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Validity of a semi-quantitative food frequency questionnaire based on food groups and cooking with a protein supplement in Japanese collegiate athletes

A semi-quantitative food frequency questionnaire (FFQ) comprising a small number of food items may allow for more accurate assessment of habitual dietary intake in Japanese collegiate athletes. This study aimed to evaluate the validity and reproducibility of a commercially available FFQ (FFQg) with a newly added protein supplement item for assessing habitual dietary intake compared to the 24-hour dietary recall (24hDR) method using multiple statistical tests. A total of 108 participants (46 males) completed the FFQg on two separate days (FFQg1 and FFQg2) and 24hDRs on three non-consecutive days (two weekdays and one weekend day). Percentage differences, cross-classification analysis by quintile and Spearman’s rank correlation coefficients (CCs) were calculated to evaluate the validity and reproducibility of the FFQg. Results showed median percentage differences and de-attenuated CCs between the FFQg2 and 24hDRs were -3.4 (range: -46.8 25.1) and 0.33 (0.03 0.55), respectively, for 34 nutrients, and -16.1 (-51.4 45.0) and 0.34 (0.03 -0.64), respectively, for 18 food groups. Cross-classification analysis showed that median proportions of the “completely opposite category” between FFQg2 and 24hDRs for energy-adjusted intakes of nutrients and food groups were both within 5%. Median CCs for energy-adjusted intake between the FFQ1 and FFQ2 were 0.61 (0.40 0.82) for nutrients and 0.52 (0.40 0.87) for food groups. In conclusion, although estimating habitual dietary intake requires careful consideration, the FFQg offers reasonably valid measurements of ranking individuals to assess intakes of macronutrients and food groups in collegiate athletes, with a relatively good reproducibility.


Introduction
Athletes engage in intense training to perform their best in games. Diet plays an important role in attaining and withstanding intense training. Dietary interventions are reportedly useful for maintaining overall health and athletic performance in collegiate athletes 1,2) . However, collegiate students reportedly have unbalanced dietary habits due to ready access to unhealthy food, irregular eating, and meal skipping [3][4][5] . Moreover, Japanese collegiate athletes, especially female rhythmic gymnasts, in whom leanness is valued, sometimes suffer from under-nutrition or diet-related medical complications 6,7) . To perform proper dietary interventions on or provide appropriate dietary support to Japanese collegiate athletes, it is important to estimate their habitual low intakes of nutrients and food groups.
Three dietary assessment methods are generally used in dietary surveys: dietary record (DR), 24-hour dietary recall (24hDR), and food frequency questionnaire (FFQ) 8) . The reference method is a DR over several days in combination with a 24hDR, with an FFQ administered as a substitute method 8) . Dietary surveys with a DR consist of a number of complicated processes and require welltrained interviewers as well as sufficient time and money. In epidemiological studies, FFQs are widely used given their feasibility and cost-effectiveness compared to the reference method. However, there are few validation studies on FFQs in athletes in Japan, and only one study was conducted in Japanese collegiate athletes 9) with an FFQ *Correspondence: y1kawano@nodai.ac.jp comprising 138 food items, 20 beverage items, and 14 questions regarding seasonings.
FFQs can provide a reasonable estimation of dietary intake, and may reduce burden in participants, who have limited knowledge of food categories (e.g., differences between beverages and juice) or food composition for mixed dishes or cooking. However, it is difficult for participants to answer questions regarding so many food items. Against this backdrop, a commercially available FFQ (FFQg) was developed for community-dwelling Japanese individuals, which comprises 29 food groups as well as 10 cooking methods, to assess habitual dietary intake 10) . However, no validation study has been performed on the FFQg. Lombard et al. 11) recently reviewed 102 studies of FFQs on statistical tests that are applied to evaluate validity of dietary intake assessment methods, and concluded that the use of multiple statistical tests, such as calculations of de-attenuated correlation coefficients (CCs) and percentage (%) differences, paired t-test or Wilcoxon signed-rank test, cross-classification (tertiles, quartiles, or quintiles), weighted kappa statistics, and Bland-Altman analysis, is helpful in analyzing the comprehensive validity of a dietary assessment method. In the present study, therefore, we set out to conduct a validation and reproducibility study for both nutrients and food groups using multiple statistical tests, for the purpose of examining the usefulness of the FFQg for ranking individuals compared to the 24hDR method.
In addition, a considerable number of athletes routinely consume protein supplements 12) . In fact, a previous study confirmed that 38% of Japanese male athletes used protein supplements on a routine basis 9) . As no FFQ contains an item regarding protein supplements in Japan, a modified version of the FFQg that incorporates a question regarding protein supplement use might allow for more accurate assessment of dietary protein intake in collegiate athletes.
The aim of this study was to examine whether the FFQg with a protein supplement item can be validated as a suitable method for ranking individuals to assess habitual dietary intake in Japanese collegiate athletes, with a relatively good reproducibility as compared to 24hDRs.

Participants.
Collegiate athletes were recruited through bulletin board announcements and recruiting fairs held at two universities in the Kanto area in Japan. Among 121 collegiate athletes who participated in this study, 13 males were unable to complete all three 24hDR surveys due to their schedules; and, ultimately, this study included a total of 108 (46 male) participants. This study was approved by the Ethics Committee of the Tokyo University of Agriculture (Approval No. 1504).

Study design.
The study design is illustrated in Fig. 1. The surveys were conducted during the two-month period from May to July in 2015. No game was planned during this period. The modified FFQg was administered to participants twice (beginning and end of the study period) to evaluate their dietary intake in the previous month [13][14][15] . The first FFQg (FFQg1) was administered to participants at the time of informed consent, followed by anthropometric measurements. Thereafter, the 24hDRs were conducted on three non-consecutive days (two weekdays and one weekend day) with an interval of approximately 1 week between each 9,15,16) . The second FFQg (FFQg2) was administered approximately one month after the FFQg1, when the third 24hDR was completed. We did not estimate detailed training data on the survey days.
Anthropometric measurements. Height was measured using a stadiometer (YL-65, Yagami, Japan). Body weight, amount of body fat, body fat percentage, and skeletal muscle mass were measured using Inbody 430 (Biospace, USA). Lean body weight was calculated by subtracting the amount of body fat from body weight. Body mass index (BMI) was calculated by dividing body weight in kilograms (kg) by height in meters squared (m 2 ). Basal metabolic expenditure (BM) was calculated using the Harris-Benedict's equation for males and females, as follows 17 24hDR survey. Participants were interviewed face-toface by registered dietitians or well-trained university students enrolled in a registered dietitian or nutritionist course. Interviewers asked participants to recall all food and beverage items consumed in the previous 24 hours from 3:00 am, as well as their physical activities.
Interviews were conducted according to the multiplepass interviewing method 9,18,19) , and the process for recall was divided into four steps as previously described 9) . In brief, step 1 involved asking participants to recall the details of their physical activities for the previous 24 hours, including the time of eating or drinking. The use of nutritional supplements was also reported.
Step 2 involved asking participants to recall menus and ingredients.
Step 3 involved estimating the weights of consumed foods and beverages, as determined from the interviews. Finally, in step 4, the interviewers checked the records and asked additional questions to ensure accuracy in dietary calculation (e.g., seasonings; with or without butter or margarine for bread; regular or low-fat milk; food categories such as beverages, fruit juice, vegetable juice, or vegetables). Finally, a registered dietitian checked the records again for any inadequate or missing information. This 24hDR survey took an average of 33 (standard deviation; SD, 8) minutes per person.
Daily energy and nutrient intake based on the Standard Tables of Food Composition in Japan (5 th revised edition, 2010) 20) and food group intake were calculated using nutrient calculation software (Excel Eiyoukun, Version 7.0, Kenpaku-sha, Tokyo, Japan). Nutritional information on package labels, attached documents, tags, or web sites was used to calculate nutrient values of commercially available protein supplements 21) . Food groups not listed in the food composition table were substituted with similar food items to calculate nutrient values. Beverages containing no energy were excluded from calculations. Furthermore, ice cream, ice milk, lactic ice, soft-type ice cream, and sherbets were categorized as 'confectioneries', as well as sweet buns, such as those containing red bean paste or cream. Milks in commercial coffee-flavored milk or milk-tea were categorized as 'beverages', as well as fruit juices except for 100% fruit juices (categorized as 'fruits') and vegetable juices (categorized as 'other vegetables'). As 97.0% of protein supplements were whey protein-based products according to the 3-day 24hDRs, we classified protein supplements into the 'milks and dairy products' group.
Modified FFQg survey. The FFQg comprising 29 food group items and 10 cooking method items (FFQg ver 4.0) 10) was partially modified to include a question about protein supplement use. We explained to participants how to complete the self-administered FFQg before they answered the questions themselves. The participants recalled their habitual diets during the previous month, and indicated a portion size and weekly frequency for each food group and cooking method. Portion size was selected from the following three categories: 'smaller than a standard portion size', 'standard portion size', and 'larger than a standard portion size', corresponding, respectively, to a 0.5-fold, 1-fold, and 1.5-fold the standard portion size. The definition of the standard portion size was provided in words along with color illustrations. Frequency was provided by free description in a one-week unit; 'never' was defined as less than twice in one month (i.e., less than 0.5 times a week). Portion size categories (in grams) and the weighted food composition table for the FFQg were basically obtained from the Tokushima Prefectural Health and Nutrition Survey data 22) .
With regard to protein supplements, participants were asked to provide weekly frequency of use (times) by the following question: "How many times do you take protein supplement?" Then, one serving for protein supplement was illustrated with a large spoon. Total daily intake of protein including protein supplements (g/day) was calculated for each participant as follows: Total daily protein intake (g/day) = 20 (g) x weekly frequency / 7 (days) + protein intake (g/day) Since participants consumed protein supplements at a mean amount of 20.5 g per one serving according to 24hDRs, we defined one serving as 20 g in the FFQg.

Statistical analysis.
Statistical analyses were performed on data from 108 participants who completed all three 24hDRs and two FFQg surveys. All analyses were performed using IBM SPSS Statistics ver. 22 (IBM Japan, Ltd., Tokyo, Japan). Values were expressed as mean (SD), median (range), or number of people (%). The Wilcoxon signed-rank test was used to compare mean intakes of energy, nutrients, and food groups between the two methods. The % difference was calculated by (FFQg2 -24hDRs) / 24hDRs x 100 to assess the validity of the modified FFQ. Spearman's rank CCs between 24hDRs and the FFQg2 were also calculated for crude and energy-adjusted values. Energy adjustment was performed using a residual method after raw data were log-transformed. De-attenuated CCs were calculated using the ratio of inter-and intraindividual variance from 24hDRs based on the following formula 9,23,24) : de-attenuated CCx = en-CCx x 1 + λx / n, where en-CCx is the CC of energy-adjusted nutrient x, λx is the ratio of inter-individual variance to intra-individual variance for 24hDRs, and n is the number of surveyed recalls (n = 3 in the present study). Furthermore, to estimate the validity of categorization, we calculated the percentages of participants classified into "same", "same or adjacent", and "completely opposite" categories, by cross-classification in quintiles of energy-adjusted intake measured with 24hDRs or the FFQg 29, 13,24) .
The reproducibility of the modified FFQg was assessed by calculating % differences and Spearman's CCs between the FFQg1 and FFQg2 for energy-adjusted values. The % difference was calculated by (FFQg2 -FFQg1) / FFQg1 x 100. The Wilcoxon signed-rank test was used to estimate significant % differences between the FFQg1 and FFQg2.

Participant characteristics.
Participant characteristics are shown in Table 1. The mean age was 19.2 (1.1) years, mean body fat percentage was 17.4 (5.4) %, and 64.8% lived in dormitories. Participants were members of a baseball, rhythmic gymnastics, track and field, gymnastics, badminton, or power lifting team. Seventy-six percent of males belonged to a baseball team, and 54.8% and 37.1% of females belonged to rhythmic gymnastics and track and field teams, respectively. Mean weekly training frequency was 5.7 (1.2) days in total participants including males and females, at a mean daily training duration of 3.5 (1.5) hours. Daily training time tended to be longer in males than in females.

Protein intake from dietary supplements.
A total of 17 participants (16 males) were dietary supplement users, and 15 males (93.8% of males) consumed 10 different types of protein supplements (whey protein, 97.0%; casein, 1.9%; soy, 1.1%) according to 24hDRs. Mean serv-ing size of the various protein supplements was 20.5 g. Based on nutrition information, the nutrient composition (per 100 g) was calculated as follows: 395 kcal energy, 70.3 g protein, 4.2 g fat, 18.6 g carbohydrate, and 1.0 g salt equivalent. As 97.0% of protein supplements were whey protein-based products according to the 3-day 24hDRs, protein supplements were classified into the "milk and dairy products" group.
A total of 46 participants (33 males and 13 females) were protein supplement users according to the FFQg; the FFQg identified more protein supplement users compared to 24hDRs. We estimated daily frequency of use of protein supplements and their contribution to total protein intake between the FFQg and 24hDRs among the same protein supplement users (n = 15). The results revealed a mean frequency of use of 1.4 (1.7) times/day and daily protein supplement intake of 20.0 (23.7) g/day by the FFQg, and a mean frequency of use of 0.8 (0.7) times/day and daily protein supplement intake of 14.3 (14.5) g/day by 24hDRs. Both the frequency and amount of protein supplement intake were similar between the FFQg and 24hDRs (frequency, p = 0.139; amount, p = 0.256), and consequently, there was no significant difference in mean habitual daily protein intake between the FFQg (73.2 g) and 24hDRs (78.5 g) in the 15 participants.  17) .

Validation of the FFQg for assessing nutrient intake.
Compared to 24hDRs, the FFQg underestimated intake of energy and 9 nutrients in total participants, energy and 13 nutrients in males, and 4 nutrients in females, and overestimated intake of 6 nutrients in total participants, 4 nutrients in males, and energy and 8 nutrients in females ( Table 2). Median % differences in mean energy and 34 nutrients intake were -3.4% (range: -46.8% for vitamin C to 25.1% for β-carotene equivalent) in total participants, -10.5% (-57.5% for vitamin C to 46.9% for β-carotene equivalent) in males, and 4.2% (-33.9% for vitamin B6 to 31.1% for saturated fatty acids) in females.
The median of Spearman's rank CCs for crude intake of energy and 34 nutrients among total participants was 0.40 (range: 0.13 for vitamin C to 0.65 for zinc; Table 2), and that for energy-adjusted nutrient intake in total participants was 0.30 (0.03 for niacin to 0.52 for phosphorus). Medians of de-attenuated CCs for total participants, males, and females were 0.33 (0.03 for niacin to 0.55 for phosphorus), 0.21 (-0.21 for monounsaturated fatty acids to 0.49 for retinol), and 0.37 (-0.06 for niacin to 0.66 for water soluble fiber), respectively, with a de-attenuated CC of 0.3 or higher for energy and18 nutrients in total participants, energy and 8 nutrients in males and 24 nutrients in females.

Validation of the FFQg for assessing food group intake.
Compared to 24hDRs, the FFQg overestimated intake of 5 food groups in total participants, 6 food groups in males, and 3 food groups in females, and underestimated intake of 7 food groups in total participants, 6 food groups in males, and 5 food groups in females (Table 3). Median % differences in 18 food groups intake were -16.1% (-51.4% for beverages to 45.0% for green and yellow vegetables) in total participants, 2.1% (-55.6% for sugar to 100.8% for green and yellow vegetables) in males, and -13.1% (-52.8% in beverages to 29.8% for fats and oils) in females.
The median of Spearman's rank CCs for crude intake of 18 food groups was 0.38 (0.10 for nuts and seeds to 0.75 for cereals) in total participants, and that for energyadjusted intake was 0.32 (0.03 for nuts and seeds to 0.60 for pulses). The median of de-attenuated CCs was 0.34 (0.03 for nuts and seeds to 0.64 for pulses) for total participants, 0.32 (0.09 for seasonings and spices to 0.44 for eggs) for males, and 0.40 (-0.07 for nuts and seeds to 0.70 for pulses) for females, with a de-attenuated CC of 0.3 or higher for 13 food groups in total participants, 12 food groups in males, and 13 food groups in females.
Reproducibility of the FFQg for assessing nutrient and food group intake. Compared to the FFQg1, the FFQg2 in total participants underestimated intake of energy and 32 of 34 nutrients and 6 of 18 food groups (Table 5). Median % differences between the FFQg1 and FFQg2 in total participants were -9.4% (-19.6 for β-carotene equivalent to -5.1 for cholesterol) for energy and 34 nutrients, and -11.0% (-41.4 for nuts and seeds to 5.3 for fats and oils) for 18 food groups.
The median of Spearman's rank CCs for crude intake of energy and 34 nutrients between the FFQg1 and FFQg2 was 0.69 (0.58 for α-tocopherol to 0.80 for protein, zinc, retinol, or pantothenic acid; Table 5) in total participants. The median of Spearman's rank CCs for energy-adjusted intake was 0.61 (0.40 for α-tocopherol to 0.82 for zinc) in total participants, 0.44 (0.20 for α-tocopherol to 0.76 for zinc) in males, and 0.70 (0.47 for cholesterol to 0.87 for phosphorus) in females, respectively. The energy-adjusted CC was 0.5 or higher for 27 (77.1%) nutrients in total participants, 12 nutrients (34.3%) in males, and 30 nutrients (85.7%) in females, respectively.
The median of Spearman's rank CCs for energy-adjusted intake of 18 food groups between the FFQg1 and FFQg2 was 0.52 (0.40 for nuts and seeds, or seasonings and spices to 0.87 for confectioneries) in total participants, 0.41 (0.05 for sugar to 0.85 for confectioneries) in males, and 0.62 (0.37 for seasonings and spices to 0.79 for milk and dairy products) in females. The energyadjusted CC was 0.5 or higher for 11 food groups (61.1%) in total participants, 6 food groups (33.3%) in males, and 15 food groups (83.3%) in females.

Discussion
The present study was conducted to examine the validity and reproducibility of a modified version of the FFQg for ranking individuals to assess dietary intake among collegiate athletes using multiple statistical tests, and to explore the usefulness of the FFQg in estimating habitual nutrient and food group intake compared to the 24hDR method. Although the modified FFQg was found to underor overestimate intake of some nutrients and food groups, it provide reasonably valid measurements of habitual nutrient and food groups intake for ranking individuals in Japanese collegiate athletes with a good reproducibility.
In the present study, 15 males according to 24hDRs and 33 males and 13 females according to the FFQg were 136 JPFSM : Sasaki K, et al. De-attenuated correlation coefficient(CC)x = Observed CCx * SQRT (1+λx/n), where λx is the ratio of intra-to inter-individual variance for nutrient x, and n is the number of 24hDRs.
Observed CCx were based on energy-adjusted values expected for energy intake.    Table 5. Reproducibility of the FFQg; FFQgs was adminisitered at an average interval of one month FFQg; food frequency questionnaire based on food groups and cooking# Values are expressed as mean (SD) in total participants. FFQgs was adminisitered at twice during an average interval of one month; the FFQg1 was first administered, and after one month interval, the FFQg2 was administered. a: % difference in total participants was between the FFQg1 and FFQg2; (FFQg2-FFQg1)/FFQg1*100. b: Wilcoxon signed rank test. *: Statistically significant (p<0.05). c: Spearman's rank correlation coeffcients (CC) between the FFQg1 and the FFQg2. nutrients and for 13 food groups in total participants. The median CCs for nutrients or food groups obtained in this modified FFQg were comparable to previous studies 9,15,26) , suggesting that the differences in estimated accuracy between the FFQg and 24hDRs were within an acceptable range for many nutrients and food groups. Furthermore, cross-classification analysis in this study showed that median proportions of the "completely opposite category" between the FFQg2 and 24hDRs for energy-adjusted intake of nutrients and food groups were both within 5%. Sunami et al. 9) also reported that the median coincident ratio of "completely opposite category" among quintile categories of energy-adjusted intake for the FFQ2 and 24hDRs were 3% in males and 5% in females, respectively 9) . Our results were also consistent with these findings 9,11) suggesting that the modified FFQg, which comprises a small number of food items, might be suitable for the ranking of individuals with regard to intake of many nutrients and food groups. However, any application of this modified FFQg to the examination of diet-disease associations, such as studies of niacin and nuts and seeds, must carefully address the problem of classification 24) . Accurate classification indicates to what extent the dietary intake assessment method is able to rank participants correctly, this reflects agreement at the individual level 11) .
With respect to reproducibility, Pearson's or Spearman's CC was mostly used for the assessment 15,23) , and an energy-adjusted CC of 0.5 or higher was deemed desirable for estimating dietary intake using FFQs 27,28) . Watanabe et al. 14) studied the reproducibility of an FFQ consisting of 82 items in female adolescents at an interval of 1 month, and reported that the CC for energy was 0.58, the energyadjusted CCs for nutrients ranged from 0.34 (fat) to 0.70 (carbohydrate and calcium), and for food groups ranged from 0.13 (fat) to 0.86 (milk). In addition, median CCs of 0.65 and 0.63 for food groups in physically active males and females, respectively, have been reported 28) . In our present study, the median CC for energy intake was 0.78 in total participants (0.67 in males and 0.59 in females). The median CCs for energy-adjusted intake of 34 nutrients and 18 food groups in total patients were 0.61 and 0.52, and the CCs for energy-adjusted intake of 27 nutrients and 11 food groups were 0.5 or higher, although median CCs for energy-adjusted intake of nutrients and food groups were relatively lower in males than in females. Gender differences might be partly due to disparities in male/female interest in dietary habits, motivation, cognitive activity, or literacy level of the participants 15) . In examining reproducibility, it is very hard to estimate or determine "true" intra-individual changes in diet. In this study, we didn't estimate whether or not the FFQg1 survey was "true". Future studies are needed to clarify the FFQg1 as a "true" value. Although we would have liked to examine the effect of random variations, both differences between the inter-individual variation and "true" identified as protein supplement users. However, no significant differences were observed between the two methods in the frequency or amount of additional daily protein intake from protein supplements. In addition, de-attenuated Spearman's CCs for habitual protein intake were 0.46 in total participants, 0.30 in males, and 0.58 in females. In a previous study 9) , de-attenuated Spearman's CCs for protein intake were 0.26 and 0.20 in collegiate male and female athletes, respectively, with an FFQ comprising 138 food items that didn't include a question about protein supplements. Henry and Almstedt 25) examined the validity of an FFQ for estimating calcium intake among athletes, and reported that obtaining appropriate information about supplement use was important for accurately estimating calcium intake. In this context, the modified FFQg that includes a question about protein supplement use might provide a more appropriate means of assessing dietary protein intake.
This modified FFQg was found to under-or overestimate dietary intakes, especially for food groups compared to nutrients. Median % differences in mean energy and nutrients intake were -3.4% (range: -46.8 % for vitamin C to 25.1% for β-carotene equivalent), and those in food groups intake were -16.1% (-51.4% for beverages to 45.0% for green and yellow vegetables) in total participants. Accordingly, absolute intake cannot be ascertained from the modified FFQg, and intake from the modified FFQg cannot compare directly with that from the 24hDRs. Further investigation will be needed to examine the effects of measurement error on "true" intake.
Wakai 26) reviewed 21 validation studies of FFQs in Japan and reported that a median CC was 0.46 (range: 0.20-0.87) for energy. In the present study, de-attenuated CC for energy was 0.62 in total participants, 0.54 in males, and 0.18 in females. The low de-attenuated CC for energy in females might be explained by the fact that 92% of the females belonged to the two groups that wanted to lose weight, and there was little inter-individual variation for energy intake. Furthermore, the lower validity for energy intake among females might be partly attributable to a lower contribution to energy by individual foods in females than in males 24) . However, de-attenuated CCs for energy in total participants and males were comparable to that reported by Wakai (0.46) 26) .
Wakai 26) also reported that median CCs for nutrients ranged from 0.22 (n-6 polyunsaturated fatty acids) to 0.58 (calcium), and that a CC of 0.3 or higher indicated fair validity 15,26) . Sunami et al. 9) examined the validity of a 138item FFQ in Japanese collegiate athletes, and reported that the median de-attenuated CCs for nutrients was 0.30 in males and 0.32 in females, and for food groups was 0.32 in males and 0.34 in females. In our study, the median deattenuated CCs for nutrients was 0.33 (0.21 in males and 0.37 in females) and that for food groups was 0.34 (032 in males and 0.40 in females) in total participants, and the de-attenuated CCs were 0.3 or higher for energy and 18 they were available from the manufacturer or displayed on food labels. Finally, in this study of reproducibility, we didn't conduct three-day 24-DRs before FFQg1. Therefore, it is unknown whether or not the FFQg1 survey represented "true" intake compared to the FFQg2. Future studies are needed to clarify the FFQg1 as "true" intake.
In conclusion, the modified FFQg was found to underestimate or overestimate intake of some nutrients and food groups, suggesting that caution is required when estimating mean dietary intake in collegiate athletes. Nonetheless, the modified FFQg could be used to screen for low consumers in intervention programs to assess mean intake of nutrients or food groups, and was validated as a suitable method for ranking individuals, among Japanese collegiate athletes, with relatively good reproducibility.

Conflict of Interests
All authors declare no conflict of interests with respect to this research study and paper.
intra-individual changes of diet might have attenuated the reproducibility of the FFQg 23) . Nevertheless, these median CC observations strongly suggested that the modified FFQg might have good potential with regard to reproducibility, and be conceptually appropriate, given that the FFQg is designed to survey dietary intake during the month leading up to the survey.
This study has several limitations. First, we could not recruit a large number of athletes. There may have been a selection bias, as roughly 80% of male participants were members of a baseball team, whereas roughly 90% of female participants belonged to either a rhythmic gymnastics team or track and field team. Cade et al. 27) reported that the required sample size for a validation study is 50 or more. Therefore, it remains unclear whether similar results can be obtained for other athletes. Second, we performed 3-day 24hRDs as the reference method, but this method might not necessarily reflect "true" dietary intake for the previous month; however, there is no consensus regarding the number of days necessary to grasp one's dietary habits. Reviews of validity studies reported a range of survey periods spanning 1 to 7 days, and reproducibility ranged 1 week to 1 year 8,15,26,27) . Third, we did not take training, playing games, or seasonal variation during the survey period into consideration. Training quantity and quality differ by season, and such differences may affect daily dietary intake. The FFQg is designed to survey habitual dietary intake for the previous month, whereas 24hDRs focus on the most recent (24h) period. There is a possibility that habitual training frequency and the presence or absence of training on the previous day might have given rise to differences in "true" dietary intake between the two methods. In the present study, we surveyed 2 weekdays and 1 weekend day, and compared with the data previously reported for Japanese collegiate athletes 9) . Under our conditions, Bland-Altman plots did not show any serious systematic bias between the FFQg and 24hDRs methods over the range of mean intakes of energy in total participants, males, and females, respectively (data not shown). Fourth, it is not certain whether the newly-added protein supplement item affected the ranking validity of FFQ, because only 13.8% of the participants were dietary protein supplement users according to 24hDRs. From now on, we need to examine the effect of newly-added protein supplement items among more users. Fifth, the composition table for protein supplements, which was newly created, did not contain data on vitamins and minerals. Although some commercially available protein supplements display vitamin and mineral content, this is not legally required 21) . In other words, nutrition labeling for vitamins and minerals content is not provided for all protein supplements, and the nutrients displayed clearly differ across products. However, energy, protein, carbohydrate, and sodium (salt equivalent) are items for which nutrition labeling is required for all products 21) . Therefore, only these items were analyzed, as