A Review of Nonpharmacological Strategies in the Treatment of Relative Energy Deficiency in Sport
Bibliographic record
Abstract
Relative energy deficiency in sport (RED-S) can result in negative health and performance outcomes in both male and female athletes. The underlying etiology of RED-S is low energy availability (LEA), which occurs when there is insufficient dietary energy intake to meet exercise energy expenditure, corrected for fat-free mass, leaving inadequate energy available to ensure homeostasis and adequate energy turnover (optimize normal bodily functions to positively impact health), but also optimizing recovery, training adaptations, and performance. As such, treatment of RED-S involves increasing energy intake and/or decreasing exercise energy expenditure to address the underlying LEA. Clinically, however, the time burden and methodological errors associated with the quantification of energy intake, exercise energy expenditure, and fat-free mass to assess energy availability in free-living conditions make it difficult for the practitioner to implement in everyday practice. Furthermore, interpretation is complicated by the lack of validated energy availability thresholds, which can result in compromised health and performance outcomes in male and female athletes across various stages of maturation, ethnic races, and different types of sports. This narrative review focuses on pragmatic nonpharmacological strategies in the treatment of RED-S, featuring factors such as low carbohydrate availability, within-day prolonged periods of LEA, insufficient intake of bone-building nutrients, lack of mechanical bone stress, and/or psychogenic stress. This includes the implementation of strategies that address exacerbating factors of LEA, as well as novel treatment methods and underlying mechanisms of action, while highlighting areas of further research.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".