Abstract
Background:
Nutritional studies on athletes have focused on the intake of nutrients and nutritional supplements. The lack of sufficient evidence on eating pattern is challenging for those advising athletes on their daily dietary patterns during training and competitions.Objectives:
To identify the eating pattern of high school athletes and its association with sport characteristics.Methods:
This cross-sectional study enrolled 248 Brazilian athletes (170 boys; age range, 14 - 20 years). We assessed the frequency of meals, unhealthy and healthy food items, and sports variables. Poisson regression model and the effect measure [prevalence ratio, PR] was analyzed.Results:
The athletes exhibited low breakfast consumption and lunch or dinner with parents, as well as a low consumption of healthy food items. More than one-third of all athletes ate vegetables and fruits on ≤ 2 days per week. We found a positive association between the time spent practicing the sport type and the regular consumption of raw salad and fruits. Soccer athletes had the worst eating pattern, including high soft drink (PR, 1.32; 1.10 - 1.60) consumption and the lowest bean (PR, 1.13; 1.01 - 1.26), raw salad (PR, 1.19; 1.04 - 1.37), and vegetable (PR, 1.10; 1.01 - 1.22) consumption, whereas the volleyball players exhibited the most adequate eating pattern.Conclusions:
Sports types and the time spent practicing these sports are associated with eating pattern. This information may be crucial for improving performance and promoting healthy habits that could benefit athletes even after their sports career.Keywords
Sports Food Groups Dietary Habits Eating Behavior Health Food
1. Background
In addition to ensuring optimal performance in sports, adequate nutrition is essential for optimal growth, development, and maturation in young athletes (1). Firstly, with regard to performance, the main focus of athletes is adequate energy and nutrient intake and the avoidance of conditions related to nutritional deficiencies, including muscle mass loss, body mass and/or hormonal imbalances, and injuries (2). Secondly, with regard to health, developing a good eating pattern (such as having breakfast, adequate choice and frequency of healthy foods) that is continued in adulthood may reduce the risk of disordered eating and chronic diseases (3). Furthermore, unhealthy eating pattern is associated with poor mental health (4) and academic performance (5).
Most of the studies on nutrition in athletes focus on the intake of nutrients (6), nutrient and water intake (7), nutritional supplement consumption (8), and eating disorders (9). This approach evaluates the intake adequacy by comparing individual nutrients with current recommendations. The lack of evidence on eating pattern is challenging for those advising athletes on their daily dietary patterns during training and competitions (10), and for those developing public policies for education and health promotion (11).
However, recent studies have increasingly been assessing the food consumption frequency and quality in adolescents and adults (12, 13), and whether the foods are unprocessed, processed, or ultra-processed (14). This evidence is crucial, as nutrition-specific education programs should address both food choices and meals, and not just the required nutrients (10). However, to our knowledge, only a few studies have evaluated the eating pattern from young athletes as well its association with characteristics of sport participation in developed countries (15), and there is a lack of studies that have been conducted in emerging countries (10).
Many adolescents do not live under ideal educational, health and training circumstances (16). A recent survey showed that only 16.3% of adolescents who participated in sports have a coach or receive any instructions for practice (17), thus emphasizing the precarious training conditions those adolescents were exposed to. These conditions reinforce the need to understand eating pattern, and to identify the factors associated with unhealthy habits.
2. Objectives
Here, we aimed to identify the eating pattern in high school athletes, and its association with characteristics of sport participation.
3. Methods
The analyses in the present cross-sectional study were based on data from the “Brazilian High School Athlete Study: Health-related Outcomes”, which is a study performed during the state phase of the 2015 Federal Institutes Games (FIGs) in Brazil (18). The FIGs are organized annually and the top-ranked athletes are selected and follow the regional and national phases. The athletes were from high school classes at federal institutes representing 12 cities of the state of Goiás in the Brazilian Midwest.
All regular athletes enrolled at FIG were invited to participate in the present study (n = 361). A total of 320 athletes were included by the following inclusion criteria: age of 14 - 20 years who had no previous history of musculoskeletal surgery and were participating in one the following sport types: soccer, handball, volleyball, and basketball. Of these, 42 declined to participate and 27 were injured. Therefore, 251 athletes were evaluated in our study.
Prior to participation, the athletes and their parents or guardians, in the case of minors, voluntarily signed an informed consent form approved specifically for this study. The present study was performed in accordance with the Helsinki Declaration, and was approved by the Ethics Committee for Human Research of the Instituto Federal Goiano (number 1.630.480).
3.1. Data Collection Procedure
To measure body weight and height, a digital scale with maximum capacity of 150 kg and accuracy of 100 g (Plenna-MEA-03140, São Paulo, Brazil) and stadiometer accurate to 0.1 mm were used, respectively. Thereafter, the body mass index (BMI) was calculated by dividing the mass by the square of the height (kg/m2); these values were then classified according to the standard deviation (SD) of the BMI Z-scores, based on the World Health Organization’s Growth Reference data (19), as follows: normal weight (-2 SD < BMI Z-score < 1 SD), overweight (1 SD ≤ BMI Z-score < 2 SD), or obese (BMI Z-score ≥ 2 SD). Students wore light clothes at the evaluations and were instructed to remain standing upright, with shoulders relaxed and the face directed forward.
The athletes reported on their weekly frequency of physical exercise (1, 2, 3, 4, and ≥ 5 days per week), annual competition frequency (1 per year, or ≥ 2 per year), sports type (volleyball, basketball, handball, or soccer), and time spent practicing this sport (months). We used a validated and reproducible questionnaire from The Brazilian National School-Based Health Survey (PeNSE) (16, 20) to assess the eating pattern. Food consumption was assessed based on the frequency of consumption of the 5 following healthy food items in the previous 7 days: bean, raw salad (cucumber, lettuce, tomato, carrot, and onion), vegetables (cabbage, chayote, broccoli, spinach, and pumpkin), fruits, and milk; as well as the 5 following unhealthy food items in the previous 7 days: deep-fried salty snacks, processed meat (hamburger, sausage, and ham), bagged salty snacks, sweets, and soft drinks. All survey questions had the following associated response options: 0, 1, 2, 3, 4, 5, and 6 times a week, and daily. Food consumption was categorized as regularly consumed (at least 5 days in the previous 7 days) or not regularly consumed for data analysis (12).
The questionnaire also evaluated healthy behaviors, such as how often the athlete usually “had breakfast”, and “lunch and dinner with parents”, as well as unhealthy behavior, such as “eating in front of the TV”. The responses to these questions were also categorized as regular practice (at least 5 days in the previous 7 days) or not regular practice (21). All athletes are regularly enrolled in school and study in the mornings, so we consider breakfast between 6 and 8:59 am (22). At the weekend it was considered the same timing of breakfast.
At the outset of the study, the researcher explained to the subjects in a group meeting how the questionnaire should be answered. The subjects then answered the questionnaires individually. The PeNSE reliability was tested on 34 high school students by the 7-day test-retest protocol. All the questions indicated good and very good values (Kappa range, 0.701 - 0.841).
Handgrip strength was evaluated using a manual force dynamometer (EMG System, model TRF_MAN200, São José dos Campos, Brazil; nominal capacity, 200 kg; sensitivity, 2 mV/V ± 10%; error < 0.03%; input resistance, 405 Ω; output resistance, 350 Ω). The equipment was adjusted according to each athlete’s size and was calibrated prior to data collection. The data were collected using a signal conditioner, with 16 channels, and an A/D converter with 16-bit resolution and 100× amplification. Data reliability was tested with the same protocol as aforementioned and a high intra-class correlation coefficient (ICC = 0.974, 0.969 - 0.981) was observed.
For the collection of handgrip strength data, the athlete sat with the elbow flexed at 90º, shoulders adducted and neutrally rotated, forearm in the neutral position, and the wrist with a 10º extension. The participants executed two valid isometric trials of 5 s for right handgrip strength, and the largest value was computed. Athletes were motivated to perform maximum handgrip strength. A resting period of 60 s between each trial was allowed. Strength were normalized by body weight (BW). Athletes were categorized according to tertiles: 1º tertile (lowest), 2º tertile, and 3º tertile (highest).
3.2. Statistical Analysis
The analyses were performed by the Statistical Package for the Social Sciences (SPSS 20.0). Data were analyzed using descriptive statistics and the Wald chi-Square test of association (bivariate analysis) for all eating pattern outcomes. The sports variables (weekly frequency, competition, sport type, time spent practicing this sport, and handgrip strength) were considered as independent variables. The independent variables with a level of significance of P < 0.20 in the bivariate analysis were included in a multivariable analysis according to the Poisson regression model with robust variance. The effect measure was the prevalence ratio (PRs) with its respective 95% confidence intervals (CIs) (23). The reference category to calculate the PR for each variable was considered the lower prevalence. The multivariable analyses were adjusted for sociodemographic (gender and age) and anthropometric (body mass index) variables, and the threshold α = 0.05 was used to indicate statistical significance.
4. Results
Of the enrolled students, 248 (98.8%) completed the protocol, including 170 male and 78 female athletes. The mean age, weight, height, and BMI of the athletes were 16.4 ± 1.4 years, 65.2 ± 12.1 kg, 1.71 ± 0.09 m, and 22.33 ± 3.15 kg/m2, respectively; 73.5% of the athletes exhibited normal BMI. The athletes exhibited low regular breakfast consumption (54.4%) and few had lunch or dinner with parents (35.5%). Moreover, we found a low consumption of healthy items, such as vegetables, fruits, and milk. In fact, more than one-third of all the athletes ate vegetables and fruits ≤ 2 days per week. With regard to unhealthy food items, the majority of athletes consumed deep-fried salty snacks, processed meats, and bagged salty snacks ≤ 2 days per week (Table 1).
Healthy and Unhealthy Eating Pattern of High School Athletes.
| Eating Pattern (N = 248) | Total, No. (%) |
|---|---|
| Healthy Eating Pattern | |
| Have breakfast, days per week | |
| 0 - 2 | 86 (34.7) |
| 3 - 4 | 27 (10.9) |
| 5 - 7 | 135 (54.4) |
| Lunch/dinner with parents, days per week | |
| 0 - 2 | 145 (58.5) |
| 3 - 4 | 15 (6.0) |
| 5 - 7 | 88 (35.5) |
| Bean consumption, days per week | |
| 0 - 2 | 17 (7.0) |
| 3 - 4 | 27 (11.0) |
| 5 - 7 | 200 (82.0) |
| Raw salad consumption, days per week | |
| 0 - 2 | 57 (23.2) |
| 3 - 4 | 59 (24.0) |
| 5 - 7 | 130 (52.8) |
| Vegetable consumption, days per week | |
| 0 - 2 | 140 (56.9) |
| 3 - 4 | 60 (24.4) |
| 5 - 7 | 46 (18.7) |
| Fruit consumption, days per week | |
| 0 - 2 | 95 (38.8) |
| 3 - 4 | 82 (33.5) |
| 5 - 7 | 68 (27.7) |
| Milk consumption, days per week | |
| 0 - 2 | 107 (43.5) |
| 3 - 4 | 48 (19.5) |
| 5 - 7 | 91 (37.0) |
| Unhealthy Eating Pattern | |
| Eating in front of the TV, days per week | |
| 0 - 2 | 166 (66.9) |
| 3 - 4 | 21 (8.5) |
| 5 - 7 | 66 (24.6) |
| Deep-fried salty snack consumption, days per week | |
| 0 - 2 | 157 (64.1) |
| 3 - 4 | 63 (25.7) |
| 5 - 7 | 25 (10.2) |
| Processed meat consumption, days per week | |
| 0 - 2 | 185 (76.1) |
| 3 - 4 | 44 (18.1) |
| 5 - 7 | 14 (5.8) |
| Bagged salty snack consumption, days per week | |
| 0 - 2 | 189 (77.2) |
| 3 - 4 | 38 (15.5) |
| 5 - 7 | 18 (7.3) |
| Sweets consumption, days per week | |
| 0 - 2 | 107 (43.7) |
| 3 - 4 | 65 (26.5) |
| 5 - 7 | 73 (29.8) |
| Soft drink consumption, days per week | |
| 0 - 2 | 100 (40.8) |
| 3 - 4 | 75 (30.6) |
| 5 - 7 | 70 (28.6) |
Bivariate analysis indicated an association between sports variables and meal consumption, healthy food items, and unhealthy food items (Tables 2 - 4). After these variables were inserted in the multivariable analysis, the following associations remained: (a) inadequate fruit and soft drink consumption with playing basketball; (b) inadequate consumption of beans, raw salad, vegetable, and soft drinks with playing soccer; (c) irregular breakfast consumption with playing basketball and soccer; (d) irregular having lunch and dinner with parents with playing handball and basketball; and (e) irregular consumption of raw salad and fruits with time spent practicing the sport type (Table 5).
Association (χ2) and Prevalence Ratio Between Meal Consumption and Independent Variables
| Variables | Total, % | Have Breakfasta | Lunch/Dinner with Parentsa | Eating in Front of the TVb | |||
|---|---|---|---|---|---|---|---|
| % | PR (95% CI) | % | PR (95% CI) | % | PR (95% CI) | ||
| Weekly frequency, d | P = 0.705 | P = 0.008c | P = 0.489 | ||||
| 1 - 2 | 48.8 | 47.5 | 1.04 (0.95 - 1.14) | 60.2 | 1 | 22.9 | 1.09 (0.71 - 1.67) |
| 3 - 4 | 40.5 | 41.8 | 1 | 63.3 | 1.02 (0.94 - 1.10) | 28.6 | 1.23 (0.81 - 1.90) |
| ≥ 5 | 10.7 | 46.2 | 1.03 (0.89 - 1.19) | 84.6 | 1.15 (1.05 - 1.26) | 19.2 | 1 |
| Competition, per year | P = 0.590 | P = 0.156 | P = 0.623 | ||||
| Only 1 | 48.5 | 46.6 | 1.02 (0.94 - 1.12) | 60.3 | 1 | 23.3 | 1 |
| ≥ 2 | 51.5 | 43.1 | 1 | 69.1 | 1.05 (0.98 - 1.13) | 26 | 1.06 (0.83 - 1.36) |
| Sport type | P = 0.033c | P = 0.045c | P = 0.576 | ||||
| Basketball | 14.1 | 62.5 | 1.22 (1.06 - 1.40) | 78.1 | 1.14 (1.03 - 1.26) | 21.9 | 1.13 (0.70 - 1.82) |
| Handball | 15.4 | 40 | 1.05 (0.91 - 1.27) | 74.3 | 1.12 (1.01 - 1.24) | 17.1 | 1 |
| Volleyball | 26.4 | 33.3 | 1 | 66.7 | 1.07 (0.97 - 1.17) | 28.3 | 1.30 (0.86 - 1.96) |
| Soccer | 44.1 | 48 | 1.11 (0.99 - 1.24) | 56 | 1 | 27 | 1.26 (0.86 - 1.86) |
| Practice, y | P = 0.715 | P = 0.214 | P = 0.986 | ||||
| 0 -1 | 27.7 | 47.5 | 1.04 (0.93 - 1.16) | 72.1 | 1.10 (0.99 - 1.22) | 24.6 | 1.03 (0.58 - 1.86) |
| > 1 - 5 | 39.1 | 41.9 | 1 | 62.8 | 1.04 (0.94 - 1.15) | 26.7 | 1.08 (0.61 - 1.91) |
| > 5 - 10 | 27.3 | 50 | 1.06 (0.94 - 1.18) | 56.7 | 1 | 25 | 1.04 (0.58 - 1.87) |
| > 10 | 5.9 | 53.8 | 1.08 (0.89 - 1.31) | 76.9 | 1.13 (0.97 - 1.31) | 23.1 | 1 |
| Handgrip strength | P = 0.775 | P = 0.652 | P = 0.604 | ||||
| 1º Tertile | 33.5 | 42.7 | 1 | 64.6 | 1.02 (0.93 - 1.12) | 25.6 | 1.12 (0.83 - 1.51) |
| 2º Tertile | 33.5 | 46.3 | 1.03 (0.92 - 1.14) | 61 | 1 | 20.7 | 1 |
| 3º Tertile | 33 | 48.1 | 1.04 (0.95 - 1.15) | 67.9 | 1.04 (0.95 - 1.14) | 27.2 | 1.16 (0.86 - 1.56) |
Association (χ2) and Prevalence Ratio Between Irregular Consumption of Healthy Food Items and Independent Variables
| Variable | Total, % | Bean Consumptiona | Raw salad Consumptiona | Vegetable Consumptiona | Fruit Consumptiona | Milk Consumptiona | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| % | PR (95% CI) | % | PR (95% CI) | % | PR (95% CI) | % | PR (95% CI) | % | PR (95% CI) | ||
| Weekly frequency, d | P = 0.686 | P = 0.875 | P = 0.881 | P = 0.814 | P = 0.399 | ||||||
| 1 - 2 | 48.8 | 16.2 | 1 | 47.9 | 1.04 (0.89 - 1.20) | 81.2 | 1.02 (0.93 - 1.13) | 70.1 | 1 | 64.1 | 1.09 (0.95 - 1.26) |
| 3 - 4 | 40.5 | 20.8 | 1.04 (0.95 - 1.14) | 47.4 | 1.03 (0.89 - 1.20) | 81.4 | 1.03 (0.93 - 1.14) | 74 | 1.02 (0.95 - 1.10) | 64.9 | 1.10 (0.96 - 1.27) |
| ≥ 5 | 10.7 | 19.2 | 1.03 (0.89 - 1.18) | 42.3 | 1 | 76.9 | 1 | 73.1 | 1.02 (0.91 - 1.14) | 50 | 1 |
| Competition, per year | P = 0.298 | P = 0.159 | P = 0.774 | P = 0.138 | P = 0.123 | ||||||
| Only 1 | 48.5 | 20 | 1.04 (0.96 - 1.13) | 42.1 | 1 | 79.8 | 1 | 67.5 | 1 | 67.8 | 1.06 (0.98 - 1.14) |
| ≥ 2 | 51.5 | 14.9 | 1 | 51.2 | 1.06 (0.98 - 1.16) | 81.3 | 1.01 (0.95 - 1.07) | 76.2 | 1.05 (0.98 - 1.13) | 58.2 | 1 |
| Sport type | P = 0.057 | P = 0.004 | P = 0.046 | P = 0.119 | P = 0.839 | ||||||
| Basketball | 14.1 | 6.2 | 1 | 46.9 | 1.12 (0.95 - 1.32) | 71.9 | 1 | 81.2 | 1.13 (1.02 - 1.25) | 62.5 | 1.02 (0.90 - 1.15) |
| Handball | 15.4 | 14.3 | 1.08 (0.95 - 1.22) | 31.4 | 1 | 74.3 | 1.01 (0.89 - 1.15) | 64.7 | 1.02 (0.90 - 1.16) | 65.7 | 1.04 (0.93 - 1.16) |
| Volleyball | 26.4 | 18.3 | 1.11 (0.99 - 1.25) | 38.3 | 1.05 (0.91 - 1.22) | 76.7 | 1.03 (0.92 - 1.15) | 61 | 1 | 66.1 | 1.04 (0.95 - 1.14) |
| Soccer | 44.1 | 22.4 | 1.15 (1.04 - 1.30) | 60.6 | 1.22 (1.07 - 1.39) | 88.9 | 1.10 (1.00 - 1.21) | 74.7 | 1.08 (0.99 - 1.20) | 59.6 | 1 |
| Practice, y | P = 0.422 | P = 0.001 | P = 0.242 | P = 0.022 | P = 0.318 | ||||||
| 0 - 1 | 27.7 | 11.7 | 1 | 44.3 | 1.34 (1.14 - 1.57) | 86.9 | 1.16 (0.97 - 1.37) | 78.3 | 1.36 (1.12 - 1.67) | 65.6 | 1.20 (0.98 - 1.47) |
| > 1 - 5 | 39.1 | 15.1 | 1.03 (0.93 - 1.14) | 51.1 | 1.41 (1.21 - 1.64) | 81.2 | 1.12 (0.95 - 1.33) | 74.1 | 1.33 (1.09 - 1.62) | 60 | 1.16 (0.94 - 1.41) |
| > 5 - 10 | 27.3 | 22 | 1.09 (0.98 - 1.22) | 52.5 | 1.42 (1.21 - 1.66) | 88.1 | 1.17 (0.98 - 1.38) | 69.5 | 1.30 (1.06 - 1.60) | 66.1 | 1.20 (0.98 - 1.47) |
| > 10 | 5.9 | 23.1 | 1.10 (0.90 - 1.35) | 7.7 | 1 | 61.5 | 1 | 30.8 | 1 | 38.5 | 1 |
| Handgrip strength | P = 0.802 | P = 0.902 | P = 0.473 | P = 0.131 | P = 0.457 | ||||||
| 1º Tertile | 33.5 | 16 | 1 | 44.4 | 1 | 76.5 | 1 | 67.9 | 1 | 59.8 | 1 |
| 2º Tertile | 33.5 | 20 | 1.03 (0.94 - 1.14) | 47.6 | 1.02 (0.92 - 1.13) | 82.9 | 1.04 (0.97 - 1.11) | 68.3 | 1.01 (0.92 - 1.09) | 61 | 1.01 (0.92 - 1.11) |
| 3º Tertile | 33 | 18.5 | 1.02 (0.92 - 1.13) | 47.5 | 1.02 (0.92 - 1.13) | 83.8 | 1.04 (0.97 - 1.12) | 79.7 | 1.07 (0.99 - 1.16) | 68.4 | 1.05 (0.96 - 1.15) |
Association (χ2) and Prevalence Ratio Between Regular Consumption of Unhealthy Food Items and Independent Variables
| Variables | Total, % | Deep-Fried Salty Snack Consumptiona | Processed Meat Consumptiona | Bagged Salty Snack Consumptiona | Sweets Consumptiona | Soft Drink Consumptiona | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| % | PR (95% CI) | % | PR (95% CI) | % | PR (95% CI) | % | PR (95% CI) | % | PR (95% CI) | ||
| Weekly frequency, d | P = 0.166 | P = 0.562 | P = 0.815 | P = 0.776 | P = 0.783 | ||||||
| 1 - 2 | 48.8 | 12.7 | 1.16 (0.98 - 1.38) | 5.2 | 1 | 6 | 1 | 26.7 | 1 | 26.7 | 1 |
| 3 - 4 | 40.5 | 7.4 | 1.06 (0.90 - 1.26) | 5.2 | 1.00 (0.90 - 1.12) | 8.2 | 1.04 (0.92 - 1.17) | 30.9 | 1.05 (0.90 - 1.23) | 30.9 | 1.05 (0.90 - 1.28) |
| ≥ 5 | 10.7 | 3.8 | 1 | 12 | 1.12 (0.90 - 1.40) | 7.7 | 1.03 (0.85 - 1.25) | 30.8 | 1.05 (0.82 - 1.34) | 26.9 | 1.00 (0.78 - 1.28) |
| Competition, per year | P = 0.681 | P = 0.899 | P = 0.511 | P = 0.848 | P = 0.112 | ||||||
| Only 1 | 48.5 | 9.6 | 1.03 (0.91 - 1.16) | 5.4 | 1 | 7.9 | 1.04 (0.93 - 1.16) | 29.8 | 1.01 (0.88 - 1.17) | 22.8 | 1 |
| ≥ 2 | 51.5 | 8.1 | 1 | 5.7 | 1.01 (0.91 - 1.12) | 5.7 | 1 | 28.7 | 1 | 32 | 1.13 (0.97 - 1.30) |
| Sport type | P = 0.656 | P = 0.194 | P = 0.645 | P = 0.647 | P = 0.006 | ||||||
| Basketball | 14.1 | 12.5 | 1.12 (0.89 - 1.41) | 16.1 | 1.24 (1.00 - 1.53) | 12.5 | 1.13 (0.92 - 1.40) | 25 | 1 | 40.6 | 1.35 (1.07 - 1.71) |
| Handball | 15.4 | 5.7 | 1 | 8.6 | 1.09 (0.92 - 1.31) | 5.7 | 1.01 (0.85 - 1.20) | 34.3 | 1.12 (0.85 - 1.48) | 20 | 1.04 (0.82 - 1.33) |
| Volleyball | 26.4 | 11.9 | 1.11 (0.92 - 1.30) | 3.3 | 1 | 5.1 | 1 | 35.6 | 1.14 (0.89 - 1.46) | 16.9 | 1 |
| Soccer | 44.1 | 11.1 | 1.09 (0.79 - 1.21) | 4.1 | 1.01 (0.91 - 1.13) | 8.1 | 1.05 (0.92 - 1.21) | 28.3 | 1.04 (0.83 - 1.31) | 37.8 | 1.31 (1.09 - 1.57) |
| Practice, y | P = 0.506 | P = 0.349 | P = 0.328 | P = 0.788 | P = 0.266 | ||||||
| 0 - 1 | 27.7 | 5.0 | 1 | 5.1 | 1 | 3.3 | 1 | 26.7 | 1.05 (0.74 - 1.48) | 19.7 | 1 |
| > 1 - 5 | 39.1 | 10.6 | 1.10 (0.95 - 1.27) | 5.9 | 1.01 (0.88 - 1.16) | 10.6 | 1.13 (0.98 - 1.30) | 29.4 | 1.09 (0.77 - 1.52) | 30.6 | 1.16 (0.96 - 1.39) |
| > 5 - 10 | 27.3 | 8.5 | 1.06 (0.91 - 1.24) | 5.1 | 1.00 (0.86 - 1.15) | 5.1 | 1.03 (0.90 - 1.18) | 33.9 | 1.15 (0.81 - 1.62) | 32.8 | 1.19 (0.97 - 1.46) |
| > 10 | 5.9 | 15.2 | 1.19 (0.86 - 1.63) | 23.1 | 1.32 (0.95 - 1.84) | 7.7 | 1.08 (0.83 - 1.41) | 23.1 | 1 | 38.5 | 1.27 (0.91 - 1.77) |
| Handgrip strength | P = 0.549 | P = 0.603 | P = 0.230 | P = 0.495 | P = 0.931 | ||||||
| 1º Tertile | 33.5 | 7.5 | 1 | 3.8 | 1 | 3.7 | 1 | 29.3 | 1.05 (0.87 - 1.25) | 29.3 | 1.03 (0.86 - 1.23) |
| 2º Tertile | 33.5 | 12.2 | 1.08 (0.93 - 1.26) | 7.3 | 1.07 (0.94 - 1.21) | 9.8 | 1.11 (0.97 - 1.27) | 34.1 | 1.11 (0.93 - 1.33) | 29.3 | 1.03 (0.86 - 1.23) |
| 3º Tertile | 33 | 11.2 | 1.06 (0.92 - 1.24) | 5.1 | 1.03 (0.91 - 1.15) | 7.7 | 1.07 (0.95 - 1.22) | 25.6 | 1 | 26.9 | 1 |
Adjusted Prevalence Ratio for Sports Variables and Meal Consumption Characteristics, Healthy Food Items, and Unhealthy Food Consumptiona
| Variable | Irregular Meal Consumptionb | Irregular Healthy Food Itemsb | Regular Unhealthy Food Itemsc | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Have Breakfast | Lunch/Dinner with Parents | Bean Consumption | Raw Salad Consumption | Vegetable Consumption | Fruit Consumption | Milk Consumption | Deep-Fried Salty Snack Consumption | Processed Meat Consumption | Soft Drink Consumption | |
| Sport type | ||||||||||
| Basketball | 1.26 (1.09 - 1.46)d | 1.13 (1.01 - 1.26)d | 1 | 1.09 (0.92 - 1.30) | 1 | 1.14 (1.01 - 1.22)d | 1.20 (0.97 - 1.50) | 1.34 (1.03 - 1.74)d | ||
| Handball | 1.05 (0.90 - 1.21) | 1.14 (1.03 - 1.25)d | 1.02 (0.89-1.17) | 1 | 1.04 (0.91 - 1.18) | 1.02 (0.90-1.16) | 1.11 (0.93 - 1.32) | 1.05 (0.82 - 1.33) | ||
| Volleyball | 1 | 1.09 (0.99 - 1.20) | 1.07 (0.94 - 1.22) | 1.05 (0.91 - 1.22) | 1.06 (0.94 - 1.18) | 1 | 1 | 1 | ||
| Soccer | 1.13 (1.01 - 1.27)d | 1 | 1.13 (1.01 - 1.26)d | 1.19 (1.04 - 1.37)d | 1.10 (1.01 - 1.22)d | 1.10 (0.99 - 1.22) | 1.02 (0.90 - 1.15) | 1.32 (1.10 - 1.60)d | ||
| Practice, y | ||||||||||
| 0 - 1 | 1.34 (1.14 - 1.58)d | 1.43 (1.17 - 1.73)d | ||||||||
| > 1 - 5 | 1.42 (1.21 - 1.67)d | 1.36 (1.23 - 1.65)d | ||||||||
| > 5 - 10 | 1.43 (1.22 - 1.69)d | 1.28 (1.05 - 1.57)d | ||||||||
| > 10 | 1 | 1 | ||||||||
| Weekly frequency, d | ||||||||||
| 1 - 2 | 1 | 1.13 (0.96 - 1.34) | ||||||||
| 3 - 4 | 1.00 (0.92 - 1.10) | 1.06 (0.89 - 1.26) | ||||||||
| ≥ 5 | 1.10 (0.98 - 1.23) | 1 | ||||||||
| Competition, per year | ||||||||||
| Only 1 | 1 | 1 | 1 | 1.06 (0.99 - 1.15) | 1 | |||||
| ≥ 2 | 1.03 (0.94 - 1.12) | 1.07 (0.97 - 1.17) | 1.05 (0.98 - 1.13) | 1 | 1.14 (0.97 - 1.33) | |||||
| Handgrip strength | ||||||||||
| 1º Tertile | 1 | |||||||||
| 2º Tertile | 1.02 (0.92 - 1.12) | |||||||||
| 3º Tertile | 1.06 (0.97 - 1.16) | |||||||||
5. Discussion
The main results show that sports types and the time spent practicing these sports are associated with eating pattern variables. Soccer athletes had the worst eating pattern, since they had high soft drink consumption and the lowest bean, raw salad, and vegetable consumption. We found a positive association between the time spent practicing sports and the regular consumption of healthy food items. In contrast with the nutrient intake data, this is the first study to assess the eating pattern of high school athletes from Latin America (10), and it provides a detailed view of the food consumption in athletes and offers important information for instituting appropriate interventions and planning public policies that could benefit athletes even after their sports career.
We found some differences in the meal consumption characteristics and food items between sport types. Although they had higher lunch or dinner with parents, soccer athletes had the worst eating pattern. A recent review (24) presented an unanimous agreement on the inadequate nutrient intake of soccer players, even though they only found a few studies evaluating the eating pattern. A study (25) of British professional soccer players described several important aspects of the soccer culture that impact the nutritional consumption of athletes, such as the traditional ideas that are prevalent in the sport; the power possessed by the managers, who were generally former players and practiced the nutrition knowledge they acquired through their own professional careers; the fact that many clubs infrequently work with sports nutritionists (25). These findings highlight the need for interventions, particularly in countries such as Brazil, where soccer is the most popular sport among young individuals (17). Although volleyball players exhibited better healthy habits, eating patterns between different sport types cannot be appropriately compared due to the lack of studies.
High soft drink consumption is associated with soccer and basketball. Considering the high glycemic index of sugary drinks and the risk of insulin resistance and obesity over time (26), it is vital to reduce the consumption of these beverages in order to improve health, along with the consumption of ultra-processed foods that are energy dense; high in free sugars, unhealthy fats, and salt; and low in dietary fiber (14). Recent studies (27-29) and the Brazilian Dietary Guidelines have recommended a new approach that extends beyond simple supply and intake of nutrients, and considers the choice of foods and meals, and the level of processing to which the food was submitted, and also advocates for the restriction of foods and beverages with low nutritional value.
Our results show that athletes who practiced sports for more than ten years had a higher regular consumption of raw salad (92.3%) and fruits (69.2%). This regular consumption prevalence is much higher than Brazilian adolescents. Azeredo et al. (30), who evaluated the eating pattern with the same questionnaire, showed that fewer than one third present a regular consumption of raw salad and fruit. Our findings suggest that sports have a positive effect on eating pattern. Similar results were found by Dortch et al. (31) who indicated that participation in sports is associated with higher consumption of vegetables and fruits. Consumption of a diet rich in fruits and vegetables is associated with lower risks of numerous chronic diseases, including cancer, diabetes, obesity, and cardiovascular diseases (32).
Although the most experienced athletes had a higher consumption of healthy food items, our results are concerning. Only few of all athletes regularly consumed vegetables (18.7%), fruits (27.8%), and milk (37%). Coutinho et al. (2), who evaluated Brazilian modern pentathletes, also found that the consumption of fruits (~20%) and vegetables (~15%) was insufficient, whereas the consumption of baked goods (~45%) and sugared soft drinks (~30%) was frequent. These data reflect the actual settings in Brazil, as other studies have also demonstrated low consumption of vegetables and fruits in the country, among adolescents, adults, and elderly people. According to the Brazilian Dietary Guidelines, these consumption levels of healthy food are much lower than those recommended for children and adolescents, and indicate the need for a re-evaluation of current national policies and nutritional regulations in schools and sports organizations.
In the present study, 54.4% of cases regularly had breakfast. This prevalence is much lower once compared with Egyptian adolescent athletes (78.5%) (33). Eating breakfast regularly is relevant, as it reduces the consumption of unhealthy snack foods during the day and may improve consumption of fiber-rich products and lead to a more even distribution of energy and nutrient intake throughout the day, that may improve glucose and insulin metabolism, appetite regulation (hunger and satiety); thus reducing risk of becoming overweight (5). Moreover, breakfast consumption may improve cognitive function and school performance (5). Thus, our findings are concerning due to the lower prevalence of regular breakfast consumption in athletes, as compared to young students from Brazil (34), who reported a frequency higher than 75%.
Although there is a consensus on literature from the importance of implementing intervention programs for athletes, there is still a lack of knowledge about the eating patterns of young athletes and its association between these eating patterns and with characteristics of sport participation (24). Once this relationship is thoroughly understood, efficient nutritional programs conducted by nutritionists can then be performed improving performance and health from athletes. In emerging countries, such information is crucial for improving the performance in the athletes and for promoting healthy habits that will benefit athletes throughout their life. Hence, nutrition and health promotion programs should not only focus on the specific nutrients, but also on food intake and eating practices (11, 24). Overall, we believe that our findings may help researchers, coaches, and health professionals to conduct more specific interventions. Future research about eating patterns should evaluate these variables by longitudinal design to improve cause and effect results.
In conclusion, sports types and the time spent practicing these sports are associated with eating pattern. Athletes who practice sports for many years have a more regular, healthy food consumption. Moreover, we found certain differences in the meal consumption characteristics and unhealthy food items among sport types, wherein soccer athletes had the worst eating pattern.
Acknowledgements
References
-
1.
Purcell LK; Canadian Paediatric Society Paediatric Sports Exercise Medicine Section. Sport nutrition for young athletes. Paediatr Child Health. 2013;18(4):200-5. [PubMed ID: 24421690]. [PubMed Central ID: PMC3805623]. https://doi.org/10.1093/pch/18.4.200.
-
2.
Coutinho LA, Porto CP, Pierucci AP. Critical evaluation of food intake and energy balance in young modern pentathlon athletes: A cross-sectional study. J Int Soc Sports Nutr. 2016;13:15. [PubMed ID: 27042167]. [PubMed Central ID: PMC4818861]. https://doi.org/10.1186/s12970-016-0127-x.
-
3.
Voracova J, Sigmund E, Sigmundova D, Kalman M. Changes in eating behaviours among czech children and adolescents from 2002 to 2014 (HBSC Study). Int J Environ Res Public Health. 2015;12(12):15888-99. [PubMed ID: 26694428]. [PubMed Central ID: PMC4690964]. https://doi.org/10.3390/ijerph121215028.
-
4.
O'Neil A, Quirk SE, Housden S, Brennan SL, Williams LJ, Pasco JA, et al. Relationship between diet and mental health in children and adolescents: A systematic review. Am J Public Health. 2014;104(10):e31-42. [PubMed ID: 25208008]. [PubMed Central ID: PMC4167107]. https://doi.org/10.2105/AJPH.2014.302110.
-
5.
Kim SY, Sim S, Park B, Kong IG, Kim JH, Choi HG. Dietary habits are associated with school performance in adolescents. Medicine (Baltimore). 2016;95(12). e3096. [PubMed ID: 27015180]. [PubMed Central ID: PMC4998375]. https://doi.org/10.1097/MD.0000000000003096.
-
6.
Briggs MA, Cockburn E, Rumbold PL, Rae G, Stevenson EJ, Russell M. Assessment of energy intake and energy expenditure of male adolescent academy-level soccer players during a competitive week. Nutrients. 2015;7(10):8392-401. [PubMed ID: 26445059]. [PubMed Central ID: PMC4632420]. https://doi.org/10.3390/nu7105400.
-
7.
de Sousa EF, Da Costa TH, Nogueira JA, Vivaldi LJ. Assessment of nutrient and water intake among adolescents from sports federations in the Federal District, Brazil. Br J Nutr. 2008;99(6):1275-83. [PubMed ID: 18053313]. https://doi.org/10.1017/S0007114507864841.
-
8.
Parnell JA, Wiens K, Erdman KA. Evaluation of congruence among dietary supplement use and motivation for supplementation in young, Canadian athletes. J Int Soc Sports Nutr. 2015;12:49. [PubMed ID: 26677351]. [PubMed Central ID: PMC4681054]. https://doi.org/10.1186/s12970-015-0110-y.
-
9.
Wollenberg G, Shriver LH, Gates GE. Comparison of disordered eating symptoms and emotion regulation difficulties between female college athletes and non-athletes. Eat Behav. 2015;18:1-6. [PubMed ID: 25841217]. https://doi.org/10.1016/j.eatbeh.2015.03.008.
-
10.
Noll M, de Mendonca CR, de Souza Rosa LP, Silveira EA. Determinants of eating patterns and nutrient intake among adolescent athletes: A systematic review. Nutr J. 2017;16(1):46. [PubMed ID: 28754133]. [PubMed Central ID: PMC5534032]. https://doi.org/10.1186/s12937-017-0267-0.
-
11.
Leech RM, Worsley A, Timperio A, McNaughton SA. Characterizing eating patterns: A comparison of eating occasion definitions. Am J Clin Nutr. 2015;102(5):1229-37. [PubMed ID: 26447152]. https://doi.org/10.3945/ajcn.115.114660.
-
12.
Azeredo CM, de Rezende LF, Canella DS, Claro RM, Peres MF, Luiz Odo C, et al. Food environments in schools and in the immediate vicinity are associated with unhealthy food consumption among Brazilian adolescents. Prev Med. 2016;88:73-9. [PubMed ID: 27050024]. https://doi.org/10.1016/j.ypmed.2016.03.026.
-
13.
Holubcikova J, Kolarcik P, Madarasova Geckova A, van Dijk JP, Reijneveld SA. Lack of parental rule-setting on eating is associated with a wide range of adolescent unhealthy eating behaviour both for boys and girls. BMC Public Health. 2016;16:359. [PubMed ID: 27117062]. [PubMed Central ID: PMC4847241]. https://doi.org/10.1186/s12889-016-3002-4.
-
14.
Fardet A, Rock E, Bassama J, Bohuon P, Prabhasankar P, Monteiro C, et al. Current food classifications in epidemiological studies do not enable solid nutritional recommendations for preventing diet-related chronic diseases: The impact of food processing. Adv Nutr. 2015;6(6):629-38. [PubMed ID: 26567188]. [PubMed Central ID: PMC4642417]. https://doi.org/10.3945/an.115.008789.
-
15.
Diehl K, Yarmoliuk T, Mayer J, Zipfel S, Schnell A, Thiel A, et al. Eating patterns of elite adolescent athletes: Results of a cross-sectional study of 51 olympic sports. Dtsch Z Sportmed. 2013;2013(5):126-31. https://doi.org/10.5960/dzsm.2012.067.
-
16.
Azeredo CM, Levy RB, Peres MF, Menezes PR, Araya R. Patterns of health-related behaviours among adolescents: A cross-sectional study based on the National Survey of School Health Brazil 2012. BMJ Open. 2016;6(11). e011571. [PubMed ID: 28186927]. [PubMed Central ID: PMC5128904]. https://doi.org/10.1136/bmjopen-2016-011571.
-
17.
Brasil. Diagnóstico nacional do esporte - ministério do esporte. 2016, [cited 2017 Jan 1]. Available from: http://www.esporte.gov.br/diesporte/diesporte_revista_2016.pdf.
-
18.
Noll M, de Avelar IS, Lehnen GC, Vieira MF. Back pain prevalence and its associated factors in Brazilian athletes from public high schools: A cross-sectional study. PLoS One. 2016;11(3). e0150542. [PubMed ID: 26938456]. [PubMed Central ID: PMC4777545]. https://doi.org/10.1371/journal.pone.0150542.
-
19.
WHO. Growth reference data for 5-19 years (WHO 2007). Geneva: World Health Organization; 2007, [cited 2016 Nov 12]. Available from: http://www.who.int/growthref/en.
-
20.
Oliveira MM, Campos MO, Andreazzi MAR, Malta DC. Characteristics of the National Adolescent School-based Health Survey - PeNSE, Brazil. Epidemiol Serv Saude. 2017;26(3):605-16. [PubMed ID: 28977184]. https://doi.org/10.5123/S1679-49742017000300017.
-
21.
Mathur C, Stigler M, Lust K, Laska M. A latent class analysis of weight-related health behaviors among 2- and 4-year college students and associated risk of obesity. Health Educ Behav. 2014;41(6):663-72. [PubMed ID: 24990599]. [PubMed Central ID: PMC5051694]. https://doi.org/10.1177/1090198114537062.
-
22.
Coulthard JD, Palla L, Pot GK. Breakfast consumption and nutrient intakes in 4-18-year-olds: UK National Diet and Nutrition Survey Rolling Programme (2008-2012). Br J Nutr. 2017;118(4):280-90. [PubMed ID: 28814349]. https://doi.org/10.1017/S0007114517001714.
-
23.
Barros AJ, Hirakata VN. Alternatives for logistic regression in cross-sectional studies: An empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol. 2003;3:21. [PubMed ID: 14567763]. [PubMed Central ID: PMC521200]. https://doi.org/10.1186/1471-2288-3-21.
-
24.
Garcia-Roves PM, Garcia-Zapico P, Patterson AM, Iglesias-Gutierrez E. Nutrient intake and food habits of soccer players: Analyzing the correlates of eating practice. Nutrients. 2014;6(7):2697-717. [PubMed ID: 25045939]. [PubMed Central ID: PMC4113765]. https://doi.org/10.3390/nu6072697.
-
25.
Ono M, Kennedy E, Reeves S, Cronin L. Nutrition and culture in professional football. A mixed method approach. Appetite. 2012;58(1):98-104. [PubMed ID: 22027271]. https://doi.org/10.1016/j.appet.2011.10.007.
-
26.
Canella DS, Levy RB, Martins AP, Claro RM, Moubarac JC, Baraldi LG, et al. Ultra-processed food products and obesity in Brazilian households (2008-2009). PLoS One. 2014;9(3). e92752. [PubMed ID: 24667658]. [PubMed Central ID: PMC3965451]. https://doi.org/10.1371/journal.pone.0092752.
-
27.
Costa Louzada ML, Martins AP, Canella DS, Baraldi LG, Levy RB, Claro RM, et al. Ultra-processed foods and the nutritional dietary profile in Brazil. Rev Saude Publica. 2015;49:38. [PubMed ID: 26176747]. [PubMed Central ID: PMC4544452]. https://doi.org/10.1590/S0034-8910.2015049006132.
-
28.
Monteiro CA, Moubarac JC, Cannon G, Ng SW, Popkin B. Ultra-processed products are becoming dominant in the global food system. Obes Rev. 2013;14 Suppl 2:21-8. [PubMed ID: 24102801]. https://doi.org/10.1111/obr.12107.
-
29.
Noll P, Noll M, de Abreu LC, Baracat EC, Silveira EA, Sorpreso ICE. Ultra-processed food consumption by Brazilian adolescents in cafeterias and school meals. Sci Rep. 2019;9(1):7162. [PubMed ID: 31073127]. [PubMed Central ID: PMC6509257]. https://doi.org/10.1038/s41598-019-43611-x.
-
30.
Azeredo CM, de Rezende LF, Canella DS, Moreira Claro R, de Castro IR, Luiz Odo C, et al. Dietary intake of Brazilian adolescents. Public Health Nutr. 2015;18(7):1215-24. [PubMed ID: 25089589]. https://doi.org/10.1017/S1368980014001463.
-
31.
Dortch KS, Gay J, Springer A, Kohl H3, Sharma S, Saxton D, et al. The association between sport participation and dietary behaviors among fourth graders in the school physical activity and nutrition survey, 2009-2010. Am J Health Promot. 2014;29(2):99-106. [PubMed ID: 24460004]. https://doi.org/10.4278/ajhp.130125-QUAN-47.
-
32.
Nguyen B, Bauman A, Gale J, Banks E, Kritharides L, Ding D. Fruit and vegetable consumption and all-cause mortality: Evidence from a large Australian cohort study. Int J Behav Nutr Phys Act. 2016;13:9. [PubMed ID: 26810760]. [PubMed Central ID: PMC4727264]. https://doi.org/10.1186/s12966-016-0334-5.
-
33.
Tawfik S, El Koofy N, Moawad EM. Patterns of nutrition and dietary supplements use in young Egyptian athletes: A community-based cross-sectional survey. PLoS One. 2016;11(8). e0161252. [PubMed ID: 27529492]. [PubMed Central ID: PMC4987011]. https://doi.org/10.1371/journal.pone.0161252.
-
34.
Prochnik Estima CC, da Costa RS, Sichieri R, Pereira RA, da Veiga GV. Meal consumption patterns and anthropometric measurements in adolescents from a low socioeconomic neighborhood in the metropolitan area of Rio de Janeiro, Brazil. Appetite. 2009;52(3):735-9. [PubMed ID: 19501773]. https://doi.org/10.1016/j.appet.2009.03.017.