Body Composition and Strength Changes in Women with Milk and Resistance Exercise
Bibliographic record
Abstract
PURPOSE: We aimed to determine whether women consuming fat-free milk versus isoenergetic carbohydrate after resistance exercise would see augmented gains in lean mass and reductions in fat mass similar to what we observed in young men. METHODS: Young women were randomized to drink either fat-free milk (MILK: n = 10; age (mean +/- SD) = 23.2 +/- 2.8 yr; BMI = 26.2 +/- 4.2 kg x m(-2)) or isoenergetic carbohydrate (CON: n = 10; age = 22.4 +/- 2.4 yr; BMI = 25.2 +/- 3.8 kg x m(-2)) immediately after and 1 h after exercise (2 x 500 mL). Subjects exercised 5 d x wk(-1) for 12 wk. Body composition changes were measured by dual-energy x-ray absorptiometry, and subjects' strength and fasting blood were measured before and after training. RESULTS: CON gained weight after training (CON: +0.86 +/- 0.4 kg, P < 0.05; MILK: +0.50 +/- 0.4 kg, P = 0.29). Lean mass increased with training in both groups (P < 0.01), with a greater net gain in MILK versus CON (1.9 +/- 0.2 vs 1.1 +/- 0.2 kg, respectively, P < 0.01). Fat mass decreased with training in MILK only (-1.6 +/- 0.4 kg, P < 0.01; CON: -0.3 +/- 0.3 kg, P = 0.41). Isotonic strength increased more in MILK than CON (P < 0.05) for some exercises. Serum 25-hydroxyvitamin D increased in both groups but to a greater extent in MILK than CON (+6.5 +/- 1.1 vs +2.8 +/- 1.3 nM, respectively, P < 0.05), and parathyroid hormone decreased only in MILK (-1.2 +/- 0.2 pM, P < 0.01). CONCLUSIONS: Heavy, whole-body resistance exercise with the consumption of milk versus carbohydrate in the early postexercise period resulted in greater muscle mass accretion, strength gains, fat mass loss, and a possible reduction in bone turnover in women after 12 wk. Our results, similar to those in men, highlight that milk is an effective drink to support favorable body composition changes in women with resistance training.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".