Consuming a lentil‐based sports nutrition bar affects metabolic and performance measures during endurance exercise
Bibliographic record
Abstract
Low glycemic index (GI) carbohydrates (CHO) may be superior to high GI CHO if consumed before exercise by providing a prolonged energy source and sparing muscle CHO. We compared pre‐exercise feeding with: 1) a low GI lentil‐based sports nutrition bar; 2) a commercially‐available moderate GI sports nutrition bar with a similar macronutrient content; and 3) diet jelly (placebo) on metabolism and performance during endurance cycling. Using a randomized, counterbalanced, cross‐over design, endurance athletes (n=6) consumed 1.5 g/kg available CHO from a lentil bar, the commercially‐available bar, or consumed placebo 1h before endurance cycling (75min, 65% of max aerobic power), followed by a 7km time trial. We also compared post‐exercise consumption of these bars on next‐day exercise performance as an assessment of recovery. Each feeding condition was separated by 1wk. Respiratory exchange ratio (RER) during the last 10 minutes of the 75min cycling trial was significantly different between conditions with control (0.82 SD 0.01) < lentil bar (0.87 SD 0.04) < commercially‐available bar (0.89 SD 0.03), respectively (p<0.05) indicating more fat oxidation relative to CHO oxidation late in exercise after consumption of the lentil bar compared to the commercially‐available bar. Time trial performance was significantly improved after consumption of the lentil bar (587 SD 42 s) and the commercially available bar (584 SD 61 s) compared to control (618 SD 72 s) (p<0.05). The next‐day recovery time trial performance was better in the lentil bar condition (547 SD 23 s) compared to the commercially‐available bar (568 SD 29 s) and control (566 SD 34 s) (p<0.05). A low GI lentil‐based sports bar offers a metabolic advantage during endurance exercise (i.e. lower RER) and enhances recovery following exercise compared to a moderate GI sports nutrition bar.
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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 | 0.001 |
| 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".