Sprint versus endurance training: Metabolic adaptations in working human skeletal muscle
Bibliographic record
Abstract
BACKGROUND: Brief, intense exercise training induces metabolic adaptations in working human skeletal muscle that are comparable to traditional endurance training (Burgomaster, J Appl Physiol 100 : –2047, 2006). However, no study has directly compared the metabolic adaptations to these diverse training strategies in a standardized manner. PURPOSE: We examined changes in skeletal muscle metabolism during a fixed bout of exercise (1 h cycling at 65% of pre‐training VO2peak) before and after 6 wks of either low volume sprint‐interval training (SIT) or high volume endurance training (ET). Twenty subjects (23±1 yr, VO 2 peak = 41±1 ml/kg/min) were assigned in a counterbalanced manner to a SIT or ET group (n=5 men and 5 women per group). Subjects performed either SIT (4–6 repeats of 30 s “all out” cycling at ~250% VO2peak with 4.5 min recovery per d, 3 d/wk) or ET (40–60 min of continuous cycling at ~65% VO2peak per d, 5 d/wk). Weekly training time commitment was ~1 h for SIT and ~4.5 h for ET, and total training volume was ~80% lower for SIT versus ET (~630 vs ~3000 kJ per wk). Training increased VO2peak and the maximal activity of citrate synthase and 3‐hydroxyacyl‐CoA dehydrogenase with no difference between groups (main effects, P<0.05). Similarly, training decreased net muscle glycogenolysis, phosphocreatine degradation and lactate accumulation but there was no difference between groups (main effects, P<0.05). CONCLUSION: Given the large difference in training volume, these data demonstrate that SIT is a time efficient strategy to induce skeletal muscle metabolic adaptations during exercise that are comparable to ET in young men and women. Supported by NSERC Canada and an ACSM Doctoral Research Grant (KAB)
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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) | 0.003 | 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".