Best practice recommendations for body composition considerations in sport to reduce health and performance risks: a critical review, original survey and expert opinion by a subgroup of the IOC consensus on Relative Energy Deficiency in Sport (REDs)
Bibliographic record
Abstract
BACKGROUND: The assessment of body composition (BC) in sport raises concern for athlete health, especially where an overfocus on being lighter or leaner increases the risk of Relative Energy Deficiency in Sport (REDs) and disordered eating. METHODS: We undertook a critical review of the effect of BC on performance (29 longitudinal, prospective or intervention studies) and explored current practice related to BC considerations via a follow-up to a 2013 internationally distributed survey. RESULTS: The review found that a higher level of body fat was negatively associated with endurance performance, while a gain in muscle mass resulted in performance benefits across sports. BC did not contribute to early talent identification, and no unique cut-off to signify a performance advantage for BC was identified. BC appears to be one of an array of variables impacting performance, and its influence should not be overstated. The survey (125 practitioners, 61 sports and 26 countries) showed subtle changes in BC considerations over time, such as an increased role for sport dietitian/nutrition practitioners as BC measurers (2013: 54%, 2022: 78%); less emphasis on reporting of body fat percentage (2013: 68%, 2022: 46%) and reduced frequency of BC assessment if ≥every fourth week (2013: 18%, 2022: 5%). Respondents remained concerned about a problematic focus on BC (2013: 69%, 2022: 78%). To address these findings, we provide detailed recommendations for BC considerations, including an overview of preferable BC methodology. CONCLUSIONS: The 'best practice' guidelines stress the importance of a multidisciplinary athlete health and performance team, and the treatment of BC data as confidential medical information. The guidelines provide a health focus around BC, aiming to reduce the associated burden of disordered eating, problematic low energy availability and REDs.
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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 | 0.082 | 0.209 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.025 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".