Abstract
[Purpose]
Protein supplements are widely used by athletes, prompting the scrutiny of their impact on low energy availability. This study investigated whether habitual protein supplement use is linked to physical characteristics and nutrient intake in Japanese university soccer athletes. In addition, an attempt was made to examine the differences in physical characteristics and nutrient intake according to muscle mass in protein supplement users using the fat-free mass index (FFMI), which reflects muscle mass.
[Methods]
A dietary survey, physical activity survey, and measurement of the physical characteristics of 38 Japanese collegiate soccer players were conducted.
[Results]
The habitual protein supplement utilization rate among the participants was 50%, and the participants exhibited an FFMI of 19.2 ± 1.3 kg/m2. Significant disparities between the two groups were noted in exercise energy expenditure (p = 0.023); carbohydrates (p = 0.039); copper (p = 0.003); and vitamins B1 (p = 0.016), B2 (p = 0.040), B6 (p = 0.003), C (p = 0.012), and D (p = 0.033), with higher levels observed in the protein-consuming cohort compared to the non-consuming cohort. Protein supplement users (n = 15) were stratified into two groups based on the median FFMI of the entire population (18.9 kg/m2). While the energy balance ratios were comparable between the groups, the high FFMI group tended to exhibit elevated energy intake (p = 0.169), consumption (p = 0.076), and carbohydrate consumption (p = 0.092), compared to the low FFMI group.
[Conclusion]
These findings suggest that adequate carbohydrate and protein intakes are crucial for preserving or augmenting muscle mass in young Japanese soccer athletes, including those consuming protein supplements.
Keywords: fat-free mass index, dietary intake, protein supplement, Japanese soccer players, relative energy deficiency in sport
INTRODUCTION
Athletes consistently strive to augment muscle mass for enhanced athletic performance. Muscle, the largest organ in the body, exhibits metabolic activity. Muscle proteins undergo cyclic breakdown and synthesis. Given that muscle mass changes are dependent on myofibrillar protein content, maintaining adequate nutritional intake, including protein intake, is crucial for athletes [1-3]. However, the International Olympic Committee introduced the concept of “Relative Energy Deficiency in Sport” (REDs) in 2014 [4], with a revised iteration published in 2023 [5]. REDs explicitly states that diminished energy availability (EA) suppresses various physiological functions, including energy and bone metabolism, thereby affecting performance [6-11]. Low EA, which was initially understood to focus on female athletes in 2014, has improved understanding, suggesting that it can also be applied to male athletes, with further evidence-building being conducted before the 2023 revision. The estimated prevalence ranges widely from 15% to 70% in men and from 23% to 79.5% in women [5]. Japan is no exception, with an estimated prevalence of approximately 50% [12], highlighting the need for nutritional interventions to improve EA.
Since the onset of the COVID-19 pandemic, the market for protein powder has expanded rapidly in Japan, and the number of users may increase further. The prevalence of protein supplement consumption among athletes has surged recently, and it is necessary to verify whether the intake of protein supplements contributes to muscle maintenance and enhancement in addition to improving EA. In a recent report, a large meta-analysis of randomized controlled trials evaluating the effect of protein intake on lean body mass proposed a dose-response relationship between protein intake and muscle mass gain, and an appropriate range of protein intake [13]. However, this meta-analysis was conducted with the implementation of resistance training, and similar large-scale studies on non-resistance training are anticipated. Furthermore, only a few studies have focused on elucidating the relationship between protein supplement intake, muscle mass, and diet in endurance ball players, such as soccer players, and data on Japanese soccer players in particular are currently lacking. This study investigated the physical characteristics and nutrient intake of young Japanese soccer players and compared them between protein supplement users and non-users. In addition, we explored the characteristics of low muscle mass protein supplement users using the fat-free mass index (FFMI), which reflects muscle mass.
METHODS
Participants
Thirty-eight male students from a university soccer club (Upper League) in Tokyo were selected as the study participants. The participants were asked to complete oral and written surveys, and informed consent was obtained from all participants. After excluding individuals with injuries or missing data, 30 participants were included in the final analysis. This study was approved by the ethics committee of the Kyoto Prefectural University (No. 207).
Dietary survey
Participants were instructed to document their meals on two non-game days within the study period (October 2023) using the approximate quantity method and capture images with a smartphone. During photography, the participants were asked to include their student ID alongside the meal to aid in portion size determination. Nutrients and other intakes were analyzed from the collected records and photographs using specialized nutrition calculation software (Eiyou Plus ver. 1.0; Kenpakusha, Tokyo). Daily intakes and energy density-adjusted values were calculated for 22 items: energy; protein; fat; carbohydrate; potassium; calcium; magnesium; phosphorus; iron; zinc; copper; retinol activity equivalent; vitamin D, K, B₁, B₂, B₆, B₁₂, and C; alpha-tocopherol; folic acid; and salt equivalent. Additionally, data on the frequency and quantity of protein supplement consumption were gathered and incorporated into nutrient and other intake analyses. However, the nutrient content of supplements other than proteins, such as multivitamins, was not included in the analysis.
Physical activity survey
The physical activity survey was conducted over two days on the same day as the dietary survey. Behavioral data were collected at approximately 5-minute intervals from waking to bedtime using recording forms. Physical activity intensity was determined based on the Physical Activity Recording Standards for Health Promotion 2013 [14] and the revised METs table for physical activity [15]. The exercise intensity and time duration of training at their club were determined by analyzing videos of the training sessions. Daily energy expenditure (kcal/day) was calculated using the formula: daily energy expenditure = body weight × 1.05 × Σ(exercise intensity × time). Participants engaged in club activities six days a week, with some undertaking independent training.
Physical characteristics
Physical characteristics were assessed using data from routine measurements conducted in September 2023. Physical attributes of the participants were evaluated using a body composition analyzer (InBody; InBody Japan, Tokyo, Japan) by bioelectrical impedance analysis. Measured parameters included body weight, body fat percentage, fat mass, fat-free mass (FFM), body water content, and body water percentage. Height was determined using a stadiometer, and body mass index (BMI) was calculated using the formula: BMI (kg/m2) = weight (kg)/height (m) × height (m). The FFMI was calculated using the following formula: FFMI (kg/m2) = FFM (kg)/height (m) × height (m).
Statistical analysis
Normality was assessed using the Shapiro–Wilk test. The non-paired t-test was used to compare normally distributed data, whereas the Mann–Whitney U test was used to compare non-normally distributed data between groups. Pearson’s correlation coefficient was used to assess the correlation for normally distributed data, whereas Spearman’s rank sum correlation coefficient was used for non-normally distributed data. Statistical analysis was performed using SPSS Statistics (version 24.0; IBM, Armonk, NY, USA), with the significance level set at 5%.
RESULTS
Physical characteristics and nutrient intake of participants
Average age of participants was 20.4 ± 1.3 years. Table 1 presents the physical characteristics, energy expenditure, and nutrient intake of the participants. BMI ranged from 18.5 to 25.0 for all participants. Average FFM of the participants was 59.6 ± 6.8 kg, with a median of 57.9 kg. Average FFMI was 19.2 ± 1.3 kg/m2, with a median of 18.9 kg/m2. Average available energy was 35.1 ± 9.0 kcal/kg FFM, with 32.3% of participants having a distribution of <30 kcal/kg FFM. The nutrients that deviated the most from the Japanese Dietary Reference Intake [16] were fats (57.9%, upward deviation), calcium (78.9%, downward deviation), retinol activity (63.2%, downward deviation), and vitamin D (76.3%, downward deviation).
Table 1.
Participants characteristics.
| n=30 | mean ± SD |
|---|---|
| BMI, kg/m2 | 21.6 ± 1.4 |
| Body weight, kg | 66.8 ± 7.8 |
| FFM, kg | 59.6 ± 7.2 |
| FFMI, kg/m2 | 19.2 ± 1.3 |
| Exercise energy expenditure, kcal/day | 777 ± 348 |
| Energy availabity, kcal/kg FFM | 37 ± 9 |
| Total energy expenditure, kcal/day | 3186 ± 547 |
| Energy intake, kcal/day | 2977 ± 677 |
| Energy intake/expenditure | 0.94 ± 0.20 |
| Dietary protein, g/day | 131.0 ± 34.2 |
| Dietary protein, E% | 17.8 ± 3.4 |
| Dietary protein, g/kg bodyweight | 1.96 ± 0.41 |
| Dietary fat, g/day | 112.3 ± 46.8 |
| Dietary fat, E% | 33.5 ± 9.5 |
| Dietary carbohydrate, g/day | 370.0 ± 110.3 |
| Dietary mineral intake/day | |
| K, mg | 3461 ± 1002 |
| Ca, mg | 633 ± 326 |
| Mg, mg | 364 ± 105 |
| P, mg | 1541 ± 441 |
| Fe, mg | 11.8 ± 3.7 |
| Zn, mg | 15.3 ± 5.0 |
| Cu, mg | 1.7 ± 0.5 |
| Dietary vitamin intake/day | |
| Retinol activity equivalent, µg | 640 ± 321 |
| Vitamin D, µg | 13.3 ± 13.3 |
| Alpha-tocopherol, mg | 10.8 ± 5.3 |
| Vitamin K, µg | 446 ± 227 |
| Vitamin B1, mg | 2.2 ± 0.9 |
| Vitamin B2, mg | 2.2 ± 1.1 |
| Vitamin B6, mg | 2.7 ± 1.3 |
| Vitamin B12, µg | 9.8 ± 13.4 |
| Folic acid, µg | 456 ± 154 |
| Vitamin C, mg | 161 ± 104 |
| Salt, g/day | 11.3 ± 2.6 |
BMI, body mass index; FFM, fat-free mass; FFMI, fat-free mass index.
Comparison of physical characteristics and nutrient intake with and without protein supplement use
The habitual protein supplement utilization rate among participants was 50%. Table 2 illustrates the physical characteristics and nutrient intakes related to protein supplement utilization. Significant disparities between the two groups were noted in exercise energy expenditure (p = 0.023); carbohydrates (p = 0.039); copper (p = 0.003); and vitamins B1 (p = 0.016), B2 (p = 0.040), B6 (p = 0.003), C (p = 0.012), and D (p = 0.033); with higher levels observed in the protein-supplement-consuming cohort compared to the non-consuming cohort. Similar findings were evident for the energy-adjusted intake. FFM, FFMI, energy expenditure, energy intake, protein intake, and protein intake per kilogram of body weight were higher in the protein-supplement-consuming cohort than in the non-consuming cohort; however, these discrepancies were not statistically significant.
Table 2.
Comparison of physical characteristics and nutrient intake with and without protein supplement use in Japanese young soccer players.
| n=30 | Protein supplement |
p | |
|---|---|---|---|
| User |
non-User |
||
| n=15 | n=15 | ||
| BMI, kg/m2 | 22.0 ± 1.3 | 21.2 ± 1.4 | 0.114 |
| Body weight, kg | 68.9 ± 8.6 | 64.8 ± 6.6 | 0.149 |
| FFM, kg | 61.5 ± 7.8 | 57.6 ± 6.3 | 0.146 |
| FFMI, kg/m2 | 19.6 ± 1.2 | 18.9 ± 1.3 | 0.134 |
| Exercise energy expenditure, kcal/day | 918 ± 335 | 636 ± 309 | 0.023* |
| Energy availabity, kcal/kg FFM | 36 ± 8 | 38 ± 10 | 0.404 |
| Total energy expenditure, kcal/day | 3342 ± 553 | 3030 ± 513 | 0.120 |
| Energy intake, kcal/day | 3096 ± 643 | 2857 ± 710 | 0.342 |
| Energy intake/expenditure | 0.93 ± 0.14 | 0.96 ± 0.25 | 0.696 |
| Dietary protein, g/day | 140.7 ± 37.1 | 121.3 ± 29.0 | 0.121 |
| Dietary protein, E% | 18.4 ± 4.1 | 17.2 ± 2.7 | 0.356 |
| Dietary protein, g/kg bodyweight | 2.0 ± 0.4 | 1.9 ± 0.4 | 0.255 |
| Dietary fat, g/day | 106.1 ± 49.5 | 118.5 ± 44.6 | 0.478 |
| Dietary fat, E% | 30.3 ± 10.2 | 36.7 ± 8.0 | 0.065 |
| Dietary carbohydrate, g/day | 411.1 ± 126.0 | 328.8 ± 75.4 | 0.039* |
| K, mg/day | 3655 ± 1021 | 3265 ± 978 | 0.294 |
| Ca, mg/day | 668 ± 294 | 599 ± 362 | 0.575 |
| Mg, mg/day | 394 ± 114 | 335 ± 90 | 0.127 |
| P, mg/day | 1525 ± 473 | 1558 ± 421 | 0.842 |
| Fe, mg/day | 12.8 ± 4.2 | 10.9 ± 2.9 | 0.156 |
| Zn, mg/day | 16.3 ± 5.7 | 14.3 ± 4.1 | 0.272 |
| Cu, mg/day | 2.0 ± 1.5 | 1.5 ± 0.4 | 0.003* |
| Retinol activity equivalent, µg/day | 621 ± 315 | 658 ± 337 | 0.757 |
| Vitamin D, µg/day | 18.4 ± 15.6 | 8.2 ± 8.4 | 0.033 |
| Alpha-tocopherol, mg/day | 11.3 ± 6.8 | 10.3 ± 3.2 | 0.596 |
| Vitamin K, µg/day | 473 ± 156 | 419 ± 285 | 0.527 |
| Vitamin B1, mg/day | 2.6 ± 1.0 | 1.8 ± 0.6 | 0.016* |
| Vitamin B2, mg/day | 2.6 ± 1.8 | 1.8 ± 0.7 | 0.040* |
| Vitamin B6, mg/day | 3.4 ± 1.4 | 2.0 ± 0.6 | 0.003* |
| Vitamin B12, µg/day | 11.1 ± 18.2 | 8.5 ± 6.1 | 0.604 |
| Folic acid, µg/day | 490 ± 152 | 421 ± 153 | 0.223 |
| Vitamin C, mg/day | 208 ± 110 | 114 ± 75 | 0.012* |
| Salt, g/day | 11.1 ± 2.8 | 11.5 ± 2.5 | 0.712 |
Value are mean ± SD.
p<0.05. Non-paired t-test or Mann-Whitney U test. BMI, body mass index; FFM, fat-free mass; FFMI, fat-free mass index.
Comparison of nutrient intake by FFMI among protein supplement users
Fifteen protein supplement users were selected and divided into two groups based on the median FFMI of the entire population: nine in the upper group and six in the lower group. Comparing each item in both groups (Table 3), the energy consumed for exercise in the upper group was significantly higher than in the lower group (p = 0.005). The energy balance ratios of the two groups were similar; however, neither energy intake (p = 0.169) nor energy expenditure (p = 0.076) were significantly higher in the upper group compared to that in the lower group. When nutrient intake was adjusted for energy, the upper group carbohydrate intake tended to be greater than the lower group carbohydrate intake, with a significance level below 10% (p = 0.092). However, no significant difference was observed in protein intake between the two groups (Figure 1).
Table 3.
Comparison by FFMI among protein supplement users in Japanese young soccer players.
| n=15 | FFMI |
p | |
|---|---|---|---|
| Upper |
Lower |
||
| n=9 | n=6 | ||
| BMI, kg/m2 | 22.6 ± 1.0 | 21.0 ± 1.0 | 0.008* |
| FFM, kg | 64.6 ± 6.7 | 56.9 ± 7.5 | 0.055 |
| FFMI, kg/m2 | 20.4 ± 0.9 | 18.4 ± 0.4 | <0.001* |
| Exercise energy expenditure, kcal/day | 1099 ± 273 | 647 ± 219 | 0.005* |
| Energy availabity, kcal/kg FFM | 33.9 ± 7.5 | 38.3 ± 8.2 | 0.301 |
| Total energy expenditure, kcal/day | 3526 ± 588 | 3034 ± 334 | 0.076 |
| Energy intake, kcal/day | 3286 ± 672 | 2812 ± 523 | 0.169 |
| Energy intake/expenditure | 0.93 ± 0.18 | 0.92 ± 0.08 | 0.872 |
| Dietary protein, g/day | 149.1 ± 43.3 | 128.2 ± 23.1 | 0.302 |
| Dietary protein, E% | 18.4 ± 5.0 | 18.4 ± 2.6 | 0.995 |
| Dietary protein, g/kg bodyweight | 2.1 ± 0.5 | 2.0 ± 0.5 | 0.847 |
| Dietary fat, g/day | 107.4 ± 43.9 | 104.0 ± 61.6 | 0.902 |
| Dietary fat, E% | 29.4 ± 9.0 | 31.6 ± 12.4 | 0.690 |
| Dietary carbohydrate, g/day | 455.9 ± 146.7 | 343.9 ± 31.8 | 0.092 |
| K, mg/day | 3817 ± 1104 | 3414 ± 921 | 0.474 |
| Ca, mg/day | 720 ± 298 | 589 ± 297 | 0.419 |
| Mg, mg/day | 407 ± 111 | 374 ± 125 | 0.594 |
| P, mg/day | 1635 ± 521 | 1360 ± 373 | 0.288 |
| Fe, mg/day | 13.2 ± 4.7 | 12.3 ± 3.7 | 0.694 |
| Zn, mg/day | 18.0 ± 6.3 | 13.8 ± 3.8 | 0.165 |
| Cu, mg/day | 2.1 ± 0.5 | 1.8 ± 0.4 | 0.157 |
| Retinol activity equivalent, µg/day | 650 ± 358 | 578 ± 262 | 0.683 |
| Vitamin D, µg/day | 20.4 ± 19.2 | 15.4 ± 8.4 | 0.559 |
| Alpha-tocopherol, mg/day | 12.7 ± 8.3 | 9.3 ± 3.4 | 0.365 |
| Vitamin K, µg/day | 451 ± 178 | 507 ± 126 | 0.474 |
| Vitamin B1, mg/day | 2.9 ± 1.1 | 2.1 ± 0.7 | 0.148 |
| Vitamin B2, mg/day | 2.9 ± 1.4 | 2.0 ± 0.7 | 0.148 |
| Vitamin B6, mg/day | 3.6 ± 1.4 | 3.1 ± 1.6 | 0.580 |
| Vitamin B12, µg/day | 14.3 ± 23.3 | 6.3 ± 3.4 | 0.423 |
| Folic acid, µg/day | 519 ± 167 | 447 ± 129 | 0.388 |
| Vitamin C, mg/day | 230 ± 120 | 189 ± 101 | 0.609 |
| Salt equivalent, g/day | 11.6 ± 2.3 | 10.3 ± 3.5 | 0.415 |
| Energy-adjusted intake (/1,000 kcal) | |||
| Dietary protein, g/day | 50.0 ± 12.4 | 46.0 ± 6.5 | 0.995 |
| Dietary fat, g/day | 32.6 ± 10.1 | 35.1 ± 13.8 | 0.690 |
| Dietary carbohydrate, g/day | 115.6 ± 37.2 | 87.2 ± 8.1 | 0.092 |
| K, mg/day | 968 ± 280 | 866 ± 234 | 0.474 |
| Ca, mg/day | 183 ± 75 | 149 ± 75 | 0.419 |
| Mg, mg/day | 103 ± 28 | 95 ± 32 | 0.594 |
| P, mg/day | 414 ± 132 | 345 ± 94 | 0.288 |
| Fe, mg/day | 3.3 ± 1.2 | 3.1 ± 0.9 | 0.694 |
| Zn, mg/day | 4.6 ± 1.6 | 3.5 ± 1.0 | 0.165 |
| Cu, mg/day | 0.5 ± 0.1 | 0.4 ± 0.1 | 0.157 |
| Retinol activity equivalent, µg/day | 165 ± 91 | 147 ± 66 | 0.683 |
| Vitamin D, µg/day | 5.2 ± 4.9 | 3.9 ± 2.1 | 0.559 |
| Alpha-tocopherol, mg/day | 3.2 ± 2.1 | 2.3 ± 0.9 | 0.365 |
| Vitamin K, µg/day | 114 ± 45 | 129 ± 32 | 0.513 |
| Vitamin B1, mg/day | 0.7 ± 0.3 | 0.5 ± 0.2 | 0.125 |
| Vitamin B2, mg/day | 0.7 ± 0.4 | 0.5 ± 0.2 | 0.148 |
| Vitamin B6, mg/day | 0.9 ± 0.3 | 0.8 ± 0.4 | 0.580 |
| Vitamin B12, µg/day | 3.6 ± 5.9 | 1.6 ± 0.9 | 0.423 |
| Folic acid, µg/day | 132 ± 42 | 113 ± 33 | 0.388 |
| Vitamin C, mg/day | 56 ± 30 | 48 ± 26 | 0.609 |
| Salt equivalent, g/day | 2.9 ± 0.6 | 2.6 ± 0.9 | 0.415 |
Value are mean ± SD.
p<0.05. Non-paired t-test or Mann-Whitney U test. BMI, body mass index; FFM, fat-free mass; FFMI, fat-free mass index.
Figure 1. Comparison of energy intake and expenditure, carbohydrate and protein intake by FFMI among protein supplement users in young Japanese soccer players.
Fifteen protein supplement users in young Japanese soccer players divided into two groups based on the median FFMI (fat-free mass index) of the entire population: 9 in the upper group and 6 in the lower group. White boxes indicate FFMI upper group; light gray boxes, FFMI lower group. Non-paired t-test. The center line of the boxplot indicates the median, the crosses indicate the mean, the top of the box indicates the 75th percentile, and the bottom of the box indicates the 25th percentile. Error bars indicate ± SD.
Relationship between FFMI and protein and carbohydrate intake of participants
Single correlation analysis was performed to examine whether FFMI was associated with dietary protein and carbohydrate intake (Table 4). Significant positive correlations were found between FFMI and intake of protein (r = 0.448; p = 0.013) and carbohydrates (r = 0.590; p = 0.001). Consistent results were observed for the energy density-adjusted nutrient intake.
Table 4.
Single correlations between dietary protein and carbohydrate intake and FFMI in Japanese young soccer players.
| Variable | r | p |
|---|---|---|
| Protein | 0.448 | 0.013 |
| Carbohydrate | 0.590 | 0.001 |
Spearman's rank sum correlation coefficient.
DISCUSSION
In athletes, maintaining or increasing fat-free muscle mass is a challenge for enhancing athletic performance. This study investigated whether protein supplement use is linked to the physical characteristics and dietary intake of high-lev-el college soccer players in Japan. These results suggest that the habitual protein supplement utilization rate among the participants was 50%, and sufficient carbohydrate and protein intake is crucial for the maintenance or enhancement of muscle mass.
Half of the participants consumed protein supplements. According to a survey by the Japan Sports Association, 66.2% of athletes belonging to college athletic teams, such as the participants in this study, used protein supplements [17]. Protein supplement users had a significantly higher energy expenditure from exercise, a component of the available energy calculation, than non-supplement users. This is presumably due to independent training outside club activities, suggesting that protein supplements are used by athletes with higher activity levels. Additionally, protein supplement users had significantly higher intakes of carbohydrates, copper, and vitamins than non-users. FFMI, energy expenditure, energy intake, protein intake, and protein intake per kilogram of body weight were also higher in protein supplement users than in non-users; however, the differences were not statistically significant, suggesting large individual differences.
In our investigation of the association between dietary intake, protein supplement utilization, and muscle mass, 40% of athletes exhibited FFM or muscle mass in the lower half of the total subject cohort despite the incorporation of protein supplements. Consequently, a comparison of the nutrient intake between the upper and lower strata was conducted based on the median FFMI of the entire subject pool. The results indicated comparable energy balance ratios between the upper and lower groups, both demonstrating higher energy intake and expenditure in the upper group, albeit insignificantly. Further scrutiny revealed similar protein intake across both groups, whereas carbohydrate intake was notably elevated in the upper group, although the significance remained below 10%. This finding implies that adequate carbohydrate consumption by protein supplement users contributes to muscle accretion. Conversely, sustaining muscle mass may pose challenges for protein supplement users with adequate protein intake but deficient carbohydrate intake. Notably, these findings underscore the importance of elevating energy rather than protein intake to augment lean body mass, thereby corroborating the existing literature [18]. Furthermore, the correlation analysis of FFMI with protein and carbohydrate intake showed significant positive correlations. This result further emphasizes the contribution of carbohydrate and protein intake to the increase and maintenance of muscle mass.
The limitations of this study include the small number of participants and cross-sectional design, precluding the establishment of a clear causal relationship. The measurement of activity in this study was performed using a time study survey and video analysis of the training sessions. This was performed to avoid the risk of damage or loss of activity meters. Although these methods may be strictly limited to a complete picture of all physical activities, we believe that we can encompass physical activity in club training, voluntary training, and daily life. Additionally, soccer players may have different activity levels depending on their position. However, the number of participants in this study was small, and it was not possible to stratify the data according to position. This is an interesting finding, because protein intake may differ on the basis of position. In the future, it will be necessary to increase the sample size and examine the data based on position. Specific protein supplement consumption amounts were not assessed. Nevertheless, little information is available regarding protein supplement utilization and its association with dietary intake status and FFMI in young Japanese soccer players. Furthermore, the data were successfully collected from the same sports competitions. These findings contribute to the foundational understanding of the relationship between muscle mass and nutritional intake during endurance ball games.
In conclusion, this study investigated whether protein supplement use is linked to physical characteristics and dietary intake in young Japanese soccer players. These results suggest that the habitual protein supplement utilization rate among the participants was 50%, and sufficient carbohydrate and protein intake is crucial for the maintenance or enhancement of muscle mass. When administering protein supplements to meet protein requirements, it is imperative to assess carbohydrate intake to avoid perpetuating low EA of athletes. These findings should help establish a basis for nutritional supplementation strategies for muscle maintenance in athletes and enhance the strategic use of protein supplements.
Acknowledgments
We thank the members of the university soccer club for their cooperation in conducting this study. This work was supported by Urakami Foundation for Food and Food Culture Promotion, Japan. All authors have no conflicts of interest.
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