Parallel assessment of nutrition and activity in athletes: Validation against doubly labelled water, 24-h urea excretion, and indirect calorimetry
Bibliographic record
Abstract
The assessment of nutrition and activity in athletes requires accurate and precise methods. The aim of this study was to validate a protocol for parallel assessment of diet and exercise against doubly labelled water, 24-h urea excretion, and respiratory gas exchange. The participants were 14 male triathletes under normal training conditions. Energy intake and doubly labelled water were weakly associated with each other (r = 0.69, standard error of estimate [SEE] = 304 kcal x day(-1)). Protein intake was strongly correlated with 24-h urea (r = 0.89) but showed considerable individual variation (SEE = 0.34 g kg(-1) x day(-1)). Total energy expenditure based on recorded activities was highly correlated with doubly labelled water (r = 0.95, SEE = 195 kcal x day(-1)) but was proportionally biased. During running and cycling, estimated exercise energy expenditure was highly correlated with gas exchange (running: r = 0.89, SEE = 1.6 kcal x min(-1); cycling: r = 0.95, SEE = 1.4 kcal x min(-1)). High exercise energy expenditure was slightly underestimated during running. For nutrition data, variations appear too large for precise measurements in individual athletes, which is a common problem of dietary assessment methods. Despite the high correlations of total energy expenditure and exercise energy expenditure with reference methods, a correction for systematic errors is necessary for the valid estimation of energetic requirements in individual athletes.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".