Regional and muscle-specific adaptations in knee extensor hypertrophy using flywheel versus conventional weight-stack resistance exercise
Bibliographic record
Abstract
This study compared the effects of the most frequently employed protocols of flywheel (FW) versus weight-stack (WS) resistance exercise (RE) on regional and muscle-specific adaptations of the knee extensors. Sixteen men (n = 8) and women (n = 8) performed 8 weeks (2–3 days/week) of knee extension RE employing FW technology on 1 leg (4 × 7 repetitions), while the contralateral leg performed regular WS training (4 × 8–12 repetitions). Maximal strength (1-repetition maximum (1RM) in WS) and peak FW power were determined before and after training for both legs. Partial muscle volume of vastus lateralis (VL), vastus medialis (VM), vastus intermedius (VI), and rectus femoris (RF) were measured using magnetic resonance imaging. Additionally, quadriceps cross-sectional area was assessed at a proximal and a distal site. There were no differences (P > 0.05) between FW versus WS in muscle hypertrophy of the quadriceps femoris (8% vs. 9%), VL (10% vs. 11%), VM (6% vs. 8%), VI (5% vs. 5%), or RF (17% vs. 17%). Muscle hypertrophy tended (P = 0.09) to be greater at the distal compared with the proximal site, but there was no interaction with exercise method. Increases in 1RM and FW peak power were similar across legs, yet the increase in 1RM was greater in men (31%) than in women (20%). These findings suggest that FW and WS training induces comparable muscle-specific hypertrophy of the knee extensors. Given that these robust muscular adaptations were brought about with markedly fewer repetitions in the FW compared with WS, it seems FW training can be recommended as a particularly time-efficient exercise paradigm.
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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".