nu14153193

AMA Style

Murakami K, Shinozaki N, Kimoto N, Masayasu S, Sasaki S. Relative Validity of Food Intake in Each Meal Type and Overall Food Intake Derived Using the Meal-Based Diet History Questionnaire against the 4-Day Weighed Dietary Record in Japanese Adults. Nutrients. 2022; 14(15):3193. https://doi.org/10.3390/nu14153193

Chicago/Turabian Style

Murakami, Kentaro, Nana Shinozaki, Nana Kimoto, Shizuko Masayasu, and Satoshi Sasaki. 2022. "Relative Validity of Food Intake in Each Meal Type and Overall Food Intake Derived Using the Meal-Based Diet History Questionnaire against the 4-Day Weighed Dietary Record in Japanese Adults" Nutrients 14, no. 15: 3193. https://doi.org/10.3390/nu14153193

APA Style

Murakami, K., Shinozaki, N., Kimoto, N., Masayasu, S., & Sasaki, S. (2022). Relative Validity of Food Intake in Each Meal Type and Overall Food Intake Derived Using the Meal-Based Diet History Questionnaire against the 4-Day Weighed Dietary Record in Japanese Adults. Nutrients, 14(15), 3193. https://doi.org/10.3390/nu14153193

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop