Effect of 24‐week strength training on unstable surfaces on mobility, balance, and concern about falling in older adults
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effects of 24 weeks of strength training on stable (ST) and unstable surfaces (UST) on the functional mobility, balance, and concern about falling in healthy older adults, younger than 70. DESIGN: A single-center randomized clinical trial. PARTICIPANTS: Sixty-four older adults (58 females and 6 males; 68 years) were randomized into control, ST, or UST groups. INTERVENTIONS: Both ST and UST intervention groups received a core muscle, upper, and lower limb moderate-intensity strength exercises using stable and unstable surfaces. The classes were performed three times per week over a 24-week period. The control group did not receive any type of active intervention. MEASUREMENTS: The primary outcome measures were the dynamic balance (Berg Balance Scale (BBS)) and functional mobility (timed up and go (TUG) test). The secondary outcomes included the sitting and rising test (SRT) and Falls Efficacy Scale-International (FESI) scores. RESULTS: There was a significant improvement in balance performance (BBS = +4 points) after 24 weeks of both ST (+1.22; 95% CI, -0.19 to 2.63) and UST (+2.26; 95% CI, 0.83-3.70) compared with the control group. Additionally, compared with the control, only UST experienced functional mobility gains (TUG = -2.44; 95% CI, -4.41 to -0.48; SRT = +1.12; 95% CI, 0.08-2.17) and decreased concern about falling (FESI = -4.41; 95% CI, -9.30 to -0.27). CONCLUSION: Long-term ST with and without unstable devices was effective to improve dynamic balance in older adults. Furthermore, the effects of UST were extended to functional mobility gains and reduced concern about falling.
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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 | 0.001 | 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 | 0.001 |
| 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".