Effects of plyometric- and cycle-based high-intensity interval training on body composition, aerobic capacity, and muscle function in young females: a field-based group fitness assessment
Bibliographic record
Abstract
High-intensity interval training (HIIT) is an effective alternative to moderate intensity continuous training for improvements in body composition and aerobic capacity; however, there is little work comparing different modalities of HIIT. The purpose of this study was to compare the effects of plyometric- (PLYO) and cycle-oriented (CYC) HIIT on body composition, aerobic capacity, and skeletal muscle size, quality, and function in recreationally trained females. Young (21.7 ± 3.1 yrs), recreationally active females were quasi-randomized (1:1 ratio) to 8 weeks of twice weekly PLYO ( n = 15) or CYC ( n = 15) HIIT. Body composition (four-compartment model), VO2peak, countermovement jump performance, muscle size, and echo intensity (muscle quality), as well as strength and power of the knee extensors and plantar flexors were measured before and after training. Both groups showed a similar decrease in body fat percentage ( p < 0.001; [Formula: see text]= 0.409) and echo intensity ( p < 0.001; [Formula: see text] = 0.558), and an increase in fat-free mass ( p < 0.001; [Formula: see text]= 0.367) and VO2peak ( p = 0.001; [Formula: see text] = 0.318). Muscle size was unaffected ( p > 0.05), whereas peak torque was reduced similarly in both groups ( p = 0.017; [Formula: see text] = 0.188) and rapid torque capacity was diminished only for the knee extensors after CYC ( p = 0.022; d = −0.67). These results suggest that PLYO and CYC HIIT are similarly effective for improving body composition, aerobic capacity, and muscle quality, whereas muscle function may express moderate decrements in recreationally active females. ClinicalTrials.gov (NCT05821504)
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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) | 0.001 | 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".