Impact of seat height and grab bars on postural stability and muscle activity of older females standing from a toilet
Bibliographic record
Abstract
BACKGROUND: Postural stability and muscle activity of older females were assessed during a sit-to-stand (STS) task completed from a standard North American toilet. Four STS toilet setups were examined: normal height (normalheight), raised seat (raisedseat), and normal and raised seats with bilateral grab bars (normalheightbars, raisedseatbars). METHODS: Eight older (72 ± 6 years) and 8 younger females (21 ± 1 years) participated. Total movement time, STS difficulty measured as the time from hindfoot to forefoot plantar pressure peaks, and center of pressure (COP) displacement were evaluated. Surface electromyography (EMG) captured muscle activity in the vastus medialis (VM), biceps femoris, calf muscles, and tibialis anterior (TA). RESULTS: Raisedseat, normalheightbars, and raisedseatbars reduced STS difficulty. Raising the seat reduced EMG activity in VM, TA, and calf muscles. Adding bars lowered biceps femoris and calf muscle EMG, and increased VM and TA activity. Normalheightbars and raisedseatbars lowered COP speed (P = .01) and displacement (P = .03) compared to normalheight and raisedseat. Conversely, raisedseat and raisedseatbars increased COP speed and displacement (P < .001) for young and older females. CONCLUSION: Normalheightbars provides the most stability and reduces STS difficulty, making it the best intervention for improving postural stability in older females standing up from a toilet.
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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) | 0.000 | 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".