Interventions to Promote More Effective Balance-Recovery Reactions in Industrial Settings: New Perspectives on Footwear and Handrails
Bibliographic record
Abstract
"Change-in-support" balance-recovery reactions that involve rapid stepping or reaching movements play a critical role in preventing falls. Recent geriatrics studies have led to new interventions to improve ability to execute these reactions effectively. Some of these interventions have the potential to reduce fall risk for younger persons working in industrial settings. In this paper, we review research pertaining to two such interventions: 1) balance-enhancing footwear insoles designed to improve stepping reactions, and 2) proximity-triggered handrail cueing systems designed to improve reach-to-grasp reactions. The insole has a raised ridge around the perimeter that is intended to improve balance control by providing increased stimulation of sensory receptors on the footsole in situations where loss of balance may be imminent. The cueing system uses flashing lights and/or verbal prompts to attract attention to the handrail and ensure that the brain registers its location, thereby facilitating more rapid and accurate grasping of the rail if and when sudden loss of balance occurs. Results to date support the efficacy of both interventions in geriatric populations. There is also some evidence that these interventions may improve balance control in younger persons; however, further research is needed to confirm their efficacy in preventing falls in industrial settings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".