Lower Limb Mechanics during Stair Descent with and without a Handrail
Bibliographic record
Abstract
In 1996, the World Health Organization outlined a framework for achieving health across the lifespan and identified ageing and health to be a priority issue. One of the most prevalent yet preventable forms of age-related risks to health is falling. Research consistently points to stairs as the most common location of falls; despite this, older adults demonstrate similar patterns and frequency of stair use compared to their younger counterparts. Moreover, well known age-related physiological changes contribute to the use of alternate stair ambulation patterns, particularly increased handrail use. To date, the mechanism of how handrail force alters the lower limb and trunk mechanics in different populations is unknown. PURPOSE: This exploratory study examined how varied amounts of force applied to the handrail effect knee joint mechanics. METHODS: Ten self-described healthy female adults 65–85 y participated. Knee joint mechanics were analyzed during stair descent under three conditions [1) no load applied to the handrail (NH), 2) a light handrail load (LH), and 3) a heavy handrail load (HH)]. Three dimensional net moments were calculated. Physical activity, health, and demographics were assessed via the Human Activity Profile, SF-36, and demographic questionnaire, respectively.FigureRESULTS: The frontal and sagittal plane moments during stance were not different across the NH, LH, and HH conditions. However, visual inspection revealed HH frontal moment profile to be shifted relative to the LH and NH frontal moment profiles. CONCLUSION: Force applied to the handrail is an important factor in stair negotiation. Future research should now examine if there is an optimal handrail load to use during stair ambulation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".