The Majority of Athletes Participating in Winter Multisport Events and Ironman Fail to Meet Recommended Intakes for Endurance Sports
Bibliographic record
Abstract
Participating in endurance sports requires optimal nutrition, with particular focus on dietary carbohydrates (CHO). Yet, little is known regarding dietary intakes of athletes involved in multisport endurance events. The objective of this observational study was to evaluate the nutritional habits 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 (IM70.3). A total of 151 athletes (44 women and 107 men) competing in a winter triathlon, winter pentathlon or in one of the IM and IM70.3 events were included in the analysis. Participants completed an online validated food frequency questionnaire. Other questionnaires assessing training habits, socio‐demographic information and sport food and supplements use were also completed. Athletes (21‐66 years) trained on average (±SD) 11.8±1.7 hours per week. Average energy intake was 3257±1091 kcal with higher energy intakes (p<0.001) reported by IM athletes compared to other groups. A significantly higher proportion of men achieved the recommended intakes of proteins (1.6 g/kg of body weight) compared with women (72.0 vs. 54.6%, p=0.04). On the other hand, small proportions of both men (37.4%) and women (27.3%, p=0.24 for difference between gender) meet the recommended CHO intakes for endurance athletes (7g/kg of body weight). Our data suggest that a majority of athletes involved in endurance multisport events fail to reach the minimal recommended intakes for CHO, highlighting the need for targeted education in this area. Funding: Natural Sciences and Engineering Research Council of Canada
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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) | 0.004 | 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".