The Sports Food Usage of Athletes Participating in Winter Endurance Multisport and Ironman Events
Bibliographic record
Abstract
Strong evidence supports the importance of carbohydrate (CHO) and protein intake in optimizing training adaptations to endurance exercise. In that context, athletes now have access to plenty of sports food products such as sports drinks, bars and gels to provide CHO/protein during and after training. Little is known regarding the use of such sports foods by multisport endurance athletes while training. PURPOSE: 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). METHODS: A total of 151 non-elite athletes (44 women and 107 men) were included in the analysis. Participants completed an online validated food frequency questionnaire. Questionnaires assessing training habits, socio-demographic information and sports food and supplement use in the month prior to investigation were also completed. RESULTS: Participants were aged from 21 to 66 years and trained on average (±SD) 11.8±1.7 hours/week. Average energy intake provided by sports food was 180±37 kcal/d (5.7±1.1% of daily calories) with highest intakes among IM athletes (314±30 kcal/d) and lowest intakes among winter athletes (WA) (85±23 kcal/d). IM and IM70.3 athletes consumed more protein bars (p=0.003) and energy-dense bars (p<0.001) than WA. Gels were consumed by 79.3% of the participants; the lowest values being seen in WA compared with IM and IM70.3 athletes (p<0.001). Sports candies were consumed by 47.3% of the athletes without difference between groups (p=0.13). Regular sports drinks (CHO + electrolytes) (85.2%) were more popular than low-calorie drinks (31.3%), drinks supplemented with amino acids (22.0%) and homemade versions (21.3%). Sodium tabs were used by 28.0% of athletes, with higher use among IM and IM70.3 athletes than among WA (p<0.001). A few athletes reported using caffeine pills (2.7%), all of whom were IM 70.3 athletes. CONCLUSIONS: Our data suggest that the use of sports food while training varies greatly among multisport endurance non-elite athletes, with apparently greater prevalence among summer triathletes than among WA. 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".