Original investigation: manipulating energy availability in male endurance runners: a randomised controlled trial
Bibliographic record
Abstract
This study investigated the effect of 4 days low energy availability (LEA) on physiological markers and mood states in male endurance runners. Twelve participants (mean (standard deviation); age: 25.8 (3.8) years; fat-free mass (FFM): 52.8 (5.5) kg) completed three 4-day conditions: adequate energy availability (AEA): 45 kcal/kg FFM/day; LEA1: 30 kcal/kg FFM/day; and LEA2: 15 kcal/kg FFM/day, in a randomized order. Participants ran on a treadmill at 65% of V̇O2max until they expended 15 kcal/kg FFM/day of energy. Energy intake was adjusted to achieve the desired energy availability. Pre- and post-measurements of bone turnover, metabolism, testosterone and estradiol (plasma), resting metabolic rate (indirect calorimetry), and mood states (Brunel Mood Scale) were assessed. The results reported a significant decrease in testosterone (condition × time interaction, p = 0.03) occurred on LEA2 (Pre: 23.8 (7.0) nmol/L vs. Post: 20.3 (7.7) nmol/L) compared with AEA (Pre: 22.9 (5.5) nmol/L vs. Post: 23.3 (6.1) nmol/L) or LEA1 (Pre: 23.6 (8.6) nmol/L vs. Post: 20.9 (8.8) nmol/L). Fatigue level significantly increased (condition × time interaction, p = 0.02) in LEA2 (Pre: 3.5 (1.7) vs. Post: 6.5 (2.9)) but did not change in AEA (Pre: 2.8 (1.5) vs. Post: 2.5 (2.7)) or LEA1 (Pre: 2.8(2.4) vs. Post: 2.9 (2.0)). Other measures were unaffected by the interventions. In conclusion, this study suggests that testosterone and fatigue may serve as early indicators of LEA in male runners. However, other physiological markers and mood states appeared largely unaffected, aligning with existing literature indicating minimal disruption of physiological functions during acute LEA in male athletes. Study registration: Australian New Zealand Clinical Trials Registry (Trial No.: 381278).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".