Wheelchair Servicing for Older Adults: Cross-Sectional Study
Bibliographic record
Abstract
Background: Wheelchairs are assistive mobility devices known to experience frequent part failures and breakdowns within 6 months of regular use. No tools or technologies exist to monitor the wheelchairs' condition or usage and inform stakeholders and users regarding when or how often they need to undergo servicing to avoid critical part failure. Objective: We aimed to test the association between wheelchair usage and manual wheelchair damage, part failures, and consequences for older wheelchair users and evaluate their preferences for a new wheelchair servicing technology. Methods: A cross-sectional study was performed with older manual wheelchair users atInstituto Nacional de Geriatría in Mexico. Demographic data, wheelchair information, failure counts, and preferences for new technology (sensor and smartphone app for servicing) were collected using surveys. Road shocks experienced by the wheelchair were collected for a week using a sensor. Results: Ten participants (mean [SD] age, 78.8 [11.8] y; 8 female and 2 male) participated. Seven experienced an average (SD) of 2.86 (1.36) self-reported part failures. Road shocks correlated with self-reported loose brake failures (r(8)=0.58, P=.09), the damaged condition of tires (r(8)=0.61, P=.1), and the damaged condition of brakes (r(8)=0.58, P=.099). No consequences were reported. Part failures increased as self-maintenance occurrences increased (r(9)=0.67, P=.04). More than 8 participants responded that they would like to monitor the wheelchair's condition using the new technology and purchase it at an average (SD) price of US $28.95 (18.13). Conclusions: In this study, the association between wheelchair usage and failures showed that data-driven wheelchair inspection schedules should be determined through a collaborative approach involving researchers and stakeholders in wheelchair repair provision and older adult users. Older adults are interested in using new technology to engage in wheelchair servicing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".