Investigation of User Satisfaction and Associated Factors in Geriatrics Using Walking Aids: A Cross Sectional Study
Bibliographic record
Abstract
Objective: We aimed to investigate user satisfaction and associated factors in geriatrics using a walking aid. Material and Methods: The Quebec User Evaluation of Satisfaction with Assistive Technology 2.0 was used to assess the satisfaction of 269 individuals aged ≥65 years using any walking aid. The relationships between satisfaction and age, years of use, body mass index, number of falls in the last year, physical activity level and health-related quality of life were analyzed. Results: The most commonly used walking aid was cane (78.8%). Ease of use was the most satisfied feature, while adjustments was the least satisfied feature. The three most important features were safety, ease of use and weight. Walking aid satisfaction had weak negative correlations with physical activity (r=-0.246) and quality of life (r=-0.131) (p<0.05). In addition, 41.6% of the participants stated that they had fallen at least once in the last year and 70.7% of them did not use a walking aid during the fall. Conclusion: Geriatrics with lower quality of life and physical activity values tend to have higher user satisfaction with walking aids with a weak relationship. Satisfaction results may contribute to the design and selection of appropriate walking aids for this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".