Six Weeks of Low‐Volume Sprint Interval Training Improves Peak Oxygen Uptake Compared to a Non‐Exercise Control: A Randomized Controlled Trial
Bibliographic record
Abstract
ABSTRACT Low‐volume sprint interval training (LVSIT) increases peak oxygen uptake (VO2peak) when performed three times a week for 6 weeks. Methodological and statistical concerns, however, constrain the veracity of prior findings. We therefore reassessed the VO2peak response to LVSIT using a randomized controlled trial design to mitigate bias and augment reporting quality. A generative model of VO2peak was constructed as a function of group, baseline VO2peak, age, sex, height, and change in body mass. Simulation experiments using previous data estimated that n = 15/group would achieve 80% power to detect a difference of 1 metabolic equivalent (MET) with a credible interval (CrI) of ≤ 1‐MET. Insufficiently active young adults (22 ± 3 years, body mass index: 24 ± 4 kg m−2, baseline VO2peak: 33 ± 7 mL kg−1 min−1) were randomized to perform 6 weeks of thrice weekly LVSIT (n = 17) or no exercise (CTL; n = 20). The LVSIT protocol involved 3 × 20‐s “all out” sprints over a 10‐min session of low‐intensity cycling. Bayesian generative multivariate modeling revealed that LVSIT increased absolute [+325 mL min−1 (101–605)] and relative VO2peak [+5.6 mL kg−1 min−1 (2.2–8.1)] versus CTL. All but one LVSIT participant (94%) were deemed a responder (i.e., mean estimate ± 95% CrI for relative VO2peak > 0). In contrast, four CTL participants (20%) met this criterion. LVSIT also improved time to exhaustion by +133 s (101–160) versus CTL. We unequivocally demonstrate that 6 weeks of thrice weekly LVSIT increased VO2peak in insufficiently active young adults compared to no exercise. By incorporating a robust design that included preregistration, concealed allocation assignment, statistical best practices, and applied Bayesian methods, and open data‐sharing, this study addresses prior methodological critiques of similar previous work.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".