A Method For Estimating Fat And Fat-free Mass Index From Anthropometric Z-score In College Athletes
Bibliographic record
Abstract
The body composition assessment is an important step for the athlete’s monitoring. Using fat mass index (FMI) and fat-free mass index (FFMI) offers a different approach to the classical body fat percentage interpretation. Even though anthropometry is widely used, the anthropometric models to estimate FMI and FFMI in athletes are lacking. In this regard, it seems plausible to use the “Phantom” approach to estimate FMI and FFMI, given that it also corrects anthropometric measurements for height. PURPOSE: To analyze the validity of Phantom-derived z-scores for predicting the FMI and FFMI in college athletes. METHODS: We recruited 114 college athletes from different sports. We took anthropometric measurements and whole-body dual-energy x-ray absorptiometry (DXA) scans to get the DXA-derived FMI and FFMI. Then we calculated the Phantom z-scores (z = ((variable * height/170.18) - Phantom average) / Phantom SD) for the eight skinfolds proposed by the ISAK (biceps, triceps, subscapular, iliac-crest, supraspinal, abdominal, anterior thigh, medial calf), and the arithmetic mean from these eight z-scores (ZSF) to estimate the FMI. Similarly, we calculated the Phantom z-scores for the corrected arm, thigh, and calf circumferences (corrected for triceps, anterior thigh, and medial calf skinfolds, respectively) and the arithmetic mean from these three z-scores (ZCC) to estimate the FFMI. RESULTS: The descriptive statistics are reported in the table. The ZSF was a statistically significant predictor of FMI in women (FMI = 7.12 + 1.90 * ZSF) (r = 0.91, SEE = 0.76) and men (FMI = 6.97 + 1.94 * ZSF) (r = 0.93, SEE = 0.60) (all p < 0.001). Similarly, the ZCC was a statistically significant predictor of FFMI in women (FFMI = 15.4 + 1.41 * ZCC) (r = 0.79, SEE = 0.87) and men (FFMI = 16.61 + 1.75 * ZCC) (r = 0.89) (all p < 0.001). CONCLUSIONS: The average Phantom-derived z-scores from anthropometric measurements is useful for estimating the FMI and FFMI in college athletes. Table. Participants’ descriptive statistics. - Women (n = 55) Men (n = 59) Age, y 21.4 ± 1.8 21.8 ± 2.0 Weight, kg 61.5 ± 8.8 71.9 ± 9.9 Height, cm 164.5 ± 6.7 175.0 ± 7.1 BF, % 26.7 ± 4.9 16.1 ± 5.2 BF, kg 16.5 ± 4.9 11.6 ± 4.9 FFM, kg 44.2 ± 4.8 59.1 ± 6.6 FMI, kg/m2 6.1 ± 1.8 3.8 ± 1.6 FFMI, kg/m2 16.3 ± 1.4 19.3 ± 1.6 ZSF -0.6 [-1.2, -0.2] -1.8 [-2.1, -1.2] ZCC 0.8 [0.1, 1.1] 1.6 [1.0, 2.0] Data reported as mean ± SD or median [25th, 75th percentile]. BF: Body fat; FFM: Fat-free mass; FFMI: Fat-free mass index; FMI; Fat mass index; ZCC: Mean z-score for corrected circumferences; ZSF: Mean z-score for skinfolds.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".