ABSTRACT
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Objective
This study aimed to compare the nutrient intake, nutrition knowledge, and dietary habits of track-and-field (TF) and weight-class (WC) adolescent varsity athletes.
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Methods
This cross-sectional study included 144 adolescent athletes (TF group, 76; WC group, 68) aged 15 to 18 years at a physical education high school in Seoul, South Korea. Their nutrient intake, dietary quality, nutrition knowledge, dietary habits, and weight control behaviors were assessed using food records and questionnaires.
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Results
Regarding nutrient intake, the consumption of carbohydrates, including energy, was substantially lower than the sports nutrition recommendations in both groups. However, the overall diet quality, including the mean adequacy ratio and nutrient adequacy ratio for key micronutrients, was significantly higher in the WC group than in the TF group, with female WC athletes consuming significantly more energy than female TF athletes (P<0.05). For nutrition knowledge, both groups lacked overall sports nutrition understanding; however, the WC group scored significantly higher in dietary fat knowledge (P<0.05). As for dietary habits, despite having better meal quality, harmful weight control behaviors, including skipping breakfast (P<0.001) and fasting (P<0.05), were significantly more common in the WC group than in the TF group.
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Conclusion
The WC group, while showing better meal quality, practiced unhealthier dietary behaviors and weight control methods more often than the TF group. Additionally, both groups exhibited insufficient energy intake and lacked overall nutrition knowledge. Therefore, tailored sports nutrition education is urgently needed among South Korean adolescent athletes.
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Keywords: Sports nutritional sciences; Adolescent; Athletes; Feeding behavior; Nutritional status
INTRODUCTION
Background
Optimal nutrient intake among athletes improves their athletic abilities by reducing fatigue and the risk of illness and injury, thereby fostering enhanced athletic performance and rapid recovery from high-intensity training [
1]. Sometimes, a “low weight” or “low fat/muscle ratio (leanness)” is a prerequisite for excellent sports performance [
2]. Despite generally requiring a higher calorie intake than nonathletes, many athletes limit energy intake to lose weight [
3]. In many sports, athletes need to change body shape and composition to improve athletic performance and qualify for a specific weight-class (WC); however, unhealthy weight loss on a diet that does not meet nutrition recommendations may be a problem for athletes [
3]. Insufficient energy intake may lead to deficiencies in nutrients, including carbohydrates, iron, calcium, vitamin E, vitamin D, zinc, and magnesium [
4]. According to several studies, the diets of adolescent athletes often do not meet the sports nutrition recommendation (SNR) and general nutrition recommendation [
5-
7].
Numerous adolescents have low levels of nutrition knowledge [
8]. Good nutrition knowledge is one of the determinants of dietary behavior improvement and contributes to strengthening the skills and abilities necessary to resist the environmental impact of an unbalanced and unhealthy diet [
8]. By improving the nutrition knowledge of adolescent athletes, they may learn good eating habits and rectify their erroneous beliefs about food [
9]. A better understanding of nutrition during adolescence may affect one’s lifelong relationship with diet in terms of eating habits, exercise, and body image relevance [
4].
WC athletes in boxing, judo, taekwondo, wrestling, and weightlifting must reach a certain weight before a competition. This prerequisite may lead them to follow extreme diets, develop eating disorders, and have impaired health and performance [
10]. Meanwhile, track-and-field (TF) athletes participating in long-distance running, walking, and jumping sports may pursue a slimmer, lighter body shape, potentially leading to improper energy intake and eating disorders [
7].
Both WC and TF athletes require optimal nutrition for their health and athletic performance. While numerous studies have investigated and compared nutrient intake, nutrition knowledge, and dietary habits among athletes participating in team sports such as football, basketball, baseball, and volleyball, only a few focused on individual sports such as the TF and WC sports.
Therefore, this study aimed to compare and analyze the nutrient intake, nutrition knowledge, and dietary habits between TF and WC adolescent athletes. The findings of this study may guide them in properly managing their nutrition.
METHODS
Study design and ethics statement
This cross-sectional study was conducted between April and May 2018, with approval from the Institutional Review Board of Kyung Hee University (No. KHISIRB-18-006). All participants provided written informed consent prior to participation. In addition, this study conformed to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines.
Participants
This study enrolled 401 adolescent athletes from a physical education high school in Seoul, South Korea. Eligible participants were then divided into TF and WC groups.
General and anthropometric characteristics of the participants
Several factors potentially affecting participants’ training (sex, age, grade level, athletic career, weekly exercise time and frequency, sleep time, and dietary supplement use) were investigated. Nutrition class participation, interest in nutrition topics, nutrition information sources, and practical application of nutrition knowledge were also assessed. Furthermore, data about the participants’ body composition, including height, body weight, lean body mass (LBM), body mass index (BMI), fat weight, and percent body fat, were collected using an InBody 720 (Biospace Co.).
Assessment of sports nutrition knowledge
Participants’ nutrition knowledge was measured using the questionnaire Nutrition Knowledge Questionnaire for Athletes (NKQA) developed by Furber et al. [
11] and classified into knowledge about carbohydrate, protein, fat, general nutrition, fluid, and sports nutrition. This questionnaire has 23 items about carbohydrate content in food, blood sugar index, and protein-saving effect specifically for carbohydrate knowledge assessment. Protein knowledge was assessed using 18 items about protein content in food, protein use by the body, and recommended protein intake. Regarding fat knowledge, 23 items about fat, saturated fat, and cholesterol were used. The general nutrition knowledge survey comprises 25 items, including macronutrient calories; food sources of vitamin C, iron, and calcium; and antioxidant nutrient function. For fluid knowledge, the questionnaire provides six items, including fluid intake before, during, and after exercise; dehydration; and sports drinks. Moreover, 14 questions, which include food intake before and after exercise and sports supplements, were used for sports nutrition knowledge. In total, the survey questionnaire has 109 questions, and each may be answered by “yes,” “no,” or “not sure.” The score for each question is “1” for a correct response and “0” for an incorrect response or an “unsure” response. Thus, the total score ranges from 0 to 109.
To assess dietary habits, physical activity, body image self-perception, and weight control, we used the Korean Youth Risk Behavior Survey, which contains related questions distributed across 15 domains and 107 indices [
12]. Regarding dietary habits, 16 questions are used to assess skipping breakfast, intake of fruit/vegetable/milk/carbonated beverages/high-caffeinated beverages/sweet drinks/instant noodles/unhealthy snacks, and use of convenience store/supermarket/store. Regarding health status, one item is used to assess self-perceived body image in terms of “very slim,” “slim,” “normal,” “fat,” and “very fat.” Moreover, the weight control survey has four items: height, weight, effort, and weight control method. The question for weight control effort is “Have you ever tried to control your weight during the last 30 days?” Meanwhile, only those who responded to the questions related to weight loss, weight gain, and weight maintenance effort could answer the question “How did you try to control your weight for the last 30 days?”
The school cafeteria provided group meals to all participants. A well-trained clinical dietitian instructed the participants on how to record their meals. After recording their food intake for 3 days, including the weekend, their answers were confirmed by a direct interview method. The participants’ nutritional intake was analyzed using Computer Aided Nutritional Analysis Program, ver 5.0 (CAN-Pro, The Korean Nutrition Society).
To assess the appropriateness of nutrient intake, we calculated the nutrient adequacy ratio (NAR) and the mean adequacy ratio (MAR) of 12 nutrients (protein, vitamin A, vitamin C, thiamine, riboflavin, niacin, vitamin B
6, folic acid, calcium, phosphorus, iron, and zinc). An NAR value of 1.0 or more was assumed to be 1.0 [
13]. NAR was calculated using the following equation: NAR=individual daily nutrient intake/recommended intake of each nutrient. For energy, NAR was calculated separately to evaluate overall energy adequacy. In the MAR calculation, energy was not included. MAR was calculated as follows: MAR=sum of NARs for each nutrient/number of different nutrients.
All statistical data were analyzed using IBM SPSS ver. 23.0 (IBM Corp.). Continuous variables, expressed as mean±standard deviation, were compared between TF and WC groups using Student t-test. To compare nutrient intakes between the two groups, we conducted analysis of covariance (ANCOVA) after adjusting for sex, age, BMI, athletic career, and weekly training duration. The significance of categorical variables was confirmed using a chi-squared test. Energy intake under-reporters were identified by estimating each participant’s basal metabolic rate using the Schofield equation for adolescents and by deriving the estimated energy requirement (EER) using an activity factor reflecting the participants’ physical activity level. Subsequently, we calculated the ratio of reported energy intake to EER and classified participants with a ratio below 0.70 as under-reporters. Additionally, a sensitivity analysis was conducted by excluding these under-reporters to evaluate whether potential energy under-reporting influenced the results [
14]. A P-value less than 0.05 was considered statistically significant.
RESULTS
General and anthropometric characteristics
This study included 144 out of 401 adolescent athletes, aged 15 to 18 years. As shown in
Table 1, 76 belonged to the TF group and 68 to the WC group. The TF group (64.5%) included slightly more males than the WC group (54.4%). The mean age was not significantly different between such groups. Body weight, BMI, and LBM were significantly higher in the WC group than in the TF group (P<0.05), although no significant differences were noted in height and body fat percentage. Underweight was observed in 2.6% of athletes in the TF group and none in the WC group (P<0.01). Conversely, overweight was significantly more common in the WC group than in the TF group (38.2% vs. 14.5%) (P<0.01). While the length of athletic careers showed no significant difference, weekly training frequency and daily training volume were significantly higher in the WC group than in the TF group (P<0.05). Meanwhile, no significant difference was noted in the mean daily sleeping time.
Table S1 presents the specific distribution of sports types among the TF and WC groups by sex. The TF group competed in five sports (track sprints, track distance, jump, throw, and pentathlon), whereas the WC group competed in four sports (weightlifting, taekwondo, judo, and wrestling).
Dietary intakes of the participants
Table 2 presents the adolescent athletes’ average daily diet intakes. The macronutrient intakes of the TF and WC groups were compared with the SNR [
5] or the Korean Dietary Reference Intake 2015 (KDRI) [
15]. The total daily energy intake of the TF group did not significantly differ from that of the WC group. Energy intake showed no significant difference between male participants in the TF and WC groups. As for females, energy intake was significantly higher in the WC group than in the TF group (P<0.001). However, the energy consumption of both sexes in the TF and WC groups was significantly less than the SNR. Moreover, the daily intake of carbohydrate, protein, and fat did not significantly differ between the two groups. The percentages of carbohydrate, protein, and fat intake were 54.6%, 15.8%, and 29.1% in the TF group and 54.8%, 15.4%, and 28.7% in the WC group, both of which were appropriate according to the KDRI criteria. The daily intake of dietary fiber was significantly higher in the WC group (P<0.001). Likewise, within the total daily energy intake, the proportion of saturated fat intake was significantly higher in the WC group than in the TF group (P<0.001).
Among vitamins, vitamin A intake was significantly higher in the TF group (P<0.001), whereas vitamin E and vitamin D intakes were significantly higher in the WC group (P<0.001). The WC group also demonstrated significantly higher intakes of niacin and folate (P<0.05). In contrast, thiamin, riboflavin, vitamin B12, and vitamin C intakes exhibited no significant difference between such groups. Both groups consumed enough iron to meet the KDRI, demonstrating no significant between-group difference. Furthermore, calcium consumption in both groups was less than the KDRI. In the TF group, micronutrients such as calcium, vitamin D, vitamin C, vitamin A, potassium, magnesium, manganese, and copper were consumed at levels below the KDRI. In the WC group, the intake levels of calcium, vitamin C, vitamin A, potassium, magnesium, manganese, and copper were also below the KDRI level.
Regarding the influence of potential energy intake under-reporting, our sensitivity analysis excluded 64 participants identified as under-reporters. As presented in
Table S2, this analysis revealed that macronutrient intakes showed patterns similar to the primary results. While the statistical significance observed in niacin, calcium, and phosphorus intake was not maintained, likely resulting from the reduced sample size, vitamin C intake significantly differed between the groups.
Tables S3 and
S4 enumerate the sex-stratified results of nutrient intake between the two groups.
Table 3 shows the MAR and NAR of each nutrient consumed by the TF and WC groups. The TF group had a significantly lower MAR than the WC group (P<0.01). Among the nutrient intakes investigated, protein had a NAR closest to the recommended intake, with no significant difference between the two groups. The NARs of energy, vitamin C, riboflavin, niacin, vitamin B
6, folic acid, phosphorus, iron, and zinc were significantly higher in the WC group than in the TF group (P<0.05). The NARs for vitamin A, vitamin C, and calcium for both groups were too low (<73%, <61%, and <66%, respectively). This discrepancy, the WC group showing a higher vitamin C NAR despite having a lower absolute vitamin C intake than the TF group (
Table 2), may be attributed to the higher proportion of males in the TF group, who have a higher recommended nutrient intake.
Nutrition knowledge of the participants
Table 4 lists the number (0–109 points) and percentage (0%–100%) of participants’ correct answers on the NKQA. The mean score of total nutrition knowledge was 27.9±14.0 points for the TF group and 29.4±15.7 points for the WC group, showing no significant between-group difference. Among the six subsections (carbohydrate, protein, fat, general nutrition, fluid, and sports nutrition) of the nutritional knowledge assessment, knowledge about fat was significantly higher in the WC group than in the TF group (P<0.05). Regarding carbohydrate knowledge, the two groups obtained similar percentages of participants achieving correct answers. While the percentage of participants with correct answers for protein knowledge was slightly higher in the TF group than in the WC group, the between-group difference remained nonsignificant. The scores for general nutrition, fluid, and sports nutrition were similar, with no significant differences noted between the groups. The TF group had the highest rate of correct answers regarding protein, followed by fluid, general nutrition, carbohydrate, sports nutrition, and fat. Meanwhile, the WC group had the highest rate of correct answers in the order of protein, fluid, fat, general nutrition, carbohydrate, and sports nutrition.
Dietary habits of the participants
Table 5 presents the adolescent athletes’ healthy and unhealthy dietary habits. In the healthy dietary habit assessment, the WC group had a significantly lower proportion of participants who ate breakfast daily than the TF group (P<0.001). The proportion of athletes consuming fruits daily was low in both groups, with only 22.4% in the TF group and 19.1% in the WC group. The TF group had a higher proportion of participants who consumed vegetables more than thrice a day than the WC group (23.7% vs. 13.2%). Both fruit and vegetables intakes did not show significant differences between such groups. In addition, only 15.8% of the TF group and 11.8% of the WC group responded that they drank milk more than twice a day.
With regard to unhealthy dietary habits, the WC group had significantly more participants drinking carbonated drinks beyond thrice a week than the TF group (P<0.001). Only 2.6% of the TF group and 5.9% of the WC group drank high-caffeine beverages more than thrice per week, demonstrating no significant between-group difference. The proportion of participants consuming sugar-sweetened beverages at least thrice a week was higher in the TF group than in the WC group (64.5% vs. 55.9%), although the difference was not significant. Regarding the intake of fast food, instant noodles, and unhealthy snacks more than thrice per week, no significant differences were observed between the TF and WC groups.
Weight control behavior and body image self-perception of the participants
Table 6 summarizes the results of the weight control behavior questionnaire. We found no significant differences between the TF and WC groups in terms of effort to control weight (no, weight loss, weight gain, and weight maintenance).
Participants controlled their weight through methods such as regular exercise, meal size reduction, fasting, oriental medicine intake, one-food diet, diet food consumption, and diet pill administration. The most commonly used method for weight control was regular exercise (TF, 93.5%; WC, 93.3%), but no significant difference was noted between the two groups. The next most common method was meal size reduction in both groups. The proportion of participants who applied fasting was significantly higher in the WC group than in the TF group (31.1% vs. 8.7%).
Table S5 compares the measured weight status with body image self-perception. The percentage of agreement of body image with weight status did not significantly differ between the TF and WC groups (61.8% vs. 60.3%). In the TF group, 25.0% perceived their weight to be lower than the BMI standard, whereas 13.1% perceived it to be higher. In the WC group, 27.9% perceived their weight to be lower than the BMI standard, whereas 11.8% perceived it to be higher. Female participants had a lower agreement on weight status than male participants. Cohen’s kappa indicated a moderate agreement in males and a slight agreement in females. Compared with the BMI standard, 30.2% of males perceived their weight to be lower, while 2.3% perceived it to be higher. Conversely, 27.6% of females perceived their weight to be higher than the BMI standard, while 20.7% perceived it to be lower.
Interesting topics and information sources about nutrition among participants
Table S6 lists the results of the participants’ interest in nutrition and the sources of nutrition information. Only 26.3% of the TF group and 38.2% of the WC group had received nutrition education, showing no significant between-group difference. Regarding the topics of interest in nutrition, the TF group was most interested in quick recovery after training (63.2%) as compared with the WC group (44.1%), showing a significant difference (P<0.05). The WC group was most interested in body fat reduction. The percentage of respondents who expressed interest in increasing muscle mass was higher in the WC group than in the TF group. Conversely, the degree of interest in nutrition supplements was higher in the TF group. Nonetheless, the degrees of interest in body fat reduction, muscle mass increase, and nutrition supplementation were not significantly different between the two groups. The degree of interest in good food habits was relatively low in both groups compared with body fat loss, quick recovery, and muscle buildup. Overall, the TF group was most interested in quick recovery, followed by fat loss, muscle buildup, and nutrition supplements, while the WC group was most interested in fat loss, followed by muscle buildup, quick recovery, and nutrition supplements. The least interesting topic for both groups was glycogen loading.
The most common nutrition information sources were coaches/trainers, followed by friends and mass media. In addition, the participants obtained nutrition information from their family, dietitian/physician, and magazines. None of these information sources significantly differed between the TF and WC groups.
Moreover, the practical application of nutrition knowledge showed no significant difference between the two groups. Only 25.0% of the TF group and 14.7% of the WC group responded that they applied nutrition to training. Those who did not apply nutrition to training chose the “Just so” or “Not at all” response.
DISCUSSION
This study revealed inadequate nutrition intake and the need for nutrition education among South Korean adolescent athletes involved in TF and WC sports. This study may be the first to compare and analyze the nutrient intake, nutrition knowledge, and dietary habits of TF and WC adolescent athletes. According to self-reported food records, the TF and WC groups consumed energy and carbohydrates below the SNR. While the WC group had a significantly higher diet quality of micronutrients than the TF group, calcium, vitamin C, and magnesium intake was less than the KDRI in both groups. Both groups also had low scores in all items of the nutrition knowledge questionnaire, likely resulting from the lack of opportunities for nutrition education.
TF sports involve athletic performance based on physical skills such as running, jumping, and throwing. They require speed, strength, power, and endurance; thus, a healthy nutrition strategy is essential to overcome the stresses of high-intensity training [
16]. For WC sports, such as taekwondo, judo, wrestling, boxing, weightlifting, and bodybuilding, athletes must reach a certain weight to qualify for a competition. As a result, many of the WC athletes resort to extreme weight control methods, including fasting, eating disorders, and unhealthy diets; therefore, establishing healthy eating habits is crucial among these athletes [
10].
Nutrition promotes optimal growth and development, making it an integral part of sporting competition for adolescent athletes [
4]. Nutrition knowledge is essential for adolescent athletes because they need to know what, when, and how much to eat before, during, and after an exercise to achieve optimal performance [
17]; however, no significant differences were identified by sex in this study. Most of the participants lacked sports nutrition knowledge, as well as basic nutrition knowledge, including nutrient sources and macronutrient and micronutrient functions. Their lack of nutrition knowledge may be explained by the findings that they did not receive proper nutrition education and opportunities to meet nutrition experts. Athletes tend to seek nutrition information from coaches, colleagues, the Internet, and family members rather than nutrition experts. With high nutrition knowledge, athletes can increase their quality of meals [
18]. Therefore, providing effective nutrition education programs for adolescents may help improve the quality of their food intake [
18].
This study was conducted within the “in season” period, during which adequate nutrient intake of athletes is crucial for energy supplementation and recovery before, during, and after competition and training. However, the daily energy intake of both participant groups was significantly lower than the SNR. The lack of energy intake may be explained by the fact that half of the TF group and only 1/5 of the WC group ate breakfast each day. Although athletes may skip breakfast to lose weight, weight reduction can become more difficult because they eat most of their calories at dinner [
19]. Erdman et al. [
20] found that 98% of elite Canadian athletes consumed breakfast, whereas only 23% of nonelite athletes did so. Breakfast is crucial for athletes because it replenishes the glycogen depleted overnight and the carbohydrates needed for exercise [
20]. Thus, athletes skipping breakfast may not meet their total required daily energy intake, as well as the carbohydrate intake, required to compete [
21]. Moreover, approximately 40% of the TF group and 50% of the WC group are currently losing weight, likely causing their energy shortages. Energy intake below the recommended level should be avoided because it negatively affects not only the healthy growth and development of adolescent athletes but also their athletic performance and health [
22]. For example, a persistent energy deficit lowers athletes’ muscle strength, glycogen storage, concentration, coordination, and training response, thereby negatively affecting their athletic performance [
1]. It may also lead to adverse health consequences, including eating disorders, dehydration, anorexia, and bulimia, as a result of hunger, fatigue, and stress [
22]. Consuming fruit, bread, milk, and cheese between meals may be helpful for adolescent athletes to optimize their daily energy intake and nutrient timing.
According to the SNR for adolescent athletes, the recommended carbohydrate intake is 50% of the daily intake or 3–8 g per kg body weight, depending on the intensity of exercise [
5]. A previous study suggested that the carbohydrate intake of adolescent TF athletes should be more than 55% of the total energy intake or equal to 6–10 g of carbohydrates per kg body weight [
23]. In our study, the mean carbohydrate intake per body weight was similar between groups but lower than the SNR. Considering the long daily exercise time of both groups, they need to increase their intake of carbohydrates, the primary energy source of exercise; guidelines for carbohydrate intake are generally 5–7 g/kg for moderate-intensity training (<1 hour, low-intensity exercise) and 7–10 g/kg for increased high-intensity training (2–5 hours, endurance training) [
24]. With carbohydrate deficiency, athletes may experience glycogen depletion, fatigue accumulation, slow recovery from training, and poor athletic performance [
24]. Adolescents having difficulty obtaining sufficient carbohydrates from their meals may additionally drink juice or sports drinks to help increase their carbohydrate intake.
The protein intake of adolescent athletes is often reported to be 1.2–1.6 g/kg/day [
25]. The mean protein intake per body weight (1.3±0.4 g/kg/day) was comparable between the TF and WC groups and is similar to a previous report, indicating that the protein intake of our participants is appropriate. In general, nonvegetarian athletes easily exceed protein recommendations [
6]. Our participants consumed protein that is approximately 1.5 times the KDRI. In the US, UK, and Australia, adolescent athletes consumed protein that is 2 to 3 times the recommendation of each country [
26]. Among elite adolescent athletes, the protein intake is 1.5–2.0 g per body weight, particularly for WC sports requiring body weight control and energy intake restriction in preparation for a competition [
6]. Nonetheless, coaches should always be concerned because vegetarian adolescent athletes who strictly limit their energy intake may have a low protein intake [
6].
The dietary fat consumption of the TF and WC groups was approximately 30% of their total energy intake, in line with the recommended range of 15% to 30% [
15]. For athletes, dietary fat intake above 30% is not recommended because it may contribute to excessive weight gain [
27]. If athletes are trying to meet their energy needs, healthy fats may be the most energy-dense option [
27].
The busy daily schedule of elite adolescent athletes may lead to an imbalanced diet, increasing the risk of micronutrient deficiency [
5]. The WC group met the recommended nutrient intake of vitamin D, whereas the TF group did not. In a cross-sectional study of South Korean adolescents, vitamin D deficiency was very common, with 73.3% of them reporting such deficiency [
28]. WC athletes trying to lose body weight are at increased risk of deficiency in micronutrients, including vitamin D, calcium, and iron [
10]. Calcium and vitamin D are essential for maintaining muscle contraction, bone health, and other physical performance, but most adolescent athletes do not consume adequate amounts [
1]. In this study, low calcium intake of the TF and WC groups may be associated with low dairy intake. Calcium deficiency has been reported in WC and TF sports [
2], and athletes trying to lose weight might experience decreased bone mineral density and impaired exercise performance [
29]. A common nutritional goal among athletes is the moderate and well-timed consumption of nutritionally balanced meals; this goal improves their health and exercise performance and promotes recovery [
30]. Despite the importance of diversity in food choice, both the TF and WC groups had an insufficient vitamin C intake, which is often associated with inadequate consumption of fruit and vegetables.
Many sports require low body weight and a low fat–muscle ratio for excellent performance. Therefore, adolescent athletes may try to lose weight to increase their physical strength, reach a certain WC, and develop a slim and lean body for sporting performance [
3]. Although nutritional optimization leads to increased self-confidence and peak performance among athletes [
31], approximately 40% of the TF group and nearly half of the WC group were currently losing weight, making their daily energy intake significantly below the SNR. The WC group showed a significantly higher rate of fasting than the TF group. Various studies have reported high fasting rates among high school and college wrestlers [
32]. Fasting in athletes not only can impair mood and concentration but can also have adverse effects on cardiovascular function, heat regulation, kidney function, and electrolyte balance [
33]. For adolescent athletes, primarily females, chronic energy deficiencies may delay puberty and lead to irregular menstrual periods, poor bone health, and increased injury risk, which, in turn, negatively affect exercise performance [
4]. When trying to lose weight or diet, adolescent athletes lacking nutrition knowledge need to consult a dietitian or physician. However, only 14.5% of the TF group and 23.5% of the WC group received nutritional information from a dietitian or physician. Of note, the TF (39.5%) and WC (32.4%) groups were acquiring nutritional information from colleagues who also lack nutrition knowledge. Rather than nutrition experts, nutrition information sources for athletes are often coaches, who may not be qualified to provide such information [
34].
In interesting topics in nutrition, the TF group was most interested in quick recovery, followed by body fat loss, muscle buildup, and nutrition supplements. Similarly, the WC group was most interested in body fat loss, followed by muscle buildup, quick recovery, and nutrition supplements. Regarding nutrition for quick recovery from training, athletes should focus primarily on carbohydrates, protein, and fluids [
35]. Despite high-intensity training for more than 4.5 hours a day, the TF group had a lower-than-recommended carbohydrate intake, which could have delayed their recovery from training. The WC group spent a longer time in training (>5.0 hours daily compared with 4.5 hours daily) than the TF group, likely because of a weight loss exercise in addition to power, strength, endurance, and technical training. As shown by interest in muscle buildup and nutrition supplements in both groups, adolescent athletes consider taking nutritional supplements for various reasons, including increased muscle mass, power and endurance, and optimized body composition [
4]. However, the potential long-term effects of nutrition supplements have not been investigated among younger ones because of ethical considerations [
5]. With regard to sports nutrition knowledge, both the TF and WC groups lacked such knowledge, showing an average of three correct answers out of 14 questions. Sports nutrition education is important among adolescent athletes to understand nutrition supplements accurately. Nutrition experts must also continue to provide nutrition guidelines for quick recovery, body fat loss, and muscle buildup, which are of primary interest to adolescent athletes.
This study has several strengths. It is a relatively large study compared with other studies involving South Korean adolescent athletes. Other studies have only fewer than 50 participants. To our knowledge, the present study may be the first to compare the nutrient intake, nutrition knowledge, and dietary habits between South Korean adolescent athletes involved in TF and WC sports. Although this study did not evaluate the biochemical factors of adolescent athletes, the results may help improve adolescent athletes’ nutrition knowledge, nutrient intake, dietary habits, health status, and athletic performance in future studies. Concerning the results, further research on the nutrition-related factors for athletes in different sports types is warranted.
Limitations
This study also has several limitations that need to be considered. For example, it relied on participants’ self-reported data on food intake, dietary habits, and nutrition knowledge, which are subject to potential recall and social desirability bias. Additionally, given that data were collected in 2018, the findings may not fully reflect the current nutritional status and dietary behaviors of South Korean adolescent athletes. However, considering the limited availability of data on nutrient intake, nutrition knowledge, and dietary habits in this population, this study provides valuable baseline information for future research. Furthermore, this study only recruited athletes from a single high school; thus, the results may not be generalizable to a broader population of South Korean adolescent athletes in TF and WC sports. Finally, nutrient adequacy was evaluated using the 2015 KDRIs despite the recent release of the 2025 KDRIs. The dietary data used in this study were collected in 2018. By utilizing the guidelines that were tailored to adolescents at that specific time, the interpretation of our results remains valid and contextually appropriate. Furthermore, the reference values for energy and major nutrients remain largely consistent between the two guidelines; thus, applying the updated KDRIs would not alter our fundamental conclusion regarding widespread nutrient inadequacy.
Conclusion
The TF group had better dietary habits than the WC group but had lower energy and micronutrient intake, diet quality, and fat nutrition knowledge. Despite having more training than the TF group, the WC group more often reported that they attempted to lose weight through unhealthy ways, including fasting and reducing meal size to less than the SNR. Nutrient intake, nutrition knowledge, and dietary habits differed between these two groups; however, both groups demonstrated nutrient intake that was considerably lower than the SNR, had no nutritional education experience and nutrition knowledge, and showed similar interests in nutrition. Considering the nutrition-related problems observed in this study, individualized nutrition intervention with continuous sports nutrition education is necessary for the health and athletic performance of adolescents.
NOTES
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Author Contributions
Conceptualization: ISL, SL. Formal analysis: SL. Investigation: SL. Methodology: SL. Supervision: ISL. Writing - original draft: SL, PK. Writing - review & editing: all authors. All authors read and approved the final manuscript.
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Conflict of Interest
None.
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Funding
None.
-
Acknowledgments
We thank all the adolescent athletes at a physical education high school in Seoul, Republic of Korea, who participated in this study.
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Data availability
The datasets are not publicly available but are available from the corresponding author upon reasonable request.
Supplementary materials
Table 1.General and anthropometric characteristics of the study participants
Table 1.
|
Variable |
All (n=144) |
TF group (n=76) |
WC group (n=68) |
|
Age (yr) |
16.7±1.0 |
16.6±1.0 |
16.7±1.0 |
|
Sex |
|
|
|
|
Male |
86 (59.7) |
49 (64.5) |
37 (54.4) |
|
Female |
58 (40.3) |
27 (35.5) |
31 (45.6) |
|
Anthropometric |
|
|
|
|
Height (cm) |
170.3±7.9 |
170.6±7.6 |
169.9±8.2 |
|
Weight (kg) |
69.1±15.6 |
66.5±16.1 |
71.0±16.7*
|
|
BMI (kg/m2) |
23.7±4.5 |
22.6±4.0 |
24.8±4.7**
|
|
Weight statusa)
|
|
|
|
|
Underweight |
2 (1.4) |
2 (2.6) |
0 (0.0)**
|
|
Normal weight |
105 (72.9) |
63 (82.9) |
42 (61.8)**
|
|
Overweight/obesity |
37 (25.7) |
11 (14.5) |
26 (38.2)**
|
|
LBM (kg) |
55.8±11.1 |
54.0±10.7 |
57.7±11.4*
|
|
Body fat (%) |
18.6±5.6 |
18.0±5.2 |
19.2±6.0 |
|
Fat mass (kg) |
13.3±7.0 |
12.5±6.6 |
14.3±7.4 |
|
Training-related |
|
|
|
|
Athletic career (yr) |
5.2±1.9 |
5.2±2.1 |
5.1±1.7 |
|
Training frequency (days/wk) |
6.2±0.5 |
6.1±0.4 |
6.3±0.6*
|
|
Training volume (h/day) |
4.7±1.1 |
4.5±1.3 |
5.0±0.8*
|
|
Sleeping time (h/day) |
6.9±0.6 |
6.9±0.6 |
6.7±0.8 |
Table 2.Dietary intakes of the TF and WC groups
Table 2.
|
Variable |
SNRa) or KDRIb)
|
TF group (n=76) |
WC group (n=68) |
|
Male |
Female |
|
Energy |
|
|
|
|
|
Total (kcal/day) |
3,650–3,925a)
|
2,875a)
|
2,143.2±669.8 |
2,260.6±514.2 |
|
Sex |
|
|
|
|
|
Male |
- |
- |
2,486.6±495.2 |
2,291.6±569.1 |
|
Female |
- |
- |
1,520.1±464.8 |
2,223.6±446.5***
|
|
Per kg of body weight (g/kg/day) |
- |
- |
33.0±11.0 |
32.7±9.6 |
|
Carbohydrate |
|
|
|
|
|
Total (g/day) |
- |
- |
292.1±96.1 |
305.7±61.6 |
|
Per kg of body weight (g/kg/day) |
5–10a)
|
5–10a)
|
4.5±1.6 |
4.4±1.3 |
|
% of total energy intake |
>50a)
|
>50a)
|
54.6±4.6 |
54.8±4.9 |
|
Fiber (g/day) |
25b)
|
20b)
|
20.3±7.7 |
25.7±8.3***
|
|
Protein |
|
|
|
|
|
Total (g/day) |
65b)
|
50b)
|
84.3±25.1 |
87.8±22.2 |
|
Per kg of body weight (g/kg/day) |
1.2–1.8a)
|
1.2–1.8a)
|
1.3±0.4 |
1.3±0.4 |
|
% of total energy intake |
15–20a)
|
15–20a)
|
15.8±1.8 |
15.4±1.3 |
|
Fat |
|
|
|
|
|
Total (g/day) |
- |
- |
69.3±23.9 |
73.4±23.7 |
|
Per kg of body weight (g/kg/day) |
- |
- |
1.1±0.4 |
1.1±0.4 |
|
% of total energy intake |
15–30a)
|
15–30a)
|
29.1±4.2 |
28.7±4.4 |
|
Saturated fat (g/day) |
- |
- |
11.2±5.3 |
16.1±4.9***
|
|
Saturated fat (% of total energy intake) |
<8b)
|
<8b)
|
4.7±1.6 |
6.4±1.3***
|
|
Vitamins |
|
|
|
|
|
Vitamin A (μg/day) |
850b)
|
600b)
|
672.8±313.2 |
477.1±118.1***
|
|
Vitamin E (mg/day) |
11b)
|
11b)
|
17.8±6.3 |
22.6±7.1***
|
|
Vitamin D (μg/day)c)
|
10b)
|
10b)
|
4.6±3.3 |
10.1±3.9***
|
|
Thiamin (mg/day) |
1.3b)
|
1.2b)
|
2.0±0.8 |
2.2±0.6 |
|
Riboflavin (mg/day) |
1.7b)
|
1.2b)
|
1.7±1.0 |
1.7±0.9 |
|
Niacin (mg/day) |
17b)
|
14b)
|
14.6±4.9 |
16.2±4.6*
|
|
Folate (μg/day) |
400b)
|
400b)
|
410.0±140.6 |
454.0±120.1*
|
|
Vitamin B12 (μg/day) |
2.7b)
|
2.4b)
|
7.1±3.1 |
7.5±3.5 |
|
Vitamin C (mg/day) |
105b)
|
95b)
|
60.0±27.3 |
53.3±13.7 |
|
Minerals |
|
|
|
|
|
Calcium (mg/day) |
900b)
|
800b)
|
526.6±184.8 |
561.0±204.3 |
|
Phosphorus (mg/day) |
1,200b)
|
1,200b)
|
1,226.8±334.6 |
1,337.3±321.6*
|
|
Sodium (mg/day)c)
|
1,500b)
|
1,500b)
|
3,768.1±1144.1 |
3,921.9±962.2 |
|
Potassium (mg/day)c)
|
3,500b)
|
3,500b)
|
2,564.2±742.2 |
2,697.3±670.1 |
|
Magnesium (mg/day) |
400b)
|
340b)
|
112.1±40.5 |
121.5±42.5 |
|
Iron (mg/day) |
14b)
|
14b)
|
19.0±14.1 |
17.6±7.5 |
|
Zinc (mg/day) |
10b)
|
9b)
|
12.0±4.1 |
14.3±4.9**
|
|
Copper (μg/day) |
840b)
|
840b)
|
744.6±426.1 |
729.1±254.0 |
|
Selenium (μg/day) |
65b)
|
65b)
|
75.1±27.6 |
78.6±21.5 |
|
Manganese (mg/day) |
4b)
|
3.5b)
|
1.7±0.6 |
2.0±0.7**
|
Table 3.Dietary quality of the TF and WC groups
Table 3.
|
Variable |
All (n=144) |
TF group (n=76) |
WC group (n=68) |
|
NAR |
|
|
|
|
Energy |
0.85±0.16 |
0.81±0.17 |
0.90±0.13**
|
|
Protein |
0.98±0.08 |
0.97±0.09 |
0.99±0.06 |
|
Vitamin A |
0.72±0.21 |
0.73±0.23 |
0.70±0.17 |
|
Vitamin C |
0.56±0.20 |
0.51±0.21 |
0.61±0.19**
|
|
Thiamin |
0.98±0.07 |
0.97±0.09 |
0.98±0.05 |
|
Riboflavin |
0.92±0.13 |
0.89±0.15 |
0.96±0.18**
|
|
Niacin |
0.87±0.17 |
0.82±0.19 |
0.92±0.14***
|
|
Vitamin B6
|
0.94±0.17 |
0.92±0.16 |
0.97±0.11*
|
|
Folate |
0.94±0.14 |
0.86±0.18 |
0.95±0.11**
|
|
Calcium |
0.63±0.22 |
0.60±0.22 |
0.66±0.21 |
|
Phosphorus |
0.92±0.14 |
0.89±0.16 |
0.95±0.11*
|
|
Iron |
0.92±0.12 |
0.90±0.16 |
0.95±0.11*
|
|
Zinc |
0.95±0.12 |
0.92±0.14 |
0.97±0.19*
|
|
MARa)
|
0.86±0.11 |
0.83±0.13 |
0.89±0.08**
|
Table 4.Subsection and total scores achieved on the nutritional knowledge questionnaire for athletes by the TF and WC groups
Table 4.
|
Nutrition knowledge subsection |
TF group (n=76) |
WC group (n=68) |
|
Carbohydrate (n=23) |
5.8±3.6 (25.1) |
5.8±3.5 (25.2) |
|
Protein (n=18) |
6.2±3.5 (34.4) |
5.9±3.3 (32.5) |
|
Fat (n=23) |
4.4±3.9 (19.2) |
6.2±5.1 (26.8)*
|
|
General nutrition (n=25) |
6.5±4.4 (26.1) |
6.6±4.4 (26.6) |
|
Fluid (n=6) |
1.7±1.3 (28.9) |
1.7±1.3 (28.4) |
|
Sports nutrition (n=14) |
3.3±2.8 (23.7) |
3.3±2.5 (23.2) |
|
Total (n=109) |
27.9±14.0 (25.6) |
29.4±15.7 (27.0) |
Table 5.Dietary habits of the study participants
a)
Table 5.
|
Variable |
All (n=144) |
TF group (n=76) |
WC group (n=68) |
|
Healthy dietary habitsb)
|
|
|
|
|
Breakfast, ≥7 times/wk |
50 (34.7) |
37 (48.7) |
13 (19.1)***
|
|
Fruit, ≥1 times/day |
30 (20.8) |
17 (22.4) |
13 (19.1) |
|
Vegetable, ≥3 times/day |
27 (18.8) |
18 (23.7) |
9 (13.2) |
|
Milk, ≥2 times/day |
20 (13.9) |
12 (15.8) |
8 (11.8) |
|
Unhealthy dietary habitsb)
|
|
|
|
|
Carbonated beverage, ≥3 times/wk |
39 (27.1) |
12 (15.8) |
27 (39.7)**
|
|
High-caffeine beverage, ≥3 times/wk |
6 (4.2) |
2 (2.6) |
4 (5.9) |
|
Sugar-sweetened beverage, ≥3 times/wk |
87 (60.4) |
49 (64.5) |
38 (55.9) |
|
Fast food, ≥3 times/wk |
17 (11.8) |
9 (11.8) |
8 (11.8) |
|
Instant noodle, ≥3 times/wk |
22 (15.3) |
9 (11.8) |
13 (19.1) |
|
Unhealthy snack, ≥3 times/wk |
38 (26.4) |
17 (22.4) |
21 (30.9) |
Table 6.Weight control behavior in the study participants
Table 6.
|
Variable |
All |
TF group |
WC group |
|
Effort to control weight during the last month |
144 (100) |
76 (100) |
68 (100) |
|
No |
53 (36.8) |
30 (39.5) |
23 (33.8) |
|
Weight loss |
63 (43.8) |
31 (40.8) |
32 (47.1) |
|
Weight gain |
9 (6.3) |
7 (9.2) |
2 (2.9) |
|
Weight maintenance |
19 (13.2) |
8 (10.5) |
11 (16.2) |
|
Method to control weighta)
|
91 (100) |
46 (100) |
45 (100) |
|
Regular exercise |
85 (93.4) |
43 (93.5) |
42 (93.3) |
|
Fasting |
18 (19.8) |
4 (8.7) |
14 (31.1)*
|
|
Reduce meal size |
73 (80.2) |
36 (78.3) |
37 (82.2) |
|
Diet pill |
8 (8.8) |
3 (6.5) |
5 (11.1) |
|
Diarrhea or diuretic medicine |
1 (1.1) |
0 (0.0) |
1 (2.2) |
|
Vomiting after a meal |
3 (3.3) |
1 (2.2) |
2 (4.4) |
|
One-food diet |
5 (5.5) |
3 (6.5) |
2 (4.4) |
|
Oriental medicine |
8 (8.8) |
5 (10.9) |
3 (6.7) |
|
Diet food |
5 (5.5) |
3 (6.5) |
2 (4.4) |
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