Effects of run sprint interval training on healthy, inactive, overweight/obese women: A pilot study
Bibliographic record
Abstract
This study examined the effects of a 12 week, treadmill-based, run sprint interval training (SIT) protocol compared with that of a moderate-intensity continuous training (MICT) protocol in healthy, inactive, overweight/obese women, on cardiovascular disease risk factors. After random assignment, the SIT group completed 4–10 × 30 s maximal sprints, with a 4 min active recovery between sprints, and the MICT group completed 30–60 min at moderate intensity (45–55% heart rate reserve (HRR)). The rate of perceived exertion (RPE) was recorded for each session, and perceived enjoyment was quantified every 3 weeks. Clinical and fitness testing were completed at baseline, 6 weeks, and 12 weeks. Twelve female participants (age: 34.1 ± 6.1; body mass index (BMI): 31.3 ± 6.8; VO 2peak : 27.0 ± 6.2) completed the intervention. There were significant main effects for time for VO 2peak ( p = 0.001), body fat percentage ( p = 0.001), and session RPE ( p = 0.006). VO 2peak improved 20.7% in the SIT group ( n = 5) and 24.4% in the MICT group ( n = 7). Body fat percentage reduced by 1.7% in the SIT group and 2.6% in the MICT group. Perceived enjoyment was similarly high between the groups despite higher session RPE in the SIT group ( p = 0.441). SIT training on a motor-driven treadmill elicits similar improvements in oxygen utilization and body composition as moderate-intensity training in this population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".