Effect of elastic band‐based high‐speed power training on cognitive function, physical performance and muscle strength in older women with mild cognitive impairment
Bibliographic record
Abstract
AIM: The effectiveness of resistance training in improving cognitive function in older adults is well demonstrated. In particular, unconventional high-speed resistance training can improve muscle power development. In the present study, the effectiveness of 12 weeks of elastic band-based high-speed power training (HSPT) was examined. METHODS: Participants were randomly assigned into a HSPT group (n = 14, age 75.0 ± 0.9 years), a low-speed strength training (LSST) group (n = 9, age 76.0 ± 1.3 years) and a control group (CON; n = 7, age 78.0 ± 1.0 years). A 1-h exercise program was provided twice a week for 12 weeks for the HSPT and LSST groups, and balance and tone exercises were carried out by the CON group. RESULTS: Significant increases in levels of cognitive function, physical function, and muscle strength were observed in both the HSPT and LSST groups. In cognitive function, significant improvements in the Mini-Mental State Examination and Montreal Cognitive Assessment were seen in both the HSPT and LSST groups compared with the CON group. In physical functions, Short Physical Performance Battery scores were increased significantly in the HSPT and LSST groups compared with the CON group. In the 12 weeks of elastic band-based training, the HSPT group showed greater improvements in older women with mild cognitive impairment than the LSST group, although both regimens were effective in improving cognitive function, physical function and muscle strength. CONCLUSIONS: We conclude that elastic band-based HSPT, as compared with LSST, is more efficient in helping older women with mild cognitive impairment to improve cognitive function, physical performance and muscle strength. Geriatr Gerontol Int 2017; 17: 765-772.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".