Many non-elite multisport endurance athletes do not meet sports nutrition recommendations for carbohydrates
Bibliographic record
Abstract
Little is known regarding the dietary intake of non-elite athletes involved in multisport endurance events. The primary objective of this observational study was to characterize the dietary intake of non-elite athletes participating in winter triathlon (snowshoeing, skating, and cross-country skiing), winter pentathlon (winter triathlon sports + cycling and running), Ironman (IM: swimming, cycling, running), and half-distance Ironman (IM 70.3) in relation with current sports nutrition recommendations. A total of 116 non-elite athletes (32 women and 84 men) who had participated in one of those events in 2014 were included in the analyses. Usual dietary intake was assessed using a validated online food frequency questionnaire. Participants (22-66 years old) trained 14.8 ± 5.3 h/week, on average (±SD). Only 45.7% [95% confidence interval, 36.4%-55.2%] of all athletes reported consuming the recommended intake for carbohydrates, with the highest proportion (66.7%) seen in IM athletes. On the other hand, 87.1% [79.6%-92.6%] of all athletes reported consuming at least 1.2 g protein·kg(-1)·day(-1), while 66.4% [57.0%-74.9%] reported consuming more than 1.6 g protein·kg(-1)·day(-1). The proportion of athletes consuming the recommended amount of protein was highest (84.6%) among IM athletes. There was no difference in the proportion of athletes achieving the recommended carbohydrate and protein intakes between men and women. These findings suggest that many non-elite multisport endurance athletes do not meet the current recommendations for carbohydrates, emphasizing the need for targeted nutritional education. Further research is needed to examine how underreporting of food intake may have affected these estimates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".