Six Sessions of Sprint Interval Training Improves Running Performance in Trained Athletes
Bibliographic record
Abstract
Koral, J, Oranchuk, DJ, Herrera, R, and Millet, GY. Six sessions of sprint interval training improves running performance in trained athletes. J Strength Cond Res 32(3): 617-623, 2018-Sprint interval training (SIT) is gaining popularity with endurance athletes. Various studies have shown that SIT allows for similar or greater endurance, strength, and power performance improvements than traditional endurance training but demands less time and volume. One of the main limitations in SIT research is that most studies were performed in a laboratory using expensive treadmills or ergometers. The aim of this study was to assess the performance effects of a novel short-term and highly accessible training protocol based on maximal shuttle runs in the field (SIT-F). Sixteen (12 male, 4 female) trained trail runners completed a 2-week procedure consisting of 4-7 bouts of 30 seconds at maximal intensity interspersed by 4 minutes of recovery, 3 times a week. Maximal aerobic speed (MAS), time to exhaustion at 90% of MAS before test (Tmax at 90% MAS), and 3,000-m time trial (TT3000m) were evaluated before and after training. Data were analyzed using a paired samples t-test, and Cohen's (d) effect sizes were calculated. Maximal aerobic speed improved by 2.3% (p = 0.01, d = 0.22), whereas peak power (PP) and mean power (MP) increased by 2.4% (p = 0.009, d = 0.33) and 2.8% (p = 0.002, d = 0.41), respectively. TT3000m was 6% shorter (p < 0.001, d = 0.35), whereas Tmax at 90% MAS was 42% longer (p < 0.001, d = 0.74). Sprint interval training in the field significantly improved the 3,000-m run, time to exhaustion, PP, and MP in trained trail runners. Sprint interval training in the field is a time-efficient and cost-free means of improving both endurance and power performance in trained athletes.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".