Accurate assessment of dietary intake is essential to understand how dietary intake influences health outcomes, particularly during childhood and adolescence, which are periods of rapid physical growth and cognitive development(Reference Uauy, Kain and Mericq1). Dietary habits formed during these stages often persist into adulthood, shaping long-term health trajectories and affecting both immediate well-being and the risk of chronic diseases in later life(Reference Mikkilä, Räsänen and Raitakari2,Reference Ness, Maynard and Frankel3) ; therefore, valid dietary assessment tools are crucial to inform public health strategies and interventions.
Dietary questionnaires, such as food frequency questionnaire (FFQ) and diet history questionnaires (DHQ), are commonly used for dietary assessment due to their time efficiency, lower cost and reduced respondent and researcher burden compared with traditional methods like dietary records (DR) and 24-h recalls(Reference Willett4–Reference Cade, Thompson and Burley6). In large-scale epidemiological studies, dietary questionnaires provide a feasible approach to assess long-term habitual diets and their relationships with health outcomes and disease risk(Reference Willett4–Reference Cade, Thompson and Burley6). However, assessing dietary intake in children and adolescents presents unique challenges(Reference Livingstone and Robson7,Reference Livingstone, Robson and Wallace8) . As children transition into adolescence, they experience increased autonomy in food choices, are influenced by peer pressure and family dynamics and develop their personal preferences, leading to greater variability in their diets(Reference Ziegler, Kasprzak and Mansouri9,Reference Story, Neumark-Sztainer and French10) . Additionally, cognitive development during this period can hinder the accuracy of dietary recall, as children’s limited memory capacity and developing sense of time make it difficult to report food consumption precisely(Reference Baranowski and Domel11,Reference Sharman, Skouteris and Powell12) . These developmental limitations highlight the importance of validating dietary assessment tools to ensure reliable capture of habitual intake in this age group(Reference Livingstone, Robson and Wallace8,Reference Burrows, Martin and Collins13) .
To address these challenges, various dietary questionnaires have been adapted for children and adolescents(Reference Shatenstein, Amre and Jabbour14–Reference Okuda and Sasaki24), including the brief-type diet history questionnaire for Japanese children and adolescents (BDHQ15y)(Reference Okuda, Sasaki and Bando22–Reference Okuda and Sasaki24). This tool was developed based on the existing comprehensive and brief versions of a validated self-administered DHQ for Japanese adults(Reference Sasaki, Yanagibori and Amano25–Reference Kobayashi, Honda and Murakami29). Previous studies assessed the validity of the BDHQ15y using biomarkers of dietary intake as a reference, but these evaluations were limited to a specific demographic and geographic region in Japan, focusing on a narrow age range(Reference Okuda, Sasaki and Bando22–Reference Okuda and Sasaki24). More importantly, the ability of the BDHQ15y to estimate a comprehensive range of nutrients and food groups remains underexplored. Given the widespread use of the BDHQ15y in large-scale epidemiological studies across Japan(Reference Kawamoto, Nitta and Murata30,Reference Murakami, Livingstone and Masayasu31) , it is essential to extend its validation to a wider range of populations and age groups in order to ensure its relevance and accuracy for broader public health applications.
Therefore, the present study aimed to evaluate the relative validity of the BDHQ15y among Japanese children and adolescents aged 6–17 years in comparison with the 8-day weighed DR, with the goal of determining whether the BDHQ15y can accurately capture habitual dietary intake across a range of nutrients and food groups in this age group.
Methods
Study procedure and participants
This study used data from the Ministry of Health, Labour and Welfare-sponsored Nationwide Study on Dietary Intake Evaluation (MINNADE study), which aims to evaluate and develop methods for assessing the intake of hazardous environmental chemicals through foods in the Japanese population. Detailed descriptions on the study design and protocol have been previously published(Reference Murakami, Livingstone and Masayasu31–Reference Shinozaki, Murakami and Kimoto33). In brief, healthy, community-dwelling Japanese individuals aged 1–79 years from thirty-two of the forty-seven prefectures in Japan, covering over 85 % of the population, were recruited based on feasibility and regional population proportions. The research dietitians in each prefecture (n 453) used snowball sampling to recruit eligible, free-living individuals who could conduct a DR independently or with the help of a guardian for children. Exclusion criteria included dietitians and their cohabitants, individuals receiving dietary counselling from a medical doctor or dietitian, those undergoing insulin or dialysis treatment, pregnant or lactating women and infants exclusively consuming human milk. Only one participant per household was eligible.
Data were collected over three 1-year rounds: November 2016 to September 2017, October 2017 to September 2018 and November 2019 to August 2020. A total of 2304 participants were recruited in the first round, comprising 256 individuals (128 males and 128 females) from each of nine age bands (1–6, 7–13, 14–19, 20–29, 30–39, 40–49, 50–59, 60–69 and 70–79 years). To account for dropouts in the first round, 110–119 individuals per sex and age group were recruited in the second round, resulting in 2051 individuals invited to participate. Additionally, 438 children aged 1–6 years were recruited in the third round to gather data on younger children. Due to the snowball sampling method, the number of individuals contacted was not recorded; thus, the response rate could not be calculated. In total, 4736 individuals (first round: n 2263, second round: n 2036 and third round: n 437) agreed to participate in the MINNADE study. Participants were asked to first complete the BDHQ and then, after a designated interval, complete the DR on two non-consecutive days for each of the four seasons, totalling 8 days of records. The protocol for the MINNADE study was in accordance with the guidelines of the Declaration of Helsinki and was reviewed and approved by the Ethics Committee of the University of Tokyo Faculty of Medicine (protocol codes: 11397 and 2019124NI; approval dates: 25 October 2016 and 17 October 2019, respectively). Written informed consent was obtained from all participants, or from parents or guardians in the case of participants under 18 years of age.
The present analysis focused on children and adolescents aged 6–17 years with at least 1-day data of DR (n 944) (Figure 1). Participants who did not complete the BDHQ15y (n 72), those with fewer than 8 d of DR data (n 1), those who conducted DR on consecutive days (n 26) and those whose DR were not conducted in the appropriate months (i.e. October, November and December for autumn; January, February and March for winter; April, May and June for spring and July, August and September for summer (n 2)) were excluded. Consequently, data from 844 children and adolescents aged 6–17 years were used in the present analysis.
Figure 1. Flow diagram of study subject selection.
Brief-type diet history questionnaire for Japanese children and adolescents
The BDHQ15y is a four-page, structured questionnaire designed to assess dietary habits over the preceding month for Japanese children and adolescents. It was developed in 2005 based on comprehensive and brief versions of a validated self-administered DHQ for Japanese adults(Reference Sasaki, Yanagibori and Amano25–Reference Kobayashi, Honda and Murakami29). The BDHQ15y consists of the following four sections: (i) intake frequencies of sixty-one food and beverage items; (ii) daily intake of rice, including type of rice (refined and unrefined) and size of rice bowl; (iii) usual cooking methods for fish and meat dishes and (iv) general dietary behaviours (e.g. preferences for fatty cuts of meat and amount of soup consumed with noodles). The BDHQ15y also includes questions on basic characteristics of the respondent, such as sex, date of birth, self-reported height and weight, person who mainly completed the questionnaire and physical activity. Written instructions on how to complete the questionnaire are provided at the beginning, along with sample responses.
Most of the food and beverage items in the BDHQ15y were selected from the adult version of the brief type self-administered DHQ (BDHQ)(Reference Kobayashi, Murakami and Sasaki28), with a focus on those commonly consumed in Japan. The BDHQ3y, validated for Japanese children aged 3–6 years(Reference Asakura, Haga and Sasaki34), was also referenced to help inform the selection of certain items, particularly for younger children in the target age range. For the BDHQ15y, several items, such as alcoholic beverages, were excluded, while others frequently consumed by children and adolescents, such as yoghurt, cheese, fried potatoes, chocolate, bread spreads, ketchup and nutritional snacks (e.g. CalorieMate and Weider in Jelly), were included based on general dietary trends and expert judgement to better capture dietary habits among Japanese youth. Seven predefined frequency scales, ranging from ‘did not drink/consume’ to ‘two times or more per day’ for food items or ‘four cups or more per day’ for beverage items, were used. In the BDHQ15y, information on portion sizes was not collected directly; instead, fixed portion sizes specific to sex and age were applied to all participants in the calculation of dietary intake. These standard portion sizes were derived from the adult version of the BDHQ, which was based on recipe books that generally represent the portion size for middle-aged women around 50 years old, who are primarily responsible for meal preparation(Reference Kobayashi, Murakami and Sasaki28,Reference Kobayashi, Honda and Murakami29) . Portion sizes for children and adolescents were determined by adjusting the reference value of the estimated energy requirement for 50-year-old women using age- and sex-specific estimated energy requirement ratios(35).
Dietary intakes of three food items commonly added during cooking, namely, salt, fat and oil and sugar, were estimated according to the diet history method, using the qualitative information from sections 3 and 4. Intakes of table salt and salt-containing seasonings at the table, such as soya sauce and soup consumed with noodles, were estimated from answers to the corresponding qualitative questions in section 4. Additionally, individual preferences for meat fat content were incorporated to refine the nutritional composition values, ensuring that the fat content was consistent with the selection of lean or fatty meat. Estimates of daily intakes of foods (68 food items in total; see online Supplementary Table ST1), energy and selected nutrients were calculated using an ad hoc computer algorithm for the BDHQ15y, which was based on the BDHQ(Reference Kobayashi, Murakami and Sasaki28,Reference Kobayashi, Honda and Murakami29) . The nutritional values (i.e. energy and nutrient contents) of each food group item were established using the 2010 version of the Standard Tables of Food Composition in Japan(36), partly supported by the 2015 version(37). For food items without data on the added sugar content, added sugar values were derived from the same or similar food items in the 2011–2012 Food Patterns Equivalents Database(Reference Bowman, Clemens and Friday38). Teaspoon equivalents in the Food Patterns Equivalents Database were converted into grams by multiplying by 4·2 (grams of added sugar per teaspoon).
Eight-day weighed dietary record
Information on actual dietary intake was collected using two-non-consecutive-day weighed DR for each of the four seasons (8 d in total). In this study, the 8-day DR was used as the reference method. Details on the procedure of DR have been described elsewhere(Reference Murakami, Livingstone and Masayasu31–Reference Shinozaki, Murakami and Kimoto33). Briefly, each participant recorded two days of dietary intake per season: half of participants recorded two weekdays (Monday to Friday), while the other half recorded one weekday and one weekend day (Saturday, Sunday or national holidays). This design aimed to capture an approximate representation of overall dietary habits with a 3:1 ratio of weekdays to weekend days (actual ratio 5:2), while maintaining survey feasibility. After receiving verbal and written instructions by a research dietitian, as well as an example of a completed diary sheet, each participant was requested to weigh and record all foods and beverages consumed, both inside and outside the home, using a digital scale (KS-812WT; Tanita, Tokyo, Japan), which measures up to 2 kg in 1 g increments. Parental or guardian assistance was encouraged for younger participants as needed. To ensure the accuracy of reported information on foods consumed outside the home – such as school lunches and snacks – parents were instructed, in accordance with the dietary assessment manual, to ask their children about the types and portion sizes of foods consumed and to supplement this information using external sources (e.g. school lunch menus or newsletters).
Within a few days after each recording day (usually the next day), the research dietitian collected the recording diary, checked the completeness of recording and recorded additional information if necessary. All the collected diaries were checked by trained dietitians at the central office in terms of coding, recorded weights and descriptions of items consumed. Finally, estimates of daily intakes of energy and nutrients were calculated using the 2015 version of the Standard Tables of Food Composition in Japan(37). Dietary supplements were not considered during calculation of nutrient intake because a nutrient composition database of dietary supplements in Japan is lacking.
Statistical analyses
All statistical analyses were conducted separately for boys and girls using SAS statistical software (version 9.4, SAS Institute, Inc.). The dietary variables analysed in this study comprised energy, forty-four nutrients and thirty-one food groups (see online Supplementary Table ST1). Descriptive statistics for participant characteristics were presented as means and SD, while dietary data were summarised as medians with 25th and 75th percentiles to account for their skewed distributions. Energy adjustment was primarily conducted using the density model for both nutrient and food group intakes; however, for nutrients, the residual model(Reference Willett4), which is widely recognised and commonly used in epidemiological studies, was also applied.
To evaluate estimation accuracy at the group level, percentage differences in median intake between the two methods (i.e. the BDHQ15y and 8-day DR) were calculated using the following formula: (BDHQ15y – 8-day DR)/8-day DR × 100. Percentage differences in median intakes were considered good (≤ 10·0 %), acceptable (10·1–20·0 %) and poor (> 20·0 %) with reference to thresholds suggested in prior research(Reference Lombard, Steyn and Charlton39). The ability of the BDHQ15y to rank individuals within the population was assessed by calculating Spearman’s rank correlation coefficients for energy-adjusted intakes of nutrients and food groups derived from both the BDHQ15y and 8-day DR. Agreement between the two methods was further evaluated using Bland–Altman plots(Reference Bland and Altman40) for selected variables, including protein, fat, carbohydrate, total vegetables, fruit, dairy and sugar-sweetened beverages (SSB) because these are key macronutrients and food groups with direct implications for dietary intake and health outcomes and are particularly relevant to public health nutrition for this age group(Reference Golley, Bell and Hendrie41). Proportional bias between the two methods was tested using linear regression analysis, with statistical significance set at P < 0·05. As a sensitivity analysis, evaluations were stratified by sex and age groups (6–9, 10–14 and 15–17 years), with results detailed in the online Supplementary Materials.
Results
This study included 432 boys and 412 girls (Table 1). The mean ages of boys and girls were 11·8 years (sd 3·4 years) and 11·5 years (sd 3·5 years), respectively.
Table 1. Basic characteristics of study subjects (Mean values and standard deviations)
* BMI was calculated by dividing body weight (kg) by the square of height (m), based on self-reported values.
The median values of daily energy and energy-adjusted nutrient intakes derived from the 8-day DR and BDHQ15y are presented in Table 2 (for boys) and Table 3 (for girls). The median energy intakes estimated from the BDHQ15y closely aligned with those estimated from the 8-day DR, with percentage differences within 4 % for both sexes. Among forty-four nutrients examined, 19 (43 %) showed differences within 10 % between the BDHQ15y and 8-day DR for both sexes and both energy-adjusted models, except for β-carotene in the residual model for girls. These nutrients included protein, total fat, SFA, PUFA, cholesterol, carbohydrate, dietary fibre, β-carotene equivalent, α-tocopherol, niacin, vitamin B6, Na, potassium, Mg, phosphorus and Zn, indicating good agreement at the group level. In contrast, the BDHQ15y overestimated fifteen nutrients in boys and fourteen nutrients in girls by more than 20 % and underestimated two nutrients (α-carotene and thiamine) by more than 20 % in both sexes, indicating poor accuracy. This trend was consistent across both energy-adjusted models.
Table 2. Median estimates of daily energy and energy-adjusted nutrient intakes using the density and residual methods from the 8-day weighed dietary records (DR) and the brief-type diet history questionnaire for Japanese adolescents and children (BDHQ15y) in 432 Japanese boys aged 6–17 years: percentage differences in median intakes and Spearman’s rank correlations coefficients (CC)
P25, 25th percentile; P75, 75th percentile.
* Sum of EPA, n-3 DPA and DHA.
† Sum of retinol, β-carotene/12, α-carotene/24 and cryptoxanthin/24.
‡ Sum of β-carotene, α-carotene/2 and cryptoxanthin/2.
§ Percentage differences: (BDHQ15y – 8-day DR)/8-day DR × 100 (%).
Table 3. Median estimates of daily energy and energy-adjusted nutrient intakes using the density and residual methods from the 8-day weighed dietary records (DR) and the brief-type diet history questionnaire for Japanese adolescents and children (BDHQ15y) in 412 Japanese girls aged 6–17 years: percentage differences in median intakes and Spearman’s rank correlations coefficients (CCs)
SFA, saturated fatty acids; MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids; EPA, eicosapentaenoic acid; DPA, docosapentaenoic acid; DHA, docosahexaenoic acid; P25, 25th percentile; P75, 75th percentile.
* Sum of EPA, n-3 DPA and DHA.
† Sum of retinol, β-carotene/12, α-carotene/24 and cryptoxanthin/24.
‡ Sum of β-carotene, α-carotene/2 and cryptoxanthin/2.
§ Percentage differences: (BDHQ15y – 8-day DR)/8-day DR × 100 (%).
Spearman’s rank correlation coefficients for energy intake between the BDHQ15y and 8-day DR were 0·67 for boys and 0·41 for girls. Among the forty-four nutrients examined, Spearman’s rank correlation coefficients in boys ranged from 0·16 (thiamine) to 0·56 (Ca) in the density model and from 0·16 (vitamin D) to 0·57 (Ca) in the residual model, with medians of 0·33 (25th–75th percentiles: 0·28–0·38) and 0·32 (25th–75th percentiles: 0·25–0·36), respectively. For girls, Spearman’s rank correlation coefficients ranged from 0·08 (α-tocopherol) to 0·46 (Ca) in the density model and from 0·10 (α-tocopherol) to 0·45 (Ca) in the residual model, with medians of 0·28 (25th–75th percentiles: 0·23–0·35) and 0·29 (25th–75th percentiles: 0·22–0·36), respectively.
Energy-adjusted food group intakes from the BDHQ15y and 8-day DR are shown in Table 4. Of the thirty-one food groups examined, only 11 (35 %) in boys and seven (23 %) in girls showed differences within 10 % between the two methods, while many food groups were under- or overestimated by more than 20 %. Food groups with the smallest median differences (%) included grains (2·2 % for boys and 3·9 % for girls), total vegetables (–6·7 % for boys and −5·2 % for girls), green and yellow vegetables (9·2 % for boys and 7·3 % for girls), dairy products (0·6 % for boys and −9·5 % for girls), full-fat milk (–8·8 % for boys and −4·2 % for girls) and SSB (–4·4 % for boys and −1·5 % for girls). Spearman’s rank correlation coefficients ranged from 0·13 for meat to 0·61 for dairy products in boys and from 0·10 for seasonings to 0·65 for dairy products in girls, with medians of 0·36 (25th–75th percentiles: 0·29–0·42) and 0·29 (25th–75th percentiles: 0·24–0·36), respectively.
Table 4. Median estimates of energy-adjusted food group intakes using the density method from the 8-day weighed dietary records (DR) and the brief-type diet history questionnaire for Japanese adolescents and children (BDHQ15y) and their correlations in Japanese children and adolescents aged 6–17 years: percentage differences in median intakes and Spearman’s rank correlations coefficients (CC)
P25, 25th percentile; P75, 75th percentile.
* Percentage differences: (BDHQ15y – 8-day DR)/8-day DR × 100 (%).
Bland–Altman plots were also used to assess agreement between the two methods for selected variables, namely, protein, total fat, carbohydrate, total vegetables, fruit, dairy products and SSB (Figure 2). The limits of agreement (mean (sd 1·96)) were relatively wide for all variables examined, indicating substantial discrepancies at the individual level. Regression lines for most variables examined were statistically significant, indicating a proportional bias in the BDHQ15y, with overestimation of intakes at higher consumption levels. In contrast, no significant linear trends were detected for SSB in boys or girls.
Figure 2. Bland–Altman plots of agreement between the brief-type diet history questionnaire for Japanese children and adolescents (BDHQ15y) and 8-day dietary records (DR) among 432 boys (a) protein, (c) fat, (e) carbohydrate, (g) total vegetables, (i) fruits, (k) dairy products and (m) sugar-sweetened beverages) and 412 girls (b) protein, (d) fat, (f) carbohydrate, (h) total vegetables, (j) fruits, (l) dairy products and (n) sugar-sweetened beverages) aged 6–17 years.
Additional analyses by sex and age groups (6–9, 10–14 and 15–17 years) revealed that, regardless of the energy-adjustment model, similar patterns of percentage differences in median intakes of nutrients and food groups (within 10 %) were observed across age groups for both sexes (online Supplementary Tables ST2–ST7). However, Spearman’s rank correlation coefficients for nutrients were generally lower in the 10- to 14-year age group than in the other age groups, with median values (from the density model results) of 0·28 in boys and 0·24 in girls, compared with 0·36 and 0·30 in the 6–9-year age group, respectively, and 0·37 and 0·34 in the 15–17-year age group, respectively. Notably, the 15- to 17-year age group had a higher number of nutrients and food groups with correlation coefficients exceeding 0·4 in both sexes than the other age groups.
Discussion
To our knowledge, this is the first study to evaluate the relative validity of the BDHQ15y in assessing intakes of a broad range of nutrients and food groups. Using the 8-day DR as the reference method, our findings demonstrate that the BDHQ15y provides reasonable estimates of median intakes of several key nutrients and food groups, although its ability to accurately rank individuals differed considerably depending on the nutrient, food group and age group, with correlations generally lower in younger adolescents (10- to 14-year age group). These results suggest the potential of the BDHQ15y for large-scale dietary monitoring and public health research, particularly for assessing dietary intake trends of specific nutrients and food groups at the group level. However, its limitations, including variability in ranking ability, should be carefully considered, particularly when interpreting results for individual dietary assessment or specific sub-groups.
Unlike the adult versions of the DHQ and BDHQ in Japan(Reference Sasaki, Yanagibori and Amano25–Reference Kobayashi, Honda and Murakami29), which tend to underestimate dietary intake compared with DR, the BDHQ15y generally overestimated median intakes for most nutrients and food groups. This aligns with broader trends observed when adult-oriented dietary assessment tools were applied to children(Reference Shatenstein, Amre and Jabbour14–Reference Slater, Philippi and Fisberg17,Reference Ambrosini, de Klerk and O’Sullivan19–Reference Kobayashi, Kamimura and Imai21) . The BDHQ15y overestimated fifteen and fourteen nutrients in boys and girls, respectively, and underestimated two nutrients in both sexes, with all discrepancies exceeding 20 % relative to the 8-day DR. However, for key nutrients such as protein, fat, carbohydrate, dietary fibre and certain vitamins (e.g. niacin and vitamin B6) and minerals (e.g. Na, phosphorus and Zn), as well as food groups like grains, total vegetables, dairy products and SSB, the BDHQ15y provided reliable estimates within 10 % of the 8-day DR, suggesting its utility for population-level dietary monitoring. However, Bland–Altman analysis revealed greater variability at the individual level, with overestimations being more pronounced at higher intake levels. This may be due to the use of fixed portion sizes, which may lead frequent consumers to overestimate their intake if the portion sizes specified in the BDHQ15y are larger than those they actually consume. Therefore, caution should be exercised when interpreting individual-level data, especially for frequently consumed foods. Given that the portion sizes in the BDHQ15y were derived from the adult version of the BDHQ, which is based on recipe books for middle-aged women and adjusted using sex- and age-specific estimated energy requirement ratios(Reference Kobayashi, Murakami and Sasaki28,Reference Kobayashi, Honda and Murakami29) , future improvements to the BDHQ15y should focus on refining portion size estimates by updating them with current consumption data from the target population to more accurately reflect typical dietary intake.
The BDHQ15y demonstrated a limited ability to rank individuals by energy-adjusted nutrient intake, with median Spearman’s rank correlation coefficients (for results based on the density model) of 0·33 for boys and 0·28 for girls. These results are generally consistent with, although in some cases they are slightly lower than, those reported in a meta-analysis of dietary questionnaires in children and adolescents(Reference Golley, Bell and Hendrie41–Reference Ortiz-Andrellucchi, Henríquez-Sánchez and Sánchez-Villegas44). In particular, low correlations (< 0·3 or 0·4), which significantly attenuate the ability to detect associations in epidemiological studies(Reference Cade, Thompson and Burley6), were observed for several specific nutrients, including fat-related nutrients (e.g. n-3 PUFA, marine-origin n-3 PUFA, eicosapentaenoic acid, docosapentaenoic acid, docosahexaenoic acid and α-linolenic acid), certain vitamins (e.g. α-carotene, vitamin D and thiamine), as well as Na and Mn. These insufficient correlations likely reflect the inherent difficulty in accurately estimating nutrient intake from diverse food sources such as meat, fish, fats and oils and seasonings(Reference Ortiz-Andrellucchi, Henríquez-Sánchez and Sánchez-Villegas44). Additionally, the self-reporting nature of the BDHQ15y limits its ability to capture individual variations in fat preferences and cooking methods, which likely further contributes to these inaccuracies(Reference Kobayashi, Murakami and Sasaki28,Reference Kobayashi, Honda and Murakami29,Reference Cui, Xia and Wu45) . In contrast, stronger correlations were observed for certain nutrients, with correlations exceeding 0·5 for Ca and phosphorus in boys and exceeding 0·4 for several nutrients, including added sugar, vitamin K, Ca and Cu, in both sexes. Additionally, boys had higher correlations (> 0·4) for riboflavin, pantothenic acid, potassium, Mg and phosphorus, while girls showed a similar trend for cryptoxanthin. In light of these findings, the limited ability of the BDHQ15y to reliably rank nutrient intakes could hinder the identification of meaningful associations between dietary factors and health outcomes for many nutrients. While the BDHQ15y may be useful for broad population-level dietary monitoring, its application in epidemiological studies investigating specific nutrient-health relationships in children and adolescents should be approached with caution. Future research should prioritise biomarker-based validation studies employing appropriate biological samples (e.g. erythrocyte membrane fatty acids) across diverse populations to assess the accuracy of the BDHQ15y, particularly for nutrients that are prone to misestimation due to the self-reporting nature of this tool, such as various types of fatty acids, vitamin A and vitamin D. Such efforts would provide a more accurate evaluation of its reliability and enhance its applicability in nutritional epidemiology.
Despite lower correlations for many nutrients, it is noteworthy that the BDHQ15y achieved correlations exceeding 0·4 for ten and seven food groups in boys and girls, respectively. Common to both sexes, these included fruit, dairy products, full-fat milk, yoghurt and SSB. For boys, additional food groups surpassing this threshold included bread, total vegetables, green and yellow vegetables and tea. For girls, these food groups included mushrooms and fruit and vegetable juices. Notably, stronger correlations exceeding 0·5 were consistently observed for fruit, dairy products and full-fat milk in both sexes. These food groups play a crucial role in understanding dietary patterns in children and adolescents, with direct implications for dietary intake and associated health outcomes(Reference Golley, Bell and Hendrie41). These findings highlight the utility of the BDHQ15y as a valuable tool for ranking individuals according to the intake of key food components in epidemiological studies, particularly those investigating the relationships between diet and health outcomes in younger populations.
Age-related differences in the validity of the BDHQ15y were observed, with the 10–14-year age group showing lower correlation coefficients than the other age groups. This may reflect the transitional nature of this developmental stage, during which younger adolescents gain greater independence in food choices and are influenced by social factors such as peer pressure, leading to more varied and less consistent dietary patterns(Reference Ziegler, Kasprzak and Mansouri9–Reference Sharman, Skouteris and Powell12). Additionally, cognitive and memory-related challenges specific to this age group may contribute to the lower accuracy of dietary reporting(Reference Baranowski and Domel11,Reference Saravia, Miguel-Berges and Iglesia42) . In contrast, younger children (6–9 years) benefit from greater parental involvement, leading to more consistent reporting, while older adolescents (15–17 years) show improved accuracy due to cognitive maturity and more stable eating patterns. These findings highlight the need for careful interpretation of dietary assessment results among younger adolescents (i.e. 10–14-year age group) and emphasise the importance of developing age-appropriate tools. Strategies such as encouraging parental involvement and tailoring methods to this developmental stage could help address these challenges and improve the validity of dietary assessments.
Our study has several strengths, including a large, nationwide sample of Japanese school-aged children and adolescents (6–17 years), comprising both boys and girls. This broad age range and inclusion of both sexes enable comprehensive analysis of dietary intake across various developmental stages, enhancing the relevance and generalisability of our findings to the wider population of Japanese children and adolescents. Despite these strengths, several limitations should be considered. First, the participants were volunteers rather than a randomly selected sample, potentially leading to a group of participants with greater health consciousness due to the demanding nature of the 8-day DR. Second, use of the 8-day weighed DR as the reference method has inherent limitations, including errors from underreporting, over-reporting and social desirability bias, as well as potential changes in dietary behaviours during recording(Reference Cade, Thompson and Burley6). Unlike memory-based dietary assessments, the weighed DR does not rely on recall, which reduces some sources of error. However, shared biases between the DR and BDHQ15y, both of which involve self-reported data, may still affect their comparability(Reference Cade, Thompson and Burley6,Reference Saravia, Miguel-Berges and Iglesia42) . Third, the BDHQ15y assessed dietary intake over the previous month, whereas the DR was conducted over the course of a year. This temporal discrepancy constitutes a methodological limitation, particularly in a population experiencing rapid developmental and behavioural changes. Additionally, seasonal variation in food consumption(Reference Yannakoulia, Drichoutis and Kontogianni46) and age-related shifts in dietary habits(Reference Ziegler, Kasprzak and Mansouri9,Reference Baranowski and Domel11) contribute to differences between the two methods. Therefore, discrepancies in estimated intake may reflect both measurement error and genuine dietary changes over time, warranting cautious interpretation of the relative validity results for the BDHQ15y obtained in this study. Fourth, alcoholic beverages were not assessed in the BDHQ15y due to legal constraints and the difficulty of obtaining reliable self-reports from minors, which may be affected by social desirability bias. While alcohol use is generally low in this age group(47), previous research suggests that it may be more prevalent among certain subgroups of older adolescents(Reference Fujii, Kuwabara and Kinjo48). Consequently, the omission of alcoholic beverages may compromise the completeness of dietary assessment in this population. Finally, while half the participants recorded dietary intake only on weekdays, the remaining half recorded dietary intake on both weekdays and weekend days. Notably, four of the eight recorded days were weekend days, potentially overrepresenting weekend dietary habits. However, sensitivity analyses showed no significant differences between the two groups (data not shown), indicating that variations in recording days did not substantially affect the relative validity of BDHQ15y estimates.
In conclusion, the BDHQ15y demonstrates reasonable relative validity to assess the intakes of key nutrients and food groups in Japanese children and adolescents at the group level, but not at the individual level. While the BDHQ15y is effective for large-scale dietary monitoring, its ability to accurately rank individual intakes is limited, especially in younger adolescents. These findings highlight the need for caution when interpreting individual-level data. Future research should focus on refining portion sizes and conducting biomarker-based validation studies to more accurately assess the validity of the BDHQ15y. This would improve the evaluation of its validity and broaden its applicability in nutritional epidemiological studies. Furthermore, considering identified methodological limitations, developing and validating dietary questionnaires tailored to developmental stages is essential to better capture age-specific dietary behaviours and support epidemiological studies on diet and health.
Acknowledgements
We thank all the children and their parents/guardians who took part in this study. We also thank the following research dietitians and staff who collected and processed the data: Tamotsu Noshiro*, Ikuko Kato, Yoshie Awa, Erika Takayama, Mari Sakurai, Mihoko Yanase, Masae Kato, Mihoko Furukawa, Yuna Nodera, Kazue Fukushi, Miwako Onodera, Yoshie Sato, Megumi Yoshida, Masako Shimooka, Kaori Takahashi and Fuki Kudo (Hokkaido); Yumiko Sato, Yutaka Shojiguchi, Kazunori Kimoto, Saori Kikuchi, Megumi Maeta, Mayumi Sugawara, Shinogu Muraoka, Kanako Takahashi, Noriko Suzuki, Yoko Fujihira and Megumi Onodera (Iwate); Yukiko Takahashi, Kaoru Honda, Chie Yamada, Miki Sato, Katsue Watanabe, Akemi Konno and Reina Kato (Miyagi); Akiko Sato, Hiromi Kawaguchi, Miyuki Awano, Chigusa Miyake, Ayako Konno, Ayumi Goto, Shizuko Taira, Yuka Takeda, Akiko Matsunaga, Nao Konta, Yumi Miura, Satoshi Numazawa, Chiemi Ito, Sachie Yokosawa, Manam Endo and Hiromi Seki (Yamagata); Yoko Tsukada, Tomoko Oga, Satoko Fujita, Hitomi Sonobe, Hanayo Kadoi, Toshie Nakayama, Hiromi Takasawa, Yoko Ichikawa, Yuko Takano, Junko Hanzawa, Kiyomi Seki, Emi Kamoshita, Yuri Kawakami and Hisako Watahiki (Ibaraki); Reiko Ishii, Yoshiyuki Tatsuki, Daisuke Mogi, Akiko Nakamura, Suguru Yagi, Yumiko Furushima, Noriko Ogiwara, Kiyomi Kimura, Kinue Takahashi, Kikue Tomaru, Kana Tsukagoshi, Fumiko Fujiu, Kyoko Maehara and Yuki Kobayashi (Gunma); Kaoru Goto, Yuka Inaba, Michiko Koresawa, Tomoko Tsuchida, Naoko Sakakibara, Fumika Shimoyama, Akiko Kato, Miki Hori, Rika Kurosaki, Hiroko Yamada, Hitomi Sasaki, Keiko Arai, Yuka Arai, Manami Honda, Akiko Utsumi, Asako Hamada, Keiko Sekine, Akiko Yamada, Mami Ono, Satoko Maruyama, Emiko Kajiwara, Taeko Takahashi, Hitomi Kawata, Satoru Arai, Ryoko Hirose, Madoka Ono and Mihoko Ainai (Saitama); Masako Shinohara, Noriko Nakamura, Mitsuko Ito, Yuka Takahara, Minako Fukuda, Masae Ito, Yayoi Sueyoshi, Hiroko Shigeno, Tomohiro Murakami, Masako Kametani, Kyoko Wada, Mika Ueda, Jun Kouno, Hiroyo Yamaguchi, Mariko Oya, Junko Suegane, Yumiko Asai, Miyuki Ono, Mitsuko Uekusa, Chieko Sunada, Yumi Tanada, Mariko Shibata, Emi Tsukii, Kae Terayama, Hiroko Iwasaki, Keiko Yokoyama, Haruna Kayano, Kazuyo Shimota, Keiko Ifuku and Keiko Honma (Chiba); Yoshiko Saito, Megumi Suzuki, Eiko Kobayashi, Yoshiko Katayama, Sonoko Yamaguchi, Tomoko Kita, Naoko Yuasa, Hitomi Okahashi, Shinobu Matsui, Yurina Arai, Sanae Togo, Eiko Horiguchi, Juri Sato, Takehiro Komatsu, Yumi Matsuura, Junko Higashi, Ayaka Nakashita, Takako Sakanashi, Yoko Kono, Naomi Nakazawa and Yukiko Shibata (Tokyo); Machiko Tanaka, Ikue Sahara, Yasue Watanabe, Kanako Yoshijima, Yuko Harashima, Yoshiko Iba, Haruko Irisawa, Junko Inose, Reiko Okui, Taeko Endo, Mayuko Sakitsu, Ikuko Endo, Haruko Terada, Chiaki Nishikawa, Ai Yasudomi, Suzuyo Takeda, Kaori Shimizu, Mari Ikeda, Yuko Okamoto, Keiko Yamada, Fumiko Nemoto, Shinobu Katayama, Yuki Takakuwa, Michiru Goukon, Megumi Koike, Masae Kamiya, Takako Okada, Yayoi Hayashi, Etsuko Abe, Akiko Hamamoto, Kumiko Ono, Kazumi Takagi, Sachiko Ito, Yuki Kumagai, Noriko Ozaki, Haruka Sato, Hisae Takahashi, Masuko Komazaki, Akiko Nako, Tatehiro Inamoto and Kimiyo Matsumoto (Kanagawa);Masako Koike, Reiko Kunimatsu, Keiko Kuribayashi, Hiroko Adachi, Yuri Shikama, Yurika Seida, Ryouko Ito, Satoko Kimura, Yoko Sato, Michiyo Nakamura, Hisako Kaneko, Hatsuyo Ikarashi, Mamiko Karo, Keiko Hirayama, Ikumi Torigoe and Fumiko Gumizawa (Niigata); Natsuko Mizuguchi, Aki Sakai, Hisako Noguchi, Chie Tanabe, Yokako Osaki, Ikiko Kawasaki, Nobuko Yoshii, Yumiko Nishihara, Izumi Takahara, Mika Minato, Yuuki Okamoto, Yukiyo Sakai, Shitomi Nakamura, Kanako Kobayashi, Emi Hano and Megumi Emori (Toyama); Kazumi Horiguchi, Michiyo Kubota, Naoya Mochizuki, Miyuki Yokokoji, Kazuko Koizumi, Megumi Ariizumi, Hozumi Kakishima, Mayumi Kawakubo, Chisato Nakajima, Yasuko Ishii, Yukie Shiogami, Yukiko Uchida, Ikuko Kayanuma and Kikuyo Moriya (Yamanashi); Noriko Sumi, Noriko Takahashi, Kuniko Watanabe, Yoko Ido, Akiko Adachi and Manami Tauchi (Gifu); Naoya Terada, Chisato Suwa, Toshihiro Tamori, Natsuko Osakabe, Toshiyuki Serizawa, Akiko Seki, Izumi Mochizuki, Nagako Matsui, Eiko Watanabe, Kyoko Yui, Yuki Murakami, Tomomi Iwasaki and Tomoko Sugiyama (Shizuoka); Keiko Kawasumi, Masako Tanaka, Kayoko Ishida, Megumi Yamatani, Shihoko Yama, Miyuki Otono, Mie Kojima, Tamaki Kobayashi, Hiroe Komaki, Miki Yanagida, Yumiko Fukaya, Syoko Sawaki, Tomomi Ota, Yasuko Kito, Mei Tobinaga, Takashi Yasue, Kuniko Hatamoto, Toru Ono, Takako Minami, Akemi Kumazawa, Masami Kato, Miyuki Kondo, Kyoko Shimizu, Sayoko Tanaka and Shizue Masuda (Aichi); Hiromi Ashida, Shintaro Hinaga, Yoshiko Shoji, Ryusuke Yamaguchi, Hiroka Morita and Atsuko Nakabayashi (Mie); Erika Shioi, Sawa Mizukawa, Miwako Ohashi, Eriko Taniguchi, Yuri Mitsushima, Mariko Teraya, Kazuko Ogawa, Yoko Minami, Megumi Ito, Yasuhiro Morimoto, Shizuka Kurokawa and Manami Hayashi (Kyoto); Yumiko Noutomi, Yoshiko Iwamoto, Junko Ikukawa, Shinobu Fujiwara, Tami Irei, Keiko Takata, Yasuka Tabuchi, Naoko Murayama, Kaori Maruyama, Hiromi Tashiro, Miki Tanaka, Miho Nomura, Shizuyo Umezawa, Minori Shintani, IkuyoMaruishi, Atsuko Toyokawa, Rumi Kitada, Yuka Takashima, Eriko Nakatani, Wakana Tsujimoto, Yumi Koori, Emi Iwamoto and Masumi Yamada (Osaka); Atsuko Konishi, Yoshiko Nakamori, Yumi Ikawa, Junko Shimizu, Mie Atsumi, Atsuko Fukuzaki, Akemi Yamamoto, Yasuko Inoue, Miyuki Nakahara, Reiko Fujii, Yumi Tanaka, Rika Miyachi, Mari Matsuyama, Ayana Honda, Tomoka Nakata, Miho Inagaki, Mikiyo Ueno, Mami Kamei, Kiyomi Kawachi, Yasuyo Hasegawa and Masayo Fukumoto (Hyogo); Sachiyo Otani, Tomomi Sugimoto, Kanako Mizoguchi, Tomomi Shimada, Shima Takahashi, Yoshiko Okuno, Takahide Kijima, Masayo Ueda, Yuko Sakamoto, Hitoshi Matsuda, Yumie Shimizu, Rie Hataguchi, Junko Nohara, Yuko Sakakitani, Hatsuki Matsumoto, Sakiko Tanaka, Moe Yoshikawa and Rena Yuki (Nara); Yurika Adachi, Akiko Notsu, Keiko Uzuki, Atsuko Umeda, Hiroko Nishio, Chikako Takeshita and Sayuri Omoso (Tottori); Hiromi Watanabe, Nami Sakane, Nao Hino, Sakiko Sonoyama, Yukiko Katagiri, Kaori Nagami, Tsunemi Moriyama and Yoshiko Kirihara (Shimane); Sachiko Terao, Akemi Inoue, Mieko Imanaka, Noriyuki Kubota, Sachiko Sugii, Yuri Fujiwara and Tomoko Miyake (Okayama); Youko Fujii, Hiroko Tamura, Kimie Tanaka, Izumi Hase, Etsuko Kimura, Akiko Hamada, Tomoko Kawai, Masako Ogusu and Emi Isomichi (Hiroshima); Kyoko Ueda, Atsuko Nakanishi, Tomoko Ishida, Nobuko Morishita, Hitomi Nakata and Takako Yagi (Yamaguchi); Yuriko Doihara, Toyoko Kitadai, Hideyo Yamada, Mariko Nakamura, Nanako Honda, Eri Ikeuchi, Kayo Hashimoto, Azusa Onishi and Manami Iwase (Tokushima); Machiko Ueda, Ayano Kamei, Reiko Motoie, Yoko Moriki, Nobumi Yoshida and Kiyoko Kubo (Ehime); Kyoko Kaku, Emi Ibushi, Miho Otsuka, Kiyoko Katayama, Hisami Kumagai, Chizuru Shibata, Miki Hamachi, Yuko Hayashida, Akiko Matsuzaki, Mika Yoshioka, Yoshie Yanase, Yoshiko Yahagi and Tomomi Ota (Fukuoka); Junko Kiyota, Hiromi Ide, Takako Fukushima, Shiho Tominaga, Satsuki Miyama, Yoko Okamura, Kayoko Kurahara, Tomomi Nagamori, Chika Shino, Akiko Taira, Yuki Kuwajima and Miyuki Matsushita (Kumamoto); Miki Hamada, Kiyomi Aso, Toshie Eto, Mitsue Kodama, Miyoko Sato, Mutsuko Shuto, Yuko Soga, Taeko Nagami, Machiko Hirayama, Mika Moribe, Junko Yamamoto, Hideko Yoshioka, Yuko Kawano, Rika Matsuoka and Satomi Sato (Oita); and Hisami Yamauchi, Satomi Moromi, Satoko Tomari, Kaoru Miyara, Chikako Murahama, Yukiko Furugen, Kana Awano, Hiromi Arakaki, Suzumi Uema, Yasuko Tomori, Nariko Mori, Ayako Iho, Michiru Hokama, Yasue Higa and Chigusa Chibana (Okinawa). The authors would also like to thank the following research team staff at the survey centre (University of Tokyo): Tomoko Doi, Hitomi Fujihashi, Akiko Hara, Nana Kimoto, Nanako Koe, Eri Kudo, Fumie Maeda, Keika Mine, Akemi Nakahara, Hiroko Onodera, Hiroko Sato, Chifumi Shimomura and Fusako Takahashi. *Deceased.
This research was funded by the Ministry of Health, Labour and Welfare, Japan (grant number 23KA1009). The Ministry of Health, Labour and Welfare had no role in the design, analysis or writing of this article.
H. O. contributed to the research plan, conducted the statistical analysis and wrote the first draft of the manuscript; R. T. contributed to data collection, management and interpretation and the manuscript preparation; N. S. contributed to data collection and management and provided critical input on the final draft of the manuscript; S. M. and S. S. contributed to the survey design and managed study-field establishment, recruitment and fieldwork; K. M. contributed to the conceptualisation and design of the survey, oversaw data collection and project administration, assisted with data interpretation and provided critical input on the final draft of the manuscript. All authors reviewed and approved the final version submitted for publication.
H. O. and R. T. belong to the Department of Nutritional Epidemiology and Behavioural Nutrition, Graduate School of Medicine, The University of Tokyo, which is a social cooperation program with Ajinomoto, Co., Inc. The authors declare that Ajinomoto, Co., Inc. had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decision to publish the results. The remaining authors declare that they have no conflicts of interest.
Supplementary material
For supplementary material/s referred to in this article, please visit https://doi.org/10.1017/S0007114525104042
Open access
