Daily Myofibrillar Protein Synthesis Rates Do Not Differ During Interval Compared to Continuous Exercise Training Matched for Duration and Work in Healthy Young Men
Bibliographic record
Abstract
High-intensity interval training (HIIT) may elicit different skeletal muscle responses compared to work-matched moderate-intensity continuous training (MICT). The effect of work-matched HIIT versus MICT on myofibrillar protein synthesis remains to be determined. In the present study, we assessed the effect of short-term HIIT versus MICT on myofibrillar protein synthesis rates using a single-leg within-participant design. Ten healthy young men (age: 20 ± 1 years) performed six to eight training sessions with each leg over 2 weeks while ingesting deuterated water to assess myofibrillar protein synthesis. One leg was randomly assigned to perform HIIT and the other MICT. Skeletal muscle biopsies were collected at rest from one leg before and after a 2-week habituation period and from both legs after the training period to assess myofibrillar protein synthesis rates. HIIT and MICT increased single-leg maximal power output (main effect, p < .01), with no differences between legs (interaction: p = .61). Myofibrillar protein synthesis rates did not differ between the habituation period, MICT, or HIIT (1.39 ± 0.16%, 1.24 ± 0.30%, and 1.42 ± 0.31% per day, respectively; p = .29). In conclusion, we observed no detectable differences in daily myofibrillar protein synthesis rates between HIIT or work-matched MICT when assessed over a 2-week exercise training period in recreationally active young adult men.
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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 | 0.001 |
| 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) | 0.001 | 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".