Supplement intake in half-marathon, (ultra-)marathon and 10-km runners – results from the NURMI study (Step 2)
Bibliographic record
Abstract
The primary nutritional challenge facing endurance runners is meeting the nutrient requirements necessary to optimize the performance and recovery of prolonged training sessions. Supplement intake is a commonly used strategy by elite and recreational distance runners to meet nutritional recommendations. This study was conducted to investigate the patterns of supplement intake among different groups of distance runners and the potential association between supplement intake and sex, age, running and racing experiences.In a cross-sectional design, from a total of 317 runners participating in this survey, 119 distance runners were involved in the final sample after data clearance, assigned into three groups of 10-km runners (n = 24), half-marathoners (n = 44), and (ultra-)marathoners (n = 51). Personal characteristics, training and racing experiences, as well as patterns of supplement intake, including type, frequency, and dosage, were evaluated by questionnaire. Food Frequency Questionnaire was implemented to assess macronutrient intake. ANOVA and logistic regression were used for statistical analysis.While 50 % of total distance runners reported consuming supplements regularly, no differences between distance groups in consumption of carbohydrate/protein, mineral, or vitamin supplements were observed (p > 0.05). In addition, age, sex, running and racing experience showed no significant association with supplement intake (p > 0.05). Vitamin supplements had the highest intake rate in runners by 43 % compared to minerals (34 %) and carbohydrate/protein supplements (19 %).The present findings provide a window into the targeted approaches of long-distance runners as well as their coaches and sport nutrition specialists when applying and suggesting sustainable nutritional strategies for training and competition.Trial registration: ISRCTN73074080. Retrospectively registered 12th June 2015.
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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 | 0.001 | 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) | 0.001 | 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".