Supplement Intake in Recreational Vegan, Vegetarian, and Omnivorous Endurance Runners—Results from the NURMI Study (Step 2)
Bibliographic record
Abstract
Nutrient deficiency is a common cause of underperformance in endurance athletes, and supplement intake is frequently considered compensatory for vegan and vegetarian athletes specifically. This study aimed to investigate the patterns of supplement intake among vegan, vegetarian, and omnivorous distance (>10 km) runners and its association with age, sex, and race distance. From a total of 317 runners who participated in an online survey, 220 distance runners (mean age: 38.5 years; mean BMI: 21.75 kg/m2) were selected for the final sample after data clearance and assigned to 100 omnivores, 40 vegetarians, or 80 vegans. Sociodemographic information, racing experience, and patterns of supplement intake, including type, frequency, dosage, etc., were collected using a questionnaire. Macronutrient intake was assessed using a food frequency questionnaire. ANOVA and logistic regression were used for data analysis. The prevalence of supplement intake was 51% for total runners and 72% among vegan runners. Age, sex, and race distance had no significant effect on the type of supplement intake (p > 0.05). Compared to omnivores and vegetarians, vegan runners reported consuming more vitamin (but not carbohydrate/protein or mineral) supplements (p < 0.05). Vitamin B12, magnesium, and multivitamin had the most prevalent use amongst micronutrient supplements. This study points to a central role for supplementary nutritional strategies in different groups of distance runners. The present findings may help future investigations by design to identify specific requirements of endurance runners when adhering to specific kinds of diet particularly plant-based diets.
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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) | 0.001 | 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".