Human skeletal muscle mitochondrial responses to single‐leg intermittent or continuous cycle exercise training matched for absolute intensity and total work
Bibliographic record
Abstract
There is renewed interest in the potential for interval (INT) training to increase skeletal muscle mitochondrial content including whether the response differs from continuous (CONT) training. Comparisons of INT and CONT exercise are impacted by the manner in which protocols are "matched", particularly with respect to exercise intensity, as well as inter-individual differences in training responses. We employed single-leg cycling to facilitate a within-participant design and test the hypothesis that short-term INT training would elicit a greater increase in mitochondrial content than work- and intensity-matched CONT training. Ten young healthy adults (five males and five females) completed 12 training sessions over 4 weeks with each leg. Legs were randomly assigned to complete either 30 min of CONT exercise at a challenging sustainable workload (~50% single-leg peak power output; Wpeak) or INT exercise that involved 10 × 3-min bouts at the same absolute workload. INT bouts were interspersed with 1 min of recovery at 10% Wpeak and each CONT session ended with 10 min at 10% Wpeak. Absolute and mean intensity, total training time, and volume were thus matched between legs but the pattern of exercise differed. Contrary to our hypothesis, biomarkers of mitochondrial content including citrate synthase maximal activity, mitochondrial protein content and subsarcolemmal mitochondrial volume increased after CONT (p < 0.05) but not INT training. Both training modes increased single-leg Wpeak (p < 0.01) and time to exhaustion at 70% of single-leg Wpeak (p < 0.01). In a work- and intensity-matched comparison, short-term CONT training increased skeletal muscle mitochondrial content whereas INT training did not.
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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) | 0.002 | 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".