Run Sprint Interval Training Improves Aerobic Performance but Not Maximal Cardiac Output
Bibliographic record
Abstract
UNLABELLED: Repeated maximal-intensity short-duration exercise (sprint interval training, SIT) can produce muscle adaptations similar to endurance training (ET) despite a much reduced training volume. However, most SIT data use cycling, and little is known about its effects on body composition or maximal cardiac output (Qmax). PURPOSE: The purpose of this study was to assess body composition, 2000-m run time trial, VO(2max), and Q(max) effects of run SIT versus ET. METHODS: Men and women (n = 10 per group; mean ± SD: age = 24 ± 3 yr) trained three times per week for 6 wk with SIT, 30-s all-out run sprints (manually driven treadmill), four to six bouts per session, 4-min recovery per bout, versus ET, 65% VO(2max) for 30 to 60 min·d(-1). RESULTS: Training improved (P < 0.05) body composition, 2000-m run time trial performance, and VO(2max) in both groups. Fat mass decreased 12.4% with SIT (mean ± SEM; 13.7 ± 1.6 to 12.0 ± 1.6 kg) and 5.8% with ET (13.9 ± 1.7 to 13.1 ± 1.6 kg). Lean mass increased 1% in both groups. Time trial performance improved 4.6% with SIT (-25.6 ± 8.1 s) and 5.9% with ET (-31.9 ± 6.3 s). VO(2max) increased 11.5% with SIT (46.8 ± 1.6 to 52.2 ± 2.0 mL·kg·(-1)·min(-1)) and 12.5% with ET (44.0 ± 2.0 to 49.5 ± 2.6 mL·kg·(-1)·min(-1)). None of these improvements differed between groups. In contrast, Q(max) increased by 9.5% with ET only (22.2 ± 2.0 to 24.3 ± 1.6 L·min(-1)). CONCLUSIONS: Despite a fraction of the time commitment, run SIT induces similar body composition, VO(2max), and performance adaptations as ET, but with no effect on Q(max). These data suggest that adaptations with ET are of central origin primarily, whereas those with SIT are more peripheral
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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) | 0.001 | 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) | 0.004 | 0.001 |
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".