Intersection of mental health issues and Relative Energy Deficiency in Sport (REDs): a narrative review by a subgroup of the IOC consensus on REDs
Bibliographic record
Abstract
Overall athlete health is a stated priority by the International Olympic Committee (IOC), yet it can be difficult for athletes to safely balance nutritional needs, training load, recovery, social interactions, expectations and other demands. The effect of energy intake and, especially, low energy availability (LEA) on athlete mental health, is understudied. In this narrative review, we examine research that has included psychological factors and mental health variables when investigating the effect of LEA, dieting/restrictive eating and Relative Energy Deficiency in Sport (REDs), since the 2018 IOC consensus statement on REDs. Based on currently available data, early psychological indicators associated with problematic LEA are mood changes, fatigue and psychological conflict. More severe mental health outcomes associated with REDs are reduced well-being, elevated anxiety, depressive symptoms and eating disorders. We propose a psychological model that helps structure how possible risk factors (eg, body dissatisfaction, environmental demands or increased training load) and moderating (eg, gender, sport) and/or potential mediating (eg, social climate, self-esteem) factors are associated with LEA and ultimately REDs. The current scientific literature underscores the importance of including mental health factors when screening for REDs and for developing a clinical approach to address the psychological sequelae of REDs once diagnosed. An interdisciplinary perspective is recommended. Lastly, and importantly, the athlete perspective urges clinicians to not underestimate the drive for success and denial of health consequences that athletes demonstrate when pursuing their sport goals.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".