Carbohydrates do not accelerate force recovery after glycogen‐depleting followed by high‐intensity exercise in humans
Bibliographic record
Abstract
Prolonged low-frequency force depression (PLFFD) induced by fatiguing exercise is characterized by a persistent depression in submaximal contractile force during the recovery period. Muscle glycogen depletion is known to limit physical performance during prolonged low- and moderate-intensity exercise, and accelerating glycogen resynthesis with post-exercise carbohydrate intake can facilitate recovery and improve repeated bout exercise performance. Short-term, high-intensity exercise, however, can cause PLFFD without any marked decrease in glycogen. Here, we studied whether recovery from PLFFD was accelerated by carbohydrate ingestion after 60 minutes of moderate-intensity glycogen-depleting cycling exercise followed by six 30-seconds all-out cycling sprints. We used a randomized crossover study design where nine recreationally active males drank a beverage containing either carbohydrate or placebo after exercise. Blood glucose and muscle glycogen concentrations were determined at baseline, immediately post-exercise, and during the 3-hours recovery period. Transcutaneous electrical stimulation of the quadriceps muscle was performed to determine the extent of PLFFD by eliciting low-frequency (20 Hz) and high-frequency (100 Hz) stimulations. Muscle glycogen was severely depleted after exercise, with a significantly higher rate of muscle glycogen resynthesis during the 3-hours recovery period in the carbohydrate than in the placebo trials (13.7 and 5.4 mmol glucosyl units/kg wet weight/h, respectively). Torque at 20 Hz was significantly more depressed than 100 Hz torque during the recovery period in both conditions, and the extent of PLFFD (20/100 Hz ratio) was not different between the two trials. In conclusion, carbohydrate supplementation enhances glycogen resynthesis after glycogen-depleting exercise but does not improve force recovery when the exercise also involves all-out cycling sprints.
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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".