Validity of Body Fat Determination in Elite Female Athletes: Developing a New DXA Based Equation
Bibliographic record
Abstract
The longest standing field based method of body composition analysis is anthropometry. Anthropometry utilizes a series of skinfold measurements, circumferences, bone lengths and breadths that are rendered in statistical analysis to represent % Body Fat (% BF). Early anthropometric prediction equations were commonly created using Hydrodensitometry (HD) as the criterion measure. Limitations to these skinfold equations arise from limitations of the HD criterion, which may no longer represent the most robust criterion available. PURPOSE: The validity of commonly used % BF prediction equations recommended by national certification programs was evaluated along with equations derived from Multicompartment (MC), and Dual Energy X-Ray Absorptiometry (DXA), in female athletes. Additionally, a new DXA based regression equation was developed for elite female athletes. METHODS: Ninety-five (n=95) female athletes aged 17-31 were recruited into the study and participated in the skinfold validity testing. Anthropometric testing consisted of measurements of skinfolds (using Harpenden callipers), circumferences, and breadths. DXA measurements were conducted using a GE Lunar Prodigy DXA which served as the criterion measure (% BF DXA). RESULTS: The DXA based equation of Ball et al. (2004) displayed the greatest validity of existing equations with R=.87, total error (TE) = 2.9% BF, and Bland Altman Limits of Agreement (BALA) = -4.7 to 6.5% BF. The newly created regression equation demonstrated a non-linear characteristic and displayed similar predictive ability R=.84, TE = 3.0% BF, and BALA = -6.1 to 6.1 % BF. CONCLUSIONS: Anthropometric equations derived from various criteria yielded dissimilar results. Long utilized popular equations advocated in national accreditation schemes show considerable bias compared to modern values obtained by current DXA technology. A new regression equation was created for female Canadian athletes, 17-31years of age using skinfolds taught in the Canadian national professional certification program (CSEP).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".