Carbohydrate Ingestion Increases Interstitial Glucose and Mitigates Neuromuscular Fatigue during Single-Leg Knee Extensions
Bibliographic record
Abstract
INTRODUCTION: We aimed to investigate the neuromuscular contributions to enhanced fatigue resistance with carbohydrate (CHO) ingestion and to identify whether fatigue is associated with changes in interstitial glucose levels assessed using a continuous glucose monitor (CGM). METHODS: Twelve healthy participants (six males, six females) performed isokinetic single-leg knee extensions (90°·s -1 ) at 20% of the maximal voluntary contraction (MVC) torque until MVC torque reached 60% of its initial value (i.e., task failure). Central and peripheral fatigue were evaluated every 15 min during the fatigue task using the interpolated twitch technique and electrically evoked torque. Using a single-blinded crossover design, participants ingested CHO (85 g sucrose per hour), or a placebo (PLA), at regular intervals during the fatigue task. Minute-by-minute interstitial glucose levels measured via CGM and whole blood glucose readings were obtained intermittently during the fatiguing task. RESULTS: CHO ingestion increased time to task failure over PLA (113 ± 69 vs 81 ± 49 min, mean ± SD; P < 0.001) and was associated with higher glycemia as measured by CGM (106 ± 18 vs 88 ± 10 mg·dL -1 , P < 0.001) and whole blood glucose sampling (104 ± 17 vs 89 ± 10 mg·dL -1 , P < 0.001). When assessing the values in the CHO condition at a similar time point to those at task failure in the PLA condition (i.e., ~81 min), MVC torque, percentage voluntary activation, and 10 Hz torque were all better preserved in the CHO versus PLA condition ( P < 0.05). CONCLUSIONS: Exogenous CHO intake mitigates neuromuscular fatigue at both the central and peripheral levels by raising glucose concentrations rather than by preventing hypoglycemia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".