Running sprint interval training induces fat loss in women
Bibliographic record
Abstract
Data on whether sprint interval training (SIT) (repeated supermaximal intensity, short-duration exercise) affects body composition are limited, and the data that are available suggest that men respond more favourably than do women. Moreover, most SIT data involve cycling exercise, and running may differ because of the larger muscle mass involved. Further, running is a more universal exercise type. This study assessed whether running SIT can alter body composition (air displacement plethysmography), waist circumference, maximal oxygen consumption, peak running speed, and (or) the blood lipid profile. Fifteen recreationally active women (age, 22.9 ± 3.6 years; height, 163.9 ± 5.1 cm; mass, 60.8 ± 5.2 kg) completed 6 weeks of running SIT (4 to 6, 30-s "all-out" sprints on a self-propelled treadmill separated by 4 min of rest performed 3 times per week). Training decreased body fat mass by 8.0% (15.1 ± 3.6 to 13.9 ± 3.4 kg, P = 0.002) and waist circumference by 3.5% (80.1 ± 4.2 to 77.3 ± 4.4 cm, P = 0.048), whereas it increased fat-free mass by 1.3% (45.7 ± 3.5 to 46.3 ± 2.9 kg, P = 0.05), maximal oxygen consumption by 8.7% (46 ± 5 to 50 ± 6 mL/(kg·min), P = 0.004), and peak running speed by 4.8% (16.6 ± 1.7 to 17.4 ± 1.4 km/h, P = 0.026). There were no differences in food intake assessed by 3-day food records (P > 0.329) or in blood lipids (P > 0.595), except for a slight decrease in high-density lipoprotein concentration (1.34 ± 0.28 to 1.24 ± 0.24 mmol/L, P = 0.034). Running SIT is a time-efficient strategy for decreasing body fat while increasing aerobic capacity, peak running speed, and fat-free mass in healthy young women.
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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) | 0.000 | 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) | 0.002 | 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".