Comparing Dual Energy X-ray Absorptiometry and Air-Displacement-Plethysmography Body Composition Evaluations in Male Collegiate Hockey Players
Bibliographic record
Abstract
Accurate assessment of body fat percentage has been a major goal of body composition research over the past 50 years. Body composition is a health and performance variable that coaches and athletes deem important for optimal performance. Two popular laboratory methods used for assessing an athlete’s body composition include air displacement plethysmography (BODPOD), and dual energy x-ray absoptiometry (DXA). PURPOSE: Compare the results of the BODPOD® with the known gold-standard measure of body composition, the DXA. METHODS: Twenty-nine elite male Canadian collegiate hockey players, (Age = 24.07 ± 1.49, BMI = 26.5 ± 2.74) participated in this study at the mid-point of their regular season. All participants underwent one BODPOD and one DXA evaluation on the same day. Paired t-tests were performed to compare differences in fat mass, fat percentage, and fat-free mass between DXA and BODPOD. RESULTS: Average fat percentage reported by the DXA and BODPOD® was 15.34 ± 3.53 and 11.66 ± 4.82 respectively, resulting in a bias score of 3.78 ± 2.33 kg (t(28) = 8.71, p ≤ .001). Average fat mass reported by the DXA and BODPOD® was 13.42 ± 3.59 and 10.15 ± 4.54 kg respectively, resulting in a bias score of 3.27 ± 1.92 kg (t(28) = 9.18, p ≤ .001. Average fat-free mass reported by the DXA and BODPOD® was 73.31 ± 5.30 and 76.25 ± 5.74 kg respectively, resulting in a bias score of -2.93 ± 2.06 kg (t(28) = -7.66, p ≤ .001. CONCLUSIONS: There is a difference in fat percentage, fat mass, and fat-free mass reported between the BODPOD® and DXA. This may have important implications for training programing and meal planning for elite athletes. Our findings may help sport scientists better interpret and make comparisons between studies that use different types of body composition methodologies amongst athletic populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".