Long-term effect of nutritional counselling on desired gain in body mass and lean body mass in elite athletes
Bibliographic record
Abstract
Lean body mass (LBM) is important in power-related sports. In athletes with heavy training loads and competitions, it may be difficult to increase and maintain LBM during the season. The purpose of this study was to evaluate the long-term effects on body composition after an 8-12 week weight-gain period with or without nutritional guidance. Twenty-one elite athletes where randomized to 1 of 2 groups: the nutritional counselling group (NCG; n = 12, 18.5 ± 1.7 y, 67.8 ± 7.4 kg) and the ad libitum group (ALG; n = 9, 19.6 ± 2.7 y, 74.2 ± 5.7 kg). The NCG followed a meal plan that provided a surplus of 506 ± 84 kcal·day(-1), whereas the ALG had an ad libitum energy intake (EI) during the strength-training (4 sessions per week) intervention. Body mass (BM) and body composition were measured pre- and postintervention, and 6 and 12 months after the intervention. EI in the NCG was normalized after 12 months, whereas EI in the ALG was unchanged during or after the intervention. BM increased more in the NCG than in the ALG during the intervention (4.3% ± 0.9% vs. 1.0% ± 0.6%) and after 12 months (6.0% ± 0.9% vs. 1.8% ± 0.7%). LBM increased in the NCG during the intervention (2.8% ± 0.5%) and after 12 months (4.4% ± 1.0%), whereas LBM in the ALG was unchanged. The NCG managed to maintain and increase BM and LBM after the intervention period. Hence, the focus on nutritional guidance, in addition to strength training, seems to be preferable for obtaining the long-term effect of weight gain in athletes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".