Efficacy of an Integrated Training Device in Improving Muscle Strength, Balance, and Cognitive Ability in Older Adults
Bibliographic record
Abstract
OBJECTIVE: To determine the effects of an integrated training device for strength and balance on extremity muscle strength, postural balance, and cognition in older adults using a combination with various rehabilitation training games, in which balance, strength, and cognitive training were configured in a single device. METHODS: This prospective study included 20 healthy participants aged 65-85 years. Participants trained for 30 minutes daily, 3 days weekly, for 6 weeks with an integrated training device for strength and balance (SBT-120; Man&Tel Inc., Gumi, Korea). Main outcomes were measured using the Korean Mini-Mental State Examination (K-MMSE), Korean version of the Montreal Cognitive Assessment (K-MoCA), Timed Up and Go Test (TUG), Functional Reach Test (FRT), Berg Balance Scale (BBS), and Manual Muscle Test. Measurements were taken at three time points: T0 (pretreatment), T1 (immediately after treatment), and T2 (4 weeks after treatment). RESULTS: All 20 patients completed the training, and TUG, FRT, and BBS scores significantly improved at T1 and T2 compared to T0. Mean TUG scores decreased by 0.99±2.00 at T1 and 1.05±1.55 at T2 compared to T0. Mean FRT scores increased by 6.13±4.26 at T1 and 6.75±4.79 at T2 compared to T0. BBS scores increased by 0.60±0.94 at T1 and 0.45±1.15 at T2 compared to T0. Moreover, muscle strength and cognition (K-MMSE and K-MoCA scores) increased after training. CONCLUSION: Our findings suggest that an integrated training device for strength and balance can be a safe and useful tool for older adults.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".