Comparing DXA and Air Displacement Plethysmography to Assess Body Composition of Male Collegiate Hockey Players
Bibliographic record
Abstract
Delisle-Houde, P, Reid, RER, Insogna, JA, Prokop, NW, Buchan, TA, Fontaine, SL, and Andersen, RE. Comparing DXA and air-displacement-plethysmography to assess body composition of male collegiate hockey players. J Strength Cond Res 33(2): 474-478, 2019-Accurate assessment of body composition is an important consideration for athletes because it is a health/performance variable. However, little is known about the variability in values obtained across different assessment methods for specific athlete populations. The purpose of this study was to compare 2 common laboratory methods that assess body composition: air displacement plethysmography (BOD POD) and dual energy x-ray absorptiometry (DXA). Twenty-nine male collegiate hockey players, (Age = 24.07 ± 1.49, BMI = 26.5 ± 2.74) participated in this study. All participants underwent back-to-back BOD POD and DXA evaluations. Paired t-tests and Bland-Altman analyses were performed to compare differences in fat mass, fat percentage, and fat-free mass between methods. Average fat percentage reported by the DXA and BOD POD 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 ≤ 0.001). Average fat mass reported by the DXA and BOD POD 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 ≤ 0.001). Average fat-free mass reported by the DXA and BOD POD 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 ≤ 0.001). Our findings can help make more insightful comparisons between studies that use different body composition methodologies among athletic populations.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| 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".