Training Adaptations Associated With an 8-Week Instability Resistance Training Program With Recreationally Active Individuals
Bibliographic record
Abstract
Instability devices are popular training modalities; however, their training effectiveness has not been well established. The objective of this study was to determine differences in physiological and performance measures after stable and unstable resistance training. Eighteen subjects (10 men and 8 women) resistance trained 3 d.wk under either stable or unstable conditions for 8 weeks. Pre and posttraining measures included chest press isometric force and electromyographic activity of the triceps brachii and pectoralis major under stable and unstable conditions and 1-legged throwing distance, balance, countermovement jump (CMJ) and drop jump (DJ) heights. There were no significant training group effects found with any measure. However, there was a tendency (p = 0.06) for the unstable training group to improve the stable to unstable chest press force ratio to a greater degree (24.8%) than the stable group (10.8%). There were significant overall pre to posttraining improvements in maximum voluntary isometric contraction (MVIC) force (13.3%: p < 0.0001), unstable/stable force (18.2%: p = 0.0005), bench press (11%: p < 0.0001), squat (14.9%: p < 0.0001), CMJs (11.2%: p = 0.002), and DJs (3.3%: p = 0.001), wobble board contacts (12.4%: p = 0.03), and wobble board on-off ratios (62%: p = 0.005). There was a significant (p < 0.0001) 42.2% greater MVIC force and 43.2 and 33.2% greater triceps (p = 0.003) and pectoral (p = 0.005) neuromuscular efficiency with stable vs. unstable isometric chest press. It appears that instability resistance training, which reportedly uses lower forces, can increase strength and balance in previously untrained young individuals similar to training with more stable machines employing heavier loads.
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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 | 0.001 |
| 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".