How to improve gait and balance function in elderly individuals—compliance with principles of training
Bibliographic record
Abstract
Abstract Most balance training regimens for elderly individuals focus on self-controlled exercises, although automatic postural responses after a balance perturbation are not under direct volitional control. We critically review the literature on this topic, and notice that several studies fail to comply with basic principles of training and therefore show little improvement in function. Some present the view that physical function in the too frail and too fit cannot be improved, which we instead argue would be the effect of nonspecific training programs. We propose a concept for balance training that incorporates voluntary exercises as well as perturbation and dual-task exercises to improve balance control. The program is performed on five different levels where levels 1–4 exercises focus on the skill to maintain balance and level 5 adds perturbation exercises that focus on the skill to recover balance as well as dual task exercises providing a cognitive load during execution of a balance motor task. Functional requirements for muscle strength and power are directly incorporated into the program. The feasibility of the concept has been demonstrated on elderly fallers. A randomized control trial is underway to investigate the effects on healthy elderly individuals. Further intervention studies using this concept are encouraged.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".