Dietary Protein for Training Adaptation and Body Composition Manipulation in Track and Field Athletes
Bibliographic record
Abstract
Track and field athletes engage in vigorous training that places stress on physiological systems requiring nutritional support for optimal recovery. Of paramount importance when optimizing recovery nutrition are rehydration and refueling which are covered in other papers in this volume. Here, we highlight the benefits for dietary protein intake over and above requirements set out in various countries at ∼0.8–1.0 g·kg body mass (BM) −1 ·day −1 for training adaptation, manipulating body composition, and optimizing performance in track and field athletes. To facilitate the remodeling of protein-containing structures, which are turning over rapidly due to their training volumes, track and field athletes with the goal of weight maintenance or weight gain should aim for protein intakes of ∼1.6 g·kg BM −1 ·day −1 . Protein intakes at this level would not necessarily require an overemphasis on protein-containing foods and, beyond convenience, does not suggest a need to use protein or amino acid-based supplements. This review also highlights that optimal protein intakes may exceed 1.6 g·kg BM −1 ·day −1 for athletes who are restricting energy intake and attempting to minimize loss of lean BM. We discuss the underpinning rationale for weight loss in track and field athletes, explaining changes in metabolic pathways that occur in response to energy restriction when manipulating protein intake and training. Finally, this review offers practical advice on protein intakes that warrant consideration in allowing an optimal adaptive response for track and field athletes seeking to train effectively and to lose fat mass while energy restricted with minimal (or no) loss of lean BM.
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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".