Mind the gap: limited knowledge of carbohydrate guidelines for competition in an international cohort of endurance athletes
Bibliographic record
Abstract
Abstract Despite the well-documented role of carbohydrate (CHO) in promoting endurance exercise performance, endurance athletes typically fail to meet current recommendations in competition. Adequate nutrition knowledge is key to drive athletes’ behaviour, but the current level of knowledge in this population is not known. The present study assessed knowledge of CHO for competition in an international cohort of endurance athletes using the Carbohydrates for Endurance Athletes in Competition Questionnaire (CEAC-Q). The CEAC-Q was completed by 1016 individuals (45 % female), from the United Kingdom (40 %), Australia/New Zealand (22 %), the United States of America/Canada (18 %) and other countries (21 %). Total CEAC-Q scores were 50 ± 20 % (mean ± sd ), with no differences in scores between the five subsections (10 ± 5 points, P < 0⋅001). Based on typical knowledge and frequency of correct answers, we defined questions with low (0–39 %), moderate (40–69 %) and high (70–100 %) knowledge at a population level. Knowledge deficiencies were identified in questions related to CHO metabolism (Low: 2 out of 5 questions (2/5), Moderate: 3/5), CHO-loading (Low: 2/5, Moderate: 1/5), pre-event CHO (Low: 2/5, Moderate: 2/5), CHO during exercise (Moderate: 4/5) and CHO for recovery (Low: 3/5, Moderate: 1/5). Current CHO amounts recommendations were identified correctly for CHO-loading, pre-competition meal, during competition >2⋅5 h) and post-competition recovery by 28% (Low), 45 % (Moderate), 48 % (Moderate), and 29 % (Low), respectively. Our findings indicate that endurance athletes typically have limited knowledge of carbohydrate guidelines for competition, and we identify specific knowledge gaps that can guide targeted nutrition education to improve knowledge as an initial step towards optimal dietary practice.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".