Resistance Training with Instability Does Not Hamper Total Training Volume and Muscle Strength Gains in Older Adults: a Secondary Analysis from REI Study
Bibliographic record
Abstract
Background Resistance training with instability (REI) emerged as a prom-ising training modality for older adults aiming to counteract age-related changes. Objectives We compared the effects of 12 weeks of REI and traditional resistance exercise (RE) on muscle strength in older adults with cognitive impairment. We further explored if total train-ing volume (TTV) significantly differs among training groups. Methods This is a secondary analysis of the REI study. Participants were randomly assigned to REI (n=22) or RE (n=23). RE protocol involved moderate-intensity, free-weight, and machines-based resistance exercises (3 sets, 10–15 repetitions). REI received a similar training protocol, in which exercises were simul-taneously performed with instability/unstable devices (e.g., squat exercise under a foam pad or Bosu® ball). Maximal isometric strength and isokinetic parameters were assessed at baseline and after completion of a 12-week intervention through a hydraulic handgrip and isokinetic dynamometer, respectively. TTV (sets × repetitions × load) was computed based on external training load over the 12 weeks. Results No differences were observed between groups (p=.35) after the intervention. Over 12 weeks, REI and RE improved iso-metric handgrip strength (p<.001) and isokinetic performance (p=.04). We also did not find differences in the TTV between training groups (p=.28). Conclusion We demonstrated that both REI and RE training induced similar gains in muscle strength. Combining unstable surfaces/instability devices did not hamper TTV, which may have clini-cal applications in the context of exercise for older adults.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".