Acute Low Energy Availability Exposure Alters Body Composition Of Elite Race Walkers But Does Not Hinder Performance
Bibliographic record
Abstract
Low energy availability (LEA) is defined as a mismatch between an individual’s energy intake (EI) and energy expenditure (EE) in exercise, leaving insufficient energy to support normal physiological function and maintain metabolic homeostasis. Periods of LEA are common amongst endurance athletes and may occur due to increased EE, reduced EI, or both. While chronic exposure to LEA is associated with negative health outcomes, to date the effects of acute LEA on parameters of endurance performance have not been characterized. PURPOSE: to determine if short-term LEA exposure negatively impacts factors contributing to endurance performance. METHODS: elite race walkers (n = 21, 18 male, 3 female; VO2peak 63 ± 5 mL/min/kg) underwent a 4-stage exercise economy test and competed in a 10,000 m race prior to and following 8-d of a high energy (HEA; ~ 40 kcal/kg fat free mass (FFM); n = 11) or LEA (15 kcal/kg FFM, n = 10) diet during a 4-week intensified training camp. DXA and resting metabolic rate were measured to calculate energy availability, while a subset of participants also had DXA measured post intervention to assess changes in body composition. RESULTS: fat oxidation rates during the economy test increased across the training camp (n = 21; p < 0.001), with a reciprocal decrease in carbohydrate (CHO) oxidation (p < 0.001), however there were no differences between dietary treatments. The oxygen cost of exercise (relative VO2, mL/min/kg) decreased across all 4 stages in both groups (p < 0.001), indicating an increase in exercise economy, while there was no change in VO2max. Athletes in the LEA intervention (n = 7) displayed a decrease in body mass (68.1 ± 6.4 vs. 66.6 ± 6.3 kg, p < 0.05) and fat mass (9.0 ± 2.7 vs. 7.9 ± 2.6 kg, p < 0.05), but maintained total FFM. Athletes in the HEA group (n = 5) displayed no changes in body mass or composition. Race performance was improved in both groups (LEA; 3 ± 2%, HEA 4 ± 2%, p < 0.001) with no difference between dietary treatments. CONCLUSION: long-term performance preparation involves integration of strategies to alternatively manage training support, physique management and fuel availability. Acute 8-d exposure to LEA resulted in a decrease in total body and fat mass, but reduced training quality. However, with acute replenishment of CHO availability, there was no short-term impairment in race performance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".