Mechanical and Metabolic Responses with Exercise and Dietary Carbohydrate Manipulation
Bibliographic record
Abstract
PURPOSE: To investigate the effects of altered muscle glycogen content on the mechanical and metabolic responses to prolonged exercise of moderate intensity. METHODS: Eight volunteers (.VO2peak = 49.3 +/- 1.2 mL.kg(-1).min(-1)) cycled to fatigue on two occasions: after a 3-d low-carbohydrate diet (Lo CHO), which had been preceded by glycogen-depleting exercise, and then after a 3-d high-carbohydrate diet (Hi CHO). Metabolic and mechanical properties were assessed at both Lo CHO and Hi CHO before exercise (Pre), at 30 min of exercise (30 min), at fatigue in Lo CHO (Post 1), and again at fatigue after a brief rest (Post 2). RESULTS: For the Lo CHO cycle, time to fatigue averaged 66.7 +/- 4.5 and 9.5 +/- 1.7 min for Post 1 and Post 2, respectively. For Hi CHO, Post 2 time to fatigue was 64.9 +/- 6.3 min. Muscle glycogen was elevated (P < 0.05) by approximately 40% in Hi CHO compared with Lo CHO. Phosphocreatine, although higher (P < 0.05) by approximately 25% during exercise in Hi CHO, was not different at Pre. Similar but reciprocal effects (P < 0.05) were observed for inorganic phosphate and creatine. Force at low frequencies of stimulation was maximally reduced (P < 0.05) by approximately 26-38% by 30 min of exercise, regardless of condition. CONCLUSION: A 7-d exercise-dietary protocol leads to both an elevation in muscle glycogen and improved energy homeostasis during exercise. Although these adaptations may explain the improved cycle performance, they are not related to the progression of muscle fatigue as assessed statically at low frequencies of stimulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".