Differences in Total Body Fat and Body Fat Percentage Between Performance and Aesthetic Collegiate Athletes
Bibliographic record
Abstract
Aesthetic sport athletes may believe that they can achieve a higher score if their body conforms to an ideally perceived body image. These athletes may follow extreme diets that can lead to a low body fat percentage, fluctuations in weight, and eating disorders in order to achieve their desirable body image. Although, performance sports do not score points from judges, these athletes may be very lean in response to their training demands. PURPOSE: To compare absolute and relative total fat, android fat, gynoid fat, between synchronized swimming (aesthetic sport) to hockey (performance sport) in female collegiate athletes. METHODS: 41 female collegiate athletes (n=22 hockey players, n=19 synchronized swimmers) completed a total body iDXA scan to evaluate total and regional body fat percentage. The average age of the athletes was 20.30 ±1.77 yrs. The average weight of the athletes was 65.36 ± 8.37 kg where synchronized swimmers weighed 62.62 ± 8.45 kg and hockey players weighed 67.73 ± 7.73kg, (F(1,40) = 4.09, p ≤0.05). ANOVA was used to compare between sport differences in total body fat and body fat percentage as well as in the android and gynoid regions. RESULTS: Synchronized swimmers had higher total body fat percentages (30.64 ± 6.76 % vs 26.74 ± 4.63 %, F(1, 39) = 4.756, p=0.035), higher android percent fat (27.83 ± 9.91% vs 22.4 ± 7.09% F(1,39) = 4.156 p=0.048) and gynoid percent fat (35.18 ± 6.96% vs 30.50 ± 4.86% F(1,39) = 6.372 p=0.016) than hockey players. Moreover, there was no significant differences in total body fat (18.79 ± 5.87kg vs 17.57 ± 4.64kg) F(1,39) = 0.550 p=0.463), android fat (1.14 ± 0.53kg vs 0.97 ± 0.46kg F(1,39) = 1.280 p=0.265) and gynoid fat (3.68 ± 1.18kg vs 3.51 ± 0.82kg F(1,39) = 0.301 p=0.586) between the two groups. CONCLUSION: Previous literature suggests that synchronized swimmers (aesthetic sports) have lower body fat percentage in order to achieve a higher score from judges. However, our results indicate that collegiate hockey players (performance sports) have lower body fat in all the tested regions. Factors such as body image perception, training, and diet during the competitive season should be further investigated in order to better tailor health and training programs for female student 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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".