The Effect of Handrail Cross-Sectional Design and Age on the Speed and Quality of Reach-To-Grasp Reactions to Recover Balance
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of handrail cross-section on the speed and quality of reach-to-grasp movements following balance loss in younger and older adults. BACKGROUND: Grasping a handrail is a common strategy for balance recovery. For handrails to be effective, the design must enable fast and accurate reactive grasping. Little is known about the effect of handrail cross-section on the timing or quality of the reach-to-grasp movement following balance loss. METHODS: Twenty-four younger and 16 older adults experienced incrementally increasing magnitudes of perturbations in the forward and backward direction until they were no longer able to recover balance. We analyzed the last trial where the participant could recover using only the handrail, without stepping or relying on the harness, the maximum withstood perturbation (MWP). Seven handrail cross-sections were tested. RESULTS: Handrail cross-section did not affect the speed or timing of the reach-to-grasp reaction for younger or older adults. However, handrail cross-section affected the MWP, the grip types used, and the likelihood of making an error or adjustment when grasping. The greatest MWP and fewest errors occurred with 1.5" round handrails. CONCLUSION: The absence of common strategies for accurately grasping complex shapes (reaching more slowly), combined with the higher frequency of errors with larger handrails, suggests that both older and younger adults prioritized quickly reaching the handrail over prehension during reach-to-grasp balance reactions. APPLICATION: This work provides new insights on the effect of age and handrail cross-sectional design on reach-to-grasp reactions to recover balance, which can inform safer handrail design standards.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".