Instability Resistance Training Across the Exercise Continuum
Bibliographic record
Abstract
CONTEXT: Instability resistance training (IRT; unstable surfaces and devices to strengthen the core or trunk muscles) is popular in fitness training facilities. OBJECTIVE: To examine contradictory IRT recommendations for health enthusiasts and rehabilitation. DATA SOURCES: A literature search was performed using MEDLINE, SPORT Discus, ScienceDirect, Web of Science, and Google Scholar databases from 1990 to 2012. STUDY SELECTION: Databases were searched using key terms, including "balance," "stability," "instability," "resistance training," "core," "trunk," and "functional performance." Additionally, relevant articles were extracted from reference lists. DATA EXTRACTION: To be included, research questions addressed the effect of balance or IRT on performance, healthy and active participants, and physiologic or performance outcome measures and had to be published in English in a peer-reviewed journal. RESULTS: There is a dichotomy of opinions on the effectiveness and application of instability devices and conditions for health and performance training. Balance training without resistance has been shown to improve not only balance but functional performance as well. IRT studies document similar training adaptations as stable resistance training programs with recreationally active individuals. Similar progressions with lower resistance may improve balance and stability, increase core activation, and improve motor control. CONCLUSION: IRT is highly recommended for youth, elderly, recreationally active individuals, and highly trained enthusiasts.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".