Effects of Instability Resistance Training on Physical and Cognitive Function in Adults
Bibliographic record
Abstract
Abstract Resistance training is an effective strategy for combating geriatric syndromes, including frailty, sarcopenia, and cognitive impairment. Performing instability resistance training (IRT) (e.g., weight-bearing, machine-based, or free-weight exercises using unstable surfaces) may provide additional benefits on health outcomes in middle-aged and older adults. With this in mind, we examined the impact of IRT on physical and cognitive functioning (primary outcomes), fall risk, and quality of life (secondary outcomes). We searched three specific databases from inception to October 2024. We included peer-reviewed, randomized trials that analysed the effects of intervention on outcomes of interest among adults aged 50 years or older. Risk of bias and quality of reporting were ascertained using validated checklists. We performed a random-effects meta-analysis using robust variance estimation. 18 studies with participants from Brazil, Spain, Germany, and South Korea (n=528, %women: 35–100%) were included in the qualitative review, and 12 studies in the meta-analysis. We found that IRT had a medium-to-large-sized effect on physical and cognitive functioning. Studies had a high quality of reporting but presented significant heterogeneity and low-to-moderate risk of bias. Our findings suggest that IRT is a promising strategy for promoting physical and cognitive benefits in middle-aged and 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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".