Comparison of seat, waist, and arm sit-to-stand assistance modalities in elderly population
Bibliographic record
Abstract
The ability to perform a sit-to-stand (STS) motion is important for ambulatory adults to function independently and maintain daily activities. Roughly 6% of community-dwelling older adults experience significant difficulties with STS, a major risk factor for institutionalization. While mechanical STS assistance can help address this problem, full dependence on STS assistance provided by devices such as lift chairs can lead to atrophy of the leg muscles. We investigated the mechanics of assisted STS motion in order to better understand how load-sharing STS mechanisms may facilitate STS motions while still requiring activation of the leg muscles. Experiments were conducted with 17 nondisabled older adults performing unassisted and assisted STS rises with grab bar, arm, seat, and waist assistance. Each mode of rise was evaluated based on a subject questionnaire and key biomechanical metrics relating to stability, knee effort reduction, and rise trajectory. Results show that the seat- and waist-assist modes provide statistically significant improvements in stability metrics and reductions in required knee torques over unassisted rises and bar assistance. The assists most preferred by the subjects were the seat and bar assists. Overall, our results favor a seat-assisted STS modality for nonclinical applications and indicate further testing of this modality with a clinical population.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".