Ad Libitum Diet, Energy Availability, Body Composition And Performance In Females During A 6-day Ultramarathon
Bibliographic record
Abstract
Ultramarathons (i.e., running >42.2 km) result in extremely high, acute, exercise energy expenditures (EEE). Adequate energy intake (EI) for optimal energy availability (EA) is vital for health and performance and is understudied in ultramarathon races. Understanding relationships between EI and EA with physiological and performance outcomes is critical for developing targeted nutrition strategies to optimize health and performance in female ultra-runners. PURPOSE: Characterize dietary intake and examine the relationships between EI and EA with body composition and total distance covered among females during a first-of-its-kind 6-day ultramarathon, producing several world records. METHODS: Professional and amateur female athletes consumed food and fluids ad libitum during a 6-day ultramarathon. Athlete dietary intake was recorded by nutrition experts and athlete support crews using gold-standard methods of weighing foods and beverages before and after consumption. EI and macronutrient intake were assessed via ESHA Food Processor Software. Fat mass index (FMI, kg/m2) and fat-free mass index (FFMI, kg/m2) was assessed via dual X-ray absorptiometry. Resting metabolic rate was assessed via indirect calorimetry. EA was calculated as EI-EEE/fat-free mass, where EEE was estimated as 1 kcal/kg/km ran, as established by Margaria et al. (JAP 1963). RESULTS: 4 professional and 6 amateur ultramarathoners participated (mean age: 38 ± 7 yrs; body mass index range: 18.8-48.3 kg/m2; FMI range: 2.0-22.1 kg/m2; FFMI range: 15.4-24.4 kg/m2). Distances covered over 6 days ranged from 181-902 km (464 ± 210 km). Mean EI ranged from 2915 to 6856 kcal/d (4188 ± 1169 kcal/d) with EA ranging from -19.1 to +48.9 kcal/kg/d (-3.2 ± 20.1 kcal/kg/d). 51 ± 12% of EI consisted of carbohydrates followed by fat (34 ± 9%) and protein (15 ± 4%). EI correlated with total distance covered (r = 0.766, p = 0.01), but not FFMI (r = 0.006, p = 0.987) or FMI (r = -0.248, p = 0.489). EA did not relate to total distance covered (r = -0.116, p = 0.751), FFMI (r = -0.006, p = 0.987) or FMI (r = -0.079, p = 0.829). CONCLUSIONS: EI - but not EA - was positively related to total distance covered; neither EI nor EA were related to body composition over a 6-day ultra. These findings highlight the importance of sufficient EI for supporting female ultramarathoners. Supported by: Part of the data presented was collected through a study supported by the MITACS (Mathematics of Information Technology and Complex Systems) Accelerate Program and funded by lululemon athletica inc.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".