Evaluating Fat Free Mass Index For Sport Specific Performance Goals In Female Collegiate Athletes
Bibliographic record
Abstract
Fat free mass index (FFMI) in an athletic female population is computed as (bone mineral content + lean mass / height2). It has been associated with performance and training status in their relevant sport, collegiate female athletes need to optimize bone mineral density (BMD), and lean mass as multiple factors impact their levels, including: the type of sport, nutrition, menstrual cycle, training program variation, lifestyle habits, and genetics. PURPOSE: To compare FFMI and lower body BMD across female collegiate athletes and sedentary control considering their desired sport-specific performance and training. METHODS: A sample of 68 women (Mean±SD; Age: 20.89±1.91 yrs, Height: 1.65±0.07 m, and Weight: 62.25±10.11 kg) including 43 females collegiate athletes (n= 23 hockey players, n= 20 synchronize swimmers) and 25 sedentary university students received a scan using dual-energy x-ray absorptiometry (DEXA). ANOVA was used to evaluate differences in: FFMI, and regional BMD in the spine and the femur. RESULTS: The FFMI of hockey players (18.11±1.33 kg/m2), was significantly (p<.05) higher than both the synchronized swimmers (15.43±1.25 kg/m2) and the controls (14.99±1.45 kg/m2). Hockey players, compared to synchronized swimmers and the control group, have a higher BMD of the spine, F(2, 65) = 13.391, p<.05, FFMI, F(2, 66)=37.06, p<.05. BMD of the total femur in the hockey players was great than the controls, F(2, 65)=4.06, p<.05. Total lean mass of synchronized swimmers (40.77±3.67 kg,) was greater (p<.05) than the control group (36.65±5.30kg). CONCLUSION: BMD and FFMI of the control group compared to female athletes show how sports’ demands influence bone’s health in a female collegiate population. BMD and FFMI of hockey players compared to synchronized swimmers may demonstrate the importance of percussive activities for bone health in female athletes. Differences in FFMI between female hockey players and synchronize swimmers suggest that establishing sport-specific norms in this new measure may help coaches and strength specialists better tailor training programs to optimize performance and bone health in varsity athletes.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".