Skinfold Thicknesses And Ultrasound Perform Similarly When Used For Correcting Girths To Estimate Fat-free Mass
Bibliographic record
Abstract
Body composition is a fundamental component of fitness. Among the available techniques for body composition assessment (including fat-free mass, FFM), anthropometry offers portability, cost-effectiveness, and relative ease of application. Incorporating corrected girths for subcutaneous adipose tissue (SAT) is valuable for estimating FFM. However, the SAT estimated from skinfold thicknesses introduces theoretical weakness that ultrasound-measured SAT may potentially mitigate. PURPOSE: To determine whether the girths corrected for ultrasound-measured SAT are better to predict DXA-derived FFM than girths corrected for skinfold thickness-measured SAT. METHODS: Sixty university students (42 females, age: 21.3 ± 2.2 y, weight: 58.4 ± 10.3 kg, height: 1.61 ± 0.07 m, FFM: 41.9 ± 6.5 kg; 18 males, age: 21.2 ± 1.8 y, weight: 73.0 ± 11.7 kg, height: 1.76 ± 0.06 m, FFM: 61.4 ± 9.1 kg) underwent a whole-body DXA scan to assess their FFM, and anthropometric measurements according to the ISAK protocol carried out by certified personnel. We calculated four corrected girths (CG) as Girth - (π * SAT) for the arm, forearm, proximal thigh, and calf girths, corrected for the triceps, biceps, thigh, and calf SAT coming from anthropometric skinfold thicknesses or ultrasound images measured at the same location. Then, we calculated a corrected girth method (CGM) as the Average CG (cm) * Height (m), where the CG could come from anthropometry (CGM-A) or ultrasound (CGM-U). We used simple linear regression models to estimate DXA-derived FFM from CGM-A and CGM-U by sex. RESULTS: The mean CGM-A and CGM-U for females was 50.20 ± 5.33 and 53.24 ± 5.66, whereas for males they were 62.58 ± 6.89 and 64.63 ± 6.89, respectively. The prediction equations for females were: FFM = -16.08 + 1.15 * CGM-A (SEE = 2.09 kg, R2 = 0.90), and FFM = -15.93 + 1.09 * CGM-U (SEE = 2.09 kg, R2 = 0.90). The prediction equations for males were: FFM = -18.43 + 1.28 * CGM-A (SEE = 2.51 kg, R2 = 0.93), and FFM = -19.19 + 1.25 CGM-U (SEE = 2.60 kg, R2 = 0.92). CONCLUSIONS: The anthropometric and ultrasound CGM are valid FFM indicators; however, ultrasound does not improve the correlation with FFM compared to anthropometry. More extensive and longitudinal studies are needed to validate our findings and assess the comparative efficacy in detecting changes in FFM.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".