Older Wheelchair Users Recommend Age-Friendly Design Improvements to a Wheelchair Maintenance App: Mixed Methods Development Study
Bibliographic record
Abstract
BACKGROUND: Wheelchair part failures have doubled over the past decade. Preventative wheelchair maintenance reduces wheelchair failures and prevents user consequences. We are developing a smartphone app called WheelTrak, which alerts users when maintenance is required, to encourage maintenance practices and compliance. OBJECTIVE: This mixed methods study aimed to develop a wheelchair maintenance app using broad stakeholder advice and investigate older adults' interaction experience with the app and their perceived barriers to and facilitators of maintenance. METHODS: Interviews were conducted with stakeholders, including mobility device users, to generate needs statements and app specifications. The app was designed in 2 stages. Stage 1 involved the development of the app according to the specifications and evaluation of the app interface by lead users. Stage 2 included the revision of the app screens and manual functionality testing. Usability testing and semistructured interviews were conducted with older wheelchair and scooter users. The System Usability Scale was used to measure app usability. RESULTS: Interviews with power and manual wheelchair users (37/57, 65%), wheelchair service providers (15/57, 26%), manufacturers (2/57, 4%), seating and mobility researchers (1/57, 2%), and insurance plan providers (2/57, 4%) informed the needs and specifications of the app technology. The 2-stage development process delivered a fully functional app that met the design specifications. In total, 12 older adults (mean age 74.2, SD 9.1 years; n=10, 83% women; and n=2, 17% men) participated in the usability testing study. Of the 12 participants, 9 (75%) agreed to use WheelTrak for preventative maintenance. WheelTrak scored an average System Usability Scale score of 60.25 (SD 16). Four overarching themes were identified: WheelTrak app improvements, barriers to maintenance, consequences related to mobility device failure, and smart technology use and acceptance. Older adults preferred the simplicity, readability, personalization, and availability of educational resources in the app. Barriers to maintenance pertained to health issues and lack of maintenance knowledge among older adults. Facilitators of maintenance included notification for maintenance, app connectivity with the service provider, reporting of device failure, and the presence of a caregiver for maintenance. CONCLUSIONS: This study highlighted age-friendly design improvements to the app, making it easy to be used and adopted by older wheelchair users. The WheelTrak app has close to average system usability. Additional usability testing will be conducted following app revision in the future.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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 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".