Pre-exercise ingestion of a low glycaemic index rice-based mixed meal increases fat oxidation and endurance running performance in a hot-humid environment
Bibliographic record
Abstract
Relative to exercise in a thermoneutral environment, there is only limited evidence demonstrating that a low glycaemic index (LGI) pre-exercise meal can enhance endurance exercise performance in a hot-humid (HH) condition. Also, previous studies predominantly utilised Western-based and single food items, with minimal focus on Asian-based mixed meals. This study aimed to investigate the impact of pre-exercise LGI and high glycaemic index (HGI) rice-based mixed meals on endurance performance among acclimatised trained athletes in HH condition (32 °C, 65% relative humidity). Twelve native-born endurance-trained male runners (age 22.0 ± 5.8 years; peak oxygen consumption (V̇O2peak) 64.2 ± 5.5 mL kg−1 min−1) completed two trials consisting of 45 min steady-state (SS) run at 70% V̇O2peak followed by 10 km performance run (TT10km). Three hours before exercise, participants consumed an isocaloric rice-based mixed meals containing either LGI (GI value = 47) or HGI (GI value = 80), providing 1.3 g of carbohydrate (CHO) per kg of body mass. Participants ran faster during TT10 km after consuming the LGI meal compared to the HGI meal (LGI: 55.18 ± 1.22 vs. HGI: 57.03 ± 2.25 min, p = 0.010). End rectal temperature did not significantly differ between trials (LGI: 39.16 ± 0.74 vs. HGI: 38.95 ± 0.46 °C, p = 0.352). Fat oxidation was higher during the SS run in the LGI compared to the HGI trial (LGI: 0.19 ± 0.05 vs. HGI: 0.13 ± 0.19 g min−1, p = 0.001). This study demonstrated that, relative to HGI, consuming a pre-exercise LGI rice-based mixed meal enhanced endurance performance in HH environment among acclimatised trained male 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 | 0.000 | 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".