Food Intake Evaluation in a Group of Elite Track and Field Athletes
Bibliographic record
Abstract
Introduction: Eating behaviours and nutritional status are key factors related to individuals’ health. In athletes, diet optimisation has a key role in training adaptation achievement, injury prevention and good health status maintenance. However, nowadays it is still common to find diet behaviours not optimised with an increased risk of energy/nutrients deficiencies that can result not only in performance impairment but also in increased injury frequencies and malnutrition. Aim: To evaluate food intakes of elite track and field athletes to evaluate the adequacy of their diet. Materials and Methods: A total of 24 healthy elite track and field athletes, aged 18-30 years participated in the study. Anthropometric measurements were performed (weight, height, body circumferences, skinfold thickness) and Food Habits were evaluated using a seven days-food diary. Data were analysed through the WinFood® software, to obtain information about the basal metabolic rate and the energy expenditure of subjects. Moreover, information about macro and micro-nutrients were obtained both as percentage and grams. Statistical analysis was carried out in order to evaluate difference between male and female and between studied group’s intakes and international nutritional guidelines (The International Society of Sport Nutrition (ISSN)’s guidelines and the Position of the Academy of Nutrition and Dietetics, Dietitians of Canada, and the American College of Sports Medicine (ACSM) on Nutrition and Athletic Performance). Results: From the analysis, it was observed that energy expenditure resulted higher than the caloric intake. On average, there was a difference of about 30% between the energy expenditure and the caloric intake. The amount of macronutrients daily assumed by athletes was lower than values suggested by the major international society of sport in case of carbohydrates especially in female. Proteins intake resulted to be adequate, on the other hand, lipids intake were higher than recommendations above all in female. Furthermore, athletes did not assume adequate intakes of the most important micronutrients respect to the values suggested by guidelines especially for Calcium both in male and female and Iron in female. Conclusion: These results underline the importance of nutritional education programs and of nutritional practitioner in order to optimise the diet with proper intakes of macro and micro-nutrients.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".