Test–retest reliability and validity of body composition methods in adults
Bibliographic record
Abstract
Abstract Cost‐effective and efficient body composition measurement devices that are reliable and valid are necessary for identifying health risk as well as for understanding the effectiveness of lifestyle interventions. The objective of this study was to evaluate the test–retest reliability and validity of three body composition measurement devices. Forty‐nine adults (mean age (SD) = 31.5 (10.7) y; BMI = 23.5 (3.0) kg/m 2 ) completed a reference air displacement plethysmography (ADP) measure, and duplicate measures using skinfold callipers (Lange), ultrasound (BodyMetrix A‐mode) and a 3‐dimensional photonic scanner (3DPS; Fit3D ProScanner). Skinfold thickness was measured at seven sites using callipers and ultrasound; percent body fat (%BF) was then estimated using population‐specific algorithms. The 3DPS was used to measure body circumferences, and then %BF was estimated using its beta‐software. While skinfold callipers showed poor absolute reliability (mean differences (Δ) [95% CI] = 0.54% [0.22, 0.87], standard error of measurement (SEM) = 0.63%), ultrasound and the 3DPS showed excellent absolute (Δ = 0.17% [−0.25, 0.58], SEM = 0.78%; and Δ = −0.01% [−0.43, 0.40], SEM = 0.67%, respectively) and relative reliability (ICC 2,1 = 0.988 [0.979, 0.993]; and ICC 2,1 = 0.983 [0.968, 0.991], respectively). Compared to ADP ( n = 43), skinfold callipers underestimated %BF (Δ = −4.53 [−7.72, −1.34]; p = 0.003), while ultrasound (Δ = −0.32 [−3.51, 2.87]; p = 0.99) and the 3DPS (Δ = 1.06 [−2.12. 4.26]; p = 0.77) were not significantly different. Bland–Altman plots showed a minimal bias of ultrasound [95% limit of agreement (LOA) = −7.87, 7.23] and the 3DPS [95% LOA = −6.66, 8.79]. In conclusion, estimating %BF from subcutaneous fat measurements using ultrasound and body circumferences using a 3DPS may be reliable and valid methods that require minimal technician expertise.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".