Running sprint interval training induces fat loss in women
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it