Energy Expenditure, Dietary Energy Intake, and Nutritional Supplements in Adolescent Volleyball Athletes versus Nonathletic Controls
Bibliographic record
Abstract
Evidence suggests that athletes competing in team sports do not follow dietary recommendations. However, only few studies have investigated energy needs and supplement use in adolescent athletes, and whether they are meeting their energy requirements. This observational study examined energy expenditure, dietary energy intake, and use of nutritional supplements in 58 adolescent (14–17 years old) volleyball athletes (15 males, 43 females) and 58 age-matched nonathletic controls (13 males, 45 females). Participants completed an online survey including questions on demographic information, body mass, and a series of standardized questionnaires assessing energy expenditure, dietary energy, macronutrient, micronutrient, and supplement intake. Energy expenditure relative to body mass was higher in athletes than nonathletes by 13 kcal/kg/day (group effect, p < 0.001), and in males compared to females by 5.7 kcal/kg/day (sex effect, p = 0.004). Athletes had higher energy intake than nonathletes (+6.4 kcal/kg/day, p = 0.019) and greater consumption of fruits (p = 0.034), vegetables (p = 0.047), grains (p = 0.016), dairy (p = 0.038), meats and meat alternatives (p < 0.001), as well as higher intakes of fat (p < 0.001), carbohydrates, protein, sugar, fiber, vitamin C, calcium, and sodium (p = 0.05) compared to nonathletes. The average protein intakes exceeded the upper recommendations in all groups, suggesting that this is not a nutrient of concern for young volleyball athletes. However, athletes were only meeting 60% of the estimated energy requirements (EER) for their age, height, body mass, and physical activity score, (3322 ± 520 kcal/day), while nonathletes were meeting 74% of the EER (p < 0.001). The relative energy balance of male athletes was lower compared to both female athletes (p = 0.006) and male nonathletes (p = 0.004). Finally, more athletes reported using performance-related supplements than nonathletes, but there were no differences in the consumption of other dietary supplements. Overall, when compared to nonathletic controls, both male and female adolescent volleyball athletes were found to match their higher energy expenditure with a greater dietary energy intake; however, all adolescents were below the estimated energy requirements, a finding more profound among the volleyball athletes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".