Body composition changes affect energy cost of running during 12 months of specific diet and training in amateur athletes
Bibliographic record
Abstract
Considering the relation between body weight composition and energy cost of running, we tested the hypothesis that by modifying body composition by means of a combined protocol of specific diet and training, the energy cost of motion (Cr) may be reduced. Forty-five healthy and normal-weight subjects were divided into 3 groups that performed a different treatment: the first group attended a dietary protocol (D), the second group participated in a running program (R), and the third group followed both the dietary and running protocols (R&D). Each subject underwent 3 anthropometric and exercise evaluation tests during 1 year (at entry (T0), month 6 (T6), and month 12 (T12)) to assess body composition and Cr adjustments. The mean fat mass (FM) values were reduced in R&D from 12.0 ± 4.0 to 10.4 ± 3.0 kg (p < 0.05 T0 vs. T12) and in the D group from 14.2 ± 5.8 to 11.6 ± 4.7 kg (p < 0.05 T0 vs. T12). Conversely, the mean fat free mass values increased in R&D (from 56.3 ± 8.8 to 58.3 ± 9.8 kg, p < 0.05 T0 vs. T12) and in the D group (from 50.6 ± 13.2 to 52.9 ± 13.6 kg, p < 0.05 T0 vs. T12). The mean Cr values of the 2 groups were significantly modified throughout the 1-year protocol (1.48 ± 0.16 and 1.40 ± 0.15 kcal·kg(-b)·km(-1) in the R&D group at T0 and T12, respectively; 1.83 ± 0.17 and 1.76 ± 0.23 kcal·kg(-b)·km(-1) in D group at T0 to T12, respectively). The R&D and D groups that underwent the diet protocol had a positive change in body composition during the year (FM/fat free mass ratio decline), which determined a Cr reduction.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".