Case Study: Nutrition and Training Periodization in Three Elite Marathon Runners
Bibliographic record
Abstract
Laboratory-based studies demonstrate that fueling (carbohydrate; CHO) and fluid strategies can enhance training adaptations and race-day performance in endurance athletes. Thus, the aim of this case study was to characterize several periodized training and nutrition approaches leading to individualized race-day fluid and fueling plans for 3 elite male marathoners. The athletes kept detailed training logs on training volume, pace, and subjective ratings of perceived exertion (RPE) for each training session over 16 wk before race day. Training impulse/load calculations (TRIMP; min × RPE = load [arbitrary units; AU]) and 2 central nutritional techniques were implemented: periodic low-CHO-availability training and individualized CHO- and fluid-intake assessments. Athletes averaged ~13 training sessions per week for a total average training volume of 182 km/wk and peak volume of 231 km/wk. Weekly TRIMP peaked at 4,437 AU (Wk 9), with a low of 1,887 AU (Wk 16) and an average of 3,082 ± 646 AU. Of the 606 total training sessions, ~74%, 11%, and 15% were completed at an intensity in Zone 1 (very easy to somewhat hard), Zone 2 (at lactate threshold) and Zone 3 (very hard to maximal), respectively. There were 2.5 ± 2.3 low-CHO-availability training bouts per week. On race day athletes consumed 61 ± 15 g CHO in 604 ± 156 ml/hr (10.1% ± 0.3% CHO solution) in the following format: ~15 g CHO in ~150 ml every ~15 min of racing. Their resultant marathon times were 2:11:23, 2:12:39 (both personal bests), and 2:16:17 (a marathon debut). Taken together, these periodized training and nutrition approaches were successfully applied to elite marathoners in training and competition.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".